111 results about "Database model" patented technology

The invention discloses a method for migrating a traditional electric power SCADA system to a new energy system, and relates to the technical field of electric power system migrating, and the method comprises the steps: building an instance object of each equipment model according to the type of electric power equipment and a four-remote configuration point table, and carrying out database model instance configuration and batch implementation through a model configuration tool, forming a model instance set configuration taking a partition or equipment as a unit; generating a power system calculation formula script by using a calculation formula configuration simulation tool, and recording the information relationship of each virtual point through a standardized data format to realize export and import of calculation script configuration; importing and exporting partition pictures by using a graphic configuration tool to realize automatic configuration from an original engineering project to a new project; and according to the equipment type, classifying the matched configuration image templates, and realizing image static loading and template dynamic configuration. The method can change few codes, smoothly upgrade to adapt to various scenes, and reduce the engineering workload.

The present invention discloses a safety inspection system and method based on a hidden danger database model, comprising the following steps: S1, establishing a hidden danger table, an inspection record table, and a rectification record table, and constructing a hidden danger rule base; S2, collecting hidden danger data, and storing it in the hidden danger database model after standardization; S3, constructing a hidden danger knowledge graph and generating a feature matrix; S4, inputting the data and the feature matrix into a time series model to generate a risk scoring matrix; S5, optimizing the hidden danger classification and updating the rule base based on the risk score and knowledge graph; S6, inputting a multimodal hidden danger identification model to classify the hidden dangers; S7, optimizing task allocation, updating the hidden danger status and storing it in the database; S8, integrating data to generate dynamic feature labels, optimizing the model, and outputting a heat map and trend report. Through multimodal data fusion and dynamic analysis, the present invention realizes intelligent management of hidden danger detection, risk assessment, task allocation, and model optimization, significantly improving the efficiency and accuracy of safety inspections.

The invention relates to a large language model driven virtual knowledge graph automatic construction method based on a mapping mode, and the method comprises the following steps: 1, extracting database table structure metadata and a constraint relation, and converting a database mode into a graph structure based on an RDF standard; 2, converting the mapping modes into query statements conforming to SPARQL grammar, querying RDF graph structures corresponding to the database modes, and obtaining extraction results of the mapping modes; step 3, based on the semantic coding model and the generative large language model, aligning the database table structure metadata and the constraint relationship into the ontology vocabulary, and obtaining an alignment result and an alignment confidence from the database to the ontology; and 4, according to a mapping mode extraction result and an alignment result from the database to the ontology, performing ontology expansion and virtual knowledge graph mapping construction based on the alignment confidence. According to the method, the virtual knowledge graph construction process can be automated, the ontology structure can be expanded according to needs, and resource consumption in the construction process is reduced.

The invention relates to an intelligent beneficiation control system based on big data, which is applied to the technical field of beneficiation, and comprises a data acquisition and transmission unit, a data analysis and decision unit and an execution control and feedback unit, the data acquisition and transmission unit comprises a data acquisition module and a data transmission module, and the data analysis and decision unit comprises a special library modeling module and a data analysis module. According to the intelligent mineral separation control system, experience knowledge of experts in the mineral separation field is fully fused, so that when facing complex and changeable mineral separation working conditions, the system can accurately make decisions according to mineral separation data and other factors like guidance of professional mineral separation teams aside; and the control instruction is generated according to the decision, and the ore dressing equipment is controlled to execute the corresponding operation according to the control instruction, so that the complex ore dressing process can be effectively and accurately controlled, the intelligent level of ore dressing production can be remarkably improved, and the ore dressing efficiency and the product quality are improved.

The invention discloses a profile alignment-based steel rail abrasion real-time discrimination method and model, and the method comprises the steps: building a steel rail standard profile database model which is used for receiving actual measurement profile data and carrying out the real-time overlapping display; acquiring a point with the minimum vertical coordinate of the outer contour of the steel rail profile from the actually measured profile data, and overlapping the point with the minimum vertical coordinate with the reference point of the standard profile to realize primary alignment of the profile; calculating a perpendicular bisector at the rail waist of the actual measurement profile, and enabling the perpendicular bisector to coincide with the center line of the standard profile to finish rough alignment of the profile; and calculating the center point of the actually measured profile rail web, performing fitting processing to obtain a fitting center line, and enabling the fitting center line to coincide with the standard profile center line to complete profile fine alignment. According to the method, the precision of profile alignment is effectively improved, meanwhile, the influence of profile form errors on an abrasion algorithm is reduced, and then the problem of jumping during alignment of the actually measured profile and the standard profile is solved.

The invention relates to the technical field of artificial intelligence, and particularly provides a visual network optimization AI assistant implementation method and device, based on Transform and LLM, the method comprises the following steps: S1, a database modeling module carries out dimension definition, index definition and term library construction; s2, the voice interaction module selects a basic model to carry out data fine tuning and keyword enhancement; s3, the LLM-SQL generation module uses a GPT-4 or an open source LLM as a base; s4, the SQL correction module carries out logic verification and term replacement; s5, establishing a mapping table of aliases and actual fields by a field mapping module, and expanding dynamic fields; and S6, performing chart engine and geographical rendering by a multi-modal presentation module. Compared with the prior art, the method has the advantages that the development cost can be remarkably reduced, the user query flexibility and the data presentation intuition are improved, and the network optimization decision is effectively supported.

The invention discloses a melamine content detection method based on a terahertz spectrum technology. The melamine content detection method comprises the following steps: preparing a standard melamine sample in a predetermined gradient content range; the method comprises the following steps: collecting THz spectral data information of a standard melamine sample in a test range of 0.5-3 Thz, and carrying out de-noising treatment on a spectrogram signal to obtain an absorption peak spectrogram of the melamine sample; the method comprises the following steps: determining a content-related characteristic peak signal in a THz spectrogram of a melamine sample, and establishing a database model between the melamine content and the characteristic peak signal; and constructing a database calculation model, collecting THz characteristic absorption peaks of melamine with unknown content, and inputting the THz characteristic absorption peaks into the calculation model to obtain a corresponding melamine content detection result. According to the melamine content detection method based on the terahertz spectrum technology, rapid response can be achieved, the detection efficiency is greatly improved, a sample is analyzed in a non-contact mode, the integrity of the sample is kept, cross contamination and operation errors possibly introduced in the traditional chemical detection process are avoided, and the detection accuracy is improved. And nondestructive, rapid and efficient detection of the components and the content of the melamine is realized. And important application values are provided for melamine content detection and food safety additive detection in the environmental field.

A Hypothesis Generation and Testing System (HGTS) includes a framework for enabling artificial intelligence (AI) systems to autonomously formulate, test, refine, and store hypotheses using structured experimental workflows. HGTS may integrate with, but operates independently from, traditional databases, model repositories, context-aware AI databases and the like to provide a persistent, traceable, and interpretable record of hypothesis lifecycles. The system incorporates a probationary hypothesis database for unverified ideas, a validation engine for controlled experimentation, a confidence scoring and lifecycle management agentic subsystem for hypothesis evaluation, and recursive learning agents that iteratively refine models and experimental methods. Modular agents autonomously propose, test, and document hypotheses using statistical, symbolic, and deep learning techniques. The system ranks outcomes and retains full contextual metadata, enabling reproducible discovery. HGTS thereby provides a foundational mechanism for reproducible, interpretable, and self-directed intelligence, forming a cornerstone technology for artificial general intelligence (AGI).

The invention discloses a dry vacuum pump vibration data analysis system and method based on big data, and belongs to the technical field of data analysis. The system comprises a vacuum pump control module, a data acquisition and transmission module, a database, a model management and analysis module, an intelligent judgment module, an early warning module and a display module, the vacuum pump control module is used for starting and closing the dry vacuum pump; the data acquisition and transmission module is used for acquiring parameter data and vibration amplitude data; the database is used for storing historical data; the model management and analysis module is used for establishing a vibration analysis model and analyzing the influence of different parameter data on the vibration amplitude in the process of extracting the gas in the container by the dry vacuum pump; the intelligent judgment module is used for judging whether the current dry vacuum pump has a fault risk or not; the early warning module is used for carrying out fault alarm; and the display module is used for providing an interaction platform and carrying out digital display.

The invention discloses an intelligent early warning method and system based on laser control industrial data, and belongs to the technical field of laser safety early warning. The system comprises an equipment control module, a data acquisition module, an analysis database, a model management module, a monitoring judgment module and an alarm module, the equipment control module is used for controlling the laser to cut the industrial product; the data acquisition module is used for acquiring cutting data and parameter data generated in the industrial product cutting process; the analysis database is used for storing historical data of the cutting data and the parameter data; the model management module is used for establishing a deviation analysis model and analyzing the influence of different influence parameters on the cutting deviation of the industrial product; the monitoring judgment module is used for judging whether current industrial product cutting is abnormal or not; the alarm module is used for carrying out abnormity alarm; according to the method, the influence of different influence parameters on the cutting deviation of the industrial product is considered, and the accuracy of system data analysis is improved.

The invention discloses an AI insole modeling system based on multi-modal biomechanics and a 3D printing method, and relates to the technical field of biomedical engineering, and the AI insole modeling system specifically comprises an external parameter calibration module, an image processing module, a three-dimensional reconstruction module, a measurement and analysis module, a foot database establishment module, an insole database modeling module and an insole 3D printing module. Based on the biomechanical principle and the digital medical technology, a full-link digital service system of three-dimensional scanning, AI modeling and 3D printing is constructed, firstly, based on a foot scanning and analysis system of multi-view three-dimensional reconstruction, through the processing flow of camera calibration, image distortion removal, semantic segmentation, multi-view three-dimensional reconstruction and measurement, a full-link digital service system of three-dimensional scanning, AI modeling and 3D printing is constructed; and finally, key parameters of the foot length, the foot width, the hallux valgus angle and the flatfoot degree are output, high-precision foot modeling and measurement are achieved, accurate customization of the orthopedic insoles is achieved, and the device is suitable for the fields of medical diagnosis, orthopedic shoe customization and sports science and has the characteristics of high precision, automation and modularization.

This invention discloses an automatic update system for website article, tag, and category matching models, relating to the field of model update technology. It includes a data input module, a recommendation query unit, and a training unit. The data input module includes inputting query data and matching keyword data with data obtained from the database by the recommendation query unit to obtain matching data and corresponding articles. The database used is the Milvus database. This application optimizes the model and its performance, accelerating the training speed. Furthermore, this application uses the Milvus database, the Roformer model, and the prompt technique to solve the problem of requiring multiple manual training and restarts for a single site. Model updates are convenient and do not affect business operations. The same model can be adapted to data groups from multiple sites, and the problem of repeated training across multiple sites is solved.

The invention relates to a resource networked modeling and scheduling optimization method based on a Neo4j electrical diagram database, and the method comprises the following steps: building a diagram database model based on a power system structure; flexible resources are extracted and managed based on a graph calculation method; and constructing a graph structure scheduling strategy system. Visual representation and multi-dimensional attribute nesting of the power system topology are achieved, real-time identification, dynamic updating and hierarchical classification are carried out on adjustable loads and controllable equipment, high expansibility, high real-time performance and high adaptability are achieved, the method can be widely applied to novel power systems represented by a power distribution network, a park micro-grid and a source-load-storage cooperative system, and the method is suitable for popularization and application. And accurate modeling and intelligent regulation and control of flexible resources are realized. Compared with an existing SQL (Structured Query Language) or relational database scheduling mode, the method has remarkable advantages in the aspects of modeling granularity, response speed and system integration.

The invention discloses a traffic influence autonomous evaluation system based on natural language processing and a working method, and belongs to the technical field of traffic management. In order to solve the problem of difficulty in urban traffic multi-scene analysis iteration, the system comprises a basic data layer, an NLU engine / intelligent processing layer and a user interaction layer, and the basic data layer, the NLU engine / intelligent processing layer and the user interaction layer are connected in sequence; the basic data layer comprises a road network facility database, a model parameter library, a traffic demand matrix and a traffic partition database; the NLU engine / intelligent processing layer comprises a natural language understanding NLU engine, a traffic analysis modeling evaluation module and a report generation module; and the user interaction layer comprises a Web / App graphical user interface, a voice / text input interface, a parameter confirmation and modification panel and an AR / Web visual report board, and realizes interaction between a user and the traffic influence autonomous evaluation system based on natural language processing. The method has the high efficiency of the whole process.

The application discloses an online education data intelligent planning system and method based on big data analysis, and belongs to the technical field of education data analysis. The system comprises a learning text management module, a data acquisition module, an analysis database, a model management module and a test question generation module; the learning text management module is used for collecting different learning texts for user learning and calculating the complexity of each learning text; the data acquisition module is used for respectively acquiring reading behavior data and test question data of different user learning texts; the analysis database is used for storing historical data; the model management module is used for analyzing the influence of different reading behaviors on the understanding degree of user learning texts; and the test question generation module is used for automatically generating a test question set about the user and pushing the test question set to the user; the application automatically generates a test question set about the user by connecting the weak knowledge points of the user, and improves the intelligent service level of the online education platform.

The present invention relates to a method based on multimodal code generation and progressive delivery, which belongs to the field of code generation technology and natural language processing. The present invention builds a visual low-code development platform based on a basic template, which sets configuration data through a visual page; generates database model data based on the configuration data, and pregenerates basic code in combination with the basic template; inputs the generated basic code into a pre-trained PLBART model; uses the PLBART model to reason and complete the language syntax and semantics, and outputs executable code; pushes the executable code to a code warehouse, and automatically builds, tests, and packages the executable code; uses a grayscale release strategy to achieve grayscale release and progressive delivery; the present invention realizes systematic code generation through multi-modal data processing, organically combines grayscale release, and achieves progressive delivery, significantly improving development efficiency, code quality, and system reliability.

The application provides a database model automatic construction method and system, and the method comprises the following steps: obtaining information in a first table structure table, wherein the information in the table structure table at least comprises the following technical fields: unique identification ID, data flag, release time, update time, update ID, valid flag, and deletion time; the unique identification ID is generated according to the release time of the information, and when multiple pieces of information are released at the same time, the unique identification ID is sorted; the data flag is used for recording data generation flag; the update ID is generated according to the update time of the information, and when multiple pieces of information are updated at the same time, the update ID is sorted; and a first database model is automatically constructed according to the information in the first table structure table. The database model automatic construction method and system provided by the application can realize automatic construction of a database by providing standardized technical fields, avoid mistakes in understanding and operation of database model establishment by developers, and greatly improve work efficiency.

A database model established for a low-code platform is characterized by comprising an engineering basic information table, a component definition table, a component type table, an engineering component association table, an engineering variable table, an engineering event registry, a data source registry, a data set registry and a data set field definition table, the model designed by the invention adopts a standardized structure, so that data storage and management are more orderly, subsequent data maintenance and query are facilitated, and the problems of data redundancy and inconsistency are reduced. Through the clearly defined table relation and foreign key constraint, the user can more effectively manage the relation between the data, the integrity and consistency of the data are ensured, and the efficiency of data operation is improved. The method can adapt to various business scenes and requirements, a user can easily adjust and expand the data model according to different business logics, and the expandability and adaptability of the system are enhanced. According to the invention, through reasonable database design, the capability of data-driven decision making is improved.

The invention discloses a finite element-based calculation method for heat distribution of a large-scale three-dimensional integrated circuit (3DIC). The method comprises the following steps of: establishing a large-scale 3DIC model; performing heat distribution calculation on the established large-scale 3DIC model; for an existing heat dissipation method, 3DIC is systematically looked up, and important parameters in the aspects of geometry, heat dissipation, heat sources and the like are collected; parameterizing geometry, heat dissipation, a heat source and the like; establishing a large-scale 3DIC model database; performing heat distribution calculation on the database model; collecting a database model heat distribution calculation result, and inputting the result into a database; and establishing an artificial intelligence / machine learning optimization system. According to the method, the finite element analysis FEA technology with an accurate solving function is combined with an efficient XSim calculation tool, high accuracy and calculation efficiency are achieved, the super-strong performance of comprehensively simulating thermal behaviors of the whole packaging large model and the surrounding thermal environment of the whole packaging large model is achieved, and the heat distribution calculation problem of large three-dimensional integrated circuit 3DIC packaging is solved.

The invention provides a database object relation mapping method based on secondary equipment of an electric power system, which comprises the following steps: analyzing and classifying the secondary equipment of the electric power system, defining attributes and parameters of different equipment, sorting association relations among the equipment, and forming a secondary equipment object information system; designing an adaptive database model according to a secondary equipment object information system, and creating corresponding data tables for different types of secondary equipment to realize effective storage and management of secondary equipment data; the method comprises the following steps: mapping secondary equipment in a power system and related data thereof into a database through a data relation mapping method of the secondary equipment in the power system, and establishing a corresponding data relation; and querying and analyzing the data according to actual requirements through the mapping relationship. The method has the advantages that the problems existing in data management of secondary equipment of a traditional power system are effectively solved, the efficiency and reliability of data management and application are improved, and powerful support is provided for safe and stable operation of the power system.

A method is described for communicating with a computer device comprising a knowledge database modelling data in the form of a knowledge graph. The method includes, on the device, receiving a first request comprising information relating to an entity of the knowledge graph; commanding the rendering of a web page containing this information; receiving a second request requesting at least one missing property of said entity, from among said rendered information; commanding the rendering of a web page containing a list of missing properties classified by the observation frequency of these properties for other entities of the same type; polling a language model, based on a prompt generated by said device in natural language, with said prompt asking the language model for the value of at least one of the properties in the list; and commanding the rendering of a web page containing that value.

The application discloses a batch processing method of an AI database based on postgresql. Based on multiple pieces of data in the postgresql database, the multiple pieces of data are input into an AI model for inference calculation. The process of the inference calculation comprises sequentially performed data loading, data preprocessing, AI model inference calculation and result type conversion. A sliding window is constructed by using a built-in window function of the postgresql database, the process of the inference calculation is batch processed by using the sliding window, and after the batch processing, the data after the inference calculation is output piece by piece in a backward sliding window mode. The batch processing method proposed by the application significantly improves the inference speed of the AI database model based on postgresql, and realizes batch processing of data without changing the database kernel.

The application discloses an intelligent splitting and assembling method of a database model based on a business scene, belongs to the technical field of database management and application, and relates to the technical field of database management and application. The splitting method comprises the following steps: converting an abstract or unstructured business model into a calculable or structured graph representation form, mapping a business function unit into a node in the graph, mapping the relationship between businesses into an edge, and constructing a business topology structure. The assembling method comprises the following steps: acquiring the business topology structure, automatically analyzing a natural language business requirement into a structured requirement model, performing feature extraction on the analyzed requirement, generating a multi-dimensional feature representation, and assembling business model slices in a progressive matching mode. The application can improve the database operation efficiency, reduce the database construction and management cost, and enhance the adaptability and flexibility of the database to business changes.

The embodiment of the invention provides a database query statement generation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out graph sampling on a database model graph, and selecting a target data column from a sampling result to construct a target node; detecting the validity of the connection edge of the target node to adjust the data column in the target node; constructing a database model sub-graph based on the target node; and based on the database model sub-graph, generating test data through a large language model. According to the embodiment of the invention, the method comprises the steps: carrying out the sampling of a database model graph, obtaining candidate nodes, selecting a target data column from the candidate nodes, and constructing a target node to reduce the subsequent database structure information needing to be processed; meanwhile, the validity of the connection edge of the target node is detected, and the target node is adjusted based on the validity, so that the reliability of the database model subgraph is improved, and the semantic integrity and rationality of the subsequently generated test data are improved; and finally, based on the database model sub-graph, generating test data on a large scale.

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 application discloses a kind of plant station end panoramic data and the dynamic linkage method of monitoring system application, comprising the following steps, in the panoramic model data of plant station end, the attribute value of each data is increased, and source end management is carried out to whole model data by integrated configuration configurator, integrated configuration configurator is wired terminal information (reference) as keyword, the mapping of model data attribute and monitoring database record field is realized, by the mapping relationship of model data and monitoring database, and then automatically realize the one-time modification of monitoring system database data and data attribute, the dynamic association of each application and database model data of monitoring system, automatically identify the change of database data and data attribute, realize the dynamic loading of data.The method reduces the workload of multiple, repeated modification data of operation and maintenance personnel, reduces the error risk of human intervention, improves operation and maintenance efficiency.

The present application relates to the technical field of database construction, and particularly relates to a construction method of a foraminifera morphological feature database, comprising the following steps: S1, collecting foraminifera images from multiple sources, identifying scale drift and shooting deviation based on principal axis normalization and local vorticity field, performing structure-preserving image enhancement, and outputting an enhanced image set consistent in shell substance, shell arrangement, shell opening, suture line and micro-pore features; S2, extracting a multi-scale shell structure atlas, constructing a structure-semantic coupled label nested tree, performing polysemy recognition and ambiguity resolution, and generating a structured annotation set; S3, fusing the image set and the annotation set, and constructing a three-layer database model containing a part layer, an attribute layer and a variation layer according to the label nested tree. Through image structure enhancement, semantic ambiguity resolution and three-layer database construction, the present application effectively improves the recognition accuracy of foraminifera morphological features, the label organization logicality and the information retrieval efficiency.

The invention relates to an intelligent application development and deployment platform, and belongs to the technical field of artificial intelligence. The platform comprises training resources, a model development module, a model training module, a model deployment module and a large model agent. According to the method, flexible scheduling of computing resources is realized on the server, universal goods shelves such as a database, a model library, an algorithm library, a lightweight library and an operator library compatible with multiple development frameworks are created, tools such as multi-modal data labeling, model conversion, mirror image packaging and online development are integrated, services such as intelligent labeling and cloud edge collaboration are preset, rapid updating and iteration of models are supported, and the development efficiency is improved. And carrying out one-key deployment on an end-side algorithm. The platform can form a replicable and generalizable product through the combination of a basic type and a specific component, and the platform can be applied to the same row in the industry and has a wide application prospect.

The invention discloses a server monitoring method based on PHP. The method comprises the following steps: installing a PHP environment and a related dependency library on a server; calling a system command through the PHP script to obtain a performance index of the server, and developing an API (Application Program Interface) for providing the performance index of the server during monitoring system calling; a monitoring system PHP script program and a corresponding database model are established and used for calling an API interface on a server to obtain storage server performance data; running a PHP script program of the monitoring system at regular time, and storing the obtained performance data into a database; establishing a user interface of a Web-based monitoring system for checking server performance indexes and threshold values in real time and setting corresponding alarm rules; and when the server performance index exceeds or is lower than a set threshold value, sending an alarm to inform server operation and maintenance personnel. According to the invention, the defects in the prior art are overcome, the real-time monitoring and alarm functions of the server can be realized, the stability and performance of the server are improved, and the normal operation of online services is ensured.

The invention discloses an apnea knowledge question and answer model construction method and device based on knowledge enhancement, and the method comprises the steps: constructing a database containing apnea knowledge and a test set, and carrying out the apnea knowledge question and answer model on the basis of the database through employing a predetermined external knowledge decoding strategy, a deep and shallow knowledge decoding strategy and preset probability distribution, and constructing an apnea knowledge question-answer model, and testing the apnea knowledge question-answer model by using the test set. Therefore, according to the apnea knowledge question and answer model construction method and device, on the basis of the question and the constructed apnea knowledge database, the model decoding emphasizes external knowledge, emphasizes high-level knowledge and reduces an algorithm of shallow-layer knowledge, so that the accuracy of answering the apnea question by a large model is improved; the problem that in a specific scene of apnea knowledge questions and answers, a natural language processing method is poor in answer accuracy due to insufficient professionality is solved.