833 results about "State-space representation" patented technology

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention discloses a directional drilling track accurate control technology based on machine learning, and relates to the technical field of drilling engineering. The directional drilling track accurate control technology comprises the following steps that underground parameters of a drilling tool during underground operation are obtained; establishing a dynamic model based on the drilling tool structure and the motion state; based on the dynamic model, introducing a long-short-term memory neural network, and constructing a hybrid prediction model; drilling parameters are input into the hybrid prediction model, and trend information of multiple tracks is output; based on current geological conditions, drilling tool configuration and operation safety constraints, performing simulation evaluation on the trend information of the plurality of candidate tracks, and screening out an optimal track meeting track precision and underground safety requirements from the candidate tracks; based on the optimal track, control strategy input is constructed, and a state space containing a tool face angle, target azimuth deviation and a drilling tool state is set; and through a deep reinforcement learning method, an advanced adjustment instruction for the guiding tool is generated, and drilling operation is executed according to the optimal track and the advanced adjustment instruction.

The invention relates to the technical field of power systems, and provides a power distribution network multi-dimensional regulation and control decision method, system and device and a medium, and the method comprises the steps: inputting the preprocessed multi-source operation data into a preset state perception model, and obtaining a multi-dimensional state vector representing the operation state of a power distribution network; a multi-dimensional state vector is used as a state space, regulation and control operation is used as an action space, a composite reward function is established according to a power distribution network operation target, and modeling is carried out to obtain a Markov decision process framework; interacting with a power distribution network simulation environment by adopting a deep reinforcement learning algorithm, obtaining a current state from a state space, selecting and executing regulation and control operation in an action space according to a strategy network, updating strategy network parameters based on feedback of a composite reward function until an optimal regulation and control strategy network is obtained, and obtaining a deep reinforcement learning strategy model; and performing strategy rolling updating based on the real-time monitoring data to obtain a target regulation and control strategy. According to the invention, comprehensive optimal regulation and control of a complex operation scene can be realized.

The invention relates to the technical field of biological feature recognition, and particularly provides an anti-migration PPG recognition method based on rate perception and a state space model. The method comprises the following steps: performing physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; performing double-flow cooperative processing on the high-dimensional shallow-layer feature sequence, and distributing the high-dimensional shallow-layer feature sequence to two parallel branches, namely a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data stream branch, acquiring a deep global feature sequence with rate invariance; obtaining multi-scale refinement features based on the high-dimensional shallow feature sequence and the deep global feature sequence; according to the multi-scale refinement features, a final biological feature recognition result is obtained, the method can actively sense the physiological rate change, and efficient nonlinear modeling can be achieved with the extremely low parameter quantity.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a container door handle cover defect detection method based on lightweight multi-receptive-field feature interaction, and the method comprises the steps: firstly constructing a three-stage feature extraction architecture with a Mama state space model as a trunk network, achieving the efficient global dependence modeling and spatial feature extraction, and designing a multi-receptive-field feature interaction module; secondly, providing a high-order semantic fusion module at the neck part of the network, dynamically selecting and enhancing key semantic features in combination with multi-scale feature fusion and a channel attention mechanism, and improving the perception and discrimination ability of the model to texture fuzzy and rusted regions; and finally, designing a lightweight shared detail enhanced convolution detection head, realizing more accurate boundary positioning and defect classification through a shared structure and a fine-grained bounding box modeling strategy, and considering the detection speed and deployment efficiency at the same time. According to the method, aiming at typical defects of the container door handle cover, a multi-module cooperative efficient sensing framework is provided, so that the detection capability of fine-grained structure abnormity is improved.

The invention discloses a GRACE and Swarm time-varying gravity field fusion filtering method based on a state space model, and the method comprises the steps: taking a spherical harmonic coefficient as a state quantity, constructing a random walk process equation, introducing three types of observations, employing a quantization parameter for observation noise and process noise covariance, constructing according to an order / power law, and carrying out the self-adaptive updating along with the monthly; a Nelder-Mead method is adopted to search for spectral index parameters, and an EM algorithm and a statistical method are utilized to update other parameters in a closed / quasi-closed mode; obtaining the state and posterior covariance of a full time sequence by using Kalman filtering and RTS smoothing; in the GRACE and GRACE-FO window period, continuous reconstruction is carried out by means of a process model and Swarm; and outputting quality evaluation information including monthly gravity field coefficients, posterior covariance, innovative variance ratio, residual whitening test, space power spectrum, uncertainty band and the like. According to the method, while physical rationality and calculation feasibility are ensured, a continuous and stable monthly time-varying gravitational field sequence with quantifiable uncertainty is realized.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention relates to the technical field of agricultural picking robot visual perception, and discloses a strawberry fruit detection and picking key point positioning method and model based on deep learning. The method comprises the following steps: acquiring strawberry RGB-D images to construct a data set; on the basis of a YOLOv8-Pose architecture, global feature extraction is enhanced by constructing a C2f-MAMBA module of a fusion state space model, an HWD module is introduced to retain detail features, and a Faster Net Block is used for weight reduction, so that an STRAW-MAMBA model is constructed; outputting coordinates of five key points such as a fruit bounding box and a shear point by the model, and performing geometric compensation on a shielding point; and finally, calculating three-dimensional space coordinates of the key points in combination with depth information. According to the method, a C2f-MAMBA module based on Mama is innovatively introduced, global feature association is effectively modeled, a multi-scale attention mechanism is combined, the defect that long-range dependence is difficult to capture in a traditional CNN is overcome, and the detection and positioning precision of strawberry fruits and picking key points thereof in a complex background is remarkably improved.

The invention provides a wind power prediction method based on improved Diffusion-Q. According to the method, a diffusion model and a Q-learning reinforcement learning mechanism are deeply fused through a data preprocessing module, a diffusion model forward process, a Q-learning module, a diffusion model reverse process, an output prediction module and a feedback and optimization module; a generative prediction system with a dynamic strategy optimization capability is constructed, a future wind power sequence is generated through probability distribution of modeling time sequence data in a forward diffusion process and a reverse diffusion process, online dynamic adjustment of diffusion process parameters is realized, and dynamic prediction of the wind power sequence is realized by defining a state space, an action space and a reward function. The model can automatically select the optimal sampling path according to current input and historical performance, prediction precision and robustness are remarkably improved, and the method can be widely applied to wind power plant short-term power prediction, power dispatching system and intelligent power grid energy management and has good engineering application prospects.

The invention relates to the technical field of electric heat tracing intelligent temperature control, and discloses a control box electric heat tracing band intelligent temperature control system and method. The method comprises the steps of collecting data of a plurality of temperature sensors in a control box, constructing a temperature sequential sequence, and forming a sequential diagram structure reflecting correlation strength among the sensors by calculating spatial proximity and time correlation. And inputting the sequential graph into a state space model, and dynamically adjusting a state transition matrix of the model according to an edge weight of the graph so as to accurately simulate a heat transfer dynamic process in the box. A state output by the model is separated into a periodic component and a trend component, the periodic component is matched with a reference pattern library to identify an abnormal fluctuation phase, and multi-scale decomposition and slope curvature analysis are performed on the trend component to quantify a change trend. According to the method, dynamic and accurate modeling of the complex thermal environment is realized, the temperature can be controlled more accurately, and abnormity can be warned in advance.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention discloses a multi-constraint path planning method and device based on intelligent heuristic search, and is suitable for a mobile robot in a complex and narrow environment. According to the method, a multi-source sensor is used for environment perception and fusion mapping, and a grid map is generated; a neural heuristic search algorithm based on a Mama structure and a selective state space model (Selective SSM) is adopted on the cost map, and a guide graph is generated; a continuous and collision-free initial path is generated through self-adaptive step size control in combination with robot kinematics constraints and channel characteristics; further establishing a multi-target constraint model based on a robot center Euclidean symbol distance field (RC-ESDF), and executing path smoothing and safety optimization by using a priori-guided particle swarm optimization algorithm (Prior-guided PSO) to obtain an optimal path meeting dynamic consistency; the system corrects the path in real time through a closed-loop feedback mechanism and triggers dynamic re-planning, so that the path feasibility, safety and real-time adaptive capacity of the robot in a limited environment are remarkably improved.

The invention belongs to the technical field of artificial intelligence and multi-modal emotion calculation, and discloses a multi-modal emotion recognition method based on a Mama state space model and cross-modal self-distillation. Through the organic combination of the efficient sequence modeling capability of the Mamba state space model and the knowledge sharing mechanism of cross-modal self-distillation, the advantages of the state space model in the aspects of time sequence modeling and calculation efficiency are fully played, and meanwhile, the limitation of a single model architecture is made up through a cross-modal attention mechanism; the technical bottlenecks of an existing multi-modal emotion recognition method in the aspects of long sequence processing efficiency, cross-modal information fusion and knowledge transfer sufficiency are effectively solved, and an efficient and reliable technical solution is provided for further development and practical application of the multi-modal emotion recognition technology.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention provides a hydrological flow long sequence prediction method and system of an improved state space model. The method comprises the steps of collecting multi-source data of a drainage basin to be predicted; constructing a time sequence sample pair by the processed multi-source data through a sliding window method, wherein the time sequence sample pair comprises an input sequence and a target sequence; a HydroMama model is constructed according to the time sequence sample pair, a HydroMama prediction model is trained, and an optimal hyper-parameter combination is searched for; based on the trained HydroMama model, traffic prediction and result restoration are realized by adopting an autoregression mechanism; the calculation efficiency is high, and the continuous state equation discretization of the state space model is utilized, so that the long-distance meteorological-hydrological hysteresis effect which is difficult to capture by a traditional cycle model can be captured; according to the method, skewed distribution of hydrological data is processed through logarithmic transformation, and an anti-overfitting objective function is combined, so that the prediction performance of the model on unseen data is remarkably improved, and the practical application value is high.

The invention belongs to the field of temperature control, and particularly relates to a reaction kettle temperature control method and system for synthesizing isothiocyanate. The method comprises the following steps: acquiring temperature, material concentration and coolant flow in a kettle in real time, calculating a reaction rate estimated value through a nonlinear mixed model, and calculating an active factor by combining catalyst input time and an index inactivation function; performing unequal weight combination on the three parameters to generate a scheduling variable, and calculating a current value and a second-order difference value of the scheduling variable; selecting a reference state space model according to the current value of the scheduling variable, and correcting the matrix by using a second-order difference value to obtain an LPV model; constructing a quadratic programming optimization problem, wherein an objective function comprises a temperature tracking error penalty term and a control input increment penalty term for adjusting the weight; when the temperature deviation exceeds the threshold value continuously, a control input value penalty term is added; and solving an optimization problem and outputting a first element of the control sequence to a temperature control execution mechanism. According to the invention, the temperature control precision and stability of the isothiocyanate synthesis reaction kettle are improved.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line tree obstacle intelligent supervision system and method based on the Internet of Things, and the system comprises an intelligent sensing module, a data cloud transmission module, a fusion prediction module, an analysis decision module and a linkage execution module. According to the coupling prediction, historical data are processed through an algorithm model, future tree growth and conductor wind deviation are predicted, and the scientificity and accuracy of prediction are ensured through cooperation of a state space model and a Kalman filtering algorithm, so that the prediction accuracy is improved through a sensor deployed at the front end and an intelligent algorithm at the cloud end. The device can continuously and automatically monitor for 7 * 24 hours, not only measures the current static distance, but also predicts the future growth trend of the tree based on historical data, and calculates the dynamic windage yaw of the lead by combining the real-time wind speed and wind direction, thereby being convenient to give out early warning before the tree actually approaches the safe distance, realizing the process from post-event disposal to pre-event prediction, and improving the working efficiency. And the preventive safety level of the power grid is improved.

The invention provides a wind power tower monitoring data dynamic compensation and fusion method for Beidou positioning, and relates to the technical field of data processing and signal processing. The method comprises the steps of obtaining original positioning data of a wind power tower, and performing data preprocessing; performing real-time analysis on the preprocessed original positioning sequence, and extracting characteristic values reflecting a multipath effect and an ionosphere delay degree; constructing a state space model, designing an adaptive module based on characteristic values, adjusting a filtering process noise matrix and an observation noise matrix in real time, and filtering the positioning data to obtain preliminary filtering data; establishing a lightweight neural network model, predicting the error compensation amount at the current moment, and compensating the preliminary filtering data; and carrying out tight coupling fusion on the compensated Beidou positioning result and high-frequency attitude data output by the IMU, and outputting a final displacement sequence. According to the method, a stable, reliable and high-precision tower drum deformation data sequence can be output.

The invention belongs to the technical field of vibration reduction control of jet printing equipment, and discloses a vibration reduction optimization control method, device and equipment for the jet printing equipment and a storage medium, and the method comprises the steps: obtaining the dynamic data of the OLED jet printing equipment, building a vibration dynamic model of the OLED jet printing equipment through a frequency domain identification method, and carrying out the vibration reduction optimization control of the OLED jet printing equipment based on the vibration dynamic model. A state space model is constructed, and a designed system excitation signal is respectively input into a discretization equation corresponding to the state space model and a disturbance calculation model corresponding to the state space model so as to calculate the predicted acceleration and the feed-forward compensation amount of the OLED jet printing equipment after active vibration reduction in combination with dynamic data. Establishing a model prediction algorithm cost function and a state parameter constraint condition corresponding to the predicted acceleration to perform vibration reduction optimization control on the OLED jet printing equipment in combination with the feed-forward compensation amount; by means of the method, damping optimization control over the OLED jet printing equipment can be achieved.

The invention provides an unmanned aerial vehicle tracking system and method based on state space modeling and scale searching, and belongs to the technical field of artificial intelligence. The invention aims to solve the problems of low recognition rate and high false alarm rate when a traditional unmanned aerial vehicle tracking method is used for processing low-altitude complex backgrounds, illumination changes and multi-shielding scenes. The tracking system comprises a target detection module, a target tracking module and a dual-threshold closed-loop control module. The tracking method comprises the following steps: performing feature extraction and candidate target identification on an input video stream or image sequence through a target detection module, and outputting a detection result in the form of a coordinate frame and confidence; the target tracking module performs continuous frame association and real-time position updating on candidate unmanned aerial vehicle targets through a multi-scale twin convolutional network; and the double-threshold closed-loop control module receives an output result, automatically judges a current task state according to a detection confidence threshold and a tracking frame number threshold, and intelligently switches a tracking mode and a detection mode.

The invention relates to a safety constraint vehicle formation distributed sliding mode control method based on a preset performance function. Compared with the prior art, the defect that vehicle formation control is difficult to meet safety constraint, preset performance and distributed cooperative control at the same time is overcome. The method comprises the following steps: establishing a vehicle formation three-order state space model; generating a following vehicle smooth reference trajectory; a preset performance constraint of the following vehicle is designed; calculating the expected speed of the following vehicle; calculating the expected torque of the following vehicle; a following vehicle driving system is controlled; and formation cooperative control is realized. According to the invention, through a reference generation mechanism of constraint perception, a preset performance function with a self-adaptive boundary and a topological structured reaching law, high-performance tracking of the track of the pilot vehicle is realized on the premise of ensuring that all following vehicles meet the time-varying safety boundary.

The invention discloses a load power balance regulation and control method and device based on knowledge graph and random sequence optimization, a medium and a product, and relates to the field of power system dispatching, and the method comprises the steps: constructing a power grid topology knowledge graph according to power grid physical equipment and corresponding topology connection and operation constraints; performing probability distribution fitting on the power output of the renewable energy according to the output data to obtain the output of the renewable energy; obtaining a plurality of typical output scenes; taking the state of the power grid physical equipment after fault isolation as an initial state, and constructing a random Markov decision process; searching in a state space by using an improved heuristic search algorithm by taking an initial state as a starting point, minimizing an accumulated cost function as a target, taking a hard constraint as a constraint condition and taking a topological distance as a heuristic function to obtain an optimal operation sequence; verifying the optimal operation sequence to obtain a final operation sequence; the power of the power grid is regulated and controlled; according to the invention, the fault response speed can be improved, and rapid adaptive decision in a complex scene can be realized.

The invention discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion, and relates to the technical field of engineering measurement. Three error sources of sensor physical characteristics, environmental interference and manual operation are defined as estimable state variables, and differentiation processing is performed according to different characteristics of errors: a dynamic model is established to embed the sensor and environmental errors into a state space, so that real-time compensation based on the model is realized; according to the scheme, a gross error recognition mechanism based on data-driven statistical characteristics is established, real-time diagnosis and processing are carried out on personal errors, joint optimal estimation is carried out on unified state vectors by adopting a recursive estimation algorithm, and high-precision corrected physical quantities and estimated values of all error states are synchronously output. The measurement precision and reliability are obviously improved; by means of robust processing of gross errors, stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced.

The invention discloses a data center global temperature control optimization method based on online-offline hybrid reinforcement learning, and the method comprises the steps: constructing an interpretable global state space fusing physical information, and guaranteeing that a state variable has a clear physical meaning; a combined action space is defined, and cooperative control of the cold source side and the tail end side is achieved; designing a multi-objective reward function based on physical model driving, and comprehensively considering energy consumption, temperature stability, carbon emission and physical optimization indexes; obtaining a basic security policy through offline pre-training, and extracting reliable behaviors from historical data by using a conservative Q learning algorithm; strategy optimization is achieved through online safety fine adjustment, and gradual adjustment is conducted under multiple constraints to adapt to real-time changes; and finally, deploying an optimization strategy to realize closed-loop control of the system, and establishing a continuous learning mechanism to cope with long-term drift. According to the method, a complete offline-online mixed learning system is established, and the energy efficiency of the data center temperature control system is improved.

The invention relates to a primary frequency modulation control method for a pumped storage coupled compressed air energy storage system, and the method comprises the steps: building a frequency modulation control model based on the core component information and operation constraints of the pumped storage coupled compressed air energy storage frequency modulation system, building a state space model, and building a discretization state equation, establishing a comprehensive optimization model by taking the minimum power grid frequency deviation and the system operation cost as targets; and establishing a multi-parameter planning model based on the reservoir level, the gas storage pressure, the frequency deviation and the comprehensive optimization model, and converting a hierarchical model prediction control problem into multi-parameter quadratic programming to form a self-healing frequency modulation strategy to perform power output tracking control. Therefore, the problem that the frequency modulation performance is limited due to the fact that a water pressure system and an air pressure system in the hybrid power station are complex in coupling and large in response difference, and a related linear control strategy or a single control framework is difficult to achieve rapid response and accurate coordination of the hybrid system in the primary frequency modulation process is solved.

The invention discloses a dynamic trajectory prediction method, system and device based on fusion of a graph attention network and a Mama state space model, and a medium, and belongs to the technical field of automatic driving, and the method comprises the steps: collecting historical trajectory data and map data, carrying out the preprocessing, and generating feature data; inputting the feature data into a graph attention-deep learning model for feature fusion; constructing a dynamic trajectory prediction model, inputting the node features into the dynamic trajectory prediction model, obtaining a future trajectory through an initial prediction stage, and obtaining an optimized prediction trajectory through a post prediction stage; constructing a trajectory quality scoring mechanism, performing quality evaluation on a prediction result, and optimizing a dynamic trajectory prediction model according to an evaluation result; the problems that an existing trajectory prediction method is insufficient in the aspect of processing long-distance dependence and complex space interaction, real-time performance and accuracy are difficult to consider at the same time, and a traditional method such as LSTM can process sequence data, but the prediction result is not ideal are solved.

The invention relates to the technical field of intelligent monitoring of power equipment, in particular to an intelligent circuit breaker state monitoring method and system. The method comprises the steps that unified time sequence modeling is carried out on multi-dimensional operation data of the circuit breaker, periodic behavior fragments with internal rhythm characteristics are extracted, state feature vectors based on historical behavior self-reference are constructed, and the state feature vectors are mapped to a continuous interpretable state space to achieve dynamic visual expression of the operation state; on the basis, the state deviation intensity and the change direction consistency are quantified, so that progressive identification and early warning from normal operation to an abnormal state are realized; and combined prediction is further carried out in combination with a state trajectory and an abnormal index, a risk quantification result and an active intervention strategy are generated, and model adaptive optimization is realized through closed-loop feedback. According to the invention, intelligent operation and maintenance closed loop from real-time monitoring and abnormal early warning to predictive maintenance is completed.

There are provided systems and methods for inversion control of turbine engines. For example, there is provided a processor-implemented method that includes a processor and a memory. The memory includes instructions which, when executed by the processor, cause the system at least to perform: simulating, by a simulated state space model, a desired dynamic response based on the sensor data and a control input; inverting the desired dynamic response as output by the state space model; determining an error between a perceived dynamic response and the inverted desired dynamic response; correcting the state space model for the determined error based on updating the one or more model parameters using a machine learning network; generating the desired dynamics based on the updated state space model; and controlling the engine based on the state space model.

The invention discloses a high-proportion new energy power system frequency response modeling method and application. The method comprises the steps that all nodes in a power system are divided into active nodes and passive nodes; establishing a linear power flow model of the power system; reducing the linearized power flow model to obtain an equivalent active node power flow injection power expression; frequency-active response models of various power resources in the active nodes and the passive nodes are established respectively; combining an equivalent active node power flow injection power expression with a frequency-active response model, and constructing a unified linear state space model taking an active node phase angle and frequency as state variables; and calculating the frequency response of the power system based on the unified linearization state space model. The unified linear state space model constructed by the method can realize quicker and more accurate simulation and stability analysis of the frequency dynamic state of the high-proportion new energy power system, and is suitable for quick simulation and online analysis of a large-scale system.