433 results about "Modeling and simulation" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention relates to the technical field of control, and particularly discloses a multivariable energy efficiency optimization control system for a heating furnace, which is used for solving the problems of local overheating, non-uniform temperature and difficulty in accurate positioning and compensation of heat loss in the operation of the existing cracking heating furnace. Comprising a parameter detection module, a multivariable coupling modeling and simulation module, an optimization control module and an execution and feedback module. According to the method, dynamic digital twinning is constructed through multi-modal online sensing and data assimilation, a Pareto frontier solution is generated based on model prediction control and improved NSGA-II parallel optimization, and the weight is adaptively adjusted; and when the hot spot / cold spot is triggered, a quadric surface fitting compensation strategy is implemented and issued for execution, so that high-precision simulation prediction, precise closed-loop control and real-time online energy efficiency optimization are realized.

The invention discloses a dynamic flow control method for a two-phase cold plate liquid cooling system based on multi-modal sensing, and relates to the technical field of equipment cooling. Comprising the following steps: S1, collecting liquid cooling monitoring data in real time, and performing data preprocessing; s2, judging the state risk of the cold plate, carrying out signal cross validation, and taking an abnormal regulation and control measure; s3, cooling liquid flow regulation and control are carried out, and the matching degree of flow regulation and control and chip thermal load is evaluated, so that flow regulation and control correction is carried out; and S4, constructing a big data modeling and simulation feedback platform, evaluating a flow regulation and control self-healing effect, optimizing each parameter and strategy, and carrying out self-learning evolution. The problems that an existing two-phase cold plate liquid cooling system generally only depends on a single temperature signal, lacks a multi-source redundancy check mechanism, has the problems of response lag, weak local reflection capacity, high risk during failure and the like, consequently, the cooling capacity distribution and adjustment precision is insufficient, and the actual requirement of a high-heat-density data center for heat dissipation is difficult to meet are solved.

The invention provides a geophysical gravity and magnetic anomaly source model construction and rapid forward and reverse modeling method, and relates to the technical field of intelligent three-dimensional geological modeling and simulation. According to geological information of a research area, source body modeling parameters of a gravity and magnetic anomalous field are set for the research area, and a three-dimensional model matrix M capable of describing a plurality of underground anomalous bodies is obtained; constructing a fast forward modeling network, establishing nonlinear mapping from the three-dimensional model matrix to the simulated abnormal data through the fast forward modeling network, and calculating forward modeling response of the three-dimensional model matrix; and constructing an inversion network based on a concurrent module CFTBlock and combining a CNN and a Transform, and establishing nonlinear mapping from the gravity and magnetic abnormal data to a three-dimensional model matrix. The method has the advantages of fast and accurate forward modeling, high-resolution inversion and the like, and is suitable for better interpretation of actually measured gravity and magnetic data.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention discloses an intelligent equivalent modeling and simulation optimization method based on machine learning and multi-physics field coupling, and the method employs a 2.5 D chip stress simulation method combining a customized material with equivalent parameters output by a Python script, and can remarkably reduce the calculation time and memory resource consumption on the premise of guaranteeing high precision. Based on DNN trained by large-scale historical data, an equivalent model can be dynamically adjusted in the simulation process, and more optimized stress analysis is achieved; according to the method, the research and development period can be greatly shortened, the design reliability is improved, and the method has very high practical value and commercial value; through construction of an equivalent model and application of an automatic script, a designer can more efficiently carry out stress analysis and optimization of the 2.5 D chip, and a brand new solution is provided for design of an integrated circuit.

The invention belongs to the technical field of machine learning and pre-stress, and discloses a pre-stress release loss value prediction method and system based on machine learning, and the method comprises the steps: carrying out the multi-working-condition modeling and simulation of a pre-stress beam through finite element numerical software, and extracting the working parameters and design parameters of the pre-stress beam, carrying out data preprocessing, distribution check and feature importance analysis to obtain an initial data set; dividing the initial data set into an initial training data set and an initial test data set, and processing the initial training data set and the initial test data set to obtain a processed training data set and a processed test data set; constructing a full-connection multi-layer perceptron neural network model, defining training, verification and monitoring functions, training the full-connection multi-layer perceptron neural network model by using the processed training data set, and testing the trained model by using the processed test data set to obtain a prediction model; real parameters of the prestressed beam are obtained, the prediction model is used for predicting the prestress release loss value, and a prediction result is obtained.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.

The invention discloses a digital twin-driven water and fertilizer real-time dynamic balance transfer system, which comprises a sensing and data acquisition module for monitoring moisture, salinity, pH value, illumination and rainfall meteorological data of soil in real time through various sensors deployed in a farmland; and the digital twinborn modeling and simulation module is used for constructing a digital twinborn model of the farmland based on the real-time data collected by the perception and data acquisition module. According to the invention, by arranging the perception and data acquisition module, the digital twin modeling and simulation module, the intelligent decision-making and regulation module, the execution and feedback module, the historical data tracing and system optimization module, the expansion module, the networking module and the edge-cloud cooperative computing architecture, the farmland environment and the crop growth condition can be monitored in real time; the digital twinborn modeling and simulation module constructs a digital twinborn model of a farmland based on real-time data, simulates a soil environment and crop growth, and optimizes a water and fertilizer proportioning strategy.

The invention discloses a situation awareness and linkage disposal system based on dynamic threat modeling driving, and the system comprises a security data collection and standardization module which is used for collecting security data of a network, a host and an application layer, and carrying out the standardization processing; the dynamic threat modeling module is used for constructing a dynamic threat model; the threat agent modeling and simulation module is used for modeling each node in the threat attack graph into a threat agent; the parameter optimization module is used for globally optimizing parameters and strategy weights of the Monte Carlo tree search network by using a seagull algorithm; the linkage processing decision module is used for configuring a Monte Carlo tree search network based on the optimal parameter group; the safety equipment scheduling module is used for analyzing the optimal linkage disposal decision scheme; and the disposal effect evaluation and feedback module is used for performing multi-dimensional analysis on the disposal feedback data set. According to the invention, accurate situation awareness and linkage processing are provided for the user.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a finite element grid graph structure construction method and system, a terminal and a medium. Comprising the following steps: analyzing original full-amount grid data exported by finite element simulation, segmenting unstructured grid data into a grid vertex coordinate set and a grid unit mark number set, and constructing a graph edge topological structure corresponding to a grid based on a unit mark number relationship; on the basis, loading physical field data and adopting a tolerance-based coordinate matching algorithm to realize accurate mapping of physical field labels and material attributes with grid nodes; and generating standardized finite element grid graph data which can be directly used for graph neural network processing. By means of the method, high-consistency and high-physical-reliability conversion from the finite element simulation data to the graph structure data is achieved, and the physical field modeling and simulation acceleration capacity based on graph learning is improved.

The invention provides a wharf and foundation integrated coupling modeling and simulation method considering large deformation and hysteretic characteristics of a soil body, comprising the following steps: step 1, constructing a wharf and foundation integrated simulation analysis model for processing real data of a wharf foundation construction site and outputting characteristic parameters; 2, constructing a multi-modal data fusion network, and performing fusion correction on the feature parameters output in the step 1 according to real data; and step 3, establishing a wharf foundation design monitoring closed-loop control mechanism, and optimizing the wharf foundation integrated simulation analysis model and the multi-modal data fusion network in combination with the real data in the step 1 and the data fused and corrected in the step 2, thereby realizing wharf and foundation integrated coupling modeling and simulation considering the large deformation and hysteresis characteristics of the soil body.

The invention relates to the technical field of digital twinning modeling and simulation, in particular to a train operation and maintenance method based on digital twinning. The method comprises the following steps: collecting vibration and acoustic sensing data from a wheel and track interaction area, and fusing train operation condition data to obtain wheel-track interaction data; carrying out adaptive filtering processing on the wheel-rail interaction data to obtain filtering contact features; analyzing the spatial correlation of vibration signals in the filtering contact features, and reconstructing a contact point distribution diagram; performing pressure field calculation according to the contact point distribution diagram to obtain a contact pressure distribution diagram; performing equivalent taper derivation according to the contact pressure distribution diagram and the contact point distribution diagram, constructing a wheel-rail contact digital twin model, and generating a contact geometric feature diagram; a potential wear pattern is analyzed based on the contact geometry map. According to the method, virtual-real mapping and closed-loop optimization of the wheels and the steel rails are achieved through the digital twin modeling and simulation technology, the train operation and maintenance period is prolonged, and operation safety is improved.

The invention discloses a multi-sub-network collaborative modeling method for unsupervised solution of a multi-domain electromagnetic field, and the method comprises the steps: carrying out the partitioning of a computational domain in a solving region according to medium parameters and field source information, and dividing the whole region into a plurality of physical sub-regions; a neural network model is independently deployed in each sub-region and is used for fitting electromagnetic field distribution in the region, so that the learning ability and modeling precision of the network for local physical characteristic changes are improved; a cross-network continuity coupling mechanism is constructed between sub-regions, and a continuity loss function of a magnetic field tangential component corresponding to a vector magnetic potential value continuity constraint and a space derivative thereof is introduced at an interface of adjacent regions, so that physical coupling between sub-networks is realized; and the boundary continuity of the electromagnetic field solution among different regions and the consistency of the overall solution are ensured. The method has the advantages of being high in universality, high in capacity of adapting to complex boundary conditions and the like, and is suitable for modeling and simulation calculation of multi-region, multi-material and multi-source coupling field problems.

The invention discloses a low-loss planar transformer design method suitable for an LLC resonant converter, and the method comprises the steps: firstly collecting the index parameters of the LLC resonant converter, setting the target parameters of a planar transformer, selecting a magnetic core model meeting the requirements according to an AP method, and then carrying out the design of a winding structure, finally, modeling and simulation of the planar transformer are completed in finite element software, and if the simulation result does not meet the set parameter and loss requirements, selection of the magnetic core model and design of the winding structure need to be conducted again; and if the set parameters and the loss requirements are met, finishing the design.

The invention discloses a sub-microsecond simulation-based network construction type multi-converter modeling method, and relates to the technical field of converter modeling and simulation. Discretizing a switch element, and constructing a generalized constant admittance switch model based on an LC equivalent circuit; then, expansion modeling from a single converter to a multi-converter system is realized through a discrete system state matrix, and a complete network-forming type multi-converter model including a control link is established in combination with inertia characteristics of a virtual synchronous generator; and finally, building a simulation model of the network construction type multi-converter system in the PSCAD / EMTDC, and verifying the correctness and effectiveness of the method. According to the method, the generalized constant admittance switch model and the inertia characteristic of the virtual synchronous generator are fused, good applicability and stability are achieved under the sub-microsecond-level simulation step length, the system simulation precision and efficiency are remarkably improved, and meanwhile rapid convergence of the transient process of the network construction type multi-converter and active supporting of the power grid voltage frequency are achieved.

The invention relates to the technical field of intelligent building and elevator dispatching control, and particularly discloses an elevator safety management method and system. The method comprises the steps that elevator structure, electrical configuration, environmental parameters, use records and historical maintenance data are collected, and an elevator twin map in a unified format is constructed; fault evolution modeling and simulation are executed based on the atlas, and a trigger factor set is generated; a multi-elevator scheduling task graph is constructed, path optimization and task cooperation are completed, and an intelligent rescue path table is generated; abnormity is identified, a fault response trigger chain is generated, and an edge control instruction is issued; constructing an emergency response rule base and a strategy template set; and carrying out virtual drilling based on the strategy template and outputting feedback data. Compared with the prior art, intelligent scheduling based on multi-source data and a path scoring mechanism under the event that people are trapped in the elevator is achieved, and the response timeliness and the system cooperation capacity are improved.

The invention relates to a nonlinear damping element based on a Modelica language, and relates to the field of mechanical system modeling and simulation. According to the technical scheme, a novel damping element is designed based on the Modelica language, corresponding damping coefficients can be defined according to different movement speeds of the element, and the limitation of the prior art is effectively broken through; specifically, according to the technical scheme, the element types of the Modelica mechanical library are enriched, the blank of nonlinear damping elements in the library is filled up, and the application range of the library is expanded; moreover, simulation requirements of a nonlinear damping system can be accurately matched, so that the modeling process better fits nonlinear characteristics of a damping coefficient in an actual working condition; besides, free switching between non-linear damping and linear damping can be flexibly realized depending on a speed-damping force two-dimensional number table, the adaptability of the element to different modeling scenes is greatly enhanced, and powerful support is provided for precise modeling of a mechanical system based on Modelica.

The invention relates to the technical field of vibration active control and artificial intelligence, and discloses an aero-engine fan rotor keyless phase virtual dynamic balance method and system based on a finite element method, and the system comprises a modeling and simulation module which is configured to obtain the first-order critical speed and vibration mode of an initial rotor dynamics simulation model; the model calibration module is configured to calibrate the initial rotor dynamics simulation model according to the first actual amplitude to obtain a calibrated rotor dynamics simulation model; the model updating module is configured to update the calibration rotor dynamics simulation model to obtain an updated rotor dynamics simulation model; and the trial weight and counterweight module is configured to substitute the second actual amplitude into particle swarm optimization, calculate the actual total unbalance amount of the rotor system and the trial weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced to realize dynamic balance. According to the invention, the position of the unbalance can be accurately determined only through amplitude analysis.

The invention discloses a cross-reset-domain script generation method for verifying a system-on-chip, and relates to the technical field of chips, and the method comprises the steps: obtaining an original text, the original text comprises a first code, a second code and a third code, the first code represents a data signal, a clock signal and a reset signal of the system-on-chip, and the second code represents a reset signal of the system-on-chip; the second code represents an output code obtained by modeling and simulation of the system-level chip, and the third code represents a user-defined cross-reset domain verification condition; the original text is analyzed, a structured text is obtained, and the structured text comprises a clock signal and a reset signal of the system-on-chip and structured information of a port and a module; according to the method, the structured text is converted into the cross-reset-domain script for verifying the system-on-chip, so that the problem of low cross-reset-domain verification efficiency of the system-on-chip in related technologies is solved, and the cross-reset-domain verification efficiency of the system-on-chip is improved.

The invention discloses a machine learning driven vehicle frame lightweight design method, which comprises the following steps: (1) performing vehicle frame parametric modeling in CATIA, constructing a vehicle frame modal analysis model and a stiffness analysis model in ABAQUS, and deducing a mathematical model of vehicle frame lightweight design; (2) performing Latin hypercube sampling and discrete Gaussian sampling on continuous and discrete parameters to generate a frame population, and establishing a database; (3) designing a variable type driven hybrid variation mechanism to obtain a candidate frame population; (4) constructing a radial basis function machine learning model, and deducing a comprehensive minimum statistical lower limit function to screen an optimal candidate frame; and (5) performing parametric modeling and simulation analysis on the optimal candidate frame, updating the frame population and the database, returning to the step (3) until the optimal frame meets the design requirement, and outputting the optimal frame. According to the method, adaptive variation strategies and screening functions are designed according to the vehicle frame parameter characteristics, and the vehicle frame lightweight design effect is good.

The invention relates to the technical field of equal-strength design, and provides a high-reliability equal-strength design method and system for an intelligent cubic press, which considers structural characteristics of the cubic press, performs physical field analysis, modeling and simulation, determines deformation, temperature, stress distribution and stress concentration areas of key components, predicts structural crack initiation of the key components, and provides a high-reliability equal-strength design method and system for an intelligent cubic press. Crack propagation simulation is carried out; establishing a fatigue strength life prediction model based on deep learning and a simulation data set; constructing a fatigue strength reliability model, and establishing a mathematical association relationship among fatigue life, fatigue damage and reliability; a reliability calculation index is introduced into multi-objective optimization of the structure, a high-precision numerical solution of reliability robustness optimization design based on structure simulation is developed, and equal-strength design of the cubic press is achieved. According to the method, the optimal combination of design parameters can be obtained, the equal-strength design of parts and the whole machine structure is formed, and development of intelligent, high-precision and large-scale cubic press equipment is promoted.

The invention discloses an undercarriage key structure weight reduction optimization design method based on reliability, and the method comprises the steps: carrying out the precise modeling and simulation analysis of key parts of an undercarriage through ANSYS, and achieving the geometric parameterization process through the command stream writing of APDL; the reliability weight reduction optimization design is completed in combination with MATLAB software and the established reliability optimization model, and the purpose of weight reduction is achieved while the reliability and strength requirements are met. According to the method, the blank in the prior art is filled, and a new thought and method are provided for lightweight design of undercarriages and other aircraft key components.

The invention discloses a hydraulic engineering management and control system and method based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: firstly collecting and preprocessing channel condition data in real time, then comprehensively processing the data to obtain a sediment deposition factor and a congestion prediction factor, and analyzing whether sediment deposition exists according to the sediment deposition factor and the congestion prediction factor; if yes, whether the channel is congested or not is predicted according to the latter, if congestion is predicted, a congestion early warning is given out, and if no deposition exists or congestion does not exist, the process is ended correspondingly or prediction continues. The method combines flow velocity and flow direction abnormal parameters to form sediment deposition factors, bypasses underwater direct detection bottlenecks by means of digital twin modeling and simulation algorithms, improves sediment deposition judgment accuracy through multi-dimensional analysis, constructs congestion prediction factors based on a standard and actual water flow difference value and the flow direction abnormal parameters, and performs threshold exceeding early warning. The blocking risk is recognized in advance, and time is won for hydraulic engineering maintenance regulation and control.

The invention discloses a composite circuit substrate material parameter pixel gray scale coding method and device and a storage medium, and relates to the technical field of composite substrate material physical property modeling simulation. According to the method, the composite circuit substrate unit image is pixelated, the pixel gray value is used as a matrix material parameter coding space, and then matrix material parameter information is embedded into the substrate unit image, so that fusion coding of wiring pattern features and material mechanical parameters is realized when two or more materials exist in the same layer of substrate; and the accuracy of predicting the equivalent mechanical parameters of the substrate unit through the wiring image information of the substrate unit and the deep convolutional neural network is improved.

The invention discloses a machine tool dynamic characteristic finite element calculation method based on bolting joint surface parametric modeling, and belongs to the technical field of bolting joint surface and machine tool overall modeling and simulation. According to the method, a gantry machine tool reduced scale model is taken as an example, contact parameter calculation is conducted on bolting joint surfaces based on the fractal theory, then finite element parametric modeling is conducted on the bolting joint surfaces through an abaqus basic connector unit, and dynamic performance analysis of the whole machine is completed. Compared with a test method, the accuracy of the method is proved. According to the method, more accurate and more efficient calculation of the dynamic performance of the machine tool is realized, and a theoretical basis is provided for subsequently exploring the influence of each combination surface on the dynamic performance of the whole machine.

The invention discloses a parameter optimization method and system for an imaging structure of an XRT ore sorting machine. The parameter optimization method comprises the steps that structure parameter information of a target XRT ore sorting machine is obtained; constructing a simulation model of the target XRT ore sorting machine in simulation software; the influence of each element in the X-ray imaging structure on the signal-to-noise ratio of the image is researched, and a final influence factor is determined; and by taking the maximum signal-to-noise ratio and the minimum X-ray source power as optimization objectives and taking actual working conditions and process conditions as constraints, constructing a parameter optimization model of the imaging structure of the XRT ore sorting machine and solving the parameter optimization model to finish parameter optimization of the imaging structure of the target XRT ore sorting machine. According to the method, accurate modeling and simulation are conducted on the target XRT ore sorting machine, targeted parameter optimization model construction and solution are conducted after the influence factors of the parameters are determined, parameter optimization of the imaging structure of the target XRT ore sorting machine is achieved, and the scheme is relatively simple, higher in precision and better in effect.

The invention relates to the technical field of metal material fracture resistance testing, in particular to a prediction method for fracture toughness of a polar region ship composite steel plate under different thicknesses, and the method comprises the following specific steps: S1, material selection and performance testing; s2, carrying out a fracture test; s3, modeling and simulation: performing modeling on the composite steel plate samples with different composite layer thicknesses, and performing fracture behavior simulation; s4, model establishment and parameter fitting; and S5, model verification and application. According to the method, the layer thickness ratio Rt is used as a key research parameter, the fracture toughness change rule of the polar ship composite steel plate under different composite layer thicknesses can be systematically represented, and the fracture toughness prediction model based on the layer thickness ratio is established, so that the problems of complexity and high cost caused by one-by-one test on all the composite layer thicknesses in a traditional method are effectively solved; therefore, the actual engineering requirements of rapid fracture resistance design and economic evaluation of the polar ship composite steel plate are met, the test quantity is small, the period is short, and the cost is low.