986 results about "Real-time simulation" patented technology

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

The present invention provides an elevator emergency rescue decision-making method and system based on reinforcement learning, which relates to the technical field of elevator emergency rescue, including real-time collection of elevator multimodal data through a distributed sensor network to generate an elevator state representation vector; inputting the obtained elevator state representation vector into a pre-trained graph convolutional neural network model, and simultaneously starting an edge computing unit for real-time simulation, simulating system responses under different rescue strategies, evaluating the feasibility of each rescue strategy, weightedly fusing the multi-dimensional risk score and the rescue strategy feasibility evaluation results, and generating a risk assessment report; sending a rescue instruction set to an elevator emergency rescue executive agency through a secure encrypted channel, and continuously monitoring the real-time feedback information of the elevator emergency rescue executive agency, and triggering a decision re-evaluation mechanism when an abnormal situation occurs, until a passenger safe evacuation signal is received and the elevator system restores a safe state signal, and the rescue mission is completed.

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention provides a heart multi-mode digital twinning simulation method and system, electronic equipment and a medium, and belongs to the technical field of digital twinning. The method comprises the following steps: S1, collecting multi-modal data of a heart; s2, preprocessing the acquired multi-modal data to obtain heart digital data; s3, based on the heart digital data, constructing a multi-modal digital twinborn model of the heart and optimizing the multi-modal digital twinborn model; s4, performing real-time simulation on the electrophysiological activity of the heart by using the optimized multi-modal digital twin model and using a Runge-Kutta method in combination with a PSO (Particle Swarm Optimization) algorithm; s5, based on the optimized multi-mode digital twinborn model and the simulation result, simulation and planning of virtual cardiac surgical treatment are carried out; and S6, dynamically evaluating the multi-modal digital twin model, and monitoring the state change of the heart in real time. According to the heart multi-mode digital twinborn simulation method and system, the electronic equipment and the medium, efficient simulation and real-time monitoring of the heart state can be achieved.

The invention provides an electric power system natural disaster early warning and response method based on real-time monitoring, and relates to the technical field of electric power early warning response, and the method comprises the following steps: data analysis and processing: carrying out the analysis and processing of real-time monitoring data in an electric power system through combining a long short-term memory network with a Bayesian network; generating a scheduling scheme: updating the state of the power system in real time and generating an optimal scheduling scheme based on a multi-source data fusion and optimization method combining a Kalman filter and a genetic algorithm; generating an emergency response strategy: simulating the influence of natural disasters on the power system in real time in the virtual model through a digital twin technology and a self-adaptive dynamic planning method, and providing the emergency response strategy; and anomaly detection and self-healing: carrying out anomaly detection and self-healing on the operation state of the power system by combining big data analysis with edge calculation.

The invention discloses a spacecraft intelligent design method and system capable of autonomously learning, and belongs to the technical field of spacecraft design and simulation. The problems that in spacecraft design, natural language requirements are fuzzy and difficult to process, instruction generation is prone to ambiguity, linkage between simulation and design is not smooth, a design scheme lacks an effective iterative optimization mechanism, and traditional design is low in efficiency are solved. The method comprises the following steps: firstly, receiving a natural language demand, extracting key parameters to generate prompt words, inputting the prompt words into Design.AI, and generating a simulation instruction sequence; simulation is started through MCP Server conversion, if the result does not reach the standard, feedback correction is carried out, and iterative optimization is carried out. And repeating the steps until a design target is met, and generating a design report containing multiple items of content. Through natural language processing, real-time simulation, iterative optimization, knowledge accumulation and other technologies, the spacecraft design efficiency is greatly improved, the accuracy is enhanced, the threshold is lowered, and the flexibility is improved.

The invention relates to a milling cutter machining method and system based on a CAM simulation technology, and the method comprises the steps: obtaining three-dimensional data of a workpiece mold and milling cutter parameters, and carrying out the process planning, and obtaining an initial workpiece machining strategy; cutter path planning and contact point calculation are conducted on the initial workpiece machining strategy, and a corresponding cutter movement path is obtained; carrying out CAM simulation analysis on the initial workpiece machining strategy to obtain corresponding real-time simulation data, and carrying out collision interference detection on the tool movement track according to the real-time simulation data to obtain a corresponding safe movement path; carrying out cutting force and cutting temperature analysis on the safe motion path to obtain a corresponding process verification result; and carrying out machining analysis on the safe motion path and the cutter predicted use data, and carrying out NC machining code optimization on the initial workpiece machining strategy to obtain a corresponding optimized workpiece machining scheme. The machining strategy can be flexibly adjusted according to the characteristics and machining requirements of different workpieces.

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 discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention discloses a digital twin architecture for a satellite-ground collaborative edge network, and belongs to the field of network simulation and intelligent management in the satellite internet. According to the digital twin architecture of the satellite-ground collaborative edge network provided by the invention, the operation state, the resource state and the association state of terminal equipment such as an unmanned aerial vehicle and edge equipment such as a satellite are returned through a sliced feeder line, and a digital twin model integrating a data layer, a model layer and a decision layer is established at a gateway station side. Based on the architecture, a satellite-ground collaborative model training task is oriented, and model training and aggregation are realized by utilizing a block chain technology. According to the architecture, full-life-cycle management of the satellite-ground collaborative edge network is realized, through real-time simulation and optimization decision, the digital and intelligent level of the satellite network is improved, the simulation precision, real-time performance and resource utilization efficiency of the network are improved, and efficient and stable operation of the satellite network in a complex dynamic environment is ensured.

The present application relates to a simulation test method and simulation test platform for an energy-storage battery management system. The method comprises: receiving an energy-storage system real-time simulation result that is sent by an energy-storage system real-time simulation module by means of a high-speed communication protocol; converting a data format of the energy-storage system real-time simulation result into a data format of a target communication protocol, and sending to a battery management control module a target energy-storage system real-time simulation result that is obtained after conversion; receiving a battery management control message that is sent by the battery management control module in response to the target energy-storage system real-time simulation result; and converting a data format of the battery management control message into the data format of the target communication protocol, and sending to the energy-storage system real-time simulation module a target battery management control message that is obtained after conversion, thus making the energy-storage system real-time simulation module perform a test on the battery management control module on the basis of the battery management control message. By using the present method, the simulation test efficiency of an energy-storage battery management system can be improved.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

The invention discloses an SWMM simulation model approximation method based on sparse attention and physical embedding, belongs to the technical field of simulation modeling, and aims to effectively process high-dimensional time sequence input through a sparse attention mechanism and embed physical constraints to ensure physical consistency of model approximation and ensure that an output result conforms to the law of flow conservation and pollutant conservation. The SWMM behavior is accurately approached; according to the method, a complex simulation behavior of the SWMM is replaced by an efficient deep learning prediction model, the calculation efficiency is remarkably improved, the credibility of a prediction result is ensured, the method is suitable for a real-time simulation scene of regional drainage optimization, and real-time simulation support is provided for regional hydrological management.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses a simulation method and device for a high-voltage direct-hanging energy storage system based on an FPGA. A power unit of the high-voltage direct-hanging energy storage system is divided into an H bridge and an energy storage unit part, the H bridge and a network side topology are placed in the FPGA for simulation, an energy storage battery branch part is placed in a CPU for simulation, and submodule voltage and current information interaction is conducted between the FPGA and the CPU through a PCIE bus. The FPGA establishes a Thevenin model of a single high-voltage direct-hanging energy storage system sub-module, algebraic superposition is carried out by using the series relation of the sub-modules, and the capacitor voltage, the terminal voltage and the bridge arm voltage of the sub-modules are calculated; the equivalent model and the network side topology run in an FPGA (Field Programmable Gate Array) of a simulator at a simulation step length of 1 us; the energy storage battery branch runs in a CPU of the simulator, and the CPU supports a user-defined energy storage battery model; and the advantages of the real-time multi-core processor and the FPGA are fully combined, so that accurate real-time simulation on the high-voltage direct-hanging energy storage system of any series with the simulation step length of 1 us is realized on the FPGA.

The invention discloses a real-time simulation platform of a floating production storage and offloading (FPSO) riser support system based on Modelica, and belongs to the field of intelligent simulation. Comprising a demand definition module, a data loading module, an environment simulation module, a riser support modeling module, a system verification module, a simulation calculation module, an optimization scheduling module, an evaluation analysis module, a control suggestion module, a real-time visualization module, a storage backtracking module and a feedback adjustment module. According to the method, a high-dimensional nonlinear space can be globally searched, a better solution can be found, premature convergence is avoided, the stability of an optimization result is improved, it can be ensured that parameter configuration always keeps the optimal performance under variable working conditions, the intelligence and adaptability of an operation strategy can be improved, and it is ensured that a riser supporting system is always in an optimal control state under different working conditions; according to the method, high efficiency of multi-objective optimization is achieved, the best balance point among vibration reduction, abrasion reduction and equipment service life prolonging can be found, and therefore double improvement of economical efficiency and safety is achieved.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention discloses an automobile door cover high-precision self-adaptive assembling and adjusting system based on physical field digital twinning, and the system comprises a multi-mode flexible sensing system which is used for collecting the geometric and mechanical data of a door cover and a door frame in real time; the digital twinning and real-time simulation prediction module is used for constructing and synchronizing a high-fidelity virtual model and predicting a gap and a surface difference after assembly based on a physical information neural network; the reinforcement learning intelligent decision model is used for outputting an optimal adjustment instruction according to the prediction deviation; and the automatic guiding and executing module is used for executing the adjusting instruction and completing assembling and tightening of the door cover. According to the automobile door cover high-precision self-adaptive assembling and adjusting system based on physical field digital twinning, high automation of door cover assembling is achieved through intelligent sensing, real-time optimization and self-adaptive control.

A rapid multiphysics inversion method and apparatus for a power device, a device and a storage medium, relating to the technical field of power device inversion. The method comprises: using simulation software to construct a multiphysics simulation model of a power device; constructing a data set on the basis of a predefined input parameter set and output parameter set; by means of using a preset data order reduction method, performing order reduction on the data set, so as to obtain an order-reduced data set and an order reduction matrix; by means of using a preset inversion method, fitting the relationship between the order-reduced data set and the input parameter set, so as to obtain an inversion coefficient matrix; and on the basis of the order reduction matrix, the inversion coefficient matrix and an input parameter set to be inverted, performing rapid inversion on the multiphysics simulation model, so as to obtain a multiphysics inversion result of the power device. The present invention can obviously reduce the amount of calculation while satisfying calculation precision, achieves high calculation efficiency, and can adapt to the requirements for online monitoring and real-time simulation of power devices.

Methods and apparatus are provided for automated analysis of airflow objectives in architectural design plans using artificial intelligence (AI). A controller evaluates design plans, such as floor plans or architectural drawings, to identify, classify, and assess the placement, type, and configuration of airflow components, including air supply and exhaust vents, ducts, and HVAC systems. The controller examines these components against relevant building deployment objectives and preferred practices, evaluating spatial relationships, airflow patterns, and system configurations. When non-compliance is detected, the system generates automated suggestions for design revisions and optimizations, in order to facilitate adherence to building deployment objectives and functional efficiency before construction begins. The apparatus includes interactive tools for real-time simulation and collaboration, allowing designers, architects, and others to make informed adjustments to the design to achieve optimal airflow management and compliance with building deployment objectives.

The invention relates to the technical field of fuel cell system anomaly detection, in particular to a fuel cell system time series data anomaly detection method based on residual analysis, and the method comprises the specific implementation steps: obtaining multi-source design simulation data of a system, and constructing a multi-source design simulation data tensor; constructing a computer aided design model, performing behavior simulation on the simulation data tensor, and outputting a simulation design behavior value; the simulation data is used as a simulation observation value to be compared with a simulation design behavior value, a multi-dimensional residual sequence is obtained through calculation, and online self-adaptive updating is conducted on the design model; establishing a physical reachability matrix, and adjusting a dynamic threshold value based on the matrix and a real-time simulation working condition; performing weighted fusion on the multi-dimensional residual sequence to generate a collaborative design constraint index, and comparing the collaborative design constraint index with the dynamic threshold to generate a computer aided design verification result; and inputting the design verification result into a preset residual-control mapping rule, and outputting a design parameter optimization instruction to optimize the design parameters.

The invention discloses a real-time simulation performance evaluation method based on an ARMv8 architecture multi-core processor, and particularly relates to the technical field of multi-core performance evaluation. Comprising the following steps that a concurrent monitoring module is arranged, and an access instruction sequence and memory barrier instruction information of a processing core are collected; analyzing an address cross relation and time sequence dependence between the access instructions, and generating an instruction rearrangement risk access group; concurrency consistency analysis is carried out on the risk access group, race conditions are identified, and a synchronization mode and memory barrier instruction distribution are recorded; high-intensity concurrent operation is injected into the simulation environment, read-write operation of the shared variables is continuously tracked, and an instruction execution sequence is analyzed; the matching degree under different simulation load conditions is counted, a real-time simulation performance evaluation index for the weak memory model is generated, and the potential consistency problem caused by instruction rearrangement can be accurately recognized.

The invention relates to the technical field of accurate calculation of mining quantity, in particular to an intelligent accurate calculation system of mining quantity based on a three-dimensional geologic model, which comprises a multi-source heterogeneous data acquisition module used for acquiring and preprocessing various data such as geologic structure and mineral distribution; the dynamic three-dimensional geologic modeling module constructs a digital geologic model; the precise reserve calculation module calculates exploitable reserves; the intelligent mining planning module is used for generating a mining path optimal solution set by adopting a hybrid taboo genetic optimization algorithm and evaluating the mining efficiency and the safety risk; the resource collaborative scheduling module is used for establishing a multi-objective optimization model of the equipment-personnel-transportation system by using a space-time constraint particle swarm optimization algorithm; and the visual decision support module supports real-time simulation and adjustment of the mining scheme through a WebGL three-dimensional visual engine and an AR interactive interface. According to the intelligent mining method, accurate calculation of the mining quantity and intelligent mining are achieved, the mining efficiency is improved, safety is guaranteed, resource allocation is optimized, and economic benefits are improved.

The invention relates to the technical field of power equipment state monitoring, and discloses a cable state fault prediction method based on multi-source data fusion. According to the method, a digital twinborn model is constructed by acquiring multi-source data, and a theoretical health baseline changing along with working conditions is simulated and calculated in real time. And comparing the base line with the measured data to generate a thermoelectric coupling matrix with quantitative deviation, and diagnosing the degradation state according to the thermoelectric coupling matrix. And when the deviation exceeds a threshold value, performing attribution analysis by using a gradient boosting decision tree model, and generating a visual spectrogram associated with the partial discharge and the root cause. And finally, the defect type is judged by intelligently identifying a high-risk visual mode on the spectrogram, and the high-risk state is predicted. According to the method, working condition interference is filtered through dynamic reference, causal diagnosis is performed, the problem of high false alarm rate in the prior art is solved, and the prediction accuracy is remarkably improved.

The invention provides a digital twin city construction and operation optimization method based on cloud computing and a big data technology. The method comprises the following steps: optimizing layout of various sensor devices through a particle swarm optimization algorithm and an adaptive sampling algorithm, and adjusting acquisition frequency; establishing a distributed database, and realizing uniform distribution and efficient access of data in the distributed database through a load balancing algorithm; mining a time rule of urban operation by using a time sequence model, and discovering association between different data; a city model is constructed by using a city three-dimensional live-action fusion modeling method which takes unmanned aerial vehicle oblique photography as a main part and takes ground laser radar as an auxiliary part, different areas in a city are identified by applying a machine learning method, and a refined model of each field of the city is constructed; real-time simulation of a city operation state is realized through virtual reality and augmented reality technologies; the system is continuously optimized by using an online learning algorithm, so that the system continuously learns and adapts to a new data mode in the operation process, and the construction and operation optimization capability of the system is improved.

The invention relates to a digital twin simulation and optimization method for a large-scale stamping production line, and the method comprises a rapid lightweight modeling technology, a multifunctional fault indicator feedback technology, a three-dimensional dynamic real-time driving simulation technology, and a big data analysis intelligent optimization decision technology. According to the method, virtual-real fusion and a three-dimensional dynamic technology are combined, a three-dimensional visual user interface oriented to the whole manufacturing process is developed based on Unity, the whole stamping production line is simulated in real time, potential problems are predicted and solved through real-time monitoring of the stamping production line, various problems influencing the production efficiency in the production process are optimized, and the manual management and control difficulty is reduced.

The invention discloses a drive-by-wire chassis electric vehicle hardware-in-the-loop simulation test bench and a power chassis domain integrated control architecture thereof. The test bench comprises a driving simulation system, an electric drive system, a drive-by-wire brake system, a drive-by-wire steering system, an active suspension system, a power chassis domain integrated control system, a vehicle real-time simulation system and a bench measurement and control system. And the power chassis domain integrated control system receives a driver instruction, road environment and vehicle model information and response information of each execution system of the power chassis and the load simulation device of the execution system to complete decision-making instruction sending. The power chassis domain integrated control system adopts a hierarchical control architecture and comprises a vehicle motion stability judgment layer, a vehicle motion integrated control layer and an actuator and load control layer thereof. The system is used for testing and verifying control algorithms of various movement modes such as transverse and longitudinal movement modes, transverse and vertical movement modes and transverse, longitudinal and vertical movement modes of the drive-by-wire chassis intelligent electric vehicle, and has the characteristics of complete drive-by-wire chassis functions, high power chassis domain fusion degree, wide test coverage and the like.

The invention discloses a thermal coupling IGBT static real-time model modeling method and a related device, and the method comprises the steps: carrying out the modeling of an electrical characteristic part of an IGBT according to an equipment data table of the IGBT, and the electrical characteristic part comprises an electrical element and power loss; according to the equipment data table and in combination with a power loss thermal resistance circuit, modeling is carried out on the electric heating characteristic part of the IGBT, and the power loss thermal resistance circuit comprises an in-module series thermal resistor, a thermal conductive adhesive thermal resistor, a radiator thermal resistor and an environment temperature voltage bias; an interaction portion of the electrical characteristic portion and the electrothermal characteristic portion is modeled. According to the method, thermal coupling refined modeling of the IGBT is realized, and the calculation efficiency can achieve a real-time effect, so that the requirements of behavior-level modeling and real-time simulation can be met in the absence of semiconductor physical parameters, and the hardware-in-the-loop test requirements of normal working on-state and closed-state power consumption requirements are met.

The invention provides a simulation scene construction and simulation method and system for a large-scale unmanned cluster, and relates to the technical field of unmanned system simulation. The method comprises the steps of modeling a target task based on execution logic for the target task of the large-scale unmanned cluster to obtain an execution structure; decoupling the target task into a plurality of weak coupling subsystems based on the execution structure, and constructing a parallel simulation environment for the target task; based on the execution function category of the target task, constructing a bus interface of a corresponding category; constructing an intelligent algorithm plug-in corresponding to the execution function category based on the bus interface of each category; integrating the intelligent algorithm plug-in into the execution structure of the target task to form an execution model of the target task; and deploying the execution model to a parallel simulation environment to obtain a simulation scene of the target task. The problems that in large-scale unmanned cluster complex task simulation, the calculated amount is large, real-time simulation cannot be achieved, and the simulation accuracy and effect of cross-platform complex tasks are poor are solved.