657 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.

A multi-fault diagnosis method for a power distribution network is provided. The method includes: performing short-circuit fault analysis on a line of a power distribution network by using a MATLAB platform, so as to obtain an electrical quantity-based fault information decision table; performing modeling and simulation on the fault information decision table by using a Simulink platform; denoising and collecting output training data of a neural network of the power distribution network by using a wavelet transformation method, and forming a related fault information decision table as a training sample of the neural network; and optimizing weights and thresholds of the neural network by using an improved Artificial Tree intelligent optimization algorithm, selecting some of the data as fault data, and using the neural network trained to perform fault detection.

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 laser power adaptive control method for laser-assisted processing. The method comprises the following steps: S1, thermal modeling and simulation experiment are carried out to obtain data; s2, designing a model prediction control algorithm; s3, controlling software by an actuator; the output power of the laser is controlled through laser control software, and the predicted output power of the model prediction control algorithm is used as the input of the control software, so that the output power of the laser is dynamically updated in real time; and S4, designing a sensor and detecting.

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 discloses an intelligent modeling tool for a building structure and an MEP system and a working method of the intelligent modeling tool. The tool comprises an information extraction and storage module, an intelligent layout and optimization module, a modeling and simulation module, a model output and application module and an NLP and user interaction module. The invention belongs to the technical field of digitization and informatization management, and particularly provides an intelligent modeling tool for a building structure and an MEP system, which realizes full-process intelligentization from building drawing input to BIM model output through cooperative work of all functional modules and is used for a building structure and an MEP system.

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 provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention relates to the technical field of photovoltaic power generation modeling and simulation, in particular to a photovoltaic model parameter identification method and system based on an improved symbiont search algorithm. A complex number coding mechanism is introduced on the basis of a traditional SOS algorithm and is expanded from original one-dimensional real number coding to a two-dimensional complex number coding space, so that the search range of a group is expanded, and the optimization capacity and speed of the algorithm are enhanced; establishing a photovoltaic model, and determining a to-be-identified parameter solution vector; adopting a root-mean-square error as a parameter identification objective function of the photovoltaic model; and an improved complex number coding SOS algorithm is operated to identify unknown parameters of the photovoltaic model so as to minimize the difference between actually measured current data and a simulation result, thereby pursuing an optimal fitting effect between the actually measured current data and the simulation result. The improved algorithm provided by the invention has good applicability in the parameter identification process of a single-diode model, a double-diode model and a photovoltaic module model and can quickly optimize, prediction errors are effectively reduced in the aspect of balancing global and local optimal solutions, and the parameter identification precision is improved.

The invention relates to the technical field of image recognition and processing, and discloses a main network energy storage power station planning system and method based on wind-solar space-time correlation, and the system comprises a data collection and preprocessing module which carries out the cleaning and preprocessing of a data collection and preprocessing module; the wind and light space-time characteristic analysis module is used for researching space-time distribution characteristics and reverse peak regulation characteristics of wind power and photovoltaic output and correlation between the space-time distribution characteristics and the reverse peak regulation characteristics; a modeling and simulation module; the scene generation and screening module is used for generating a typical daily operation scene; and the energy storage technology evaluation module is used for comprehensively evaluating the technical performance of different types of stored energy. According to the method, the spatial-temporal distribution characteristics, the reverse peak regulation characteristics and the correlation of wind power and photovoltaic output are deeply researched, the output spatial-temporal correlation model suitable for large-scale wind and light is constructed, the level of accepting and utilizing renewable energy sources by a power grid is greatly improved, and the effect of remarkably improving the absorption capacity of the power grid to the renewable energy sources is achieved.

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 an MBSE-based aircraft undercarriage system model integration method, and belongs to the technical field of system engineering. The method comprises the following steps: constructing an aircraft undercarriage system architecture, a classification mapping of simulation design information and a GOPPRR-E element model, and a meta-model library; constructing an architecture model according to a demand-function-logic-physical process, and generating a Simulink simulation model according to the architecture; a simulation model semantic mapping rule is constructed according to an OSLC specification, and design information is analyzed to establish an integrated data model; and a Simulink simulation tool integration adapter is constructed, and a tool interoperation service based on Web is provided. According to the method, a multidisciplinary model can be accurately constructed, uniform expression of architecture and simulation models and semantic consistency of heterogeneous models are realized, two-way transmission of cross-domain information and interoperation between tools are supported, the problem of interoperation between architecture modeling and simulation modeling tools in aircraft undercarriage system design is effectively solved, and the design efficiency is improved.

The invention discloses an aircraft control system graphical modeling and simulation platform, and relates to the technical field of flight control. The platform comprises a data processing module used for acquiring and preprocessing system related data of the flight control system; the model building module is used for carrying out framework management, configuration parameter setting and module selection on the basis of a flight control overall framework model and a flight control professional model library and generating a flight control system model according to the framework management, the configuration parameter setting and the module selection; the quality evaluation module is used for evaluating and optimizing a simulation result of the flight control system model; and the simulation test module is used for carrying out simulation test on the flight control system in each test period. A simulation platform for simulating operation is provided for the flight control system, full-process functions such as data preprocessing, part generation, rapid model construction and rapid system evaluation are achieved, and the development efficiency, robustness, safety and accuracy of the flight control system are improved; and practical guidance is provided for actual operation of the aircraft.

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 present invention discloses a crystal plasticity finite element modeling simulation method, comprising the following steps: obtaining grain information based on EBSD analysis; importing the ctf format file generated by the EBSD analysis into Dream.3D software to realize automatic reconstruction of the grain structure, and outputting the input file and the geometric orientation data of each grain; then processing the grain geometric orientation data through a Python language script to obtain the average geometric orientation information of each grain; combining the average geometric orientation information of the grain with the input file using a Python script to generate a complete input file with geometric model information and material property information; assigning boundary conditions to the model in ABAQUS finite element analysis software and setting its constitutive relationship using the UMAT subroutine, thereby realizing the entire crystal plasticity finite element analysis process. The present invention realizes the process of crystal plasticity finite element modeling and simulation analysis.

The invention is suitable for the technical field of vehicle dynamics modeling and simulation, provides a commercial vehicle dynamics modeling method considering transient excitation of a power system, establishes a power transmission rotation dynamics quasi-transient model containing engine and transmission shaft excitation, further improves a riding dynamics model of a commercial vehicle, and improves the reliability of the commercial vehicle. And the coupling relationship between the power platform and the chassis platform is mainly analyzed. In addition, aiming at the key vibration conduction intermediate link of the frame, the invention constructs a rigid-flexible combined frame model. A final simulation result shows that the model can effectively simulate quality problems such as parking idling buffeting caused by power system excitation and structural resonance under various vehicle speeds in the acceleration process. The method has real-time performance and reliability, the transient effect of commercial vehicle power system excitation can be accurately expressed, the frequency domain response range of a commercial vehicle model is widened, and the vehicle abnormal buffeting problem is effectively managed and optimized through the virtual prototype simulation iteration technology.

The invention belongs to the technical field of metamaterial structure design, and aims to solve the problem that metamaterial structure design depends on experience and manual adjustment. The invention provides a forward and reverse fusion design and performance prediction method for a mechanical metamaterial structure, and the method comprises the steps: designing N original metamaterial structures, and extracting N combinations of structure parameters and material parameters; carrying out modeling and simulation to obtain N characteristic parameter groups; constructing a dual-deep learning model comprising a front neural network and a rear neural network, and performing training based on the data; a combination of structure parameters and material parameters of the new metamaterial structure and performance parameters needing to be achieved are given, such as a low-frequency band gap value and a Poisson's ratio; inputting a new combination of structure parameters and material parameters into the double-deep learning model, and judging whether required performance is achieved or not based on characteristic parameters output by the neural network; and if so, outputting the reconstructed optimal original material structure by the post-neural network. According to the invention, the design complexity of the metamaterial can be reduced.

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 discloses an urban multi-domain collaborative automatic modeling simulation method, and belongs to the technical field of model fusion. The problem that in the prior art, due to the fact that an urban multi-domain collaborative modeling simulation method is high in modeling complexity, the accuracy of an output result is poor is solved. According to the invention, research scene analysis is carried out; determining an interactive collaboration object, constructing a knowledge graph to perform polling retrieval, performing clustering and causal relationship inference on obtained interactive collaboration elements having a field interaction relationship with modeling field elements and attributes thereof, and identifying to obtain high-dimensional covariables and a relationship between the high-dimensional covariables; modeling domain elements needing to be researched are determined, related variables of the modeling domain elements are selected, and the relation between the modeling domain elements is obtained according to the Pearson's correlation coefficient between the related variables; and constructing a topological model to realize multi-domain collaborative modeling simulation. According to the method, the accuracy of a model result obtained by a cross-domain automatic modeling method is effectively improved, and the method can be applied to simulating the running state of urban multi-domain collaboration.

The present invention discloses a two-stage transient stability prediction method based on an improved convolutional residual memory network and a gated cyclic unit, comprising: in the first stage, the underlying measurement data required for modeling and simulation is obtained, and a transient stability prediction model based on the improved convolutional residual memory network is established for qualitative and quantitative analysis; in the second stage, the underlying measurement data required for modeling and simulation is obtained, and a generator power angle trajectory prediction model based on a gated cyclic unit is established, and the unstable samples in the qualitative analysis and the samples of the confidence interval with a certain confidence level in the quantitative analysis are predicted for the power angle trajectory, and the final transient stability prediction result is determined by comprehensively judging the results of the qualitative analysis, the quantitative analysis and the power angle trajectory prediction. The present invention can improve the accuracy of transient stability prediction, and at the same time, improve the reliability of transient stability prediction without losing the response speed.

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.