6453 results about "Simulation methods" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The present invention provides a digital twin simulation method and system for an industrial simulation platform, the method comprising: collecting the operation data of the industrial training production line through a sensor array to obtain a real-time mapping data stream; constructing a digital twin model according to the real-time mapping data stream, using a semantic ontology library and a graph neural network to perform correlation analysis on the multimodal data of the physical space and the digital twin model, and obtaining a twin knowledge graph of the physical-digital dual space; according to the twin knowledge graph and a preset expert knowledge base, using a reinforcement learning method to perform collaborative learning on the operation conditions of the physical-digital dual space to obtain an elastic fault-tolerant boundary; according to the twin knowledge graph and the elastic fault-tolerant boundary, the deviation between the real-time action of the operator in the physical space and the standard action in the digital space is graded and judged, and corresponding feedback and warning information are output. The present invention overcomes the limitations of the existing fixed standard action model and provides personalized guidance for operators with different proficiency levels.

The invention discloses a power equipment operation state prediction and anomaly simulation method based on digital twinning, and relates to the technical field of power grid operation. According to the method, through establishment of a digital twinborn model of the power equipment and real-time data analysis, prediction and anomaly simulation of the operation state of the power equipment are realized. The method comprises the following steps of: analyzing a physical structure, operation characteristics and load distribution of power equipment, constructing a digital twin model, performing multi-dimensional analysis on real-time data by utilizing edge calculation, extracting electrical characteristics and behavior patterns, generating an equipment state data set, and constructing an abnormal causal chain model based on the model and the data set. The method comprises the steps of identifying a fault mode caused by uneven load or overload, evaluating the influence of an abnormal state on the stability and efficiency of a power system through virtual simulation and fault propagation analysis, and obtaining an abnormal influence index of power equipment. The accuracy of power equipment fault prediction is improved, the stability and efficiency of a power system are optimized, and support is provided for intelligent operation and maintenance and fault early warning.

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

The invention discloses an enterprise operation global analog simulation method based on digital twinning. The method comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data; s2, constructing an enterprise operation digital twinning model, and defining an optimization objective function and constraint conditions; s3, optimizing the model by adopting a sea elephant optimization algorithm, and dynamically adjusting structural parameters, control variables and strategy configuration; s4, multi-scene simulation drilling is carried out based on the optimization model, and enterprise operation prediction results under different strategies are obtained; s5, evaluating and comparing key performance indexes corresponding to the strategies, and determining an optimal service configuration scheme; and S6, deploying an optimal service configuration scheme, collecting feedback data, and dynamically updating the model. According to the method, the enterprise operation digital twin model which can be optimized, simulated and fed back is constructed, so that operation simulation and optimal business scheme output under multi-strategy configuration are realized, and the enterprise decision-making intelligence level and the operation efficiency are improved.

The invention provides a numerical simulation method suitable for a three-dimensional spherical flame, and belongs to the field of electrical data processing, and the method comprises the steps: building a computational domain model according to the requirements of a computational scene, dividing a grid unit, and obtaining the node information of the required grid unit; establishing a Navier-Stokes equation set of a coupling chemical reaction and diffusion process model; estimating convection and diffusion term variable gradients in the conservation equation; calculating a time step length according to a set Kulang number, and carrying out time advancing; estimating an expected value of a velocity field, and solving a predicted velocity; the divergence of the velocity field and the correction value of the pressure field are calculated; obtaining a corrected velocity field; a self-adaptive grid means is introduced to dynamically adjust the grid resolution; performing iterative correction for multiple times until the velocity field and the pressure field converge to convergence conditions; performing spherical flame numerical simulation, and monitoring the convergence of the solution. According to the invention, the flame dynamics and cellular instability of the premixed spherical expansion flame can be reproduced.

Disclosed in the present invention is a non-autoregressive Transformer-based modeling method for a 4-level pulse amplitude modulation high-speed transmitter. The method involves establishing a deep learning model having an encoder-decoder architecture to predict the behavior of a 4-level pulse amplitude modulation transmitter. An encoder processes unordered non-sequential inputs, including an input signal parameter and link parameters, to generate a context vector and then transmit same to a decoder. The decoder uses both the context vector generated by the encoder and a transmitter output signal sequence to generate a categorical probability distribution for each point in the sequence one by one. The model is trained using a random masking strategy, and inference is performed by means of non-autoregressive decoding and filtering, so that the model can perform parallel prediction on an output sequence, and perform a filtering process to predict an output signal. Compared to traditional simulation methods, the present invention achieves a significant acceleration effect, particularly when processing multi-link systems.

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

The invention provides a tunnel grouting dynamic adaptive simulation method and system based on multi-physics field coupling, and belongs to the technical field of grouting simulation, and the method comprises the steps: obtaining original geological information, constructing a three-dimensional geological geometric model and a fracture network, constructing a multi-physics field coupling mechanism, and carrying out discrete solution; further predicting slurry diffusion and crack filling to obtain an isobaric envelope diagram and an early warning area, and simulating a grouting process; acquiring real-time sensor data, inputting the isobaric envelope diagram, the early warning area and the real-time sensor data into an adaptive parameter prediction model, dynamically updating the weight of the adaptive parameter prediction model according to the change of the real-time sensor data by adopting rolling training to obtain a prediction index, and constructing a feedback control chain based on the prediction index. Determining an optimal grouting parameter combination by adopting a particle swarm optimization algorithm; and the optimal grouting parameters are fed back to the simulated grouting process for automatic parameter adjustment, and the optimized grouting strategy is executed. The intelligent numerical values of the grouting parameters can be dynamically and adaptively adjusted.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention discloses an interactive simulation method for calculating aging parameters of a superconducting cable and a superconducting cable system.The system comprises the steps that a cable body electromagnetic transient model, a cooling system transient thermal model and a three-dimensional multi-field finite element model are built, and cable material characteristics, environment parameters and operation condition data are input; a model interaction interface is constructed through a simulation platform, data interaction and coupling among multiple models are realized, simulation parameters are dynamically adjusted, accurate matching of model step length and dynamic characteristics of the system is ensured, and aging parameters of the superconducting cable system under complex working conditions are calculated. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

The invention discloses a probe card simulation method and system based on counter-acting force analysis, which are used in the field of wafer simulation test, and the probe card simulation method comprises the following steps: integrating multiple physical field working conditions to construct a probe card geometric model, and importing the probe card geometric model into finite element simulation analysis software; carrying out grid division on the geometric model of the probe card by utilizing the hybrid voxel tree model, adjusting the grid density according to the distribution requirement of the multi-physical field, and setting a simulation boundary condition by utilizing a preset boundary condition template according to the actual simulation requirement; starting a simulation calculation process based on the adjusted grid density and simulated boundary conditions; and providing a visual interface of physical field distribution and a structural stress state in the simulation calculation process, and performing iterative optimization on the performance of the probe card in the simulation process according to a reactive force distribution result. According to the method, the grid density is automatically adjusted to adapt to changes of a physical field, and it is ensured that higher simulation precision is obtained in a key area.

The invention provides a keyword-based file information identification method and system, and the method comprises the steps: obtaining existing files, building a file database based on the existing files, carrying out the clustering of the existing files in the file database according to the obtained preset industry term data, and obtaining a first clustering result of keywords; the method comprises the following steps: acquiring a correlation score of an existing file and a query keyword based on a BM25 model, performing optimization clustering on a first clustering result of the keyword based on the correlation score, acquiring a second clustering result of the keyword, constructing a keyword semantic association magnetic field according to the second clustering result of the keyword in combination with an electromagnetic field simulation mode, and acquiring historical query data of a user. A personalized dynamic weight is established based on historical query data of the user, a third clustering result of the keywords is obtained according to the personalized dynamic weight and the keyword semantic association magnetic field, and a corresponding existing file is recognized and matched in the file database for the user based on the third clustering result of the keywords. Through the method, the accuracy of querying the matched document file by the user can be improved.

The invention discloses an artificial intelligence-based intelligent environment simulation method and system, and relates to the technical field of artificial intelligence environment simulation, and the method comprises the steps: forming a scene tensor based on an updated initial scene and a POMDP belief state set, using an optimized VAE model to generate an initial image sequence, using a U-Net diffusion model to optimize the initial image sequence, and using a U-Net diffusion model to optimize the initial image sequence; the method comprises the steps of calculating reconstruction loss of a VAE encoder and diffusion denoising loss of a U-Net diffusion model, calculating a joint loss function of the VAE encoder and the U-Net diffusion model, generating a high-quality image sequence by using the joint loss function through a Monte Carlo simulation method, and optimizing the high-quality image sequence through a conditional diffusion model to generate a final scene image sequence. According to the method, efficient fusion of multi-modal data is achieved, the defect that in the prior art, vision and semantics are inconsistent is overcome, task specific parameters are rapidly generated through inner circulation, meta-model generalization ability is optimized through outer circulation, a model can be rapidly matched with an edge scene, and the generation efficiency and fidelity of a rare scene are remarkably improved.

The invention relates to the technical field of image processing, and discloses a virtual operation analogue simulation method and system based on a mixed reality technology. The method comprises the steps that three-dimensional space information is collected, feature extraction and registration are conducted on a real operation environment and video data, and mixed reality mapping and monitoring data are generated; performing medical image registration processing based on mixed reality mapping to obtain virtual scene data; processing instrument sensing data according to the mapping data and the monitoring parameters to obtain interaction data; performing surgical process simulation according to the virtual scene and the interaction data; performing operation trajectory analysis and identification based on the simulation data, and generating evaluation and early warning information; and analyzing the evaluation and early warning information, and outputting an analysis report. Through multi-source data fusion and a real-time evaluation feedback mechanism, the authenticity and accuracy of operation simulation training are effectively improved.

The invention relates to the technical field of energy consumption simulation, in particular to a simulation method and system based on BIM building energy consumption, and the method comprises the following steps: importing structural data in a BIM building model, recording the actual sizes and material characteristic information of building walls, windows and floors, and generating a building structure parameter table through parameter extraction and data integration. According to the method, the BIM technology and the energy consumption simulation process are utilized, the accuracy of building energy consumption prediction and the practicability of operation are remarkably improved, the building structure parameter table is constructed, the thermal bridge effect is deeply analyzed, so that thermal performance data are more accurate, dynamic adjustment of actual climate change parameters is provided in thermal load simulation, the simulation adaptability is improved, and the simulation efficiency is improved. According to the method, the consistency of an energy consumption prediction result and an actual climate condition is ensured, the simulation precision and practicability are ensured through continuous matching and iterative optimization of actual data and a simulation result, powerful data support is provided for energy efficiency management and optimization, an energy-saving target is helped to be achieved, and the environmental performance of a building is optimized.

The invention relates to the technical field of migration simulation, in particular to a drainage basin water pollution migration and transformation simulation method and system based on an MT-DHM model. The method comprises the following steps: carrying out data acquisition on a watershed multi-source space, carrying out data preprocessing, and generating a hydrological response unit standard data set; performing vertical hierarchical calculation based on the hydrological response unit standard data set to obtain land runoff vertical analysis data; carrying out land pollutant migration simulation on the land runoff vertical analysis data, and carrying out real-time correction with an iteration step length of 1 h to obtain a dissolved state / adsorption state load matrix; therefore, by constructing a full-process multi-source heterogeneous data fusion and pollution migration simulation mechanism, the problems that a traditional model is insufficient in data uniformity, low in parameter calibration precision and weak in spatial distribution simulation capability are solved, and the precision, stability and management and control operability of drainage basin pollution load calculation are improved.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention provides an electric power system transaction simulation method based on multi-agent agency, and belongs to the technical field of electric power system simulation, and the method comprises the steps: collecting the historical operation data of an electric power system, constructing a multi-agent system comprising a power generation agent, a flexible load agent and an electricity selling agent, and carrying out the Monte Carlo simulation and scene generation. According to the method, a deep reinforcement learning algorithm is adopted, a revenue function and a decision model of each agent are defined, market clearing calculation is executed, and transaction simulation is performed by constructing a verification scene library. The system can evaluate agent strategies, calculate market key indexes and feed market information back to agents, intelligence and dynamic optimization of electricity transaction decision are achieved, and the operation efficiency and transaction accuracy of the electricity market are effectively improved. The method provided by the invention solves the technical problem in the prior art that the interaction and learning mechanism between market participants are not fully considered, so that the simulation result and the actual market performance have large deviation.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of intelligent man-machine interaction, and discloses a customized virtual pet interactive simulation method, which comprises the following steps: S1, perceiving the emotional state of a user, S2, analyzing the behavior and emotion of the user, S3, establishing a virtual pet behavior model, and S4, establishing a virtual pet interaction model. The invention provides a customized virtual pet interactive simulation system, which comprises a sensing module, a decision-making module, an execution module and a feedback module. The method comprises the following steps: S1, selecting a virtual pet behavior, S2, selecting an optimal behavior, S5, carrying out deep reinforcement learning optimization, S6, executing the virtual pet behavior, and S7, feeding back and adjusting a behavior strategy. The invention also provides the customized virtual pet interactive simulation system, which comprises a sensing module, a decision-making module, an execution module and a feedback module. Through long-term emotional state tracking, personalized behavior pattern recognition and long-term reward correction of reinforcement learning, the virtual pet can continuously optimize a behavior strategy, dynamically adapt to long-term emotional changes of a user, and improve the personalized matching degree and long-term stability of interaction.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

The invention provides a self-adaptive efficient simulation method and system for electric propulsion plasma oscillation, and relates to the technical field of electric propulsion plasma oscillation simulation. According to the method, a high-precision electric propulsion plasma numerical simulation model is adopted to ensure the precision of a simulation result; according to the method, the calculation tasks of the simulation model are decomposed through various adjustment parameters, that is, all the calculation tasks are completed in a parallel calculation mode, and therefore the simulation efficiency can be effectively improved. Moreover, when the simulation model is subjected to simulation solving, the electromagnetic field solving model is solved by adopting a multi-grid method, and the simulation calculation efficiency can be further improved based on the solving mode. Besides, the target neural network model is utilized to process the simulation result so as to update all the adjustment parameters in the process, so that a plurality of adjustment parameters used in the simulation process are continuously and adaptively optimized, and the technical effect of considering the simulation efficiency and precision of the electric propulsion plasma oscillation simulation model is achieved.

The invention provides a data transmission method in an edge computing power secure channel, and the method comprises the steps: obtaining a real-time residual energy value, node position distribution information and node historical energy consumption data of each edge node in a network, constructing a node energy prediction model through a time sequence analysis method, obtaining a predicted energy consumption rate and predicted residual energy of the node in a future period of time; the signal-to-noise ratio, the stability and the interference level of links between weak nodes and adjacent nodes in the energy depression area are obtained, the weak nodes are nodes of which the predicted residual energy is lower than a network average value preset threshold value and which are located in the energy depression area, and adaptive power control and data compression are performed on the weak nodes; and in combination with edge computing power secure channel connectivity, edge computing power secure channel coverage rate and end-to-end data transmission delay factors, network life cycles under different data transmission strategies are evaluated through a Monte Carlo simulation method, and optimal transmission parameter configuration for prolonging the network life cycles is obtained.

The invention provides a multi-level service priority dynamic scheduling simulation method and device, a medium and a product. The method comprises the following steps: constructing a priority label system; establishing a dynamic binding relationship between the service flow and the priority label; when a service layer generates a data packet, embedding a priority label for the data packet based on the dynamic binding relationship; extracting a priority label carried by the data packet on a protocol stack MAC layer, and ensuring that the priority label is unvarnished to a lower layer protocol in a lossless manner; and based on the priority labels carried by the extracted data packets, classifying the data packets into logic queues with corresponding priorities, and executing a differentiated scheduling strategy. The method can adapt to diversified requirements of data services of different types and priorities for network resources, effectively improves the simulation model accuracy, improves the simulation experiment efficiency and simulation real-time performance and reliability in a multi-service priority 5G communication network simulation scene, more accurately reflects the performance of a real network, and improves the simulation experience of the 5G communication network. And meanwhile, the simulation platform has higher expansibility and flexibility.

The invention discloses a computer numerical simulation method, system and equipment for a composite material, particularly relates to the technical field of analog simulation, and is used for solving the problem of simulation distortion caused by neglecting the randomness of a microstructure in the prior art. The method comprises the following steps: acquiring microstructure image data of a composite material, extracting fiber arrangement direction and pore distribution parameters, and generating a fiber direction probability density function and a pore topological relation; constructing a non-uniform grid model constrained by a local coordinate system according to the fiber direction probability density function; identifying a weak connection area based on a pore topological relation and marking nodes; applying dynamic decoupling constraint to the weak connection area, wherein the dynamic decoupling constraint comprises initial rigidity and rigidity attenuation rate; applying an external load to the model and calculating a real-time stress coefficient of the weak connection area; dynamically adjusting the constraint parameter through threshold judgment and iterative correction until the stress coefficient is converged; and finally, outputting a corrected macroscopic mechanical property result.