19 results about "Model order" patented technology

A method for identifying a model for modeling cable stress relaxation dynamics of an imprint head is provided. The method includes performing a non-contact imprint test run for a predetermined number of wafers. Data from a force trace and a position trace of the imprint head during the non-contact imprint test run are collected to compute cable stress relaxation forces. A model having a plurality of different model orders is generated. A set of parameters of the model for each model order is identified. Residual sum of squares for wafer average error for each of the model orders is calculated based on the obtained cable stress relaxation forces. The set of parameters of one of the plurality of model orders may be based on a difference in the residual sum of squares for wafer average error between the one of the plurality of model orders and a next higher model.

The application discloses a mixed order regional ionospheric model construction method, which comprises the following steps: establishing a mixed order regional ionospheric model based on observation data; according to whether the ionosphere is in a quiet period or an active period, using different orders and model parameter update intervals to establish corresponding regional ionospheric models; designing a model order self-adaptive adjustment strategy to realize switching of the mixed order regional ionospheric model to a first-order polynomial model or a second-order polynomial model; and unifying generation approaches of mixed order regional ionospheric model parameters to realize estimation of first-order / second-order polynomial model parameters. The mixed order regional ionospheric model has the advantages that, compared with the first-order polynomial method, the problem of low modeling accuracy in the ionosphere active period is overcome; compared with the second-order polynomial method, the limitations of model overfitting and modeling instability in the ionosphere quiet period are avoided; and in terms of parameter generation and modeling accuracy, the mixed order regional ionospheric model combines the advantages of the second-order model and the first-order model, thereby ensuring the accuracy and applicability of regional ionospheric modeling.

This invention provides a radar motion parameter estimation method and apparatus based on information criterion values. It acquires raw radar echo data, performs range processing and target extraction to obtain a target slow-time complex sequence. Based on the target slow-time complex sequence, it constructs multi-order motion candidate models, calculates the residual sum of squares, and calculates the information criterion value based on the residual sum of squares. The optimal model order is determined based on the information criterion value, and the target radial motion parameters are inverted and output based on the phase coefficients corresponding to the optimal model order. Instead of pre-fixing the use of a first-order, second-order, or third-order model, it determines the optimal model order after constructing multi-order candidate models and evaluating them using the calculated information criterion value. This approach adaptively determines the appropriate motion model order for the target based on the phase evolution characteristics of the signal itself, and completes motion parameter estimation accordingly. This ensures estimation accuracy while suppressing unnecessary model complexity, thus improving its engineering application value.

This invention discloses a method for verifying and confirming numerical prediction thrust spectrum results of propellers, belonging to the field of computational fluid dynamics (CFD) result verification and confirmation. While maintaining consistency between the physical model and boundary conditions, this invention encrypts the CFD calculation settings with joint discrete parameters and quantitatively characterizes simulation errors and uncertainties based on Richardson extrapolation. This invention employs the Lomb-Scargle periodogram method to directly process non-uniformly sampled data, avoiding spectral distortion caused by resampling interpolation and improving the accuracy of spectrum estimation. This invention automatically selects the optimal AR model order through information criteria, models colored noise backgrounds in actual engineering, and achieves adaptive noise modeling. This invention simultaneously evaluates the statistical significance of the global maximum peak value and each expected harmonic frequency in a single Monte Carlo simulation cycle. This invention uses a block bootstrap method to calculate the frequency band power confidence interval, maintaining the local correlation of the time series and achieving reliable confidence interval quantification.

The invention discloses a model sequence quantification method and system based on channel balance and related equipment, and belongs to the technical field of model compression. According to the method, for a target operator in a quantitative model, output feature maps of a floating point model and the quantitative model under the same input are respectively obtained; respectively carrying out independent normalization processing on each channel of the two feature maps, calculating the similarity between the corresponding channels after normalization, and carrying out aggregation to obtain a channel balance evaluation index representing the quantitative fidelity; if the index does not reach the preset threshold value, the quantization precision of the layer is improved, the model is updated, and then the model is updated to re-execute the evaluation process; the process is iterated layer by layer according to a network topology sequence until all layers are processed. According to the method, through finer channel-level evaluation and dynamic error suppression on the quantization effect, the quantization bit width can be allocated more accurately, and the hardware resource utilization efficiency is improved while the model quantization precision is ensured.

The application discloses a kind of long-acting time-varying monitoring methods for dam reinforcement based on Bayes real-time updating, comprising: S1. ARMA model is constructed to simulate dam state and determine model order;S2. using prior distribution and likelihood function, using Bayes method to calculate and obtain the posterior density function of each parameter of dam in running state;S3. using last posterior distribution as prior distribution in real-time calculation, repeat the S2 step, in the calculation process, least square method is used to realize model parameter estimation, variable forgetting factor is introduced, and recursive algorithm is used, different forgetting factor is used to past monitoring data, realize parameter adaptive adjustment.The application can adapt to the time-varying characteristics of dam working behavior, real-time fusion historical data and new monitoring information, more accurately and reasonably reflect the nonlinear time-varying relationship between influence quantity and effect quantity, so as to fully embody the typical time-varying characteristics of dam working behavior.

This invention discloses a method, system, and device for identifying subsynchronous oscillation modes under environmental excitation, belonging to the field of power system oscillation detection. Kalman filtering (KF) is used to estimate the coefficients of an autoregressive (AR) model to fit measured environmental data. The damping factor and frequency of the subsynchronous oscillation (SSO) mode can be directly obtained by solving for the roots of the characteristic polynomial corresponding to the AR model. Furthermore, multi-model segmentation filtering (MMPF) is applied to KF to select the correct AR model order. This invention combines the KF algorithm with MMPF, enabling the algorithm to directly identify weak SSO mode parameters from environmental data. The method has simple operation steps and short response time, thus possessing good application prospects.

The invention discloses a method for predicting the rotating speed of a power generation steam turbine based on differential thermal inertia, and aims to overcome the defects of thermal inertia processing homogenization, easy drifting of dimensionless numerical values, low feature screening precision, poor model adaptability and the like of an existing method for predicting the rotating speed of a steam turbine. A prediction system is constructed through four core innovations: a differential thermal inertia lag step number adaptation formula is proposed, lag time is set in a grading manner based on thermal parameter physical characteristics, and a sampling interval is adapted; a full-process dimensionless number [0-1] forced constraint formula is designed, and the numerical drift problem is thoroughly solved; establishing a three-dimensional IMF component screening formula, and fusing correlation, error and frequency characteristics to purify effective characteristics; and constructing a residual-driven self-adaptive ARIMAX model formula to realize dynamic adjustment of the order of the model. The method is suitable for various sampling rate scenes, the prediction precision and stability are obviously better than those of the prior art, and the method can be widely applied to power generation turbine rotating speed real-time prediction and unit regulation and control and provides support for safe operation of a unit.

The invention belongs to the technical field of power system simulation and information interaction, and particularly relates to an information interaction method for joint simulation of a main network and an active distribution network, and the method comprises the steps: building a multi-rate clock synchronization and prediction compensation mechanism, and achieving the coordination of the simulation step size of the main network and the distribution network and the perspective compensation of boundary data; constructing a hierarchical boundary information model containing static topology, dynamic electrical quantity and aggregation characteristic data, and supporting high-fidelity equivalent representation of dynamic external characteristics of the distributed power supply; executing a self-adaptive interaction strategy based on the running state, and dynamically adjusting the interaction frequency, the data granularity and the model order according to the normal, transient or topological change state; boundary data consistency verification and closed-loop correction are implemented, and numerical value stability is ensured through sensitivity information exchange and iterative correction. According to the invention, the precision, efficiency and convergence of joint simulation are improved.

The invention discloses a power system inertia on-line evaluation method and device. The method comprises the steps that a measurement data signal of a grid-connected device is collected and preprocessed; determining the occurrence moment of large disturbance of the system from the preprocessed data, and extracting observation data; establishing a controlled autoregression model according to the observation data, and determining a model order; a self-adaptive two-factor robust recursive least square algorithm is adopted, a robust factor is introduced into the least square algorithm, model parameters are recognized and identified, and a transfer function of each device is obtained; impulse response is applied to the transfer function of each device, a response initial value is calculated to determine the inertia of the device, and the total inertia of the system is obtained after aggregation. According to the invention, the system disturbance is monitored on line through the DFA, the observation data range for inertia evaluation is accurately judged, and the real-time performance and accuracy of inertia evaluation are ensured.

The application discloses a hybrid reduced-order control method for large-scale dynamic systems and relates to the technical field of system reduced-order control.The application builds a multi-time-scale state space model, quantitatively divides fast and slow subsystems based on a preset time scale threshold value, accurately screens dominant state variables by using eigenvalue dominant factors and participation factor analysis, performs energy-optimal reduction on the slow subsystem by adopting balance truncation and singular perturbation principles, and introduces a closed-loop deviation evaluation and automatic optimization mechanism, thereby solving the problems that the division of fast and slow subsystems is subjective in the prior art, dominant modes are easily lost, transient accuracy is poor, decoupling and reduction links are mutually fragmented and depend on artificial trial and error, and realizing automation, high precision and strong universality of the reduction process, so that the model order can be reduced while the dominant modes and transient characteristics of the system are completely retained, and an efficient and reliable technical scheme is provided for analysis, simulation and control design of large-scale dynamic systems.

The application discloses a modal identification method based on an unsupervised optimization covariance stochastic subspace method, and comprises the following steps: arranging vibration sensors on a to-be-measured structure with noise interference or weak excitation modes, and acquiring structural dynamic response data; calculating a covariance matrix and constructing a toply matrix; calculating an extended observable matrix and a controllable matrix based on the weighted toply matrix; defining a toply matrix row block number and a model order parameter range, and iteratively calculating a parameter optimization index; calculating a cumulative contribution rate based on a singular entropy increment of the toply matrix and determining a critical model order; calculating a cumulative parameter optimization index in an interval from a minimum model order to the critical model order under different toply matrix row block numbers, and determining an optimal parameter combination through an average minimum value; substituting the optimal parameter combination into a covariance stochastic subspace algorithm to identify a system matrix and calculate structural modal parameters; and automatically identifying each order physical mode from candidate modes by using a DBSCAN clustering method. The application further discloses a system and an electronic device.

The invention relates to the technical field of power systems and automation thereof, in particular to a small-interference stability analysis method suitable for a flexible direct current asymmetric working condition, and the method comprises the following steps: S1, building an MMC-HVDC nonlinear periodic time-varying state space model; s2, solving a steady-state periodic trajectory of the MMC-HVDC system and calculating a Jacobian matrix time sequence; and S3, solving the state transition matrix based on the Floquet theorem and carrying out eigenvalue decomposition. The technical problems of high model order and large calculation amount in MMC-HVDC system small interference stability analysis under the asymmetric working condition are solved.

The invention discloses a lightning field positioning method and system based on RSSI adaptive ranging model regularization optimization, and the method specifically comprises the steps: collecting RSSI data of anchor nodes in a communication range through a reference node, and carrying out the Kalman filtering preprocessing, and obtaining a stable estimation signal; combining the real distance between the reference node and the anchor node and the filtered RSSI value into a calibration data set; the method comprises the following steps: establishing an adaptive ranging model fitted by a polynomial, introducing L1 Lasso regression or L2 ridge regression regularization, defining a hyper-parameter space comprising a model order and regularization intensity, searching the hyper-parameter space, selecting an optimal regularization method, determining an optimal model order and regularization intensity, and generating an optimal adaptive ranging model; and measuring the distance between the unknown node and the anchor node through the optimal adaptive ranging model to obtain the coordinate position of the unknown node. According to the invention, the precision, robustness and generalization ability of the distance measurement model are improved, so that the reliability of the whole positioning system is improved.

The present invention belongs to the technical field of hydraulic pump anomaly detection. Disclosed is a mining area load spectrum anomaly detection method based on canonical variate analysis. The method comprises: S1, performing data preprocessing to obtain standardized data, and in combination with operating conditions of an excavator, constructing a typical operating condition data set of a hydraulic pump; S2, constructing a historical vector and a future vector, and on the basis of the historical vector and the future vector, constructing a historical observation matrix and a future observation matrix; S3, constructing a Hankel matrix on the basis of an auto-correlation matrix and a cross-correlation matrix, decomposing the Hankel matrix, and determining a model order; S4, mapping original data into a canonical variate space and a residual space, and evaluating the total variation of canonical variates in a state space and the sum of squared variation errors in the residual space; and S5, determining an evaluation threshold value, and if a control limit is exceeded, determining that the hydraulic pump operates abnormally. In the present invention, pressure pulsation data of a hydraulic pump is used to perform anomaly detection on the basis of canonical variate analysis, and the method in the present invention is sensitive to the internal operating state of the pump, is not prone to the impact of an external environment, and enables early warning of faults in the hydraulic pump.

The present application relates to a kind of artificial intelligence driven new energy vehicle power system energy efficiency optimization method, the present application constructs vehicle dynamics model to determine total torque demand;Based on mechanical power and adaptive fitting motor loss, the motor power of motor is obtained;Order prediction model is according to current data density and noise level adaptive prediction motor loss model order, after determining order, using square sum programming to adopt least square fitting monotone increasing and positive definite nature quasi-convex function, modeling motor loss;Each motor consumption electric energy and whole vehicle motor consumption electric energy are obtained;Collect whole vehicle operating condition in the process of driving, and the weight of each optimization subgoal of total optimization target is adjusted according to whole vehicle operating condition by pre-training multi-objective decision model, in the satisfaction vehicle total torque demand, with total optimization target optimal to optimize each motor torque distribution.Considering actual operating condition, power system energy efficiency optimization is realized.

The invention relates to the technical field of power system testing, in particular to a model parameter sensitive sorting method based on track matching. The method comprises the following steps: respectively acquiring system inertia center frequency trajectory curves with / without the action of a speed regulator, constructing a system frequency response model according to the curves, determining a final model order by comparing the matching precision of models with different orders, adjusting model key parameters, acquiring a modified output trajectory, and comparing the modified output trajectory with an original simulation trajectory to obtain a simulation model. And in combination with trace sensitivity analysis, the influence degree of each parameter on the frequency deviation is quantified, so that dominant parameters are automatically identified and priority ranking is realized. According to the method, the frequency trajectory is obtained only through simulation and theoretical calculation, dependence on multi-point actual measurement data is reduced, hardware and communication cost is saved, precision is guaranteed while the model is simplified through trajectory matching and sensitivity analysis, and core parameters which play a leading role in frequency dynamics can be effectively screened out.

The application discloses a laser communication coarse tracking system comprehensive modeling and multi-target complementary control method, and belongs to the field of laser communication technology control. The laser communication coarse tracking system is used for control, comprising linear modeling of the coarse tracking system, combination of low frequency bands of sinusoidal sweep signals and middle and high frequency bands of pseudo-random signals to obtain system frequency response, and system transfer function obtained through Hankel matrix identification method; nonlinear modeling of the coarse tracking system, establishment of a Stribeck friction model, and design of a feedforward compensation algorithm based on the Stribeck friction model to obtain a multi-target complementary control method for controlling the coarse tracking system. The application realizes accurate identification of model order and parameters in the linear part, significantly improves low-speed tracking performance in the nonlinear part, effectively solves the contradiction between system performance and robustness, and significantly improves tracking precision and anti-interference ability.

The application discloses a high arch dam operation modal parameter automatic identification method and system based on outflow excitation, and the method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer based on an adaptive multivariate variation modal decomposition algorithm, obtaining the best IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a covariance matrix driven random subspace identification algorithm; and 3) automatically identifying modal parameters based on an intelligent clustering algorithm. The application automatically optimizes the modal component reconstruction signal of the multi-sensor vibration signal to suppress noise, combines the Monte Carlo theory and the covariance driven random subspace method to establish a Monte Carlo three-dimensional stability diagram to determine the model order, and applies the improved fuzzy clustering to realize automatic interpretation of the stability diagram, so that the high arch dam operation modal parameters can be accurately identified.