137 results about "Database model" patented technology

The invention provides an otoscope image processing system for establishing database model analysis based on deep learning, and relates to the technical field of image processing, the otoscope image processing system comprises an acquisition layer, a processing layer, an extraction layer, a modeling layer and an analysis layer, the processing layer adopts an AI image expansion algorithm to carry out fusion splicing and interference elimination processing on acquired images, and the extraction layer adopts a mixed feature extraction method to extract color, texture and shape features of the images processed by the processing layer and introduce MPEG-7 descriptors to express the features; according to the method, otoscope images of a patient are collected, features and feature values of the images are extracted, a focus global shared image library is constructed through deep learning modeling, the focus global shared image library is compared with the otoscope images of the patient for training, and corresponding focuses are determined, so that different ear diseases can be automatically identified and diagnosed by learning and analyzing a large amount of ear image data, and the diagnosis accuracy is improved. And more accurate and timely diagnosis information is provided for doctors.

The invention relates to a task-driven multi-agent emergency decision support method and device, and the method comprises the steps: constructing a sub-task for a risk guidance decision support model, and dividing a complex task into simple sub-tasks, so as to achieve a more precise emergency decision support process; constructing an interaction agent of each subtask, and constructing corresponding prompt engineering contents according to the definition to realize the construction of analysis agents and inspection agents of different tasks; collecting safety analysis report data, and establishing a sample database supported by emergency operation instruction decision; performing model training, handing over the sample database to an intelligent agent, and constructing a success case library and a failure case library; over-benchmark originating event information is input, and a decision support procedure for the described originating event is generated. Therefore, the problems that an existing emergency plan decision-making method is extremely dependent on expert knowledge and is limited by human cognition, so that field personnel are difficult to make quick decisions on events exceeding the emergency plan and the like are solved.

The invention relates to a venipuncture target recommendation method based on image processing and an artificial intelligence algorithm. The venipuncture target recommendation method comprises the following steps: S1, performing image enhancement processing on an input infrared image; s2, executing a hand image blood vessel segmentation task; s3, judging whether a blood vessel region exists in the hand graph or not; s4, segmenting the center line of the blood vessel, and dividing the blood vessel into a plurality of independent sections according to the bending characteristics of the blood vessel; and S5, summarizing a target spot rule suitable for puncture by utilizing the database model, and determining key characteristics of the puncture target spot according to the target spot rule. According to a blood vessel puncture rule, a linear regression model is established, the weight of each feature of a puncture target is calculated, the score of each potential puncture target is obtained in a weighted summation mode, and the puncture target with the highest score is selected for recommendation. The target spot and the puncture direction which accord with the puncture habit of a nurse can be more accurately selected. And the accuracy of puncture operation is improved, so that the success rate and the safety of puncture are remarkably improved in practical application.

The invention discloses a safety check system and method based on a hidden danger database model, and the method comprises the following steps: S1, building a hidden danger table, a check record table and a rectification record table, and constructing a hidden danger rule base; s2, collecting hidden danger data, and storing the hidden danger data into a hidden danger database model after standardization processing; s3, constructing a hidden danger knowledge graph, and generating a feature matrix; s4, inputting the data and the feature matrix to the time sequence model, and generating a risk scoring matrix; s5, based on the risk score and the knowledge graph, optimizing hidden danger grading and updating the rule base; s6, inputting a multi-modal hidden danger recognition model, and classifying hidden dangers; s7, optimizing task distribution, updating a hidden danger state and storing the hidden danger state into a database; and S8, integrating the data to generate a dynamic characteristic label, optimizing the model, and outputting a heat map and a trend report. Through multi-modal data fusion and dynamic analysis, intelligent management of hidden danger detection, risk assessment, task allocation and model optimization is realized, and the safety check efficiency and accuracy are greatly improved.

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 coal mine natural fire prediction and alarm method based on a CO isotope database, and relates to the technical field of coal mine fireproof safety. S1, sample collection; s2, temperature programming; s3, isotope analysis; s4, constructing a database; and S5, real-time monitoring. Gas samples at specific positions under a coal mine are collected in real time, CO isotope composition of the gas samples is analyzed, data are input into an established database model, the occurrence probability and the development trend of the natural fire of the coal mine are rapidly and accurately predicted through comparative analysis, and an alarm signal is sent out in time when a preset danger threshold value is reached. Scientific basis and sufficient response time are provided for coal mine disaster prevention and control.

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 provides a batch drawing method and device based on multiple intelligent drawing models, equipment and a medium, and the method comprises the steps that a batch drawing task is created, the batch drawing task comprises a plurality of drawing sub-tasks, and each drawing sub-task corresponds to a first drawing prompt word; obtaining a model database, wherein the model database comprises a plurality of AI drawing models; based on the first drawing prompt word corresponding to each drawing subtask, a corresponding target drawing model group is determined from a plurality of AI drawing models included in a model database, and the target drawing model group comprises at least two target drawing models; and performing image batch drawing based on the first drawing prompt word corresponding to each drawing sub-task by adopting the at least two target drawing models corresponding to each drawing sub-task to obtain a batch drawing result corresponding to the batch drawing task. Therefore, the corresponding images are intelligently drawn through the multiple AI drawing models built in the system, and the image generation efficiency is effectively improved.

The invention provides a belt inspection robot system, and relates to the technical field of safety inspection, the belt inspection robot system comprises a robot, a track and a remote control station, the robot moves along the track, one end of the track is provided with a charging unit and a traction device, and base stations are distributed along the track; a walking module, a charging control module, a central control module, a multi-parameter acquisition module and a communication module are arranged in the robot, and the central control module is used for receiving acquisition information transmitted by the multi-parameter acquisition module and transmitting the acquisition information to a remote control station through the communication module and a base station; the remote control station is used for controlling the robot in real time and analyzing and storing data, images and sound transmitted by the base station in real time. According to the invention, continuous 24-hour automatic inspection is realized; multiple parameters such as images, sound, gas and temperature can be acquired and judged at the same time; various database models are automatically established, and automatic analogy is facilitated; and the inspection efficiency in a complex environment is improved.

The invention provides an enterprise knowledge robot system based on LLM and RAG, which comprises a data preprocessing module, a vector database, an RAG model and a user interaction layer, and is characterized in that the data preprocessing module is used for preprocessing unstructured data of an enterprise and converting text data into a high-dimensional vector; the vector database is used for storing the preprocessed vector data and designing an index structure for optimizing the retrieval efficiency of the vector data; the RAG model generates a question and answer result through LLM, a vector database and a Rerank rearrangement technology; and the user interaction layer interacts with the RAG model to perform question answering and result display. According to the method, the LLM and RAG technologies are utilized, the system can extract most relevant information from a huge knowledge base, generated answers are more accurate and have context correlation, and specific requirements of users are met.

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 user self-management system embedded with a seasonal allergic rhinitis expert database, and belongs to the technical field of big data health management, and the system comprises an expert database construction module which is used for constructing an expert database; the model supervised training module is used for supervising and training the IMB intervention model; the engine connection establishing module is used for establishing engine connection, and the role permission and the index matrix have a corresponding relation; the data formatting module is used for receiving the rhinitis data, identifying and carrying out semantic analysis, and converting and determining formatted data; the guidance information determination module is used for user guidance information; and the user self-management module is used for carrying out terminal visual display on the user guidance information and carrying out user self-management. According to the method, the problem of how to provide targeted guidance according to the seasonal environment variable and the user state to realize effective self-management of the user is solved.

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.