45 results about "Intelligent database" patented technology

The invention discloses a zero-configuration knowledge graph construction method, equipment and a medium, belongs to the technical field of knowledge graphs, and aims to solve the technical problem of how to overcome the hard coding defect in traditional knowledge graph construction, realize a more flexible construction mode with lower difficulty in use, further realize data source entity association and semantic fusion and improve the knowledge graph construction efficiency. According to the technical scheme, field name semantic analysis and data content analysis are conducted through a dynamic semantic analysis algorithm, and intelligent database deep analysis is achieved; the same entities in the multiple tables are automatically recognized and merged through a cross-table entity intelligent merging algorithm, and multi-table collaborative intelligent search is achieved; constructing an intelligent knowledge graph: automatically selecting an optimal construction strategy through a multi-strategy relation construction engine according to keyword role features, and independently constructing a local knowledge graph for each data table; and optimizing and intelligently exporting the knowledge graph.

The invention provides a database relationship intelligent discovery and ER graph construction method and a self-evolution learning method, and belongs to the technical field of ER graph construction, and the database relationship intelligent discovery and ER graph construction method comprises the steps of S1, metadata collection and preprocessing; s2, vectorizing domain sensing metadata; s3, recalling a multi-strategy candidate relationship; s4, performing LLM structured reasoning and verification; s5, carrying out data sampling inspection and cross validation; s6, hierarchical negative sample self-evolution learning; and S7, generating and visualizing an ER graph. According to the invention, a layered negative sample self-evolution learning mechanism and the structured reasoning ability of LLM are organically combined, and LoRA rapid fine tuning and a multi-stage verification process are supplemented. The defects of accuracy, generalization ability, cold start efficiency, reasoning transparency, automation integration and the like in the aspect of discovery of the primary and foreign key relationship of the relational database in the prior art are effectively overcome, and a more advanced and practical solution is provided.

The invention discloses an intelligent database anomaly detection and repair method and system, and belongs to the technical field of database operation and maintenance. The method comprises the following steps: collecting multi-source heterogeneous data through a distributed probe cluster; preprocessing the data and classifying the data into time sequence and structure data; extracting time sequence features by using a multi-scale CNN; constructing a dynamic dependency graph and extracting structural features by using GNN; abnormal joint judgment is carried out through a gating fusion mechanism; calling the knowledge graph to carry out root cause tracing; generating and verifying a repair instruction set; and safe execution and closed-loop feedback are realized. The system includes corresponding modules connected by a data bus. According to the method, full-process automation and intelligentization of database anomaly detection and repair are realized, and the availability and the operation and maintenance efficiency of a database system are remarkably improved.

A context-aware AI database (CAAD) integrated with modular, hierarchically organized and collaborative, goal-driven context agents that perform specific cognitive functions such as sentiment analysis, priority ranking, and feature extraction through standardized APIs. The system generates and manages multiple types of metadata: data-associated metadata (source attributions, embeddings, statistical features, timestamps) and model-associated metadata (model provenance, analysis conditions and parameters, performance scores, methodological details), using agentic AI approaches to improve data accuracy, experimental transparency, traceability, reproducibility, contextual explainability in both training and inference operations. The architecture enables automated context refinement and includes innovative “pointers” and “deep pointers” that link analytical insights directly to source data and sub-features, enabling interpretable and auditable iterative reasoning. Furthermore, the architecture support a probationary context capability for experimental knowledge, memory decay logic for dynamic and configurable forgetting, as well as a hierarchical context layering to manage global, task specific, or otherwise ephemeral knowledge collection.

The invention discloses a construction site multi-dimensional collaborative management method and system driven by an intelligent database, and relates to the technical field of engineering project collaborative control, and the method comprises the steps: building an object, relation and evidence ledger in the intelligent database, automatically expanding an affected range after items are put in storage, and solidifying the affected range into a unified basis; carrying out on-site disposal, classification and judgment according to quality, safety and resources around an affected range, and generating a gap list and evidence requirements; distributing a disposal action, returning and warehousing, and updating a gap closed-loop state to form a precondition; forming an effective judgment according to the plan, the cost and the change, and presetting a re-checking requirement for the judgment of the field condition; if the judgment meets the threshold, the trace takes effect, and the ledger is synchronously updated. According to the method, the affected range is automatically expanded and solidified through the database, and multi-dimensional decision marks are supported to take effect through a gap closed loop and evidence gating and can be played back for reconciliation, so that the coincident range of construction site collaborative management, the checkable decision, the traceable process and the quick reconciliation of disputes are realized.

The invention relates to the technical field of automobile lightweight design and intelligent databases, and provides an automobile lightweight intelligent decision database system and method based on artificial intelligence. The invention aims to solve the technical problems of low design efficiency, low performance prediction and parameter optimization precision and difficulty in balancing multi-target requirements caused by data heterogeneous dispersion, difficulty in intercommunication and information splitting in the existing automobile lightweight design. According to the scheme, an intelligent decision database system is constructed through five core modules: a multi-source heterogeneous data acquisition module realizes data standardization processing and secure storage; the lightweight knowledge graph construction module establishes an association system and dynamically updates a standard; the intelligent algorithm module integrates a deep residual network, Bayesian optimization and a Transform architecture, and completes performance prediction, parameter optimization and scheme generation; the multi-objective optimization decision module screens an optimal scheme through multi-dimensional evaluation and constraint processing; the visual interaction module provides a visual interaction function and supports multi-mode deployment.

The invention relates to the technical field of data query, and provides a database query method and device, electronic equipment and a storage medium. The method comprises the steps of receiving a query request input by a user based on a natural language, and determining a target database from a database set based on the query request; obtaining session context data corresponding to the query request, and generating a structured query instruction for querying the target database based on the session context data and the query request; according to the method, the natural language query request of the user and the current session context data are combined, the query intention can be accurately recognized, the structured query instruction is automatically constructed, intelligent query of the database is achieved, the database query mode is simplified, the database query efficiency is improved, and the user experience is improved. The problem of low data query efficiency caused by a complex database query mode in the prior art is avoided.

The invention provides an equipment quality and supplier intelligent evaluation method based on a space-time coupling Bayesian model and a two-ticket graph database, and belongs to the technical field of intelligent databases. The two-ticket graph database is firstly constructed, and a station environment, equipment full life cycle maintenance and supplier data are integrated; obtaining a supplier initial score by using an entropy weight method and a Sigmoid function; constructing a space-time coupling Bayesian model by means of ST-LSTM and VAE, and clustering equipment health states; introducing an environment coefficient and a KL divergence gradient to correct a score, and separating environment responsibilities from supplier responsibilities; calculating an equipment target score by combining an aging model and cosine similarity; and aggregating scores through a graph attention network, considering quality fluctuation, and generating a supplier global score. According to the invention, dynamic, precise and traceable evaluation is realized, a basis is provided for power grid supply chain management and the like, and the method is suitable for supplier evaluation in electric power and other industrial fields.

This invention relates to an intelligent database performance evaluation method, belonging to the field of database performance evaluation. It addresses the problems of low accuracy, long training times, and inefficient model construction in existing database performance evaluation methods. In the early stages of model training, this invention categorizes multiple variables into frequent and infrequent variables. A linear regression model is used to learn the impact of infrequent variables on database performance. The system parameters of the linear regression model are then input into a second-stage hierarchical machine learning model. Frequent variables are directly input into this model to learn their impact on database performance. The trained hierarchical machine learning model is then used as the database performance evaluation model. This invention is primarily used for evaluating database performance.

The application relates to the technical field of data processing, in particular to an AI intelligent database data cleaning method and system. The method comprises the following steps: acquiring external event data and internal data of an AI intelligent database; acquiring a driving effect coefficient of the external event data on any breakpoint data of the internal data; acquiring an event driving score of the external event data on any breakpoint data according to the driving effect coefficient; and performing adaptive data cleaning on the internal data of the AI intelligent database according to the event driving score. The application confirms the rationality of internal data changes of the database by analyzing the external event driving influence of data breakpoints, thereby dynamically identifying breakpoint data and performing adaptive cleaning on the breakpoint data, can effectively retain structural data changes with business significance, avoids information loss caused by false cleaning, and significantly improves the data quality management capability and decision support level of the intelligent database in a high dynamic environment.

The invention relates to the technical field of intelligent database optimization, in particular to a reinforcement learning dynamic compression strategy tuning method for a column storage database, and the method comprises the steps: collecting the data feature information of a current column storage database; based on the data feature information, judging whether the current column storage database meets a preset compression condition or not; and if the current column storage database meets the preset compression condition, determining a target compression strategy according to the data feature information, and executing a corresponding compression action on the current column storage database based on the target compression strategy. Therefore, data features and loads are sensed in real time through the reinforcement learning algorithm, the optimal compression algorithm is intelligently selected, the problems of low storage efficiency, poor query performance and the like caused by the fact that traditional static compression cannot adapt to dynamic changes of data in the prior art are solved, and the comprehensive performance of an HTAP system is remarkably improved.

The invention provides an intelligent database table structure self-adaption and data import system and method based on Excel, and the system comprises a file uploading and analyzing module which is used for receiving and analyzing an Excel file uploaded by a user, and extracting header information and a data sample; the database configuration management module is used for storing and managing connection configuration information of a plurality of databases; the intelligent table structure inference engine is used for carrying out multi-dimensional statistical analysis based on Excel data samples and automatically inferring an optimal database table structure design; the multi-database dialect adapter is used for converting the general table structure definition into SQL grammar of a specific database; and the transactional execution engine executes table establishment and insertion operations in an atomic transaction mode to ensure data consistency. According to the system and the method, the Excel spreadsheet data can be automatically imported into the database, the database table structure is intelligently inferred and created according to the data characteristics, and the system and the method are particularly suitable for enterprise data migration, data warehouse construction and data analysis preprocessing scenes.

The invention discloses a logistics vehicle information digital query method and system, and relates to the technical field of vehicle data management, and the method comprises the following steps: obtaining the real-time position data of a logistics vehicle, and transmitting the real-time position data to a cloud platform; processing and judging the data; constructing an intelligent database for classification; providing an intelligent query interface; according to the query condition, dynamically adjusting the priority of the query result, and displaying the query result; generating an analysis report and providing suggestions; and generating a fault early warning, notifying related personnel through a short message and an APP, and adjusting a transportation plan or scheduling a vehicle according to the early warning information. According to the invention, the real-time data of the vehicle is obtained through the intelligent device, and the data is dynamically processed and predicted and analyzed in combination with big data and a deep learning technology, so that the intelligent analysis of the transportation efficiency, the real-time monitoring of the fault risk and the query interface based on the natural language can be realized; therefore, more accurate and more efficient decision support is provided for logistics managers.

Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.

The invention relates to the technical field of database operation and maintenance, in particular to an intelligent database operation and maintenance method and system. The method comprises the following steps: acquiring a real-time Internet of Vehicles data set, and constructing edge node-center node mapping structure information according to the real-time Internet of Vehicles data set; analyzing data access characteristics in the real-time Internet of Vehicles data set, and determining a three-dimensional temperature hierarchy data set; based on the edge node-center node mapping structure information, according to a multi-dimensional data cooperative processing strategy, hierarchical optimization storage is carried out on the three-dimensional temperature hierarchical data set, and multi-dimensional storage load change information is determined; and on the basis, determining a multi-dimensional database expansion strategy, carrying out dynamic database expansion according to the multi-dimensional database expansion strategy, and determining and outputting a database operation and maintenance report. According to the invention, the matching degree between the database expansion strategy and the corresponding vehicle network data operation and maintenance requirements is improved, so that the diversification requirements of different types of vehicle network data are met, and the vehicle network data delay is reduced.

The present disclosure provides a method, device and electronic equipment for data processing instruction generation and large model training, and relates to the field of computer technology, in particular to the fields of artificial intelligence, databases, natural language processing and large language models. The specific implementation scheme is: obtaining the natural language question information of the current user; obtaining the user rules related to the current user, and determining the user rule description information of the user rules; generating a first prompt information based on the natural language question information, the user rule description information and the preset prompt information template; inputting the first prompt information into the first large language model to obtain the data processing instructions output by the first large language model, and the data processing instructions are used to process the data in the target database. Based on this scheme, the large language model can take the user rules related to the current user into consideration when generating data processing instructions, which helps to improve the accuracy and effectiveness of the generated data processing instructions.

Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.

The invention relates to the technical field of electrical digital data processing, and discloses an intelligent database operation and maintenance method based on a large language model, which comprises the following steps: establishing a reference window buffer queue and a detection window buffer queue in a memory, and respectively storing historical and real-time data to construct steady-state and transient probability distribution; the probability distribution divergence of the key index between the two windows is calculated, when the divergence exceeds a threshold value, local subgraph condition independence test is triggered, and the causal topology connection weight is updated; extracting an association index subset based on the updated topology, encoding the association index subset into a structured prompt vector, and inputting the structured prompt vector into a large language model to generate a candidate instruction; and executing the instruction in the isolation shadow database based on the snapshot, and generating a formal instruction if the state vector distance is smaller than a convergence threshold, the causal topology is repaired in real time by detecting data distribution drift, the problem of inference illusion caused by causal mismatch under the non-stationary load is solved, and the fault attribution accuracy and the operation safety are improved.

The invention discloses a ternary linkage type heart rehabilitation training method and related equipment, and relates to the field of medical nursing, and the method comprises the steps: constructing a hospital-community-family ternary linkage intelligent database and a remote rehabilitation guidance platform; the hospital screens the patients according with the rehabilitation indications and incorporates the patients into a remote rehabilitation guidance platform, and a rehabilitation revival number is generated; when the target patient is discharged from the hospital, the hospital formulates a personalized rehabilitation prescription, uploads the personalized rehabilitation prescription to the remote rehabilitation guidance platform, and synchronizes the personalized rehabilitation prescription to the community medical terminal and the patient family terminal rehabilitation instrument; follow-up visit, medicine prescription continuing and rehabilitation execution supervision are carried out on a patient through the community end, remote guidance and prescription adjustment are carried out by the hospital end according to platform dynamic data, and hospital-community-family ternary closed-loop management is achieved. According to the method, the defects of information isolated island and execution disjunction in a traditional rehabilitation system are overcome, and the safety, the intelligence and the patient compliance are improved.

The invention discloses an intelligent database system and method for material and test data management, and belongs to the technical field of computer software. The system comprises a material management module which supports management and intelligent verification of material information with labels; the parameter management module supports dynamic creation of parameter types and parameters and configures labeling classification of'basic attributes', 'variable attributes' and'other attributes' for the parameters, and the'variable attributes' support association of a plurality of conditions; the experiment management module is used for inputting experiment data based on a parameter system and supporting template import; the data analysis module is used for comparing and displaying various attribute data of the selected experiment by adopting partition layout, and providing a chart drawing function for variable attributes; and the data query module supports advanced screening based on range combination of a plurality of basic attributes and visualizes a comparison result by using a histogram. According to the method, the problems that existing material management software is rigid in parameter, weak in data relevance and insufficient in analysis capability are solved.

The invention relates to the technical field of image recognition, in particular to a cable defect X-ray intelligent database construction method based on AI, and the method comprises the steps: determining an optimization factor of each edge point based on the distance between all feature points of each edge point and the characteristic degree of each feature point, so as to optimize the characteristic degree of each edge point; based on the optimization characteristic degree, screening out defect edge points from all the edge points; classifying all the connected domains based on the length-width ratio of the minimum enclosing rectangle of each connected domain; and on the basis of the texture features, geometric features and spatial distribution of the various connected domains and in combination with defect feature values, identifying real defect regions from the various connected domains so as to construct a cable defect database. According to the method, the problem that the false alarm rate of a traditional method is high due to the fact that complex background textures are highly similar to real defect features in the cable X-ray image is solved, and the accuracy of cable defect identification is improved.

This invention discloses a method for identifying foreign key relationships in databases based on metadata, relating to database technology, data processing, and natural language processing. The method first extracts database metadata and statistically analyzes core field features, generating a standardized metadata set through standardized preprocessing. Then, it parses this set to filter explicit primary keys, determines semantic primary keys based on metadata statistical features and semantic rules, and integrates these to generate a global primary key set. Subsequently, it encodes string features in the metadata set to generate candidate foreign key field pairs, and calculates the structural dependency degree (SDS) by fusing three metadata-derived features: value range coverage, primary key uniqueness coefficient, and reference dependency rate. Finally, it verifies and filters valid and suspected foreign key relationships based on a preset threshold, generating a standardized foreign key relationship result set. This invention does not rely on field naming, is robust to data noise, accurately identifies database foreign key relationships, and achieves full-process traceability and verifiability based on metadata, providing reliable structural support for data governance and intelligent database applications.

This invention discloses an intelligent database-driven method and system for multi-dimensional collaborative management of construction sites, relating to the field of collaborative control technology for engineering projects. The method includes constructing an object, relationship, and evidence ledger in an intelligent database; automatically expanding the affected scope and solidifying it as a unified basis after an item is entered into the database; classifying and adjudicating on-site handling based on quality, safety, and resources within the affected scope, generating a gap list and evidence requirements; dispatching handling actions and recording receipts in the database, updating the gap closure status to form preconditions; generating effective adjudications for plans, costs, and changes, and pre-setting review requirements for adjudications of on-site conditions; and recording and updating the ledger once an adjudication meets a threshold. The method of this invention automatically expands and solidifies the affected scope through a database, supports multi-dimensional adjudication recording and effectiveness with gap closure and evidence gating, and allows for playback and reconciliation, thereby achieving consistency in the scope of collaborative management of construction sites, verifiable decisions, traceable processes, and rapid reconciliation of disputes.

The invention relates to a head and neck tumor multi-modal image database construction method and a computer storage medium, and relates to the technical field of multi-modal image database construction.The method comprises the steps that S1, CT and MRI data of head and neck tumors and related clinical, pathological and gene cross-scale multi-modal data are collected through an image archiving and communication system (PACS) and a hospital information system (HIS); data quality control is carried out; s2, performing data anonymization and data desensitization processing on the acquired CT and MRI images; s3, carrying out standardization processing on all CT and MRI images, wherein the standardization processing comprises data resampling and data annotation; s4, multi-modal CT and MRI images and corresponding marks are classified, arranged and updated in real time; and S5, carrying out cleaning, conversion and quality control on the unstructured data. The system has the advantages that the blank of head and neck tumor image artificial intelligence databases at home and abroad is filled; a solid foundation is laid for research, development and training of an artificial intelligence model, and the method has great value for promoting precise diagnosis and treatment, mechanism research and clinical transformation of head and neck tumors.

The invention provides a database auditing rule intelligent generation system and method based on a large language model, and belongs to the technical field of database management, and the system comprises an intelligent domain specific language generation module and a rule explanation and verification engine. Wherein the intelligent domain specific language generation module is separated from the rule explanation and verification engine, and the rule explanation and verification engine does not depend on a large language model when executing auditing. The core idea of the invention is to separate responsibilities: the role of LLM is converted from an uncontrollable code generator into a controllable advanced demand analyst responsible for translating human intentions into a structured blueprint (namely DSL), and the final execution and verification of rules are completed by an absolutely safe and deterministic interpreter.

The invention discloses a clinical test (research) intelligent platform AI which integrates clinical test whole process operation practice and systematic, normative and standardized management and operation integration platform through intelligentization, systematization, a big data model and the like. The system comprises a researcher module, an applicant (applicant) module, a third-party module, an intelligent database module and an assistant AI which enriches clinical test operation experience, has knowledge such as domestic and overseas clinical test specifications, guidelines and medical data and can reply at any time. From the perspective of applicants (project owners or organizers) and clinical trials, on the basis of the implementation process and result of datamation and standardization, intelligent (computing power, model and the like) technologies are utilized, resources are integrated, and project management and operation platform AI, AI, AI, AI, AI, AI, AI, AI, AI and AI are invented for realizing systematicness, integration, standard, normative and large-scale clinical trials. The intelligent platform AI solves the problems of insufficient technology and personnel or resource waste, long project development time and the like of an applicant in a clinical test, and the clinical test can be completed normatively, efficiently and scientifically with less resources and manpower cost through the intelligent platform AI, so that the purpose that the applicant can complete the clinical test in a compliant and efficient manner by means of the intelligent platform AI is achieved; and the clinical test value of taking patients as the center is realized.

This invention discloses a method and system for full-process quality traceability of medical consumables, relating to the field of medical consumables technology. The method includes: collecting basic management information of medical consumables and performing preliminary processing to output initial medical consumables management data; inputting the initial medical consumables management data into a pre-constructed intelligent database for storage; tracking dynamic medical consumables usage data in real time, comparing the dynamic medical consumables usage data with the initial medical consumables management data stored in the intelligent database, and generating a real-time updated medical consumables quality status data table; performing anomaly analysis on the real-time updated medical consumables quality status data table, generating quality analysis results, and determining whether the quality analysis results exceed a preset threshold. If they exceed the preset threshold, a path backtracking analysis is performed on the quality analysis results using a data traceability model to generate a comprehensive traceability report. This invention ensures that every quality problem can be traced back to its source through comprehensive data tracking.

Embodiments of the present application provide a method for constructing a drilling and completion fluid AI large model for mineral resource drilling, which comprises: constructing a mineral resource drilling database and a drilling and completion fluid knowledge graph; constructing a basic model intelligent agent group based on the mineral resource drilling database and the drilling and completion fluid knowledge graph, obtaining a professional model tool set by standardizing the interfaces of the basic model intelligent agent group through encapsulation technology; based on a search enhancement generation mechanism, a knowledge graph and an intelligent database, dynamically searching related knowledge subgraphs, historical cases and engineering rules to determine knowledge nodes; based on an engineering and physical rule constraint training mechanism, inputting the output results of the knowledge nodes and the professional model tool set into a large language model to obtain a drilling and completion fluid AI large model for mineral resource drilling. The method realizes the full-domain intelligentization of "drilling and completion fluid design and regulation-drilling risk early warning and prevention-reservoir damage diagnosis and plug removal and production increase".

The application discloses a quality control method suitable for a thin plate Rockwell hardness automatic line, and relates to the technical field of Rockwell hardness measurement of cold-rolled thin plates. The quality control method suitable for the thin plate Rockwell hardness automatic line is characterized in that: the specific operation is as follows: S1, the measurement method is intelligentized, and an automatic integrated software is connected to a hardness tester PLC, different force value scales of the hardness tester can be remotely controlled, detection rules are sorted out according to national Rockwell hardness measurement standards and product standards, and specifically include: 1) "original generation rule": corresponding historical data according to the grade and product thickness. The quality control method suitable for the thin plate Rockwell hardness automatic line realizes intelligentization of automatic Rockwell hardness measurement of thin plates, writes the inspection rules in the national standards into the automatic measurement system program, forms "original generation rule", "subsequent switching rule" and "intelligent database", and realizes intelligent Rockwell hardness measurement of thin plates with different thicknesses and grades.

The application provides a drug delivery system intelligent database construction method and system, relates to the technical field of drug delivery, and comprises the following steps: constructing a drug delivery field ontology model, the ontology model comprises four levels of a carrier composition layer, a physicochemical property layer, an in-vivo behavior layer and a clinical efficacy layer, and the inter-layer causal relationship and the intra-layer correlation relationship are defined; using a language model to extract associated data of the carrier composition, the physicochemical property, the in-vivo behavior and the clinical efficacy, and generating standardized four-tuple data; storing the standardized four-tuple data into a database to construct a structured database; and training a prediction model based on the structured database. The application first realizes the structured expression of a complete causal link of a delivery system, greatly improves the automatic extraction efficiency of delivery knowledge, and endows the database with the quantitative prediction and formula reverse optimization capabilities, effectively accelerates the delivery system design iteration, and reduces the experimental trial and error cost.