1325 results about "State-space representation" patented technology

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

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 belongs to the related technical field of artificial intelligence, and discloses a robot time sequence imitation learning method and system based on a Mama coding complete history, and the method comprises the steps: receiving a multi-modal observation sequence in a task execution process of a robot, the multi-modal observation sequence comprising observation data of at least one sensor; processing the multi-modal observation sequence by using a sequence processing module based on a state space model, and updating a time sequence output of complete historical information of one code up to the current time step at each time step; and predicting the next step or a series of future actions of the robot based on the time sequence output of the current time step so as to control the robot to simulate. The time sequence processing module based on the state space model is utilized to process and encode the complete observation history in the task execution process of the robot, so that a non-Markov decision-making imitation learning method is realized, and the learning efficiency and the execution success rate of the robot in a complex and state-dependent long time sequence operation task are improved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

The invention provides a transient overvoltage risk assessment method, device and equipment for an AC / DC system containing high-proportion new energy, and a storage medium. Relates to the technical field of power systems and automation thereof. The method comprises the following steps: modeling a power system into a discrete nonlinear power system based on phase change measurement unit data, constructing a state space, introducing a Koopman operator to determine an observation function, and establishing a dynamic time sequence track prediction model based on the observation function to output a predicted dynamic time sequence track; an Informer model is constructed, in response to an input predicted dynamic time sequence track, feature weights are distributed through a ProbSparse self-attention mechanism, hierarchical feature down-sampling is performed by using a distillation mechanism, and a predicted transient voltage amplitude is output; and quantifying the transient overvoltage risk level according to the predicted transient voltage amplitude. According to the method, the problems of low isolated time section feature prediction precision and poor long-sequence dynamic evolution feature evaluation timeliness in the prior art are solved.

The invention relates to a hyperspectral image classification method and device based on a state space model. The hyperspectral image classification method based on the state space model comprises the following steps: sequentially carrying out feature extraction and serialization processing on hyperspectral image data to obtain a shallow feature projection vector; performing global-local feature extraction on the shallow feature projection vector by adopting a neural network based on a state space model to obtain a fused feature projection vector; and carrying out pixel-by-pixel classification and dimension rearrangement on the hyperspectral image data in sequence to generate a classification result of the hyperspectral image data. According to the hyperspectral image classification method based on the state space model, long-range dependence modeling is achieved through the neural network based on the state space model with linear complexity, the calculation complexity is effectively reduced, and through feature fusion and residual error connection, the classification accuracy of the hyperspectral image is improved. And the perception capability of the neural network on different scale space-spectrum structures in the hyperspectral image is effectively enhanced.

The invention discloses a millisecond regulation and control method for hydrogen fluoride production based on reinforcement learning and model prediction control, and relates to the field of hydrogen fluoride production regulation and control. In the multi-scale neural symbol dynamics modeling step, GNN, a cellular automaton and a state space model are fused, and parameters are updated in real time; in the causal reinforcement learning decision optimization step, an effect is calculated through a causal graph, and a reward function is optimized; in the time-space fractional order sliding mode control step, a fractional order sliding mode surface and a controller are designed, and rapid and stable control is achieved; in the memory enhancement element learning adaptation step, a DNC storage strategy is utilized, new working conditions are quickly adapted through element gradient, and millisecond-level precise regulation and control are achieved. According to the method, prediction errors are greatly reduced, the response speed is increased, and overshoot is reduced; the product yield is improved, the energy consumption is reduced, and new working conditions are quickly adapted; fault detection and risk early warning are more accurate, equipment operation is more stable, production efficiency is effectively improved, cost is reduced, and safety is enhanced.

The invention relates to the technical field of inverter control, and provides an MBD-based reinforcement learning inverter control algorithm optimization method, which comprises the steps of S1, establishing a state space model of an inverter, designing a baseline controller of double-loop control, and constructing an MBD simulation platform containing multiple load models; s2, defining a state space of the 12-dimensional state vector, designing a continuous action space, and constructing an adaptive multi-target reward function; s3, training a control parameter optimization strategy by adopting an Actor-Critic algorithm architecture in combination with an experience playback mechanism and a hybrid exploration strategy; s4, establishing a security constraint mechanism of three-layer security protection, designing a fault detection and isolation strategy, realizing a closed-loop online learning strategy of pre-training-transfer learning-online fine tuning, and meanwhile, adopting a self-adaptive updating mechanism; and S5, verifying the optimization effect of the control algorithm through quantitative index evaluation, an experimental verification scheme and a real-time performance requirement test. And the upgrade of inverter control from model driving to data and model cooperative driving is realized.

The invention discloses a reasoning optimization method and device for a code generation large model, equipment and a medium, and relates to the technical field of model reasoning, and the method comprises the steps: in the reasoning process of a code generation task of a target code generation large model, executing a multi-granularity uncertainty quantification step in parallel every time a new Token is generated, obtaining a multi-granularity uncertainty score; constructing a state space vector, and utilizing a preset reinforcement learning strategy network to evaluate the selection probability of a plurality of preset reasoning optimization strategies based on a preset smooth decision mechanism so as to determine a target reasoning optimization strategy; if the strategy is a preset reasoning acceleration strategy, optimization processing is carried out through speculation decoding; if the strategy is a preset exploration optimization strategy, performing optimization processing by using a preset multi-path sampling technology and a preset knowledge enhancement technology; if the strategy is the preset fuzzy processing strategy, taking the plurality of candidate outputs as target reasoning outputs for optimization processing; and evaluating the decision effect according to the reasoning result to optimize the preset reinforcement learning strategy network.

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 belongs to the technical field of expressway operation and maintenance, and relates to an intelligent operation and maintenance method and device for an expressway strong current system, and the method comprises the following steps: collecting and preprocessing multi-source data; constructing a state space model and defining a system state vector; obtaining an estimated state value by adopting a state estimation algorithm; performing fault anomaly detection based on the estimated state value and the multi-source data; extracting a device feature vector and calculating a health index; predicting the remaining service life of the equipment and judging the potential fault risk, and generating a maintenance work order and determining the priority when the threshold value is exceeded. According to the technical scheme of the invention, work orders are generated through multi-source data acquisition, state modeling and estimation, anomaly detection, health assessment and life prediction, so that the whole-process intelligent operation and maintenance of the highway strong current system is realized, and the real-time sensing and diagnosis capability of the operation state can be improved. And the requirements of operation and maintenance real-time performance improvement, data unified management, intelligent diagnosis enhancement and emergency disposal high efficiency are met.

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 bridge engineering intelligent construction, discloses a digital twinborn and multi-physics field simulation-based lattice pier assembly error intelligent prediction method, and aims to construct a lattice pier assembly error prediction model. The lattice pier assembly error prediction model is composed of a channel independent feature extraction module of multi-physics field data, a self-adaptive space-time coupling and multi-factor dynamic fusion module and a training lattice pier assembly error prediction model, the multi-physical field data channel independent feature extraction module maps data of each physical field to a high-dimensional feature space through calculation, and according to a state space model, the adaptive space-time coupling and multi-factor dynamic fusion module introduces a plurality of factors to dynamically adjust weights of the physical fields in different time steps. Carrying out adaptive fusion on the processed physical field data through a space-time coupling function to obtain coupled physical field data; and finally, taking the output of the two as the input, and combining a regression model to predict the lattice pier assembly error.

The invention discloses a layered optimization regulation and control method and system for electricity-hydrogen coupling in a multi-energy complementary system, and relates to the technical field of multi-energy complementary system optimization regulation and control, and the method comprises the following steps: obtaining the operation data of the multi-energy complementary system, and carrying out the aggregation of the operation data based on the correlation of an optimization target, and forming a layered optimization data set; constructing a corresponding state space model based on the hierarchical optimization data set, defining an action space of each level, and designing a multi-target reward function; inputting the state space model, the action space and the multi-target reward function into a reinforcement learning agent for training to obtain a strategy model; based on the strategy model and the multi-target reward function, collaborative optimization is carried out on the strategies of all levels to generate a global optimization strategy; inputting the global optimization strategy and the state space model into a prediction optimization module, and performing rolling optimization to generate an optimization scheduling scheme; and according to the optimized scheduling scheme, generating a scheduling instruction after security constraint inspection, issuing the scheduling instruction to a system for execution, and updating the strategy model based on operation feedback.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention relates to the field of control, in particular to a zone control method and system for a temperature field of a copper material heat treatment furnace. In the off-line stage, a strategy library containing various high-efficiency affine control laws is calculated in advance by solving a multi-parameter planning problem, the furnace temperature state space is divided into a plurality of explicit control subareas according to the coupling strength of all temperature areas in the furnace, and a set of alternative control laws are assigned for each subarea; during on-line operation, the partition to which the current furnace temperature belongs is determined according to the current furnace temperature, the partition with the minimum performance cost is quickly selected from the alternative control laws of the partition through short-time domain prediction to serve as the current optimal law, and when the predicted furnace temperature enters the boundary of the partition, the optimal control laws of the current partition and the adjacent partition are weighted and mixed; otherwise, directly adopting the current optimal law to generate a control instruction and applying the control instruction to the heater.

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.

A hardware emulator and an emulation system including the hardware emulator are provided. The hardware emulator includes an artificial neural network-based reconstruction model configured to reconstruct dynamics of a dynamical system based on input data and a memristor-based circuit configured to emulate state space representation of the dynamical system based on the reconstruction model.

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 relates to the technical field of traffic detection, in particular to a traffic abnormal event cooperative detection method and system based on vehicle-road cooperation, and the method comprises the steps: collecting vehicle end data and road end data from a vehicle end and a road end respectively, and carrying out the timestamp alignment, coordinate transformation, noise filtering and missing value supplementation of the vehicle end data and the road end data; establishing target state estimation based on a state space motion model, performing recursive estimation on a target, and identifying abnormal candidates based on observation residual errors; constructing a space-time diagram based on the vehicle end data and the road end data, reconstructing node features by adopting a time sequence diagram neural network, generating an anomaly score according to a reconstruction error, and outputting an anomaly candidate; and according to the state space motion model and the anomaly candidates of the time sequence diagram neural network, confidence fusion is carried out according to confidence, and whether an anomaly alarm is triggered and whether an anomaly type and positioning information are output are judged based on a fusion result. And through confidence fusion, false alarms triggered by isolated noise can be effectively suppressed.

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 provides a hyperspectral anomaly detection method based on double-stage attention guidance and a state space model, which relates to the technical field of hyperspectral image processing and comprises the steps of scene background modeling based on an auto-encoding network, generation of a reconstructed background image and a reconstructed residual image. Carrying out target signal enhancement on the original hyperspectral data based on the reconstructed residual image to obtain attention enhancement data, carrying out target feature depth extraction by adopting a state space model based on the attention enhancement data, generating an abnormal semantic feature image, and fusing a background guide feature image and the abnormal semantic feature image to obtain a hyperspectral image; and an abnormal probability graph is generated through adaptive gating fusion, and collaborative optimization is carried out based on a multi-task loss function. According to the method, the internal contradiction of a single network architecture is fundamentally solved, the prior guiding capability of the reconstruction method and the strong feature representation capability of the state space model are fully combined, and the accuracy and reliability of hyperspectral anomaly detection are remarkably improved.

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

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 relates to the technical field of education intelligent analysis, and particularly discloses a learner cognition level fine-grained tracking method and system based on a state space model. The method aims at solving the problems that an existing knowledge tracking method is low in cognitive level modeling granularity, weak in long sequence processing capacity, poor in educational interpretation and the like. According to the method, a Bloom cognitive classification system and a state space modeling technology are combined, and fine-grained and multi-level dynamic modeling and future learning performance prediction of the knowledge mastering state of the learner are achieved. The core steps of the method comprise: constructing a semantic mapping relationship between knowledge points and cognitive hierarchies (S101); collecting and encoding multi-source learning behavior features (S102); learning a cognitive state evolution trajectory based on the state space model (S103); and outputting the cognitive hierarchy classification and the answer performance prediction (S104). According to the method, by fusing the multi-dimensional learning behavior data and the state space modeling capability, the accuracy and personalized analysis depth of cognitive tracking are improved, and technical support is provided for precise teaching and intelligent decision making.

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.