634 results about "Real-time simulation" patented technology

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 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 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 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 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 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.

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 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 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 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.

The invention discloses a multi-scene wireless network rapid deployment method based on an electromagnetic field digital twinning technology, and relates to the technical field of computer application, and the method comprises the following steps: S1, constructing a three-dimensional digital twinning model of a corresponding scene, and integrating a building structure, equipment layout and material attribute data, and establishing a scene special material database containing metal equipment, a concrete wall body and an electromagnetic shielding layer. According to the multi-scene wireless network rapid deployment method provided by the invention, the three-dimensional digital twin model is constructed, and the building structure, the equipment layout and the material attribute data are integrated, so that the precise simulation of the environment is realized, the wireless network deployment accuracy is improved, the deployment scheme can better adapt to the dynamic change of the environment, and the deployment efficiency is improved. The improved O-FDTD algorithm is adopted to simulate electromagnetic field propagation in a building in real time, and the problem that the calculation efficiency is low when a traditional FDTD algorithm processes the metal edge effect and a moving object is effectively solved.

The invention discloses a real-time motion control algorithm simulation debugging method, which relates to the technical field of real-time simulation crossing, and comprises the following steps of: acquiring real-time track data and multi-scale simulation parameters and preprocessing the real-time track data and the multi-scale simulation parameters; based on the multi-scale simulation parameters, a multi-scale simulation environment is constructed, and multi-physics field coupling control parameters are obtained; performing multi-physical field simulation according to the real-time trajectory data in a multi-scale simulation environment, and obtaining trajectory error data with a safety verification identifier; based on the trajectory error data with the security identifier, filtering out high-frequency components and separating low-frequency contour deformation components by adopting empirical mode decomposition to obtain low-frequency contour deformation features; and based on the optimal parameter adjustment instruction set, updating the multi-physics field coupling control parameters in real time and generating a simulation debugging report. According to the invention, a multi-scale simulation environment is constructed, dynamic configuration and synchronous simulation of multi-physical field coupling control parameters are realized, and a high-fidelity virtual test platform is provided for motion control under complex working conditions.

The invention discloses a granary monitoring and early warning system based on a digital twin technology, and relates to the technical field of granary monitoring. The system comprises a server, a conduction analysis module and an early warning management module, through complete coverage of sensor data, a mechanism model, numerical simulation, three-dimensional visualization, risk quantification and intelligent response, landing, online acquisition, real-time simulation and dynamic early warning of digital twinning in grain storage management are truly realized, and blind areas and time delay of traditional manual and local sensing are solved.

The invention discloses an unattended screw ship unloader material and ship control method and system, and the method comprises the steps: collecting multi-dimensional sensing data including visual images, material weight, material types and spatial poses in real time through multiple types of sensors disposed on a screw ship unloader; performing space-time registration and fusion processing on the acquired multi-dimensional sensing data to obtain an operation scene situation sensing result for comprehensively representing the real-time state of the ship unloading operation scene; based on the operation scene situation perception result, driving a screw ship unloader digital twin model to perform real-time simulation to obtain a simulation result including a future material accumulation spatial form, equipment operation performance parameters and / or potential risk probability; and according to a simulation result of the digital twinborn model, an optimization control instruction is generated based on a preset decision rule, so that self-adaptive adjustment of ship unloading operation parameters and intelligent disposal of abnormal working conditions are realized.

The invention provides a cloud factory production control system for a carbon-based microbial fertilizer, and the system comprises the steps: obtaining associated data for producing the carbon-based microbial fertilizer, carrying out the feature extraction of the associated data, and constructing a time sequence association matrix; inputting the time sequence incidence matrix into a long short-term memory network model for dynamic proportion prediction to obtain an optimal raw material proportion scheme; performing real-time simulation verification on the optimal raw material proportioning scheme, and generating an equipment control instruction after the verification is passed; wherein the equipment control instruction comprises production process parameters; executing the equipment control instruction in a distributed manner based on edge computing nodes so as to produce the carbon-based microbial fertilizer; and performing causal analysis on the quality data and the production process parameters of the produced finished product, and identifying key influence factors to generate a quality optimization strategy. According to the method, the defects that the standard production requirement is difficult to meet and the root of the quality problem is difficult to quickly position at present are overcome.

The invention relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention provides a progress and resource management linkage application system based on BIM, relates to the technical field of building management, and is suitable for full life cycle management of design, construction and operation of a building. The system comprises a demand modeling module, an intelligent equipment optimization module, a collision detection and progress optimization module, a multi-professional data integration and real-time simulation module, a building full-life-cycle intelligent management module and the like. By optimizing arrangement and streamline design of equipment, the space utilization rate and the medical service efficiency are improved; in the construction stage, a collision detection function and a digital twinning technology of the BIM are utilized to realize multi-specialty collaborative design and progress optimization; in the operation stage, the operation state of the equipment is monitored in real time in combination with an Internet of Things sensor, a preventive maintenance plan is generated, and energy consumption management is optimized through energy consumption data analysis. The system improves the design precision and construction efficiency of the building and the intelligent level of equipment management and operation maintenance, and has a wide application prospect.

The invention relates to the technical field of wind driven generator control, in particular to a thermal management system and method for a wind driven generator, and the system comprises a data collection layer, a prediction module, a digital twin model, an intelligent control and diagnosis engine, and a heat dissipation execution mechanism. In the prior art, an open-loop or simple feedback control scheme based on a real-time temperature threshold value is mainly adopted, the problem of response lag exists, and all heat dissipation parts always work independently and lack cooperation, so that the energy efficiency is low; according to the invention, a closed-loop intelligent thermal management scheme based on a digital twin model is adopted, and real-time simulation and future state prediction are carried out by constructing virtual mapping of a generator thermal system, so that a control system can actively and cooperatively manage all heat dissipation resources according to a simulation result; the method has the core advantage of converting passive into active, and can intervene in advance before excessive accumulation of heat, so that collaborative optimal control of all parts of the system is realized, and the accuracy, timeliness and overall energy efficiency of heat management are remarkably improved.

The control device comprises a control module, an ice melting power supply module, a boundary switch, a short circuit switch and an ice melting switch, the boundary switch, the short circuit switch, the ice melting switch and a protection switch are arranged on a high-voltage cable, and the control module sequentially drives the boundary switch, the short circuit switch, the ice melting switch and the protection switch to execute state switching through action instructions. The conduction control of the ice melting loop is realized; the control module integrates a finite element algorithm function, collects current, voltage and temperature distribution parameters in an ice melting loop in real time, and dynamically corrects ice melting current parameters through three-dimensional electromagnetic-thermal coupling field simulation; setting a parametric modeling unit, and calculating an optimal voltage regulation gear based on a heat balance equation and a Joule heat effect to realize adaptive voltage regulation; the demarcation switch, the short circuit switch and the ice melting switch are arranged, the effect of cable short circuit heating ice melting is achieved by sequentially controlling the on-off states of all the switches, finite element real-time simulation and parameterization voltage regulation decision are combined, and the cable ice melting efficiency, reliability and safety are remarkably improved.

The invention discloses an application method, system and device of a dynamic scene generation technology in regulation and control operation simulation training and a medium, and belongs to the technical field of power system regulation and control operation simulation training. Generating a standardized scene parameter matrix containing power grid operation parameters and meteorological parameters; generating a dynamic simulation scene according to the standardized scene parameter matrix; simulation deduction is carried out, a simulation result is generated, and evaluation and feedback are carried out. According to the method, a highly realistic dynamic scene is generated through multi-source data fusion and the generative adversarial network, and scene parameters are optimized in combination with reinforcement learning, so that the scene fidelity and the emergency disposal capability are remarkably improved. The high-precision simulation platform supports second-level real-time simulation and hour-level continuous deduction, and the training period is shortened and the resource utilization rate is improved in combination with dynamic scoring and a self-adaptive difficulty adjustment mechanism.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention belongs to the technical field of wind power generation, and particularly relates to a fault diagnosis and early warning method and system for a wind power plant, and the method comprises the steps: collecting the state data of a plurality of physical fields through a plurality of types of sensor arrays disposed at key parts of a fan; performing preprocessing and feature extraction on the data to obtain high-dimensional feature data; driving the field-level digital twinborn model to perform real-time simulation based on the high-dimensional feature data, and determining an initial fault recognition result and severity by comparing a simulation predicted value with actual sensing data and combining with an unsupervised anomaly detection algorithm; a diagnosis result is calibrated through multi-modal high-dimensional feature data fusion; predicting the remaining useful life of the key component based on the fault severity and the evolution trend thereof, and generating a collaborative operation and maintenance decision by using a multi-objective optimization algorithm; and an operation and maintenance decision is automatically executed through a closed-loop feedback execution mechanism. According to the invention, the accuracy of fault diagnosis and early warning of the wind power plant is improved.

The invention relates to the technical field of power distribution network monitoring, in particular to a power distribution cabinet line intelligent detection method and system, and the method comprises the steps: collecting node voltage, phasor current and active / reactive power of each power distribution cabinet, attaching a sampling period and a quality identifier, and carrying out the arrangement according to a timestamp; carrying out edge calculation on harmonic waves, flicker, unbalance, missing measurement compensation and initial estimation, and generating a straight-through UDP stream and a storage stream; a real-time interface is aligned and fused with a topology version according to a unified time reference, state estimation is carried out, and a node initial value set is output; s4, according to the initial value and topological modeling node digital twinning (index recovery load), obtaining online parameters through four-stage sub-step discrete + UKF joint estimation; accessing a real-time simulation platform through HiL, and recharging in the same period; a variable damping impedance stable interface is adopted, and when consistency and a threshold value are met, a digital snapshot containing an online parameter, a steady-state quantity and a transient abstract is generated. According to the method, low time delay of measurement alignment, credible state estimation, digital twinborn self-adaption, hardware-in-loop stability and verifiable result release can be realized, so that the fault detection, positioning and evaluation capabilities of the power distribution network are improved.

The invention discloses a carbon fiber wear resistance detection system, and relates to the field of carbon fiber material performance detection, the system comprises the following components: a data acquisition and model construction module, a working condition simulation and simulation calculation module, an analysis prediction and scheme optimization module, an interaction and visualization module and a system control and management module; through the data acquisition and model construction module, various physical performance parameters of the carbon fiber material can be accurately acquired, a high-precision digital twin model is constructed, and in the working condition simulation and simulation calculation module, the system can flexibly set diversified working condition parameters, perform real-time simulation calculation and dynamically track physical quantity changes in the wear process, so that the wear performance of the carbon fiber material is greatly improved. The multi-module cooperative working mode enables the system to more accurately predict the wear resistance of the carbon fiber under different working conditions, thereby improving the detection precision.

The invention belongs to the field of analog simulation, and particularly relates to a runway dynamic friction coefficient real-time simulation method, system and equipment based on multi-source data. The problem that taxiing simulation is insufficient in authenticity is solved. The method comprises the following steps: acquiring runway surface temperature data, accumulated water depth and / or accumulated snow depth; judging a runway type based on the runway texture features, matching a basic friction coefficient according to the runway type, and dynamically correcting the basic friction coefficient in combination with the accumulated water depth and / or accumulated snow depth and runway surface temperature data to obtain a corrected friction coefficient; acquiring a tire vertical load, an initial sideslip angle and tire pressure data of the aircraft, calculating a tire lateral force, and calculating a final friction coefficient in combination with the updated sideslip angle, the corrected friction coefficient and the sideslip attenuation coefficient; and inputting the data into a ground taxiing model of the flight simulator to realize real-time simulation. Through the attenuation effect of the dynamic process on the friction coefficient, the simulation result better fits the real runway state.

The invention belongs to the field of electromechanical comprehensive thermal management, and particularly relates to a real-time analogue simulation method and system for electromechanical comprehensive thermal management of an airplane. The method comprises the following steps: constructing a comprehensive thermal management environment model of an aircraft operation environment; constructing a comprehensive thermal management system model; performing real-time coupling on the comprehensive thermal management environment model and the comprehensive thermal management system model, and simulating a real-time comprehensive thermal management process of the airplane based on a comprehensive thermal management strategy to obtain a real-time simulation result; and performing optimization iteration on the comprehensive thermal management system model according to the real-time simulation result. According to the method, through accurate simulation of the electromechanical comprehensive thermal management process of the airplane, powerful support can be provided for performance optimization, safety improvement and cost control of the airplane.

The invention belongs to the technical field of automobile testing, and particularly discloses a digital twinning-based automobile instrument testing method, which comprises the following steps of: automatically generating a structured test case through a large language model, constructing a double-digital twinning architecture by relying on a high-fidelity rendering engine and a real-time simulation engine, and realizing synchronization of a virtual model and a physical instrument; multi-modal data are collected through a multi-sensor array, and defect intelligent analysis and grade judgment are completed in combination with a large language model; and test strategy self-optimization is realized through reinforcement learning based on historical test data. According to the invention, a double-engine architecture in which a high-fidelity rendering engine and a real-time simulation engine cooperatively work is adopted, and the requirements of large-scale scene and refined rendering are met at the same time. And seamless switching and data fusion between the double engines are realized through the scene transformation matrix, so that the consistency of a virtual environment and a physical entity is ensured. Meanwhile, a large language model is introduced into a test case generation and result analysis link, and natural language understanding level test script generation and anomaly analysis are achieved.