116 results about "Model order reduction" patented technology

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

The invention relates to a rapid prediction method for a temperature field and a stress field of a dangerous part of a key component of a small turbojet engine, belongs to the field of optimization design of small turbojet engines, and solves the problems of high cost and low efficiency of simulating the temperature field and the stress field of the turbojet engine by an existing conventional numerical simulation method. Comprising the steps of establishing a turbojet engine key component geometric model; generating temperature field and stress field data sets under different working conditions; reducing the order by using a POD flow field order reduction model and obtaining a transformation relation; training the deep learning model to obtain a mapping relation; inputting to-be-calculated working condition data to obtain reduced-order data; the distribution of the temperature field and the stress field under the specific working condition is obtained through recalculation by means of the transformation relation, rapid prediction can be achieved, and cost is saved. According to the method, deep learning and a flow field reduced order model are adopted, local dangerous parts under the given working condition can be predicted in time, calculation and time cost is saved, and timely data feedback is provided for prolonging the service life of an engine and keeping good performance of the engine.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention belongs to the field of hydraulic machinery state monitoring, and particularly discloses a digital twinning-based pumped storage unit shafting operation state monitoring method and system, and the method comprises the steps: building a multi-physics field coupling finite element full-order model of a pumped storage unit shafting, carrying out the model reduction of the finite element full-order model, and obtaining a digital twinning model; utilizing a multi-physics field coupling reduced-order model to construct a digital twinborn body of the pumping and storage unit shafting for real-time simulation; acquiring operation state data corresponding to the monitoring signal, and establishing an operation state knowledge base of the pumped storage unit; and according to the established virtual-real interaction subsystem, the three-dimensional visualization subsystem and the operation state knowledge base, constructing an operation state monitoring system of the pumping and storage unit shaft system, and performing real-time data transmission of the operation state monitoring system by using a sensor so as to evaluate the operation state of the pumping and storage unit in real time and realize operation state early warning of the pumping and storage unit. According to the invention, the real-time performance of monitoring the operation state of the shaft system of the pumping and storage unit can be improved, and the monitoring efficiency is further improved.

The invention discloses a pressure vessel lower chamber model order reduction method combining sparse grids and POD, and the method comprises the steps: adding one to the grade of the sparse grids after iteration initialization, and obtaining test grid points according to important grid points; executing the lower chamber full-order computational fluid mechanics model corresponding to the test grid points to obtain a first snapshot; reconstructing a second snapshot by using the mapping relation and the POD modal matrix; calculating a root-mean-square error of the two snapshots, and if the root-mean-square error is smaller than a threshold value, completing construction of a reduced-order model Otherwise, the grid points with the errors meeting the requirements are stored in a non-important grid point set, the grid points which do not meet the requirements are marked as important grid points, and the snapshot matrix is updated. Performing singular value decomposition on the snapshot matrix, updating the mapping relation, judging whether the maximum sparse grid grade is reached or not, and continuing iteration if the maximum sparse grid grade is not reached. According to the method, the field distribution of the thermal hydraulic parameters in the lower chamber can be quickly obtained through matrix algebraic operation, and the method can be used for application scenes such as parameterization analysis, design optimization and digital twinning.

The invention discloses a power transmission and transformation model order reduction method, system and device and a storage medium, and belongs to the technical field of electric power, and the method comprises the steps: obtaining the original data of a power transmission and transformation device, carrying out the standardization processing of the original data, and constructing a power transmission and transformation device training data set; performing dimension reduction on the power transmission and transformation equipment training data set to obtain power transmission and transformation equipment dimension reduction data; training a multi-layer self-encoding model and a classification model according to the dimension reduction data of the power transmission and transformation equipment; constructing a power transmission and transformation equipment data classification model through the multi-layer self-encoding model and the classification model; building a digital twin reduced-order model of the power transmission and transformation equipment through the dimension reduction data of the power transmission and transformation equipment and the data classification model of the power transmission and transformation equipment; and obtaining high-dimensional data of the power transmission and transformation equipment, and performing order reduction on the high-dimensional data through the digital twin order reduction model of the power transmission and transformation equipment. The calculation efficiency of the digital twin model of the power transmission and transformation equipment is improved.

The invention discloses a new energy system frequency domain modeling and equivalent circuit order reduction analysis method, and relates to the technical field of new energy power system modeling and stability analysis. The method is used for solving the problems that in an existing frequency domain modeling means, the automation degree of data acquisition is low, fitting precision and stability are poor, equivalent circuit universality is poor, and order reduction analysis is not systematic. The method comprises the following steps: acquiring system frequency domain response data based on disturbance injection; constructing a system rational function model by adopting an improved vector fitting algorithm; and then model order reduction is realized through state space modeling and an SVD-based balanced truncation method, and response consistency and stability analysis is carried out in a frequency domain and a time domain. The method can be widely applied to modeling analysis of new energy equipment such as wind power equipment, voltage source converters and high-voltage direct-current power transmission equipment, and has the advantages of being high in automation degree, high in modeling precision, good in visualization, clear in structure, suitable for simulation and control design and the like.

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 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 is suitable for the technical field of model simulation optimization, and provides a model order reduction system and a model order reduction method.The model order reduction system comprises a model import module, a method selection module and an order reduction processing module which are in communication connection in sequence; the model importing module is used for importing a model to be subjected to order reduction; the method selection module is used for displaying identification information of the multiple model order reduction methods and obtaining a first method selection instruction, the first method selection instruction is used for indicating a first target model order reduction method, and the first target model order reduction method is any one model order reduction method in the multiple model order reduction methods; and the order reduction processing module is used for performing order reduction processing on the model to be subjected to order reduction based on the first target model order reduction method to obtain a first order reduction model. According to the technical scheme, the multiple model order reduction methods are provided, the target model order reduction method selected by the user from the multiple model order reduction methods is used for carrying out order reduction processing on the model to be subjected to order reduction, and the accuracy of model order reduction is improved.

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 an integrated circuit electrical characteristic simulation method and device, a terminal and a storage medium, and the method comprises the steps: obtaining a to-be-simulated integrated circuit model, carrying out the mesh generation of the to-be-simulated integrated circuit model, so as to obtain an integrated circuit mesh data set composed of a plurality of mesh units, constructing a corresponding charge basis function and a current basis function based on the grid units; step-by-step fusion updating of the charge primary function is realized by taking the charge primary function as a fusion object, and meanwhile, corresponding fusion updating is performed on the current primary function in a geometric mapping mode; and after all fusion updating is completed, obtaining an integrated circuit primary function data set, and performing electrical characteristic simulation on the to-be-simulated integrated circuit based on the integrated circuit primary function data set. According to the method, fast updating and low-order representation of a complex coupling capacitance matrix are achieved, compared with a traditional post-order reduction method, the theoretical complexity is reduced from O (n3) to O (n), and meanwhile the model order reduction efficiency is greatly improved while the model precision and the order reduction effect are guaranteed.

The invention discloses a time delay calculation algorithm based on implicit circuit modeling and a model reduction algorithm, and the algorithm comprises the steps: S1, traversing the serial number of a circuit to a node, and representing a topological structure through a vector; s2, creating an equivalent implicit differential model of the circuit; s3, solving a linear system; s4, model order reduction is carried out through an improved Arnoldi algorithm; and S5, calculating the final time delay of the circuit through an iterative algorithm. The invention discloses a time delay calculation algorithm based on implicit circuit modeling and a model order reduction algorithm, and the time delay calculation efficiency and precision are remarkably improved through the methods of circuit model optimization, order reduction processing, characteristic value calculation, incremental updating of an iterative algorithm result and the like.

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 power station water level prediction control method, system and equipment based on model order reduction and a medium, and relates to the technical field of hydropower station engineering automatic regulation and control. The method comprises the following steps: constructing a river channel one-dimensional hydrodynamic model, performing single river channel roughness coefficient calibration, and determining different operation conditions and corresponding constant flow states; when the river channel one-dimensional hydrodynamic model is in a constant flow state, applying a flow sudden change disturbance to perform parameter identification, and establishing an ID model and an IDZ model under different operation conditions; constructing a power station MPC model, and embedding the ID model as a prediction part into the power station MPC model to obtain a power station ID-MPC open-loop control model; and the IDZ model is coupled with the power station ID-MPC open-loop control model, and the output of the power station ID-MPC open-loop control model is transmitted to the IDZ model for water level deviation control. According to the invention, the control precision and real-time performance can be effectively improved.

The invention discloses a three-dimensional model order reduction method for a special valve and electronic equipment, and belongs to the field of special valves. The method comprises the following steps: firstly, performing three-dimensional modeling according to a design file of the special valve to obtain an internal flow channel model; calculating a flow field in the special valve flow channel under different working conditions by utilizing computational fluid mechanics software; performing modal decomposition on a sample data set of the original three-dimensional model obtained by flow field calculation to obtain a spatial mode, a modal coefficient and modal energy; selecting the modal quantity according to the modal energy; fitting the modal coefficient, and establishing a relationship between the working condition parameter and the modal coefficient; and quickly predicting the internal flow field of the special valve under the unknown working condition according to the fitting model obtained by fitting and the spatial mode. According to the method, the calculation amount is effectively reduced while the calculation precision is ensured, the calculation efficiency is greatly improved compared with a numerical simulation method, and the requirement of a user for obtaining the internal flow field information of the special valve with high efficiency and high real-time performance can be met.

The invention discloses a rapid prediction method for wing flow field area decomposition. According to the method, a k-means clustering decomposition method is adopted, effective decomposition of a flow field area is achieved, area sampling is carried out based on area decomposition, main modal energy extraction is carried out on a sampled flow field data set through a model order reduction method, a basis matrix of flow field data is obtained, the basis matrix is trained through a machine learning method, and the flow field data is obtained. And finally, a wing flow field region decomposition rapid prediction framework is realized. According to the method, the calculation efficiency of a traditional numerical simulation method is remarkably improved, and the method has wide applicability.

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 application discloses a semi-airborne full-time electromagnetic forward method based on an Octree grid, which performs spatial discretization on time-domain Maxwell equations, thereby effectively reducing unknowns of equations to be solved. For full-time electromagnetic field numerical simulation, the application combines the high calculation precision of the second-order backward Euler method with the high OFF-Time calculation efficiency of the model reduction algorithm, adopts the second-order BDF and the model reduction algorithm to respectively solve ON-Time and OFF-Time time-domain control equations, forms a joint algorithm to realize simulation of semi-airborne time-domain electromagnetic full-time response, and finally completes the forward calculation process. Through numerical simulation verification, in the full-time transient electromagnetic forward process, the application can keep small occupied memory and also has higher calculation efficiency, and the calculation precision is not lower than that of other methods.

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 present invention discloses a method for reducing the order of a transformer temperature field model based on the Krylov subspace, belonging to the technical field of temperature field simulation of oil-immersed three-phase transformers. The present invention includes: Step 1. According to the actual physical dimensions of the transformer, construct a simulation model of the oil-immersed three-phase transformer and appropriately simplify the model. Step 2. Establish an electromagnetic field simulation model, use an eddy current solver, and simulate to obtain the transformer loss distribution. Step 3. Take the obtained transformer loss distribution as the excitation source of the fluid field, establish a fluid field simulation model, realize the coupled analysis of the electromagnetic field and the fluid field, and obtain the temperature field distribution characteristics of the oil-immersed three-phase transformer. Step 4. Build a reduced-order model for the transformer temperature field based on the Krylov subspace method. The present invention realizes the rapid simulation calculation of the physical field, reduces the simulation time, and further constructs a digital twin model of the transformer.

The application provides a method and system for offshore wind turbine integrated dynamic feature reservation. The full state time series data of the offshore wind turbine multi-physical field coupling system is obtained, the modal parameters of the system are extracted by using a dynamic modal decomposition method, the participation factor matrix is calculated, the participation degree of each state quantity and the corresponding physical link to each mode is quantified, the dominant state quantity of the extracted mode and the corresponding physical link are located, the dominant state set is constructed, the time scale distribution of each mode is quantified, the time scale distribution characteristic atlas of different physical links of the overall system is formed, the fast time scale, analysis time scale and slow time scale boundaries in the atlas are divided according to different research needs, and the offshore wind turbine integrated reduced order model is constructed based on the singular perturbation theory. The application can effectively reserve the dynamic features of the offshore wind turbine multi-field coupling characteristics, and provides a precondition and quantitative basis for physical-based model reduction modeling.

The invention discloses a gate-containing Saint-Venant equation set modeling method based on a model order reduction technology, and belongs to the technical field of hydraulic engineering, and the gate-containing Saint-Venant equation set modeling method comprises the following steps: S1, constructing a gate-containing original Saint-Venant equation set; s2, discretizing the constructed original Saint-Venant equation set to obtain a nonlinear function in a high-dimensional system; s3, based on a simulation purpose, performing system simulation by using the original Saint-Venant equation set, and collecting state variables to construct a snapshot matrix; and S4, based on the nonlinear function in the high-dimensional system, simplifying the snapshot matrix by using a model order reduction technology, and further constructing a low-dimensional order reduction model corresponding to the original Saint-Venant equation set, thereby completing the construction of the Saint-Venant equation set containing the gate. According to the method, the model is subjected to order reduction processing, so that the simulation precision is ensured, the calculation efficiency is greatly improved, and the requirements of real-time simulation and control are met; the method has a wide application prospect and can be used in the fields of river scheduling, water conservancy project optimization, flood control early warning and the like.

The invention discloses a river and lake hydrodynamic model reduced-order inversion initialization method for intelligent water conservancy, and the method comprises the steps: dividing a computational domain into a plurality of sub-domains according to hydraulic characteristics, building a physical constraint equation represented by reduced-order description parameters, and determining an independent to-be-inverted parameter set through the analysis of the degree of freedom; estimating an initial state of a part of sub-domains by using observation information or a priori value at a starting moment; determining an inversion window according to the hydraulic disturbance propagation time; constructing an agent model, and performing joint inversion on the to-be-inverted parameters and the boundary process line parameters to minimize the cost function; and driving the proxy model to generate a global initial state by using the optimal parameter obtained by inversion, mapping the global initial state to a basic model grid, and taking the global initial state as an initial condition after free evolution and boundary smoothing processing. According to the method, actual measurement data are fused through order reduction-inversion, global initial field construction can be rapidly and accurately completed, and the requirement of an intelligent water conservancy system for cold start of a hydrodynamic model is met.

The invention discloses a method and a system for predicting stress response and displacement response of a jacket platform, and belongs to the technical field of health monitoring, and the method comprises the following steps: establishing a finite element model of the jacket platform, and obtaining the inherent frequency and local strain of the jacket platform through simulation; correcting the finite element model of the jacket platform by adjusting model parameters of the finite element model by respectively taking errors corresponding to the simulated and real-time acquired inherent frequency and local strain as variables; taking real wind wave load data as excitation, inputting the real wind wave load data into the finite element correction model of the jacket platform, obtaining stress and displacement response data of the jacket platform through simulation, and respectively constructing a stress response reduced-order model and a displacement response reduced-order model according to optimal modal basis vectors and modal coefficients corresponding to different data sets, and stress response and displacement response of the in-service jacket platform are monitored in real time. According to the method, finite element model correction and model order reduction technologies are combined, so that high-precision prediction of jacket platform stress and displacement response is realized.

The invention provides a global response reconstruction method and device based on an intelligent enhanced reduced-order model, and the method comprises the steps: obtaining the local structure data of a to-be-reconstructed scene, and enabling the local structure data to be obtained through the collection of the to-be-reconstructed scene based on a sensor; an orthogonal base space matrix under model order reduction is obtained, the column vector of the orthogonal base space matrix is an orthogonal base vector, and the orthogonal base vector is expanded into a low-dimensional subspace; and based on the local structure data and the orthogonal base space matrix, performing reconstruction to obtain a high-dimensional global response of the to-be-reconstructed scene matched with the local structure data. A high-dimensional and complex structure response field is projected to a low-dimensional subspace by using an orthogonal base space matrix with physical consistency, so that the high-dimensional global response of a to-be-reconstructed scene can be obtained through accurate reconstruction and deduction based on local structure data of the to-be-reconstructed scene.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

The invention discloses an HB-AFT-based shell-rotor system model rapid solving method, which comprises the following steps of: establishing a kinetic model of a complex shell-rotating shaft-motor coupling system, firstly performing primary model order reduction based on an intrinsic orthogonal decomposition (POD) method, and then performing model rapid solving based on the HB-AFT method. According to the method, the rotor system has higher calculation efficiency, the complex nonlinear vibration problem can be rapidly solved, and the dynamic behavior mechanism of the core cabin of the complex underwater vehicle can be clarified. Compared with a traditional numerical method, the method has the advantages that an unstable solution can be obtained through the semi-analysis and semi-numerical method, so that researchers are helped to better analyze the vibration phenomenon of the underwater vehicle core cabin system, and then optimization design is conducted on the underwater vehicle core cabin system.

The embodiment of the invention provides an alternating current servo system control method, system and device based on an internal model principle and a medium, and belongs to the technical field of motor control. The method comprises the following steps: performing mechanism analysis, system identification and model order reduction processing on the alternating current servo system to obtain a four-order model corresponding to the alternating current servo system; on the basis of the fourth-order model, designing the corresponding optimal state feedback of the AC servo system under a linear quadratic regulator; determining a state observer of the inner ring object model, and determining an optimal observer parameter by using a first LQ criterion function according to the state observer; determining a first state space expression corresponding to the AC servo system according to the optimal state feedback and the optimal observer parameter; and determining a target control parameter corresponding to an optimal controller of the AC servo system according to the first state space expression, so that the AC servo system realizes optimal control according to the target control parameter.