1630 results about "Systems modeling" patented technology

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention discloses a cloud control self-driving automobile control method considering automobile cloud communication time delay. By constructing a time delay prediction model based on historical communication delay data, the back-and-forth communication time delay at the current moment is accurately predicted, and forward compensation is carried out on the vehicle state by adopting an iterative prediction formula in combination with a vehicle transverse dynamic model. According to the method, a process disturbance correction item is introduced, system modeling errors caused by environment disturbance are effectively corrected, and the state prediction precision is improved. And deducing a future vehicle state through the predicted time delay step number in combination with a historical vehicle state and control input so as to realize dynamic compensation of the time delay influence. The method effectively solves the problems of control lag and state information lag caused by communication delay in vehicle-cloud cooperative control, improves the control real-time performance and precision of a cloud control automatic driving system, enhances the robustness and safety of the system in a complex environment, and is suitable for automatic driving scenes with high dynamic and high safety requirements.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

The invention relates to the field of factory intelligent cooling, and discloses an MPC-based large factory intelligent cooling system and a cooling control method, the system comprises a data acquisition module, an AI control module, a cooling tower control module, a cooling water pump control module and a cooling-water machine control module; the data acquisition module acquires outdoor environment temperature and humidity, cooling tower outlet water temperature, cooling water pump inlet and outlet water temperature difference and flow and water chiller operation state data in real time. The AI control module constructs a system model through real-time data, predicts and optimizes by using an MPC algorithm, and calculates optimal values of the running frequency of a cooling tower fan, the running frequency of a cooling water pump and the power of a cooling-water machine compressor by adopting sequential quadratic programming, so as to realize cooperative dynamic control of equipment in the system; the overall operation efficiency of the cooling system under the outdoor temperature and humidity change condition is effectively improved, and the overall energy consumption of the system is minimized in real time.

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

A computer-implemented method for simulating an aircraft system in a closed-loop functional simulation environment is disclosed. The computer-implemented method may include: receiving, at a computer system, one or more control inputs from an interface; processing, by a control system model, the one or more control inputs to generate one or more system command signals; applying the one or more system command signals to one or more virtual system models; simulating a physical response of the aircraft based on output generated by the one or more virtual system models in response to application of the one or more system command signals; generating, responsive to monitoring the output generated by the one or more virtual system models, sensor data; transmitting the sensor data to the control system model; and outputting simulation results, wherein the simulation results at least comprise an indication of the physical response of the aircraft.

The invention discloses a deep learning-based middle-deep layer geothermal pipe group multi-field coupling simulation method, which comprises the following steps: carrying out numerical simulation cross mutual verification to obtain a middle-deep layer buried pipe heat pump system model, and carrying out system configuration by adopting a Monte Carlo method to obtain a first parameter environment set; screening the first parameter environment set according to a parameter environment function to obtain a second parameter environment set, performing data augmentation to obtain mid-deep layer geothermal pipe group simulation data, constructing a mid-deep layer geothermal pipe group multi-field coupling deep learning model, and obtaining simulation heat exchange data and prediction coupling heat exchange data; and adaptive grid optimization is carried out to obtain an optimized grid size, and the optimized grid size is adopted to adjust the mid-deep layer buried pipe heat pump system model to output the mid-deep layer buried pipe multi-field coupling model. According to the method, accurate and rapid simulation of the multi-field coupling process of the medium-deep geothermal pipe group can be realized under complex geological conditions, and the method has important practical significance for promoting large-scale development and utilization of medium-deep geothermal resources.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention discloses a hybrid attack defense control method and system for a heterogeneous cluster in an urban confrontation environment, terminal equipment and a medium, and relates to the technical field of unmanned platform cluster control, and the method comprises the steps: constructing an ideal system model of an unmanned platform cluster comprising a leader and a plurality of followers, constructing an information physical hybrid attack model based on the model; using the attack model to simulate an attack behavior of an attacker on an ideal system to obtain an attacked system model; based on an attacked system model, constructing a distributed elastic security estimator with a compensation mechanism, compensating attack influence for each follower and estimating an expected position; and based on the expected position of the follower, constructing a distributed elastic safety controller with a compensation mechanism, and controlling the follower. By designing the estimator and the controller, attack interference is counteracted, control input is generated, and safe collaboration of the heterogeneous nonlinear unmanned cluster under the cyber-physical hybrid attack is guaranteed.

The invention relates to an Internet of Vehicles channel selection method based on a multi-agent depth deterministic strategy gradient algorithm, and belongs to the technical field of communication. The method comprises the following steps: establishing an Internet of Vehicles system model, and setting local and global environment variables, a vehicle action space and a reward function of the Internet of Vehicles system model; establishing a dynamic channel model of the Internet of Vehicles based on the system model of the Internet of Vehicles, and determining a state space and steady-state probability distribution under multi-channel combination; a graph neural network GNN is introduced into a multi-agent depth deterministic strategy gradient algorithm, and a GNN-Critic network is established to process dynamic vehicle input information; the intelligent body vehicle learns a channel selection strategy by using a global environment state so as to complete centralized training; and after training is completed, the intelligent body vehicle observes local channel state distribution and executes channel selection. According to the method, the channel utilization rate can be remarkably improved, the vehicle can make a better channel selection decision according to the global environment information, the channel idle time is reduced, and the overall network performance is improved.

The invention discloses a modeling and optimization control method for a gas supply system of a proton exchange membrane fuel cell, and belongs to the technical field of hydrogen energy power. The method comprises the following steps: establishing a nonlinear state space model of a proton exchange membrane fuel cell gas supply system; determining the optimal oxygen excess ratio of the system under different load currents through experiments, and fitting the optimal oxygen excess ratio into a reference function about the load currents; based on the nonlinear state space model, a model prediction control problem with tracking of the optimal oxygen excess ratio and minimization of the cathode and anode pressure difference as control targets is constructed and expressed as a constrained quadratic programming problem; and decomposing and iteratively solving the quadratic programming problem by adopting an alternating direction multiplier method to obtain the optimal control input of the current control period and act on the system. The method effectively solves the problem that traditional model predictive control is difficult to deploy in real time in a vehicle-mounted controller due to large calculated amount, so that efficient and accurate cooperative control of the proton exchange membrane fuel cell gas supply system is realized.

The invention discloses a multi-agent reinforcement learning method. The method comprises the following steps: step 1, system initialization and edge device modeling; 2, generating a data source and constructing a task package; step 3, edge equipment trajectory planning and movement acquisition; 4, local task execution and unloading strategy decision making; 5, communication link modeling and bandwidth resource allocation are carried out; step 6, scheduling optimization driven by an information age and depreciation mechanism; step 7, multi-agent strategy optimization based on Actor-Critic is carried out; and step 8, reward function design and reinforcement learning process. The technical problems that an existing centralized scheduling or heuristic algorithm cannot perform efficient learning and scheduling in a resource heterogeneous and data dynamic environment, and cannot still have good learning ability and generalization ability under the condition of lack of global information are solved; the method is suitable for system modeling and optimization work of efficient strategy learning and collaborative decision making of multiple agents in a complete cooperation task.

The invention provides a multi-modal knowledge graph construction method and system in the agricultural field, and relates to the technical field of data processing. The method comprises the following steps: performing recognition and structured expression on agricultural entities, relationships and events by fusing texts, images and time-space data to form an agricultural semantic representation model; calculating a confidence coefficient based on the evidence template and resolving conflicts, and generating a structured knowledge unit; an agricultural multi-modal knowledge graph is constructed on the basis of the event network, and dynamic updating is achieved through increment correction and self-correction; and finally, outputting an agricultural aid decision result with confidence by combining causal reasoning and confidence evaluation. The agricultural multi-modal knowledge graph construction method solves the problem that an agricultural multi-modal knowledge graph construction method in the prior art lacks a system modeling and closed-loop optimization mechanism for cross-modal alignment credibility, ontology constraint, evidence tracing, causal verification and dynamic updating.

The invention relates to an internet of vehicles edge computing task unloading and resource scheduling optimization method, which comprises the following steps: constructing an internet of vehicles edge computing system model fusing a task jump mechanism and dual-priority scheduling, the system model comprising a road test unit deployment model, a communication model, a computing model and a task jump model; establishing a joint optimization function aiming at minimizing the total time delay and the total energy consumption of the system; decomposing a joint optimization problem into three sub-problems of unloading decision, computing resource allocation and communication strategy selection; a deep reinforcement learning algorithm is adopted to intelligently sense a road environment state, an optimal communication priority strategy combination is dynamically selected, and a task unloading decision and computing resource allocation are collaboratively optimized; and in each scheduling time slot, the edge server allocates communication bandwidth and computing resources to the vehicle tasks according to the selected strategy, and triggers a task preemption and cross-server jump mechanism, thereby realizing maximization of system throughput and minimization of task processing time delay.

The invention discloses a suspension control fault detection and recovery method and device for a maglev train and a medium, and relates to the field of maglev trains, and the method comprises the following steps: S1, constructing a linearized suspension system model; s2, reconstructing a state vector, and generating a suspension gap estimation value; s3, constructing a health probe set, and executing a signal selection strategy; s4, detecting a slow change fault based on the statistical accumulation characteristic of the residual error, and outputting a slow change fault detection result; s5, monitoring the variation trend of the residual error, and triggering a reset mechanism to clear historical accumulated data when a reset condition is met; and S6, generating a final fault mark, and forcibly updating the fault mark to a normal state if a reset mechanism is triggered. According to the hierarchical collaborative decision-making architecture based on the maglev train lap joint structure dynamic model, the condition of performance degradation or failure does not occur in a real system by the algorithm, so that the engineering applicability and reliability of fault diagnosis are improved.

The invention discloses an LSTM (Long Short Term Memory)-based harmonic frequency coupling model optimization method for a network construction type converter, which considers the influence of a dead zone and establishes a converter model controlled by a virtual synchronous generator based on a harmonic state space method. In the power electronic modeling process, due to approximate simplification, switch neglecting and other nonlinear relations, the model is not accurate enough, meanwhile, in the harmonic state space model modeling process, system state variable parameters need to be considered, and the precision of the model affects harmonic transmission characteristics and coupling analysis, so that the modeling efficiency is greatly improved. Errors caused by power electronic modeling are reduced through an LSTM neural network algorithm, the harmonic amplitude estimation difference is reduced, state variable parameter values in a system model are accurately estimated, the model precision is further improved, and then the harmonic coupling condition under the dead zone influence is effectively and accurately analyzed.

The invention discloses a hybrid energy storage optimization configuration method based on a multi-time scale regulation and control demand, and the method comprises the steps: collecting the operation data of a power system, and obtaining the power fluctuation and the frequency deviation of the power system under a short time scale through a preset high-proportion renewable energy power system model; on the basis of power fluctuation and frequency deviation, the regulation and control requirements of the power system are divided according to different time scales, different energy storage modules are correspondingly configured, and a multi-level collaborative energy storage regulation and control structure matched with the time scales is formed; based on the power system model and the mathematical model of each energy storage module, constructing a hierarchical nested optimization model oriented to multiple time scales; and selecting a typical scene from the operation data by adopting a time sequence clustering method, and obtaining the optimal capacity and power configuration of each energy storage module based on a hierarchical nested optimization model, and obtaining a cross-time-scale collaborative operation strategy. The global optimal configuration is realized by establishing a multi-time-scale collaborative hierarchical nested optimization architecture.

The invention discloses a data-driven event trigger control method for a linear variable-parameter nonlinear system, which comprises the following steps: for a discrete-time nonlinear system with an unknown model, expressing the nonlinear system as a parameter-dependent linear form by using a linear variable-parameter embedding method; constructing a convex polyhedron-based noise matrix set for unknown bounded noise in the measurement data, and proposing polyhedral data-driven LPV system representation by using offline collected state, input and scheduling variable data; designing a bounded dynamic event trigger transmission mechanism, and optimizing the use of communication resources by adjusting a trigger threshold in real time and setting the upper and lower bounds of a transmission interval; based on a discrete time cycle functional analysis method, controller gain and trigger parameters are jointly designed through a linear matrix inequality tool. The method does not depend on an accurate system model, stable control over a nonlinear system is achieved only through historical data, the communication frequency is remarkably reduced, and meanwhile the system performance and robustness are guaranteed.

The invention discloses a micro-grid group operation optimization method and system based on a multi-target dragonfly algorithm, and the method comprises the steps: constructing a system model of a micro-grid cluster, and enabling the system model to comprise a plurality of micro-grids; taking the connection state of the plurality of micro-grids as a decision variable to represent the communication mode between the micro-grids; a dynamic grouping mechanism is introduced, and the connection relation of the multiple micro-grids is adjusted according to actual requirements; establishing a multi-objective optimization model, and designing operation constraint conditions; combining decision variables and constraint conditions, designing a structure-scheduling joint optimization strategy, and carrying out collaborative optimization on a micro-grid group structure and a scheduling strategy; and according to an optimization result, outputting a Pareto optimization scheduling solution set covering different operation preferences, and obtaining a low-carbon economic multi-energy-flow cooperative operation scheme of the micro-grid group. According to the method, the problems of local optimum and low convergence speed during multi-objective optimization in the micro-grid dispatching process can be solved.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention discloses a reinforcement learning-based cluster spacecraft multi-target intelligent cooperative tracking method. The method comprises the steps of establishing a relative motion relationship between a tracking spacecraft and a target spacecraft through a nonlinear orbit kinetic equation; designing a double-layer game framework, constructing a non-zero sum game in the cluster to drive cooperation, and introducing a zero and maximum and minimum game between the cluster and a target to describe a confrontation relationship; based on communication topology and relative state information, defining a local error term and a performance index function of the tracking spacecraft and the target spacecraft, and defining an optimization target of each agent under cooperation and confrontation; and iteratively updating the value function and the control law. According to the invention, collaborative tracking can be realized without defining a system model. The method can be widely applied to the field of cluster control.

The invention discloses a new energy power prediction method and system based on a multi-scale state decomposition mechanism, and the method comprises the steps: collecting the multi-source historical data of a wind power station or a photovoltaic station, carrying out the preprocessing of the multi-source historical data, and constructing a training data set; constructing a new energy power prediction network, wherein the new energy power prediction network comprises a knowledge guide layer, a historical sequence encoder, a continuous dynamic system modeling layer and a physical perception decoding network; the knowledge guiding layer is connected with the historical sequence encoder in parallel, and the output of the knowledge guiding layer and the output of the historical sequence encoder are sequentially connected with the continuous dynamic system modeling layer and the physical perception decoding network; training the new energy power prediction network by using the training data set; and deploying the trained new energy power prediction network to a device end, and performing power prediction to obtain a prediction result. According to the method, the stability and the convergence speed of the prediction model in the early training stage and the robustness under the extreme meteorological condition are remarkably improved.

The invention belongs to the technical field of electric power system information physical fusion, and discloses an electric power information physical system modeling method and device based on digital twinborn and cross-domain joint simulation, equipment and a medium. The device comprises a physical layer used for acquiring basic data of a power network; the communication layer is used for constructing a communication network model and simulating communication among different subsystems in the power network; the digital twin layer is used for constructing a protocol conversion gateway to align semantics of the physical layer data and the communication layer data, and performing state estimation of the physical layer and the communication layer by adopting digital twin virtual-real interaction based on the physical layer data and the communication layer data which are subjected to semantic alignment; performing joint simulation of the physical layer and the communication layer in different communication time delays and communication fault risk conditions to obtain a simulation result; and the application layer is used for obtaining critical time delay and time delay-frequency sensitivity based on the simulation result.