119 results about "High fidelity simulation" patented technology

The invention relates to the field of digital twinborn technology and industrial equipment intelligent operation and maintenance, and discloses an electric power screen cabinet heat dissipation efficiency improvement and thermal management comprehensive analysis system, which comprises an offline modeling module for constructing a thermal order reduction model with efficient calculation through a model order reduction method based on high-fidelity CFD simulation data; the real-time data acquisition module is used for acquiring real-time power consumption, sparse sensor temperature and an expected load curve; the online state estimation module is used for fusing physical prediction of the thermal reduced-order model with real-time sparse measurement data and reconstructing a global three-dimensional temperature field of the power screen cabinet in real time; and the prospective scheduling analysis module is used for taking the current global temperature field as an initial condition, driving the thermal reduced-order model to perform prospective deduction by utilizing an expected load curve, and calculating and outputting a future thermal margin to early warn a thermal risk. According to the invention, comprehensive real-time sensing, prospective risk prediction and self-adaptive correction of the thermal state of the electric screen cabinet are realized, and thermal management is converted from passive response to active prevention.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention relates to the technical field of operation management, in particular to a resource optimization-oriented operation activity data quantitative analysis method and system, and the method comprises the steps: obtaining multi-modal data to construct enterprise operation digital twins; on the basis of the twin data, inferring a causal relationship between variables through a causal discovery algorithm, quantifying a causal effect by using a graph neural network, and constructing a high-fidelity simulation environment on the basis; and training and generating an optimal resource allocation strategy model in the environment by adopting a deep reinforcement learning engine. In the decision-making stage, causal intervention simulation is carried out on the candidate schemes through a do operator, so that the net effect of the candidate schemes is evaluated, and the optimal scheme is selected. And finally, the scheme is deployed and continuously monitored, and a new data iteration optimization model is utilized to form a self-adaptive closed loop of'data-insight-decision-optimization '. According to the resource optimization-oriented data quantitative analysis method and system, the problems that a decision-making link is split and depends on artificial experience in the prior art are solved.

The invention discloses an Internet of Things alarm audio call scheduling method and system, and relates to the technical field of Internet of Things intelligent decision, and the method comprises the steps: receiving an alarm request message from an Internet of Things sensing terminal, and carrying out the alarm audio call scheduling according to a device identifier and a region code in the alarm request message; collecting and generating a multi-dimensional information report of real-time environment noise data, audio playing equipment state information and user state information; according to the multi-dimensional information report, generating a dynamic priority score of the alarm request message by querying a preset strategy library; and sorting the dynamic priority scores of the alarm request messages to be processed, and generating a preliminary scheduling strategy for suggesting playing equipment and broadcasting parameters for the alarm request message with the highest priority. According to the method, high-fidelity simulation is performed through the acoustic digital twin model to generate the intelligibility index report, the optimal strategy is selected based on the simulation result to generate the execution instruction, and the execution instruction is issued to the target audio equipment, so that full-process intelligent scheduling from multi-dimensional dynamic perception to acoustic effect optimization is realized.

The invention relates to a multi-target aerodynamic-structure optimization method based on a deep neural network proxy model, and solves the problem that an existing method is insufficient in efficiency, feasibility and credibility, and the method comprises the steps: firstly, carrying out the sampling in a parameterized design target, building an aerodynamic-structure database through CFD / FEA high-fidelity simulation; the method comprises the steps that firstly, a multi-task neural network is used for predicting the lift-drag ratio, quality and stress at the same time, uncertainty is output, then, in NSGA-III multi-target optimization, a proxy model is used for rapidly evaluating and searching out a Pareto leading edge meeting constraints, finally, the precision of the model is gradually improved through high-fidelity checking and incremental updating, and an optimal design scheme is output. According to the method, sub-optimization and repeated iteration caused by'pneumatic first and then structure 'are avoided; the design universality and robustness are improved; a compromise scheme set of the system is directly obtained; and the optimization cost and the optimization period can be obviously reduced.

The invention discloses an unmanned ship control method based on deep reinforcement learning in a multi-task scene. The method comprises the following steps: constructing dynamics and kinematics models of an unmanned ship; the method comprises the following steps: constructing a high-fidelity simulation environment based on Isaac Sim and a parallel training framework thereof, and respectively designing a state space and an action space for various unmanned ship tasks; reward functions are respectively designed; constructing an unmanned ship control strategy, and designing an algorithm framework of a centralized importance sampling and shearing strategy optimization mechanism based on an end-to-end deep reinforcement learning algorithm; and for different task scenes, multiple times of training-verification are performed on the strategy network through a PPO algorithm, and the trained strategy network is used for realizing multiple control tasks of the unmanned ship. According to the method, a high-fidelity simulation environment is constructed through Isaac Sim and a parallel training framework thereof, parallelization support which is crucial to deep reinforcement learning training is achieved, and various actual control tasks of the unmanned ship can be achieved.

The invention belongs to the technical field of advanced manufacturing and intelligent design, and provides a topological optimization method and system for a liquid cooling plate of a lithium ion battery pack, and the method comprises the steps: building a parameterized simulation model of a battery pack liquid cooling system, constructing a multi-dimensional design variable space of the liquid cooling plate, and formally defining a multi-objective optimization problem; on the basis of the initial training data set, independently constructing a probabilistic agent model capable of predicting a target value and quantifying uncertainty for each optimization target function; performing quantum behavior enhanced multi-universe optimizer iterative optimization on the constructed agent model, selecting new sample points from a multi-dimensional design variable space of the liquid cooling plate according to a set criterion after a set number of iterations is completed, performing high-fidelity CFD simulation to obtain real data, supplementing the real data to a data set, and updating or reconstructing the agent model; and judging whether an iterative optimization condition is met or not, and if so, screening and outputting a final Pareto optimal solution set from all samples subjected to high-fidelity simulation verification. And efficient multi-objective optimal design of the liquid cooling plate is realized.

The invention discloses a small modular prismatic high-temperature gas cooled reactor core optimization design method and system based on data driving. The method comprises the following steps: constructing a training data set; a data-driven agent model is trained to replace and execute high-cost simulation calculation; and exploring a design space and generating a group of Pareto optimal candidate solutions by using a multi-objective evolution algorithm and taking the proxy model as a fitness function evaluator. The key feature of the method is a closed loop of iterative verification and model updating: verifying the candidate solution through high-fidelity simulation, if the prediction precision does not meet the convergence criterion, expanding the verified new data point to a training data set, and retraining the agent model. According to the method, the calculation cost can be remarkably reduced, a complex design space can be efficiently explored, and a physically reliable reactor design scheme with better performance can be obtained.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the steps: generating an initial sample point set which is uniformly distributed and has representativeness through an improved random sampling method KMODMC, enabling the initial sample point set to comprise a low-fidelity sample set and a high-fidelity sample set, training a BP neural network through employing the low-fidelity sample set, and carrying out the training of the BP neural network through employing the high-fidelity sample set; a low-fidelity BP neural network model is obtained; meanwhile, based on an error training Kriging model of a high-fidelity sample set and a low-fidelity model predicted value, an error correction Kriging model is constructed, the low-fidelity BP neural network model and the error correction Kriging model are combined to form a multi-fidelity mixed agent model, and adaptive iterative optimization is performed through a double-model alternate point adding sampling strategy. And finally obtaining a high-precision multi-fidelity hybrid agent model for structural reliability evaluation. According to the method, the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model are solved, and the adaptive capacity and prediction precision of the model in a complex nonlinear problem are effectively improved.

The invention discloses a ground fault simulation method under different scenes based on an RTDS platform, and aims to solve the problem that in the prior art, systematic deviation exists between simulation waveforms and a physical true model test on key transient characteristics due to isolated modeling of fault influence factors. According to the method, a high-fidelity simulation model fusing multi-dimensional fault variables and a dynamic coupling mechanism of the multi-dimensional fault variables is constructed on an RTDS platform, and multiple variables such as multiple grounding modes of a neutral point, fault positions, phases, transition resistance, three-phase unbalance degree, PT disconnection and in-phase two-point grounding are covered; and fault waveform output and multi-terminal collaborative closed-loop test which are highly consistent with that of a real-type test under full frequency bands and full working conditions are realized.

The invention belongs to the technical field of logistics supply chain management, and particularly relates to an international logistics supply chain collaborative management system which comprises a unified data access gateway, a multi-modal data fusion engine, a supply chain digital twinborn body, a distributed collaborative decision module and a dynamic resource scheduling executor. Through high-fidelity simulation and risk prediction, dynamic perception and prospective insight of a physical supply chain operation state are realized by a supply chain digital twinborn body, so that a system can identify a bottleneck and a risk in advance, passive response is changed into active intervention, a distributed collaborative decision module is combined with a multi-target optimization and consensus mechanism, and the system performance is improved. According to the method, a scientific and reasonable cooperation scheme can be generated on the premise of giving consideration to interests of multiple parties, the decision conflict and trust problems in cross-border cooperation are effectively solved, the dynamic resource scheduling executor ensures that optimization decisions can be accurately and efficiently executed in a landing mode, and a complete closed loop from sensing, decision making to execution is formed.

The invention relates to the technical field of compressed air energy storage, and discloses a fault prediction and health management method for a compressed air energy storage power station, which comprises the following steps: establishing an accurate physical model of a key component of the compressed air energy storage power station, constructing a fault simulation injection mechanism to simulate a real fault, and obtaining a fault mode library; setting different severity levels for each fault mode in the fault mode library; collecting multi-source data in a simulation process under multiple working conditions; extracting sensitive features from the multi-source data and associating the sensitive features with corresponding fault tags; constructing a hybrid deep learning fault prediction model to output fault classification and residual service life; and evaluating the health state of the equipment based on multi-source information fusion. According to the method, massive fault data are generated through the high-fidelity simulation model, the hybrid deep learning fault prediction model is trained, fault prediction and health assessment of the compressed air energy storage power station are achieved, and the requirements of the large compressed air energy storage power station for high reliability, high availability and intelligent operation and maintenance are met.

The invention relates to the technical field of sliding bearing design in mechanical engineering, in particular to a sliding bearing multi-mode end-to-end intelligent design system based on a large model. Comprising a multi-modal input and feature recognition module, a high-fidelity performance prediction module, a structural parameter intelligent optimization decision module and a parametric modeling and multi-modal output module which are connected in sequence and form an intelligent design closed loop, and all the modules are seamlessly connected through data interfaces. According to the method, multi-modal input and a high-fidelity simulation closed loop are driven through a large language model, the sliding bearing design efficiency is remarkably improved, the technical threshold is greatly reduced, interaction between a natural language and a drawing is supported, and non-experts can complete high-performance design; full-process automation from requirements to drawings is achieved, and manual intervention errors are avoided; optimizing in a wide-area parameter space by using the large model reasoning capability to realize multi-target global optimization; and multi-modal input and output of texts, drawings and models are supported, and the engineering applicability is enhanced.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.

The invention provides a multi-agent simulation system based on a large language model, and the system comprises a man-machine interaction setting module which is used for providing an interaction interface between a user and the system, enabling the user to carry out the scene setting and parameter setting used in a simulation operation process through the man-machine interaction setting module, and providing the data information needed by the operation of the system, in combination with real scene information, high-simulation simulation scenes and parameters are set for specific simulation; the multi-agent collaboration module is used for specifically performing multi-agent simulation operation based on a large language model, processing main body and environment information and realizing a development process of a real event along with time lapse; the data bus communication module is used for communicating the intelligent agent with the database and connecting the intelligent agent with the database in a bus mode so as to improve the information exchange efficiency; and the log causal analysis module is used for analyzing the simulation process and result. The simulation system provided by the invention can fully reflect the running condition of the real world.

The invention discloses a missile aerodynamic parameter rapid correction method based on a residual learning neural network algorithm. A small number of pneumatic data points obtained through accurate calculation are fully utilized, a large number of pneumatic data sets obtained through learning rough calculation are fused, and therefore rapid and accurate correction of original pneumatic parameters is achieved. According to the method, a neural network algorithm based on residual learning is firstly provided, and a neural network is utilized to directly learn a residual term between a theoretical curve and high-fidelity simulation data, so that an original theoretical model is corrected based on residual prediction, and a fitting curve closer to high-precision data distribution is obtained. By combining the algorithm, efficient modeling under the condition of extremely small high-precision data volume is realized, an original low-precision theoretical model does not need to be modified, only a residual error part is modeled, limited high-precision data is smoothly fitted, and rapid and accurate correction of original aerodynamic parameters is realized.

The invention discloses an automatic equipment fault diagnosis and intelligent repair system oriented to complex working conditions, and belongs to the technical field of intelligent operation and maintenance of automatic equipment. The edge computing node is internally provided with a spatial-temporal feature separation convolutional network ST-FSCN and is used for processing multi-modal sensing data in real time; the cloud decision center runs a dynamic reinforcement repair strategy library and comprises a deep Q network based on a physical model, a meta-learning strategy optimizer MAML-RL and a security verification engine; the execution feedback loop is connected with the equipment PLC through an industrial bus, adjusts the operation parameters of the equipment in real time, and feeds back the execution effect to the self-evolution digital twin system; and the self-evolution digital twin system comprises a high-fidelity simulation model and a lightweight real-time model, and adopts a dual-channel updating mechanism. The method has the advantages that the composite fault recognition accuracy is improved, the average repair decision time is shortened, and accidental shutdown of equipment is reduced.

The invention relates to the technical field of building energy conservation and green buildings, and discloses a design parameter optimization method for a tropical zero-carbon building integrated system. According to the method, an input correlation-output uncertainty-global sensitivity three-dimensional analysis framework is constructed, through high-fidelity simulation modeling, Latin hypercube sampling and Gaussian process proxy model construction and verification, and in combination with Pearson correlation analysis, Monte Carlo propagation and Sober sensitivity calculation, a dominant parameter set and key interaction items are accurately recognized, and the accuracy and the reliability of the system are improved. And finally, solving the optimal low-carbon parameter combination by adopting a sequential quadratic programming algorithm under the engineering constraint. The system comprises twelve functional units such as a meteorological acquisition unit, a parameter definition unit and a simulation modeling unit, and outputs a parameter list which can be directly used for enclosure construction, unit type selection and photovoltaic arrangement. According to the method, scientificity and implementability of parameterization design of the tropical zero-carbon building are improved, annual net carbon emission can be reduced through actual measurement, and intelligent decision support is provided for the building double-carbon target.

The invention discloses a satellite autonomous task planning method and system based on a ground digital mirror image, and belongs to the technical field of spacecraft measurement and control. Through the states of a satellite-ground link synchronous satellite and a ground digital mirror image, a task request is received in the ground digital mirror image, a planning algorithm is operated, a candidate scheme is generated for high-fidelity simulation deduction and verification, a feasible scheme is evaluated and selected and converted into an instruction sequence to be uploaded to a satellite, the satellite executes an instruction and feeds back the state, and a closed loop is formed. According to the method, a complex planning and verification process is placed in a ground digital mirror image, the efficiency and reliability of task planning are remarkably improved by using the strong computing power of the ground, meanwhile, the autonomous operation capability of a satellite is improved, and the problems that traditional satellite task planning is slow in response and poor in reliability, and the autonomous planning capability of a pure satellite is weak can be solved.

The invention discloses a generative AI operation decision-making system based on an organization strategy and a vehicle context. A multi-source situation data access module, a strategy knowledge graph construction module, a multi-modal situation fusion module, a generative AI decision module, a digital twinborn simulation verification module, a strategy compliance arbitration module, a decision optimization and explanation generation module and a decision execution and monitoring module are integrated into a closed-loop system. A candidate decision is dynamically generated by structuring an organization strategy and deeply fusing the organization strategy with real-time data, then feasibility and risk are predicted through high-fidelity simulation, high consistency of the decision and a rule is ensured through automatic compliance verification, and finally a reliable decision which is subjected to multi-objective optimization and attached with natural language explanation is output. Therefore, the illusion problem of application of the generative AI to vehicle operation is solved, the defects of lack of constraints and unpredictable output are overcome, and safe, reliable, compliant, controllable, stable and explainable intelligent operation decision is realized.

The invention discloses an energy management system and a hydrogen production dynamic optimization method for alkaline electrolytic water hydrogen production based on superposition and coupling of multiple algorithms, and electronic equipment. The system comprises a hybrid modeling module, a layered optimization controller, a cloud edge collaborative incremental learning module and an uncertainty management module. The hybrid modeling module constructs an electrolytic cell digital twinborn body by fusing a physical model and a data driving model; the hierarchical optimization controller is divided into a top layer reinforcement learning scheduling unit, a middle layer model prediction control unit and a bottom layer self-adaptive PID control unit according to a time scale; the cloud edge collaborative incremental learning module deploys a lightweight edge model and is connected with a cloud high-fidelity simulator; and the uncertainty management module integrates Bayesian deep learning and a robust optimization algorithm. The influence of different working conditions on the electrolytic cell, the power module and the auxiliary equipment can be evaluated in advance in the virtual environment, and the actual debugging risk and energy waste are reduced.

The invention discloses an unmanned cluster dynamic target allocation and collaborative decision-making method and system based on multi-agent reinforcement learning, relates to the field of unmanned aerial vehicle cluster collaborative control, and aims to solve the problem that an existing method is difficult to realize effective collaboration of task allocation and path planning in a dynamic multi-target environment when an unmanned aerial vehicle cluster is used as a key execution carrier on the edge side of the Internet of Things. And the cluster cooperation efficiency and the environment adaptability are insufficient. According to the invention, a multi-agent near-end strategy optimization algorithm for dynamic allocation perception is provided, a perception-allocation-decision integrated collaborative navigation framework is constructed by embedding a real-time target allocation module and designing a hierarchical collaborative reward mechanism, and cluster adaptive decision under local observation and dynamic multi-target constraints is realized. In a high-fidelity simulation test, the task success rate of the algorithm in dynamic multi-target scenes with different obstacle densities reaches 72%-91%, and is improved by more than 27% compared with a reference algorithm. In addition, when the target is converted from a static state to a dynamic state, the performance attenuation amplitude is reduced by more than 20% compared with a reference algorithm, and in all tests, the DA-MAPPO achieves the shortest average track length and the minimum decision step number. And the task reliability, safety and execution efficiency of the unmanned cluster in the dynamic Internet of Things environment are remarkably improved.

The invention relates to the technical field of wireless communication, in particular to a multi-scene communication high-fidelity simulation method and system, computer equipment and a medium. The method comprises the following steps: acquiring a channel state information matrix from a plurality of physical environments, converting a complex channel matrix in the channel state information matrix into a dual-channel real number tensor, and constructing an input tensor in combination with a binary mask matrix; the steps of local fine feature and global context dependency extraction, iterative feature fusion and adaptive dimension reduction are executed through a channel processing module, meanwhile, time domain sparsity constraint is applied, and high-fidelity broadband channel state information is reconstructed; and converting the format of the high-fidelity broadband channel state information into a standard complex number channel matrix format according to a real complex number conversion rule. Through the mode, the technical problem of lack of asymmetric information processing capability in the existing channel simulation technology is solved, and the fidelity, the practicability and the environmental adaptability of channel simulation in a complex scene are improved.

This invention provides a method and system for high-fidelity physical modeling and detection performance evaluation of space targets, relating to the field of deep space exploration technology. The method first acquires the relative motion parameters of the spacecraft and the asteroid, as well as camera parameters, and determines the static apparent magnitude based on observation geometry and the asteroid's physical characteristics. Then, it calculates the angular velocity modulus through relative velocity, combines it with camera parameters to obtain tail parameters, and quantifies the equivalent magnitude loss and effective apparent magnitude based on a piecewise model. Subsequently, it constructs a two-dimensional parameter grid of focal length and exposure time, calculates the effective signal-to-noise ratio and detection margin for each combination, and selects the optimal parameters using a heatmap. Finally, it generates a high-fidelity simulation image and feeds back the observation values ​​to the guidance, navigation, and control system via a UDP asynchronous interface. This invention achieves quantitative assessment of tail loss and automated parameter optimization, balancing high fidelity and real-time performance, and improving detection accuracy and system reliability.

The application provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, which comprises the following steps: constructing an accident chain knowledge graph and a physical trigger graph, establishing the causal relationship and physical constraints among equipment, state, event and consequence; mapping the historical records, expert rules and online observation data into graph entities and relationships to form a baseline accident scene and calculate baseline indicators; generating candidate paths by using graph reasoning and coupled multi-physical field simulation, and realizing the closed loop of graph reasoning and physical calculation through consistency checking; scoring and perturbation simulation analysis on the candidate paths, screening the robust target path; carrying out high-fidelity simulation on the target path, identifying key nodes combined with sensitivity analysis and minimum cut set, and generating disposal suggestions and action priority. The application realizes high credible prediction of accident evolution and closed loop linkage of emergency response, and has high precision, high robustness and engineering implementability.

The invention discloses a natural gas pipeline network operation optimization method integrated with high-fidelity simulation, which comprises the following steps of: constructing a pipeline network high-fidelity simulation model and a numerical solution method as a calculation basis in the optimization method; a linear optimization model only containing operation constraints is constructed by integrating a nonlinear coupling relationship among operation variables of a high-fidelity simulation implicit representation pipe network, so that the solving complexity is reduced; a simulation drive linearization method is provided, a linear relation between pipe network operation variables can be established based on a multi-scheme simulation result of an initial scheme local area, and a local area linear optimization model is formed; the initial scheme is updated through continuous iteration in the optimization process, a local area linear optimization model based on the current initial scheme is established and solved in each iteration, and approximation to the globally optimal solution is carried out step by step. The method can be effectively compatible with the black box characteristics of high-fidelity simulation, effectively improves the practical feasibility of an optimization scheme, and has the performance advantages of high solving speed and good convergence stability.

The invention discloses a two-point grounding fault model analysis method in a neutral point non-effective grounding mode, and aims to solve the problem that a unified accurate mathematical model suitable for a neutral point non-grounding and arc suppression coil grounding system under an in-phase two-point grounding fault is lacked in the prior art. According to the method, a fault network topology is reconstructed, a multivariate function equation set containing a fault position, ground capacitance, arc suppression coil parameters and a power supply initial phase angle is established, explicit analytical expressions of zero-sequence voltage current and phase voltage current are deduced, and a transient process is modeled in combination with a state space method. And accurate description of a fault characteristic quantity spatio-temporal evolution rule is realized. By adopting the above technical scheme, the method can provide a modeling basis which can be directly called for high-fidelity simulation platforms such as RTDS, and supports intelligent protection algorithm development and fault diagnosis system optimization.

The application relates to the technical field of energy storage safety, and discloses a multi-scale multi-physical-field energy storage emergency space twin modeling method, which comprises the following steps: presetting macro low-dimensional and micro high-dimensional models in an edge computing device, measuring residual computing power, setting a concurrent threshold, calculating space-time gradient characteristics by using a macro model based on annular buffer data to mark a candidate region, calculating a thermodynamic urgency index, sorting the candidate region, and screening out an activated region, calling historical data to drive a micro model to perform super-real-time operation, completing internal state initialization, establishing a heterogeneous model physical boundary bidirectional coupling by using flux impedance matching, outputting a fault evolution prediction result, and generating a fixed-point emergency control instruction. By means of thermodynamic urgency scheduling and boundary coupling technology, the application solves the high-fidelity simulation problem under the condition of limited computing power on the edge side, and improves the prediction accuracy and response speed of the multi-scale multi-physical-field energy storage emergency space twin modeling.