2615 results about "Data model" patented technology

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention relates to the technical field of pipeline monitoring and artificial intelligence, in particular to a multi-source data fusion pipeline monitoring method and system. The method comprises the steps of performing field sorting, structure unification and risk segmentation processing by obtaining pipeline line data, historical operation archives and strategy update configuration records, and generating a session primary key configuration table; a multi-source acquisition time window is configured, an acquisition task is issued, time anchor point registration and field aperture unification are completed, and a multi-source session data packet set is generated; performing session and risk unit association, performing multi-modal feature extraction and cleaning aggregation based on artificial intelligence, and constructing a pipe network risk map structure by using a map structure data model; and calling a multi-task reasoning model and a rule component based on the atlas, and performing risk type reasoning and grade judgment to obtain a risk assessment result and a strategy updating record. According to the invention, intelligent fusion of multi-source data and closed-loop optimization based on machine learning can be realized, and intelligence and reliability of pipeline safety management are effectively enhanced.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention provides an Internet of Vehicles data processing method and system. The method comprises the following steps: determining a protocol type of a protocol message through a protocol parser and calling a matched parsing rule to parse the protocol message; judging whether a target processing logic rule exists or not through a semantic mapping engine according to the combined protocol information corresponding to the protocol message; if not, querying and reading a first mapping configuration file matched with the combined protocol information from a configuration file library, constructing a target processing logic rule based on configuration data of the first mapping configuration file, and processing the analyzed protocol message into target Internet of Vehicles data according to the target processing logic rule; if so, directly processing according to the target processing logic rule to obtain target Internet of Vehicles data; and generating an Internet of Vehicles unified data model object according to the target structure and the target Internet of Vehicles data through an object generator, and sending the Internet of Vehicles unified data model object to a service system. According to the method and the device, the thorough decoupling of the upper layer service and the bottom layer protocol is realized, the overall maintainability and expansibility are improved, and the data processing efficiency is improved.

The invention provides a grouting simulation method and system based on time sequence interaction and self-adaptive grid reconstruction, and relates to the technical field of grouting engineering.The grouting simulation method comprises the steps that monitoring data of the grouting construction process are obtained, the monitoring data are input into a grouting diffusion prediction model, a grouting pressure prediction value is output, and a series of prediction responses are conducted based on the grouting pressure prediction value. Obtaining a grouting pressure predicted value, a boundary point set of the grouting diffusion area, the area and the volume of the grouting diffusion area and a diffusion response result of the pressure distribution diagram; constructing a geometric model of the grouting area based on the diffusion response result, and performing grid unit division and grouting diffusion area mapping on the geometric model; and performing local grid encryption and reconstruction on the grid units according to a density control rule to obtain a structured grid data model, adaptively updating the structured grid data model by using real-time monitoring data, and performing real-time visual grouting optimization control. According to the invention, the high-precision prediction response of grouting is improved.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a DFT test task management method and device, electronic equipment and a storage medium, and belongs to the technical field of chip design verifier.The DFT test task management method comprises the steps that a data model is constructed on the basis of a design module list and a test step list, the data model is used for indicating testability design DFT test steps of all design modules, and the test steps of all the design modules are stored in the test step list; the data model comprises a plurality of DFT test tasks, attributes of each DFT test task comprise a selection state, and the selection state comprises a selected state and a non-selected state; based on the interactive input, determining at least one DFT test task of which the selection state is selected as an executable DFT regression object; and according to the DFT regression object, driving a DFT test task to be executed. According to the method, the DFT test tasks are normalized through the data model, only the DFT test tasks need to be selected for making the test plan, efficient management of the DFT test tasks is achieved, and the regression test efficiency is improved.

The invention discloses an intelligent construction and tracing method and device for an attack graph, and relates to the technical field of network security. The method comprises the steps of performing semantic analysis and entity relationship extraction on a multi-source heterogeneous security log according to a predefined structured security data model, and generating a standardized security entity relationship triple set; based on the set, taking an entity in an initial alarm as a starting point, and adopting an iterative closed loop driven by a large language model to dynamically construct an attack graph; and carrying out attack technique and tactics mapping and threat attribution based on the final map, and generating a response strategy of priority ranking. According to the method, automatic and high-precision source tracing and response of the attack chain are realized, and the problems that the prior art depends on static rules and semantic segmentation and lacks dynamic reasoning capability are effectively solved.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention relates to the technical field of data processing, discloses a digital twin adaptive decision platform for TBM tunneling and a control method, and aims to solve the problems of single sensing dimension, mutual separation of physical and data models, disjunction of early warning and control links and low man-machine interaction efficiency in the prior art. According to the scheme, the method mainly comprises the steps that multi-source real-time data is collected through a hardware sensing layer, after the multi-source real-time data is processed through a data management and fusion layer, a digital twin model layer constructs a physical-data fusion model and completes risk assessment, finally risk visualization is achieved through an augmented reality interaction layer, and an intelligent control decision-making layer generates a control instruction and outputs the control instruction to a TBM main control system. According to the integrated intelligent decision-making platform and the control method, multi-source perception deep fusion, physical and data model online coupling, risk assessment and equipment control second-level closed loop can be realized, and visual interaction can be realized through augmented reality.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

The invention discloses a bearing interpretability fault diagnosis method based on digital-analog collaborative federated learning, and relates to the field of fault diagnosis and federated learning. The method comprises the following steps: S1, generating first-level embedded mechanism information of a user local end based on a dynamic state issued by a central server; s2, generating second-level embedded mechanism information of a user local end based on a fault frequency index issued by the central server; s3, constructing an expert knowledge matrix based on the signal features to generate third-level embedded knowledge information of a user local end; and S4, constructing a loss function based on a multi-constraint regular term, and carrying out physical-knowledge three-level embedded digital-analog linkage learning based on the loss function. According to the method, the feasibility of deep interaction of the physical model and the data model is shown, and a feasible solution is provided for implementing interpretable digital-analog collaborative diagnosis among users under a federated framework.

The invention discloses a road internal damage assessment method, device and equipment based on three-dimensional GPR data and a storage medium, and the method comprises the steps: determining a comprehensive disease weight corresponding to each disease in each layer of horizontal slice based on disease distribution feature information of a three-dimensional GPR horizontal slice data model, determining the internal damage rate of each layer of horizontal slice based on the comprehensive disease weight, determining the initial internal damage index of each layer of horizontal slice based on the internal damage rate, and performing through punishment correction on the through damage related layer to obtain the internal damage index; performing road internal damage evaluation on the road structure based on the internal damage index of each layer of horizontal slice; according to the method, the three-dimensional GPR horizontal slice data model is modeled, the disease distribution characteristics are analyzed in the horizontal direction, the three-dimensional geometric characteristics of the disease are accurately represented, the weight is objectively given to the disease, the internal damage index of each layer of horizontal slice is accurately calculated, and the internal damage of the road is accurately evaluated.

The invention relates to a query method based on multi-agent collaboration, and the method comprises the steps: generating a query structured representation after obtaining a natural language query text input by a user, and obtaining an associated database structure representation in combination with a constellation data model of a target database; and inputting the query structured representation and the database structure representation into a preset query statement generation agent to generate a plurality of first candidate query statements. And checking and correcting the first candidate query statement, and carrying out iterative checking and correcting on the corrected query statement which does not meet the condition. And obtaining a second candidate query statement with higher reliability through an iterative correction mechanism. And then, carrying out confidence evaluation on each second candidate query statement, selecting the second candidate query statement with the highest confidence as a target query statement, and executing query in the target database. According to the embodiment of the invention, the accuracy, reliability and practicability of natural language query are improved, and more convenient, efficient and accurate query experience is provided for users.

The invention discloses a multi-protocol hardware communication adaptation module based on an energy storage system and a configuration method, the module comprises a protocol template library, an intelligent mapping engine and a model generator, and the method comprises the following steps: storing structured protocol point location information through the protocol template library; an intelligent mapping engine is used for shielding the isomerism of Modbus, MQTT, IEC104 and other underlying protocols, and a unified data interface is provided; intelligent matching among different protocol terms is realized by adopting a natural language processing technology through a semantic level mapping unit; and finally, generating a target data model by a model generator through one key. The method effectively solves the problems that when the energy storage system integrates multi-manufacturer equipment, protocol configuration depends on manpower, efficiency is low, errors are prone to occurring, and expansibility is poor.

Systems and methods for custom filtering of large language model content are provided. A request associated with a user device may be received at a platform associated with a secure network environment. The request may concern usage of an online service associated with a large language model (LLM). A user interface may be generated that includes an input field. Prompt data may be provided by the user device via the input field of the user interface. The prompt data may be determined to include sensitive data in accordance with a data model trained to recognize patterns associated with sensitive data. The received prompt data may be modified to remove the sensitive data. The modified prompt data may be routed over a communication network to the online service associated with the LLM.

The invention discloses a transformer fault detection scheme generation method and system. The method comprises the steps of obtaining multi-source heterogeneous data of a transformer; establishing a data set covering the full life cycle state quantity of the transformer according to the multi-source heterogeneous data, and constructing a standardized data model according to the data set; determining a key state characteristic quantity based on the standardized data model, and performing time sequence correlation analysis on the key state characteristic quantity to obtain a transformer health attenuation trend and a dynamic fault risk level label; and determining a potential defect type according to the dynamic fault risk level label and a preset fault mode library, and generating a fault detection scheme based on the transformer health attenuation trend and the potential defect type. By constructing a technical framework of multi-source data integration and standardized modeling, the problem that the transformer fault detection efficiency and accuracy are not high enough is systematically solved.

The invention discloses a dynamic data docking method based on a rule engine, which comprises the following steps: S1, data source registration and automatic detection: a user inputs various heterogeneous data source information through a system console, and a system automatically detects the data source type, the data structure and the connection availability and generates an adaptive connection configuration file; according to the method, a dynamic data docking method with the rule engine supporting hot updating as the core is introduced, seamless integration and flexible conversion of multi-source heterogeneous data are achieved, and the problem of business interruption caused by code modification and service restarting due to rule change in a traditional scheme is thoroughly solved; through visual rule management, a unified data model and a closed-loop verification feedback mechanism, the development and maintenance cost of the system is remarkably reduced, and meanwhile, the flexibility, reliability and traceability of data processing and the continuous self-adaptive optimization capability of the system are greatly improved.

The invention provides a three-dimensional visualization analysis method for a real scene of an ancient earthquake exploring groove, which belongs to the technical field of seismic geological survey and comprises a data acquisition module used for acquiring point cloud data of target exploring grooves in different regions. And the model building module is used for carrying out scene format conversion on the point cloud data of the target exploring grooves in different regions, and carrying out exploring groove three-dimensional model building by utilizing the converted SLPK file. And the visualization module is used for carrying out live-action three-dimensional display on the three-dimensional models of the target probe grooves in different regions through a three-dimensional scene Web server based on the SLPK files in different regions, and rendering node data of different detail levels of the probe grooves according to user requirements. The ancient earthquake event can be better understood, and a reliable three-dimensional digital file and a new shared analysis normal form are provided for the ancient earthquake.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention relates to the technical field of distributed architectures, and discloses a multi-screen cooperative control method based on an OpenHarmony distributed architecture and an all-in-one machine system.The method comprises the steps that a broadcast frame found by equipment is sent through an OpenHarmony distributed soft bus, security authentication is conducted on a plurality of display terminals responding to the broadcast frame, a virtual bus channel is established, and the virtual bus channel is sent to the display terminals; obtaining a virtual device group; creating a distributed industrial data object on the main control equipment of the virtual equipment group and synchronizing the distributed industrial data object to each slave display terminal to obtain a distributed data model; mapping the anonymous shared memory to an annular buffer area; and writing real-time data acquired by the industrial field equipment into an annular buffer area, and transmitting the real-time data to each slave display terminal of the virtual equipment group, in the method, each display terminal accesses an agent instance of an industrial data object through a distributed data management framework; and the requirements of high reliability, low delay and high consistency on multi-screen cooperative control in an industrial field are met.

The invention discloses a low-code platform design system based on modularization and a dynamic extension component, and relates to the technical field of low-code development, and the system comprises a form module which is used for standardly defining a data model and dynamically rendering a user interface, realizing unification of form configuration through a structured metadata specification, and establishing a form module; bidirectional binding of the view and the data model is realized; the process module is used for realizing modeling, execution and state management of a business process, supporting automatic task scheduling and multi-instance management based on standardized process elements, and keeping consistency of a process state through an increment synchronization mechanism; and the data processing module is used for cooperatively managing platform data resources, supporting dynamic expansion and decoupling dependence between the form module and the process module through an event-driven mechanism. According to the method, the strong binding relationship between the decoupling form and the process is established, the reusability of the process module is improved, and the process module is independent of the form and can independently provide API service to the outside, so that the method is adaptive to more business scenes.

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

The invention relates to the technical field of data processing and optimization decision making, in particular to a dynamic optimization processing system for multi-source heterogeneous data, and aims to solve the problems that the integration efficiency of the multi-source heterogeneous data is low, and the real-time performance of multi-target collaborative optimization is poor. Comprising the steps that a multi-source data acquisition and preprocessing module realizes standardized fusion; constructing a multi-objective function and constraint conditions based on the dynamic optimization model of the spatial-temporal characteristics; an NSGA-II algorithm is improved to generate a Pareto optimal solution set; and closed-loop optimization is formed by real-time monitoring and a feedback adjustment mechanism. Through a data-model-scheme-feedback system, dynamic collaborative optimization of economy, ecology, livelihood and space targets is realized. The method is suitable for complex decision support scenes such as urban and rural planning, resource allocation and environment monitoring, and improves the multi-source heterogeneous data processing efficiency and optimization precision.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to the technical field of intelligent scheduling of sanitation vehicles, and discloses an intelligent scheduling system for an electric sanitation vehicle, and the system comprises an information collection module which is used for obtaining the operation state information of the electric sanitation vehicle; the state modeling module is used for constructing a multi-dimensional data model for describing a vehicle scheduling state based on the operation state information; the task relation modeling module is used for constructing an association structure between the vehicle and the task; the scheduling optimization module is used for executing scheduling strategy optimization operation and generating a vehicle task allocation result; the instruction control module issues the scheduling instruction to a target vehicle to drive the target vehicle to execute a task; and the scheduling monitoring module is used for receiving manual intervention operation, and the intervention operation is fed back to the state modeling module and the task relation modeling module. According to the method, a tensor expression structure based on the vehicle, electric quantity, task and time four-dimensional state is constructed, and a sparse tensor compression mechanism is introduced, so that the storage and calculation cost can be remarkably reduced while the task performability is reserved.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.