160 results about "Order reduction" patented technology

The invention relates to a nonlinear system reduced-order model generation method fusing physical constraint and digital twinning, and belongs to the technical field of digital twinning. The method comprises the steps of performing sparse sampling on high-dimensional dynamic response data, extracting a dominant mode of high-fidelity model data, and retaining a mode of a physical abrupt change region through physical field gradient constraint in the extraction process; the high-fidelity finite element model and the low-fidelity simplified model are combined, and a mixed order reduction framework based on a multi-fidelity agent model is constructed; fusing a data driving error and a physical mechanism residual error in data-physical joint driving model training and optimization based on a mixed order reduction framework, and updating a primary function of a dominant mode based on online stream data; and performing cross validation on the output of the high-fidelity finite element model and the reduced-order model, and improving the precision of the reduced-order model through an iterative optimization strategy. The intelligent order reduction problem of the model is realized, and the accuracy and rapidity of the order reduction model are ensured.

The invention discloses a calculation and measurement fused high-fidelity digital twinning dynamic monitoring method for a cantilever beam structure. Firstly, a real-time updated database is constructed based on a physical entity system; secondly, constructing a three-dimensional digital model by using SolidWorks, and performing multi-physics field simulation analysis in combination with Ansys to obtain high-precision mechanism data of a structural key area; thirdly, performing reduced-order calculation on a simulation result by using MWorks, training and optimizing a reduced-order model through a feedforward neural network, and reducing an error between simulation data and reduced-order data; fusing the one-dimensional system data order reduction model and the three-dimensional field data order reduction model based on a Modelica language; and finally, integrating a mechanism-data hybrid model, constructing a dynamic response characteristic and spatial distribution characteristic integrated high-fidelity digital twinborn body, comparing twinborn body calculation data with sensor actual measurement data in real time, and accurately representing geometric morphology, mechanical properties and dynamic behaviors of a physical system. The method is suitable for high-precision monitoring, real-time state evaluation and performance optimization of the cantilever beam structure.

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

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 discloses an electromechanical composite transmission IGBT junction temperature prediction method based on a NODE reduced-order model. A thermal simulation model is constructed according to a real physical model mechanism, a material and a thermal transfer principle of an IGBT; in the constructed thermal simulation model, performing simulation calculation according to the monitored time sequence data to obtain the junction temperature of the IGBT corresponding to the time change, jointly establishing a data set for training and testing with the monitored data, establishing a NODE neural network order reduction model comprising an input layer, a hidden layer and an output layer, and training the NODE neural network order reduction model by using the data set; and inputting the IGBT related time sequence data into the constructed NODE neural network reduced-order model for prediction to obtain the time sequence data of the junction temperature. According to the method, the IGBT junction temperature can be predicted in real time, the calculation efficiency is high, the interpretability is high, the long-term dependence problem can be better solved, and the method is suitable for complex working conditions.

The invention relates to the technical field of power supply thermal management control, and discloses a high-voltage variable-frequency power supply thermal management control method for improving energy efficiency, and the method comprises the following steps: carrying out the order reduction processing in combination with a solar high-temperature thermal power generation system, and obtaining a low-dimensional model; carrying out data fusion by using Gaussian process regression and a Kalman filtering algorithm, and reconstructing temperature field information of the system; establishing a multi-objective optimization function, and dynamically adjusting a weight optimization objective; generating a control parameter sequence by using an improved particle swarm optimization algorithm, and adjusting a control decision in real time through iterative optimization; the optimized control parameters are input into a multi-rate hierarchical control system for division execution; and detecting a fault type and switching to a corresponding fault-tolerant control mode. According to the method, a multi-physical field coupling model and an intrinsic orthogonal decomposition technology are combined, an accurate coupling model of an electromagnetic field, a temperature field and a flow field in the high-voltage variable-frequency power generation system is established, through order reduction processing, the dimension of the model is reduced, the calculation complexity is remarkably reduced, and the calculation efficiency is improved.

The invention discloses a non-stationary flow modal decomposition method based on image features, which belongs to the technical field of intersection of fluid mechanics and imageology, adopts a local registration strategy, tracks spatio-temporal evolution of a main flow structure through a local adaptive sampling grid, and simultaneously aligns space coordinates and flow field variable values in a storage space. The dominant mode representing the potential mechanism of the non-stationary flow is extracted; the problem that low-dimensional approximation precision is insufficient due to the fact that Kolmogov n-width attenuation is slow in non-stationary flow in a traditional linear mode order reduction method is solved. The problem of grid entanglement suffered by a traditional registration-based method is solved, and the application scene of the registration-based method is widened; and the dimensionality of the online approximate solution manifold is reduced and is far beyond the linearity and the existing improved method. The method shows modal compression, reconstruction and prediction performances superior to those of an existing method, and provides an efficient and accurate low-dimensional agent model for optimization, design and control tasks of fluid mechanics.

The embodiment of the invention provides a small-disturbance stability optimization method and device for a single-network converter grid-connected system. The method comprises the following steps: constructing a small-signal model representing dynamic behaviors of the system based on control parameters and network parameters of the single-network converter grid-connected system; performing preprocessing operation on the small signal model to obtain a small signal model only comprising an active synchronization control link; performing order reduction processing on the small signal model to obtain a corresponding equivalent second-order model; determining the dynamic influence of active outer loop control, voltage inner loop control and short-circuit comparison on the stability of the current system according to the equivalent second-order model; adjusting a current system control parameter according to the dynamic influence to meet a small disturbance stability requirement; according to the method, the control parameters of the single-network converter can be optimized based on the small signal model, so that the small disturbance stability of the system is improved, and an optimization strategy is provided for stable operation of the network converter.

The invention belongs to the technical field of reduced-order model development, and particularly relates to a POD-SVD random forest reduced-order method and system for digital twin engines. Adjusting a plurality of simulation data generated by the digital twin engine under different working condition boundary conditions; extracting a physical quantity of the region of interest; combining and forming an original matrix required by PODSVD decomposition; decomposing an original matrix into three matrixes by using a PODSVD algorithm, and converting the three matrixes into a modal coefficient matrix, a modal matrix and an energy matrix through a calculation formula; training a random forest algorithm; inputting a boundary condition parameter corresponding to a working condition needing to be predicted into the trained random forest model, and generating a prediction modal coefficient; calculating and restoring a physical quantity needing to be predicted; and adding grid information for the obtained reduced physical quantity, and generating a. Dat file for post-processing. The method is used for breaking through the limitation of tradeoff between cost and model precision in traditional researches such as test / CFD, and meanwhile, the model precision and the evaluation speed are improved.

The invention provides a labor insurance article order dynamic scheduling and inventory management method based on the industrial internet, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining the order demand information of each enterprise in an industrial park, and the real-time inventory data and historical delivery records of suppliers, and dynamically constructing a probability density function of the delivery capability of the suppliers, and a gravitation model is combined to calculate the adaptation degree between the supplier and the order demand, so that an accurate order distribution scheme is generated. In this way, order allocation rationality and efficiency can be improved, and order delay and resource waste are reduced. Besides, through an improved isochronous circle division method and a dynamic space-time combination optimization algorithm, clustering analysis and scheduling optimization are performed on the distribution path, so that the timeliness of distribution and the rationality of the path can be remarkably improved, the logistics cost is reduced, and the overall logistics management efficiency of the industrial park is improved.

The invention relates to an equivalent reduced-order modeling method of an offshore wind power flexible direct output system. The method comprises the following steps: constructing an equivalent reduced-order model of a wind power plant; constructing an equivalent order reduction model of the modular multilevel converter; constructing a broadband equivalent reduced-order model of the receiving-end power grid; constructing an equivalent order reduction model of the offshore wind power flexible direct output system based on the equivalent order reduction model of the wind power plant, the equivalent order reduction model of the modular multilevel converter and the broadband equivalent order reduction model of the receiving end power grid; according to the method, the wind power plant, the MMC and the receiving-end power grid are subjected to order reduction and integration, the number of overall nodes and elements is reduced in a multiplied mode, the voltage-current transient consistency of grid-connected points is still kept, the CPU calculation amount is remarkably reduced, and a larger-scale fan group can be loaded on an HIL platform at a time.

The invention discloses an intelligent monitoring system for multi-source data fusion of a biomass intelligent power plant, relates to the technical field of intelligent monitoring, and solves the problems proposed in the background technology. The biomass multi-source data acquisition module is used for acquiring biomass fuel characteristic data, equipment operation data, environment-friendly emission data and raw material supply chain data in real time; the data preprocessing and order reduction module is used for carrying out denoising, normalization and coupling order reduction processing on the biomass fuel characteristic data and the equipment operation data; the multi-source data dynamic fusion module is used for carrying out weighted fusion on the multi-source data according to the fuel heat value, the water content and the environmental protection weight; the biomass combustion optimization diagnosis module is used for constructing a deep learning model based on the fusion data and outputting combustion efficiency optimization parameters and a fault diagnosis result; according to the method, multi-dimensional data of fuel characteristics, equipment operation, environmental protection emission and the like are integrated, through links of preprocessing, order reduction, fusion and the like, an information island phenomenon is eliminated, and scientificity and accuracy of decision making are improved.

The invention belongs to the technical field of mathematical modeling of a network construction converter, and discloses a network construction converter model order reduction method, system and equipment based on optimal Hankel norm approximation and a medium, so as to solve the problem of applicability of a network construction converter model under the condition of a high short circuit ratio. The method is based on a full-order model and comprises the steps of S1, current loop order reduction processing, S2, voltage loop Hankel approximation order reduction and S3, construction of a nine-order model. According to the method, through dq decoupling, feedforward offset and optimal Hankel norm approximation, a full-order system is reduced into a nine-order model, and the calculation complexity is greatly reduced while the control precision is ensured. According to the method, active and reactive high-frequency component quantity links filtered by a filter and a line resistance-inductance equation are considered, the nine-order model of the network construction converter is constructed, and the model precision is effectively improved.

The invention relates to the field of bulk grain pneumatic conveying, and provides a bulk grain pneumatic conveying system model order reduction method and device in order to improve the accuracy of an order reduction model, when a function library is constructed, a traditional function is introduced, and a function for reflecting nonlinear and chaotic characteristics generated by interaction of particles and airflow is also introduced; during data processing, a dynamic sliding window is adopted for data collection, and when data changes drastically, the window is enlarged properly to obtain more comprehensive information; when the data change is small, the window is correspondingly reduced, and the real-time response capability is improved; model optimization is carried out based on chaos indexes, so that the model accuracy is improved; by regularly verifying the accuracy of the model, the model can continuously and accurately describe the dynamic characteristics of the system.

The invention provides a digital twinning implementation method for mechanical properties of a cantilever beam under a moving load, which is characterized in that mechanical influence distribution of different positions of the cantilever beam is calculated in real time under live load (vector) excitation by utilizing strong form weakening of a differential equation of a mathematical model, and comprises the following steps: S1, obtaining a mechanical component constraint model; obtaining a position function model according to boundary conditions; s2, separating the position equation under the weak formalization; s3, separating the load equation under the weak formalization; and S4, carrying out coupling operation on the position function and the load function which are obtained through order reduction. According to the method, the mechanical distribution condition of the cantilever beam under excitation of different loads can be observed in real time, the calculation speed is greatly increased through model order reduction, and high calculation precision is kept.

The invention discloses an order early-warning method and system based on index configuration, and the method comprises the steps: carrying out the structural processing of early-warning demands, determining the type of an early-warning index to which each early-warning demand belongs, and generating an early-warning index according to the early-warning demands after the structural processing; configuring an early warning rule according to the early warning index and the early warning rule template; extracting an order corresponding to the trigger event, and determining a target order corresponding to each early warning rule; performing early warning judgment on the order data of the target order to obtain an early warning index result; and if the early warning index result is that the order data of the target order meets the trigger logic in the early warning index, performing order early warning on the target order, generating an early warning record, and sending an early warning notification. According to the method and the corresponding system, the early warning delay of a large number of orders can be reduced, the query pressure of an order database is reduced, the data consistency of hundred million-level orders is ensured, and the early warning efficiency of hundred million-level risk orders is effectively improved.

The invention discloses a singular perturbation order reduction method matched with a network construction type wind power plant. The method comprises the following steps: firstly, establishing a small signal model matched with each module of a network construction type wind power plant grid-connected system; constructing a full-order small signal model, solving a broadband oscillation mode, and calculating damping ratios of all modes to obtain an extremely weak damping mode; dividing fast and slow state variables based on the modal participation degree and Hankel singular value weight, and establishing a criterion to supplement the slow state variables based on a singular perturbation method to form a slow state variable set; and determining the order after order reduction, and establishing an order reduction model of the matching network construction type wind power plant grid-connected system in a singular perturbation form. According to the method, the problem that a matched network construction type wind power plant order reduction system of an existing order reduction scheme cannot replace a full-order system to efficiently analyze broadband oscillation can be solved, the order number of the matched network construction type wind power plant can be reduced, and the method has efficient and powerful broadband oscillation analysis capacity while the dynamic characteristics are reserved.

The invention is suitable for the technical field of automobiles, and provides an automobile passenger compartment modeling method based on neural network model order reduction, and the method comprises the steps: building a three-dimensional passenger compartment and dummy model, combining with a human body thermal comfort evaluation index to obtain data, carrying out the steady-state order reduction through a neural network, and verifying the precision through a decision coefficient and an error index. And carrying out transient order reduction by adopting a long-short-term memory network optimized by a genetic algorithm to finally obtain a one-dimensional model. According to the method, the problems that traditional three-dimensional modeling is time-consuming in simulation and the one-dimensional modeling precision is insufficient are solved, the temperature and wind speed determination coefficient of each position of the established model reaches about 0.94, the model can be coupled with an air conditioning system, the simulation operation time is shortened, and support is provided for design of an automobile thermal management control strategy. The method comprises the steps of model establishment, steady-state and transient order reduction, working condition analysis, model verification and the like, and has the advantages of precision and efficiency.

The invention relates to the technical field of computational fluid mechanics and biological microfluidic design, in particular to a model order reduction method for rapid optimization of a circulating tumor cell (CTC) sorting structure, which comprises the following steps of: constructing a three-dimensional full-order computational fluid mechanics model by collecting channel geometric structure parameters and fluid working condition parameters, and generating a training data set; organizing a flow field snapshot into a matrix form, extracting a dominant mode, constructing a low-dimensional modal space by taking the dominant mode as a base vector, establishing a low-dimensional ordinary differential model through Galerkin projection, and training a parameter-modal coefficient mapping relation by adopting a deep neural network to form a complete reduced-order model; and finally, constructing a multi-objective optimization problem based on the reduced-order model, carrying out optimization iteration by adopting an evolutionary algorithm, and returning an optimization result to the full-order model for verification, so that rapid optimization design of the CTC sorting structure is realized, and an effective solution is provided for intelligent design of a biomedical microfluidic device.

The invention discloses a method and device for constructing a reduced-order model of a direct-driven wind turbine generator grid-connected system, and the method comprises the steps: constructing an electromagnetic transient model of the direct-driven wind turbine generator grid-connected system based on the transient response characteristics of each control link in a direct-driven wind turbine generator, and constructing a reduced-order model of the direct-driven wind turbine generator grid-connected system based on the electromagnetic transient model of the direct-driven wind turbine generator grid-connected system. The method comprises the following steps: obtaining the dominance of each state variable in a direct-driven wind turbine generator grid-connected system, determining the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system according to the dominance of each state variable in the direct-driven wind turbine generator grid-connected system, and determining the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system according to the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system. And a singular perturbation method is adopted to construct an order reduction model of the direct-driven wind turbine generator grid-connected system. According to the method and the device provided by the embodiment of the invention, the constructed order reduction model can better reflect the transient response characteristics of the system, the calculation time can be greatly shortened, and a foundation can be laid for subsequent stable analysis.

The invention relates to the technical field of data processing, in particular to a digital twinning-oriented three-dimensional field model order reduction system, which comprises an acquisition module, a judgment module, a first screening module, a second screening module, a determination module, a simulation module, an order reduction module and an adjustment module. According to the invention, through multi-level screening and order reduction processing, optimization balance of calculation efficiency and data precision is realized. A temperature-pressure synchronous screening and temperature-stress combined screening mode is adopted, a key area can be accurately identified, redundant calculation is reduced, a dynamic adjustment mechanism based on a simulation index is introduced, a screening threshold value can be adaptively optimized according to actual working conditions, an order reduction module is combined with a preset three-dimensional field order reduction model, and the screening efficiency is improved. The key feature information is reserved while the data dimension is reduced, and the problems of low order reduction precision and low complex dynamic scene processing speed caused by excessively depending on finite element software for simulation and adopting an SVD (singular value decomposition) method for order reduction are effectively solved.

The invention discloses a wind power plant broadband oscillation suppression method and system based on amplitude-phase characteristic remodeling. The method comprises the following steps: respectively establishing a frequency domain impedance model of each wind turbine generator set and performing dynamic order reduction, analyzing amplitude-frequency and phase-frequency characteristics of wind power plant multi-machine parallel coupling based on the order reduction model, determining an interaction rule of a module value and a phase angle, designing additional damping control and active damping control, and determining a wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic of the wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic. A weak position of a system is positioned based on an s-domain node admittance matrix method, a function is set by taking improvement of amplitude-phase characteristics as a target and optimizing and setting control parameters for a target wind turbine generator corresponding to the position, the optimized parameters are applied to the target generator, and effective suppression of broadband oscillation is realized. According to the application, the broadband oscillation problem caused by high-proportion new energy grid connection can be systematically solved by synergistically remodeling the frequency domain amplitude-phase characteristics of the wind power plant.

This invention relates to the field of contract reconciliation management technology, specifically disclosing a contract settlement and automatic reconciliation system and method that deeply integrates business and financial data. After acquiring business performance data sequences and financial settlement data sequences, the system maps them into a two-dimensional time-series directed graph. Upon receiving an abnormal settlement trigger instruction, it traces back along the flow of funds and penetrates forward along the business-finance mapping to capture affected node clusters. Then, based on the comparison results between the accounting period identifier and the current closing time, it generates inter-period reversal action instructions and original order reduction action instructions, constructs alternative reversal path chains, and imports them into a virtual sandbox memory for verification. When the updated amount value is lower than a preset lower threshold, it triggers backtracking and reorganization until a legal reversal path scheme is selected. Finally, it calculates a physical disturbance score by combining the total number of nodes and the total number of inter-period actions, selects the scheme with the lowest score, and outputs it to the accounting platform to complete the synchronous update of business-finance reconciliation.

The present invention discloses a method for dynamic aeroelastic calculation based on a structural order reduction model. The method comprises: constructing a three-dimensional model of the component to be analyzed and performing modal analysis to obtain the various modes and structural vibration shapes of the component structure; simplifying the three-dimensional model of the component to obtain the component's aerodynamic shape; generating a flow field grid for aerodynamic calculation based on the component's aerodynamic shape; using a radial basis function neural network to interpolate the flow field grid based on the various modes and structural vibration shapes of the component structure to obtain interpolated results of the structural vibration shapes; based on the interpolated results of the structural vibration shapes, using computational fluid dynamics methods to first calculate unsteady aerodynamic forces, and then using structural model order reduction methods to calculate transient displacements to obtain aeroelastic response results. The method of the present invention can effectively improve the accuracy and efficiency of aeroelastic calculations.

The invention relates to a model reduced-order acceleration method for HIL simulation of an automobile thermal system model, and belongs to the technical field of automobile thermal system modeling and simulation, and the method comprises the following steps: building a thermal system model: building the thermal system model, generating an input-output data set covering all working conditions, and setting a control equation of the thermal system model; performing principal component analysis (PCA) data preprocessing: compressing high-dimensional space-time data into a low-dimensional feature space, and reserving a dominant dynamic mode; neural state space modeling: designing a nonlinear dynamic system to describe state evolution after dimensionality reduction, establishing a low-dimensional nonlinear dynamic model, and performing network training and regularization; and model integration simulation: integrating the heat exchanger model after order reduction into a complete thermal system model, and performing real-time simulation optimization.

The application provides a precise simplification method and system of a passive parasitic parameter network. The method comprises the following steps: obtaining a passive parameter parasitic network of a to-be-processed circuit; obtaining device information of all parasitic parameters; labeling node names of active device ports according to an active device naming rule; if a current parasitic parameter crosses multiple active device ports, deleting the current parasitic parameter; if a branch where the current parasitic parameter is located is an open circuit, deleting the branch and the parasitic parameters thereon; after traversing all parasitic parameters, obtaining a precise passive parasitic parameter network; and performing order reduction processing on the precise passive parasitic parameter network to obtain a simplified passive parasitic parameter network. The method precisely processes the passive parasitic parameter network, excludes active device ports and invalid ports, so that the passive parasitic parameter network can be correctly processed during simplification and order reduction processing, and the efficiency and accuracy of the order reduction and simplification processing are improved due to the exclusion of invalid paths.

This application discloses a method, apparatus, equipment, medium, and product for predicting damage evolution based on dynamic order reduction, relating to the field of aero-engine health management and monitoring. The method includes: conducting finite element fatigue / creep-fatigue simulation tests on structural components covering stress states under various operating conditions, and extracting the time-physics field matrix from the finite element fatigue simulation results under each operating condition; dynamically reducing the order of the time-physics field matrix to reduce its dimension, obtaining a reduced-order matrix; constructing a reduced-order model; restoring the equivalent stress field distribution based on the reduced-order model to obtain the reduced-order physical field; predicting the damage distribution evolution and fatigue life under fatigue or creep-fatigue loading based on the reduced-order physical field, and making corrections based on the fatigue life prediction model. This application can improve the efficiency and universality of life prediction.

The invention discloses a vulnerability analysis method of a multi-time-scale micro-grid under FDI attack, which comprises the following steps: decomposing system dynamics of a direct current micro-grid into a fast boundary layer subsystem and a slow order reduction subsystem by using a singular perturbation theory, respectively deducing stability conditions of each subsystem under a false data injection attack condition, and analyzing the vulnerability of the multi-time-scale micro-grid under the false data injection attack condition; and the input state stability of the low-speed subsystem under the FDI attack is proved. The worst deviation of the system state under the attack condition is calculated by constructing a zootope reachable set analysis framework, and the maximum allowable attack amplitude capable of ensuring safe operation of the system is quantified. According to the method, through numerical simulation and hardware experiment verification, a novel attack surface introduced by multi-time scale characteristics can be effectively revealed, and theoretical support and guidance are provided for safe and stable control of the DC micro-grid.

The application belongs to the technical field of simulation analysis, and particularly relates to a switch cabinet condensation margin evaluation method based on simulation analysis and order reduction reconstruction, steps of which comprise: establishing a simulation analysis model of the switch cabinet, adding a physical field and performing physical field coupling and mesh partitioning; solving the simulation analysis model to obtain the temperature and humidity distribution inside the switch cabinet; optimizing the simulation analysis model; replacing electromagnetic heat with loss data as a heat source input into a non-isothermal flow field; constructing a physical field snapshot library for order reduction model training; processing the physical field snapshot library based on a POD method to construct an order reduction model; training a Kriging model to reconstruct the temperature and humidity field of the switch cabinet; constructing a condensation margin evaluation cloud map; in the actual operation process of the switch cabinet, calculating an actual dynamic dew point temperature to generate an actual operation condensation margin cloud map, and completing online real-time evaluation. The application can realize efficient and accurate reconstruction of the temperature and humidity field inside the switch cabinet and efficient evaluation of the condensation margin of the switch cabinet.

The invention provides an online identification and input design method and device for a wireless power transmission system, and the method comprises the steps: carrying out the online estimation of a transfer function parameter of a target system, and building an initial small signal model; constructing a full-order model corresponding to the target system, and performing order adaptive order reduction processing; determining a loss function of the target system by taking minimization of disturbance of the excitation signal to the target system as a target; on the basis of defining the loss function, constructing an optimization problem of the target system under disturbance constraint, and performing optimal excitation input design; and constructing a robust controller of the target system by adopting a robust control method in combination with the initial small signal model. According to the method, the system modal change can be tracked in real time under the closed-loop working condition, the modeling precision and the real-time performance are improved, and the problem that the identification precision is poor in the prior art is solved.