98 results about "State model" patented technology

The invention discloses an improved Kalman filtering ocean current estimation method based on inertial information and DVL speed. The method comprises the following steps: S1, system initialization and data acquisition; s2, performing inertial navigation system (INS) resolving, namely performing mechanical arrangement resolving based on IMU data; s3, constructing a fusion filtering state model and an observation model; s4, adaptive Kalman filtering iteration estimation: after discretizing the state equation and the observation equation, executing filtering iteration according to a period; and S5, state feedback correction and ocean current output. According to the improved Kalman filtering ocean current estimation method based on inertial information and DVL speed, a fusion framework of an augmented state model and adaptive Kalman filtering is constructed, and high-precision ocean current estimation and navigation correction are realized through five key processes.

The invention provides a long-life design method for a shield tunnel structure, and relates to the technical field of shield tunnel structure design, and the method comprises the steps: firstly, carrying out the classification of environmental conditions, hydrogeological data and the like through a K-means clustering algorithm, extracting key parameters of a multi-field coupling environment, and carrying out the real-time monitoring, experiment and numerical simulation based on the key parameters; and establishing a constitutive model and a deterioration state adaptive model of the material. Then, probability distribution and statistical characteristics of surrounding rock and lining design parameters are obtained through Monte Carlo simulation, finite element analysis is carried out based on the probability distribution and statistical characteristics, and a tunnel numerical simulation model is established; and further obtaining a performance evolution equation and a key index system of long-life design through regression analysis and analytic hierarchy process. And finally, through a performance limit state model and life cycle cost analysis, optimizing structural design parameters of the shield tunnel, formulating a maintenance and replacement scheme, and ensuring long-life design of the tunnel. According to the invention, long-term reliability and economical efficiency of the structure are ensured.

The invention provides an integrated navigation method combining starlight refraction positioning with inertial navigation in a near space, and belongs to the technical field of starlight refraction navigation positioning. The method comprises the following steps: firstly, determining a state model of starlight refraction positioning in a near space by taking an error equation of an inertial navigation system of a near space aircraft as a state equation; designing a near space starlight refraction positioning algorithm, identifying a refraction star by using a star sensor on the aircraft, acquiring a refraction angle as a measurement, and establishing a measurement model of starlight refraction positioning in a near space; and obtaining the optimal estimation value of the system state variable by using an extended Kalman filter (EKF) method. The method has the advantages of being suitable for near space, high in reliability, high in autonomy and the like, and can be finally used for pure astronomical positioning and astronomical / inertial integrated navigation.

The invention discloses a deep hidden variable state space model bearing degradation prediction method and system, and the method comprises the steps: correcting the simulation degradation data and real degradation data of a bearing through a data transformation function Box-Cox, fusing a plurality of time domain degradation features to obtain a degradation health index, and obtaining a degradation state representing the degradation rate of the bearing through differential transformation; constructing a prediction model based on a deep hidden variable state space model under a state space model framework; performing feature extraction, fusion and difference on the simulation degradation data and the real data to obtain degradation states of the simulation degradation data and the real data; the method comprises the following steps: acquiring a degradation state pre-training depth hidden variable state space model by using simulation degradation data, initializing the weight of the space model, predicting the degradation state obtained by real data by using a prediction model based on the depth hidden variable state space model, and accumulating the predicted degradation state to obtain a bearing degradation prediction value. The technical problems that an existing process is complex, and a large amount of time and resources are consumed are solved.

The invention discloses a satellite-based enhanced satellite orbit correction calculation method and system based on Hill difference, and relates to the technical field of satellite navigation wide-area difference, and the method comprises the steps: determining a monitoring station which observes a target satellite; smoothing the pseudo-range observed quantity by using the carrier phase observed quantity of the GNSS satellite j observed by each monitoring station; based on the smoothed pseudo-range observed quantity, ionospheric delay is eliminated; calculating the ephemeris position and the satellite clock deviation of a GNSS satellite j by using the orbit parameter and the clock parameter broadcasted by the navigation message, and calculating the inter-satellite distance by combining the position of the monitoring station i; based on the calculated distance between the stations and satellites, the influence of satellite clock errors on satellite orbit errors is eliminated, and a state model of the satellite orbit errors is established based on a Hille difference equation; and resolving the state model to obtain the satellite orbit correction, and the method can improve the precision of the satellite orbit differential correction.

A method is disclosed including providing a process fluid that can be described as having a plurality of components where each of the plurality of components has a respective mass fraction. In addition, the method includes providing an initial estimate of a compositional description for the plurality of components of the process fluid, measuring an ultrasonic sound speed of the process fluid, predicting a predicted sound speed of a liquid phase of the process fluid using an equation of state model, generating an error function using the ultrasonic sound speed of the process fluid and the predicted sound speed of a liquid phase of the process fluid, minimizing the error function and updating the respective mass fractions of the plurality of components and determining an optimized compositional description of the process fluid. Also disclosed are corresponding computer systems, apparatus, and computer programs configured to perform the actions of the methods.

The invention discloses an extended target tracking method based on a non-stationary abnormal noise environment, and the method comprises the steps: S1, carrying out the initialization of a motion state and an extended state of a target, and obtaining the initialization information of the states; s2, obtaining a measurement model of a target according to the initialization information of the state; s3, taking the measurement model and the motion state model of the target as GSTM distribution, and constructing a layered Gaussian state space model by introducing a Bernoulli variable and further predicting the density and the measurement likelihood function of the target state; s4, on the basis of the layered Gaussian state space model, predicting and updating the motion state and the extension state of the target; and S5, the target state parameters are measured and updated through a variational Bayesian method, whether tracking continues or not is judged, if tracking continues, the step S4 is executed, and otherwise, extended target tracking is completed. According to the method, the problem that the extended target tracking effect is poor in a complex noise environment is effectively solved, and higher robustness and adaptability are achieved.

This application provides a method and apparatus for simulating the performance degradation of a high-power power supply. The method includes: obtaining a spatial state equation for the output voltage of the high-power power supply system based on an equivalent circuit model of the high-power power supply; acquiring performance degradation models of multiple components with degradation characteristics in the high-power power supply system, and importing the performance degradation models of the multiple components with degradation characteristics into the spatial state equation to obtain a spatial state model; using the spatial state model to obtain performance degradation array data of the high-power power supply corresponding to each number of runs; and concatenating the performance degradation array data of the high-power power supply corresponding to each number of runs to obtain performance degradation time sequence data of the high-power power supply.

The invention provides an edge-enhanced remote sensing image segmentation method and system fusing attention and a space state model. The invention discloses an edge-enhanced remote sensing image segmentation method and system fusing attention and a space state model. The method comprises the following implementation steps: constructing an edge texture feature enhancement structure; the edge texture enhancement structure is introduced into a SegNext semantic segmentation model; dividing the remote sensing image segmentation data set to generate a training sample set, a verification sample set and a test sample set; preprocessing the data set; the method comprises the following steps: preliminarily extracting fine features of an optical remote sensing image by using a neural network, and enhancing a decoder training model by using channel attention and edge texture of a spatial state model; and finally, sending test sample data into the edge texture enhancement model of the trained attention and space state model to obtain a test result. According to the method, the constructed edge texture feature enhancement module and the SegNext semantic segmentation model are used for cooperative training, the edge texture features are enhanced while the ground feature features are ensured, and the segmentation accuracy is improved.

The invention belongs to the field of system simulation, and particularly discloses a power electronic system simulation method and system based on an event triggering update state space model and a medium, and the method comprises the steps: respectively constructing an incidence matrix corresponding to each coefficient matrix in the system state space model, whether the corresponding coefficient matrix needs to be updated is determined; simulating the system according to a preset integral step size, and in the simulation process, updating the current integral step size according to the moment of an event discrete point whenever the system passes through the event discrete point, and determining whether each coefficient matrix needs to be updated or not according to the incidence matrix; and for the coefficient matrix needing to be updated, according to the system mathematical model described by the state equation, a new value of the coefficient matrix is calculated through a perturbation method, and updating is achieved. According to the invention, timely and efficient online model conversion can be realized, and the simulation speed of the power electronic system is improved.

The invention relates to the technical field of plate glass cutting, in particular to a plate glass online cutting method based on model predictive control. Comprising the steps of establishing a state space model of a cutting control system; carrying out difference on the state space model at adjacent time steps to obtain a change relation between a state vector and an input vector in the state space model, and outputting a control system state model; measuring and acquiring a space state vector of a sampling point of the cutting control system, calculating a future trajectory of the cutting control system by using the control system state model, and calculating a prediction state variable and prediction output of the cutting control system; and calculating to obtain an optimal control signal. According to the plate glass online cutting method based on model predictive control, the defects of traditional proportional integral (PI) control can be overcome, the response speed and stability of a glass cutting control system are improved, and the good real-time adjusting effect on the state of the cutting system can be achieved.

The invention relates to a self-adaptive control method for a multi-mechanical-arm system with unpredictable states. The method comprises the following steps: constructing a communication topological structure of the multi-mechanical-arm system by utilizing a graph theory; a dynamic model of the multi-mechanical-arm system is established, and the dynamic model of the system is converted into a state model; setting a leader trajectory and defining a collaborative error model; establishing a state observer, and estimating an unmeasurable system state variable by using the state observer; constructing a radial basis function neural network model, and estimating an uncertain nonlinear function existing in the system; and a self-triggering mechanism is utilized to design triggering conditions so as to dynamically prolong a triggering interval, and a controller based on cross coupling errors is designed so as to control the multiple mechanical arms to track an expected trajectory within the specified time. Aiming at a multi-mechanical-arm system with unmeasurable states, the invention designs a self-adaptive neural network specified time controller based on a self-triggering mechanism, so that all mechanical arms can be converged to a specific track established by a leader within specified time.

The present invention relates to the field of radio telescope structure analysis, and discloses a method for tracking and analyzing the star of the active cable net reflector of the FAST telescope, comprising: step 1: establishing a dynamic relaxation method numerical model of the FAST cable net reflector structure; step 2: setting the accuracy limit of the telescope reflector, model parameters, and convergence conditions; step 3: identifying the main cable nodes within the irradiation range; step 4: calculating the radial distance from the main cable point to the working parabola; step 5: applying forced displacement to the lower end point of the lower cable; step 6: performing iterative calculation to obtain an equilibrium state model; step 7: repeating steps 3 to 6 until all main cable nodes to the working parabola meet the accuracy limit requirements of the telescope reflector; step 8: repeating the previous steps for tracking and analysis. The present invention innovatively adopts the dynamic relaxation method to perform star tracking and analysis, avoiding the problems of singular stiffness matrix and non-convergence of iteration when performing complex structure analysis using the traditional finite element method.

The invention provides a setting calculation method and system based on different tenses, equipment and a storage medium, and belongs to the field of electric power systems.The method comprises the steps that first order reduction is conducted on a multi-tense model, and a first calculation model and a second calculation model are obtained, the first calculation model comprises a common model of a past tense model, a current tense model and a future tense model, and the second calculation model comprises a difference model of the past tense model, a difference model of the current tense model and a difference model of the future tense model; performing double order reduction on the first calculation model to obtain a plurality of common calculation units; and performing triple order reduction on the plurality of common calculation units and the second calculation model based on the target setting demand to obtain a target model corresponding to the target demand, and determining a setting strategy corresponding to the target demand according to the target model. According to the invention, rapid conversion calculation of the power grid setting constant value in different time states can be realized.

This application provides a target localization method, apparatus, electronic device, and storage medium. The method obtains the initial position of the target object by allocating distance through optimal sub-patterns; it pre-constructs a motion state model of the target object, predicts the state vector of the target object at the next moment based on the state transition equation of the motion state model, and determines the prior position of the target; it filters the current real observation set based on the prior position of the target to obtain a first observation set; it searches and locates the target based on the first observation set, using the previous position of the target object as the center, and determines the real-time target position; it narrows the search range to improve the real-time performance of localization; it fuses the real-time target position with the prior position of the target to obtain a corrected target position, and uses the corrected target position as the target localization result. By fusing the prediction and observation, the respective errors are offset, solving the problem of excessive localization error and improving localization accuracy and reliability.

By accurately predicting the industrial aging process (IAP), such as the slow deactivation of catalysts in chemical plants, maintenance events can be scheduled further in advance, ensuring the cost - effectiveness and reliable operation of the plant. So far, these degradation progressions have typically been described by mechanical models or simple empirical prediction models. To accurately predict the IAP, data - driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, and feed - forward neural networks) with more complex state - recursive neural networks (echo state network and long - short - term memory network). In addition, variants of the stateful models are discussed. In particular, stateful models that use mechanical pre - knowledge about the degradation dynamics (hybrid models). Stateful models and their variants may be more suitable for generating near - perfect predictions when trained on a sufficiently large dataset, while hybrid models may be more suitable for better generalization in the case of smaller datasets under changing conditions.

The invention relates to the technical field of intelligent control, in particular to a cut-tobacco dryer steady-state model predictive control method based on Kalman filtering time delay correction, which comprises the following steps of: utilizing second-level historical production data and a system identification technology to process a cut-tobacco drying steady-state production process; according to the method, a dynamic transfer function prediction model and a state-space equation model from the moisture removal air door opening degree and the cylinder wall temperature to the outlet moisture are established, an appropriate Kalman filter (KF) is selected to carry out online correction on a cut-tobacco dryer prediction model with time delay, and the control of the process parameters in the cut-tobacco drying steady-state production stage is realized by using a model predictive controller (MPC) algorithm. Therefore, ideal outlet material moisture is achieved, and the problems that in the cut-tobacco drying process of an existing cut-tobacco dryer, dynamic characteristics of a system are ignored in a control method applied to the cut-tobacco drying process of the cut-tobacco dryer, and the control effect of the cut-tobacco drying process of the cut-tobacco dryer is poor due to the fact that the system with large inertia for controlling the cut-tobacco drying process of the cut-tobacco dryer is poor in prediction effect are solved.

The invention discloses a high-order extended Kalman filtering method for a nonlinear system. The method comprises the following steps: S1, establishing a nonlinear system composed of a linear state model equation and a nonlinear measurement model equation; s2, establishing a calculation algorithm for solving the statistical characteristics of the Kronecker product type composite random variables; s3, analyzing the high-order characteristics of the state estimation error of the nonlinear system by using the algorithm established in the step S2; s4, combining the known measurement information with the high-order features of the errors, designing and optimizing extended Kalman filtering, and gradually estimating the next state in the nonlinear system; and S5, circulating the process of the step S4, estimating the state of the nonlinear system step by step until the state estimation of the set step number is completed, and obtaining the state estimation value of each step. According to the method, the estimation model in the filtering process is adaptively adjusted based on the high-order statistical characteristics of the system state estimation error and the known measurement information, so that the stability and the precision of the filter are improved.

A complex sea condition sea target positioning and tracking method constrained by dependent variables belongs to the technical field of intelligent maritime affair monitoring and ship navigation, and comprises the following steps: S1, constructing a sea target positioning and tracking state model including a state equation based on uniform linear motion and two observation equations adapted to AIS / GNSS and radar; s2, establishing a limited dependent variable observation model, and quantitatively observing nonlinear correlation between loss / truncation and the real state of the target; s3, performing prior prediction of the current target state and the covariance based on the historical state; s4, fusing the prediction state and actual observation (including censored data) to complete state and error covariance updating; and S5, introducing a Bernoulli random variable to calculate an observation state adaptive weight. Through combination of the limited dependent variable model and the filtering method, the problems of trajectory breakage and target missing detection of a traditional tracking method caused by incomplete observation data under complex sea conditions are solved, and the method is directly integrated to a shipborne radar monitoring system and a maritime affair supervision platform.

Techniques are provided for adjusting a spectral density for computing a filtering solution. In an embodiment, a rolling history of correction data for a current epoch and previous epochs is created to compute a statistical value. The statistical value can be used to compute long-term and short-term averages for the current epoch. The long-term and short-term averages can be used to compute a ratio value that can be a scale factor for adjusting the spectral density. In an embodiment, sensor data for a current epoch can be obtained and compared to one or more threshold values and the spectral density can be adjusted when the threshold values are exceeded. In an embodiment, misclosure values from two different filters may be used to determine if a steady-state model of a first filter or a disturbed model of a second filter should be used to compute a filtering solution.

The invention provides a large maneuvering target modeling and tracking method based on an improved Singer model. According to the method, the jerk of a target is introduced as a state variable, a state model is established, the observability of the nonlinear state model is verified, the accelerated speed and the line-of-sight angular rate of the strong maneuvering target are estimated through the information of the relative distance, the relative speed and the line-of-sight angular rate obtained by a seeker, and simulation is carried out for the guidance process. It is proved that the target acceleration and the line-of-sight rate estimated by the method are high in precision, and the maneuvering target interception performance is improved.

The embodiment of the invention discloses a GNSS receiver clock modeling method based on power-law noise, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: obtaining phase difference data between a local clock and a reference clock, and obtaining a diffusion coefficient of clock noise through extraction based on the phase difference data; establishing a clock state space equation containing a clock offset parameter and a frequency offset parameter, and calculating a process noise covariance matrix of the clock state space equation based on the diffusion coefficient; and embedding the two-dimensional clock random state model and the process noise covariance matrix into a process noise embedding filtering estimation algorithm, and calculating to obtain a clock offset parameter, a frequency offset parameter and a three-dimensional coordinate parameter of the receiver. According to the method, estimation noise of receiver frequency deviation can be remarkably suppressed, and the short-term stability of the elevation direction in GNSS dynamic positioning is effectively improved. The method and the device are suitable for the GNSS terminal adopting a low-cost clock so as to realize high-precision time synchronization and positioning.

The application provides a multi-parameter joint calibration method based on graph optimization of inertial and ultra-short baseline, applies the graph optimization technology to the multi-parameter joint calibration of inertial / ultra-short baseline, and the whole process comprises the following steps: establishing a multi-error parameter state model, constructing a multi-error parameter calibration factor, calculating based on a nonlinear optimization problem of an incremental interval balance, and finally obtaining accurate multi-error parameters. Compared with the prior art, the application can simultaneously estimate the responder position error, the rod arm error and the installation angle error, sufficiently utilizes historical observation data, solves the nonlinear problem of the optimization equation through repeated iteration and relinearization, and has the advantages of high calibration precision and fast calibration speed.

The present invention relates to the field of equipment testing technology, and specifically to an intelligent thermostat durability testing system, comprising: a state modeling unit, the state modeling unit being used to define a state set and an extended variable set of a target thermostat, defining a state transition condition of the target thermostat based on the extended variable set and the state set, and constructing a state transition diagram based on the extended variable set, the state set, and the state transition condition. By defining the state set, the extended variable set, and the state transition condition of the target thermostat, the present invention can accurately simulate and describe the behavior and state changes of the thermostat under different operating conditions. By constructing a state transition diagram, the present invention can ensure that test cases cover all operating states of the thermostat and comprehensively test the system, thereby better evaluating the reliability of the device.

The application discloses a kind of based on micro-grid operation optimization and energy efficiency management method, it is related to micro-grid control and integrated energy management field.The method comprises: real-time acquisition micro-grid multi-source operation data;Distributed power output prediction model is constructed;Establish the state model of energy storage considering dynamic degradation cost to evaluate the evolution of state of charge and life-shortening cost;Establish load demand response model based on price elasticity;Establish ladder type carbon trading cost evaluation model;Based on model predictive control, the above-mentioned model is integrated into discrete state space equation, and multi-objective optimization problem is solved by rolling and output closed-loop optimal scheduling instruction.The application explicitly constrains energy storage dynamic degradation and carbon trading mechanism, effectively suppresses renewable energy fluctuation, realizes multi-objective global optimization of extending equipment life, reducing comprehensive cost and reducing carbon emission, and improves system disturbance robustness.

The invention provides a Mars landing section autonomous navigation method and device fusing point and line features. The method comprises the following steps: acquiring measurement information of the surface of Mars; analyzing point features and line features in the measurement information, and constructing a measurement model; constructing a state model according to the measurement information and a dynamical model; establishing an autonomous navigation model by using the measurement model and the state model; and updating the navigation state through extended Kalman filtering according to the autonomous navigation model. The advantages and disadvantages of the point features and the line features in different scenes are analyzed, the fact that the line features have high robustness compared with the point features in the low-texture and complex environment is analyzed, and a nonlinear error with higher precision is introduced to define the measurement error of the line features. A digital simulation result shows that compared with a traditional measurement model which only uses point feature measurement, the method has higher navigation precision.

This invention discloses an ADRC method for attenuating periodic disturbances in time-delay systems, belonging to the field of automatic control technology. The method includes: 1. Obtaining the controlled process model and disturbance frequency, and establishing an extended state model containing the periodic disturbance; 2. Constructing a reduced-order extended state observer and tuning the gain using the bandwidth parameter method; 3. Introducing a pure time-delay module into the observer's output channel to construct an ADRC structure; 4. Revealing the mechanism by which periodic disturbances in time-delay systems cannot be attenuated through two-degree-of-freedom equivalent transformation analysis; 5. Based on the frequency domain characteristic equation, analytically determining the delay of the time-delay module to achieve effective attenuation of periodic disturbances at specific frequencies. This invention solves the problem of attenuating periodic disturbances in time-delay systems without significantly increasing structural complexity. Compared to traditional schemes that require high-gain or complex prediction structures, this invention only requires adjusting the pure time delay of the observer's output channel, and the parameter tuning process is clear and intuitive, significantly lowering the threshold for engineering applications.

The invention discloses an in-situ monitoring data anomaly detection method and device based on trend decomposition and state modeling. The method comprises the following steps: preprocessing an ontology sequence and an environment sequence acquired in an in-situ monitoring area; decomposing the two types of sequences into a long-term trend term, a seasonal term and a residual term, normalizing, fragmenting and splicing into a fragment set; a fragment set is extracted in batches, and value, position and time coding and fusion are carried out on each component fragment; inputting each coding segment into a spatial state modeling module and an attention mechanism module, fusing results to obtain a predicted long-term trend, seasons and residual errors, and carrying out reverse normalization and splicing fusion to obtain a final prediction result; and processing a to-be-predicted ontology sequence and a corresponding environment sequence according to the previous steps, reasoning by using the trained model to obtain ontology prediction fragments, splicing the ontology prediction fragments into a continuous prediction sequence, calculating a prediction residual error, and performing bilateral exponentially weighted moving average anomaly detection. According to the invention, the accuracy and stability of anomaly detection can be effectively improved, and the timeliness and reliability of early warning are improved.

The present invention relates to the field of cloud computing technology, and discloses a distributed management system and method for a cloud container cluster, including: a data collection module, configured to collect the remaining resources of each node in the cloud container cluster and the resource requirements of tenants, and provide support for real-time data update; a state modeling module, configured to establish a state variable model describing the dynamic changes of tenant resources based on the resource data collected by the data collection module, wherein the state variables include the resource occupancy, resource application volume, and resource release rate of tenants; a scheduling optimization module, configured to generate a resource allocation strategy based on the dynamic change characteristics of tenant resources and the multi-tenant competition relationship; through the system architecture of modular design such as data collection, state modeling, and scheduling optimization, the present invention introduces a dynamic collection frequency and modeling of resource dynamic changes, improves the real-time performance of data collection and the accuracy of resource allocation, and solves the problems of low resource utilization rate and slow scheduling response in the prior art.