63 results about "Kalman filtering algorithm" patented technology

The invention relates to the technical field of automatic control, and discloses a PID (Proportion Integration Differentiation) intelligent pressure reducer controller, which comprises the following steps of: judging whether an environment disturbance or a load sudden change event exists according to a system load change rate and sensor feedback stability, constructing a master control-controlled coupling characteristic matrix by adopting a dual-channel data fusion method, performing attribution splitting on each disturbance channel, and obtaining a PID (Proportion Integration Differentiation) intelligent pressure reducer. PID parameters are dynamically adjusted according to the principal component weight of a disturbance channel, a variable parameter PID adjustment matrix is constructed, filtering smoothing is carried out on sampled signals through an extended Kalman filtering algorithm, a feedback closed-loop correction vector based on sampling residual errors is constructed, a historical offset tracking and switching value boundary judgment mechanism is introduced, fine tuning suppression is carried out on control signals of a motor driving mechanism, and the control signals of the motor driving mechanism are output. Judging whether a mechanical limiting protection device needs to be activated or not; and if yes, automatically triggering voltage limiting protection logic and locking the current control quantity. The method has the advantage of improving efficient and stable operation.

A motion state observation method for autonomous navigation of a ship. The method uses a determination method for system matrixes Ad, Bd, Ed and Hd, and a determination method for a related measurement covariance matrix R and noise covariance matrix Q, and uses a discrete Kalman filtering algorithm to perform motion state observation and prediction for autonomous navigation of a smart ship. The method fully uses ship position and heading measurement information of a smart ship during autonomous navigation, uses a ship motion model and the discrete Kalman filtering algorithm, and selects an effective state observation variable, thus calculating reasonable and effective observer parameters. The state observation method can effectively calculate information such as the actual position, heading, speed and angular velocity of a smart ship at each time instant during autonomous navigation, and predict motion information at a next time instant, thus helping to reduce the difficulty of smart ship motion controller design, ensuring effective implementation of navigation control strategies, reducing ineffective thrust output and rotation of propulsion systems, and improving the autonomous navigation capability of smart ships.

The invention discloses a pumped storage unit fault diagnosis method based on multi-modal data fusion, and the method comprises the steps: collecting a voiceprint signal, an infrared thermal imaging image and historical operation data of a pumped storage unit, and carrying out the noise reduction of a voiceprint set through employing an improved unscented Kalman filtering algorithm, a COMRes + model is used to train the voiceprint signal after noise reduction and historical operation data, a future voiceprint signal is predicted, an improved Deeplabv3 + model is used to train an image set, and features of an infrared thermal imaging image are extracted; the voiceprint feature, the historical operation data text feature and the image feature are input into an improved CentraNet model for multi-modal data fusion, and a fault category is output through a classification recognition module, so that diagnosis of the pumped storage unit fault is completed; according to the method, through fusion of the multi-modal data and optimization of the deep learning model, the accuracy and efficiency of fault diagnosis can be effectively improved, and a powerful guarantee is provided for safe operation of the pumped storage unit.

The invention relates to the technical field of power system safety monitoring, in particular to a power transmission line online safety monitoring method, system and device based on quantum communication and a storage medium. The multi-modal fusion sensor synchronously collects multi-parameter electric signal data such as current, voltage and temperature of the power transmission line, and performs quantum encryption instantaneous recording on the data; based on quantum encryption instantaneous record extraction data, a deep learning adaptive Kalman filtering algorithm is combined with a neural network to predict the operation state of the power transmission line; generating a new Manchester code according to the multi-parameter electric signal data and the operation state predicted value, and encrypting by adopting quantum key distribution to generate a ciphertext; and transmitting the ciphertext to a client based on a terahertz communication channel, decrypting and recovering the data through the quantum key, and carrying out safety early warning. Theoretically uncracked data security assurance is realized by using the unclonable characteristic of the quantum state, high-rate data transmission is realized by terahertz communication, and the transformation from passive monitoring to active early warning is realized by a prediction algorithm.

The invention discloses a hydropower station signal source side data anti-interference acquisition method and system. The method comprises the following steps: constructing a multi-source interference feature recognition network according to an environment state of a hydropower station, obtaining an interference source distribution feature spectrum, and obtaining an original signal through space-frequency domain joint filtering preprocessing based on the spectrum; performing recursion processing on the original signal by adopting an adaptive Kalman filtering algorithm to obtain a high-fidelity target signal; processing the high-fidelity target signal based on a multi-scale feature extraction and verification mechanism to obtain a credibility quantitative purification signal; and processing the credibility quantitative purification signal based on a compensation strategy to obtain a high-reliability output signal. According to the method, the influence of interference signals is gradually removed from interference source identification, filtering processing, signal reconstruction and quality evaluation to signal compensation, and finally, a result subjected to multiple anti-interference processing is obtained, so that various interferences in a complex environment of a hydropower station can be effectively resisted, and high-reliability data acquisition is realized.

The invention discloses a remaining oil distribution prediction method and system, a control device and a storage medium, and the method comprises the steps: obtaining a multi-scale fusion feature vector according to wide-area apparent resistivity data, seismic attribute data, optical fiber time sequence signal data, nano tracer tracking data and pore network point cloud data, inputting a physical constraint neural network to obtain a residual oil saturation initial value and a pressure gradient initial value of each region of the target layer, wherein a loss function comprises data fitting item loss, Darcy law physical constraint loss and mass conservation constraint loss; the real-time water content and the bottomhole flowing pressure of the production well serve as observation data, and a static parameter correction value, a remaining oil saturation correction value and a pressure gradient correction value are obtained through correction based on an ensemble Kalman filtering algorithm; the static parameter correction value and the pressure gradient correction value are fed back to a physical constraint neural network for loss function updating; and determining a remaining oil distribution result of the target layer. According to the invention, the accuracy and timeliness of residual oil distribution prediction can be improved.

The invention discloses a dynamic calibration control method for the detection precision of a non-standard automatic testing fixture, and particularly relates to the field of industrial automation and control, which comprises the following steps of: constructing a real-time dynamic error field model of the testing fixture; a distributed optical fiber sensing network and a miniature laser interferometer cooperatively collect real-time detection data and spatial-temporal variation errors under a multi-physics coupling effect, a precision degradation track is predicted based on spatial-temporal variation error data, a pre-compensation signal is generated, and a self-adaptive Kalman filtering algorithm is adopted to generate a precision compensation parameter. Environment temperature, vibration and electromagnetic interference data are monitored in real time, environment disturbance data are generated, a hybrid controller combining nonlinear model predictive control and reinforcement learning is adopted, multi-source data are fused, a calibration instruction is generated through multi-target optimization, and an execution mechanism is driven to achieve online dynamic calibration. And digital threads and transfer learning are integrated to realize full-life-cycle data closed-loop management and continuous optimization.

The embodiment of the invention provides a temperature drift compensation device, method and equipment for contact impedance of a high-frequency probe card. The system is applied to the technical field of semiconductor testing and comprises a temperature monitoring module, an impedance modeling module, a compensation execution module and a cooperative control module which are connected in sequence. In this way, the technical problems that in the prior art, temperature drift compensation is slow in response and insufficient in precision are solved through the sub-millisecond-level temperature monitoring network, the nonlinear compensation model and multi-probe cooperative compensation in combination with the self-adaptive Kalman filtering algorithm.

The present application relates to a kind of unmanned aerial vehicle impact interception methods based on monocular vision, the azimuth measurement information and angle measurement information of target unmanned aerial vehicle are calculated by target detection algorithm based on monocular vision;Azimuth measurement information and angle measurement information are used, through orthogonal projection matrix and algebraic transformation, non-linear measurement is converted into pseudo-linear form, then pseudo-linear adaptive Kalman filtering algorithm is carried out to estimate the position of target unmanned aerial vehicle, finally, unmanned aerial vehicle maneuvering strategy is used to control impact unmanned aerial vehicle to realize the impact interception of target unmanned aerial vehicle.The method of the present application is obviously improved in system noise robustness, target state estimation accuracy and pursuit efficiency.The present application also relates to impact unmanned aerial vehicle guided by the above impact interception method.

The invention relates to the technical field of medical instrument volume detection, and discloses a medical instrument internal volume detection system. A continuous wave and a pulse modulation signal are simultaneously transmitted through an annular phased array microwave antenna array, three-dimensional dielectric constant distribution is reconstructed by adopting a compressed sensing algorithm, and Doppler time-frequency domain characteristics are extracted through short-time Fourier transform; and inputting the phase gradient field and Doppler velocity field features into a convolutional neural network to generate a four-dimensional space-time volume evolution model, separating a macroscopic volume trend and a microcosmic pulsation component by adopting an adaptive Kalman filtering algorithm, and predicting an abnormal hemodynamic event through a long-short-term memory network. The technical problem of real-time precise detection of the multi-dimensional volume parameters of the blood circulation medical instrument under the complex pulsating flow condition is solved, and accurate evaluation of the instantaneous volume value, the pulsation amount and the pulsating frequency spectrum and early warning of abnormal events are achieved.

The invention discloses an open vocabulary multi-target tracking method based on confidence coefficient adjustment and wavelet convolution, belongs to the technical field of computer vision and target tracking, and can enhance the adaptive modeling capability of a target motion mode by designing a confidence coefficient weighted Kalman updating mechanism and developing a confidence coefficient noise adaptive Kalman filtering algorithm. And meanwhile, the Haar wavelet transform is introduced, so that the receptive field of convolution operation is remarkably expanded, and the capturing capability of high-frequency motion details and the maintaining capability of low-frequency contour information are effectively improved. And in combination with the joint cost matrix of the motion features and the appearance features, a more adaptive target tracking model is formed, and the tracking accuracy and stability are effectively improved. The method can effectively deal with rapid movement and temporary shielding of the target, accurately tracks various types of targets with undefined types, and is suitable for open vocabulary multi-target tracking tasks in various complex environments.

The invention discloses a buried gas pipeline multi-leakage parameter identification and concentration distribution rapid prediction method, and belongs to the technical field of buried gas pipeline safety detection. The method comprises the following steps: firstly, based on a porous medium flow control equation and a component transport equation, establishing a leakage diffusion physical model by utilizing computational fluid mechanics and generating a multi-working-condition sample data set; training a neural network model by using the sample data, and constructing a rapid concentration prediction agent model; in combination with measured data of a monitoring area, an ensemble Kalman filtering algorithm is adopted, the agent model serves as a forward prediction operator, and collaborative inversion of parameters such as the soil type, the leakage direction and the flow is achieved through iterative updating; and finally, outputting complete underground gas concentration distribution in the monitoring area based on the leakage parameters obtained through identification. According to the method, rapid identification of multiple leakage parameters and accurate prediction of concentration distribution are realized under the condition of limited measuring points, and the method has remarkable disaster early warning and engineering application values.

The invention relates to the technical field of energy spectrum data processing, in particular to a germanium spectrometer energy spectrum data adaptive optimization method and device, equipment and a storage medium. Performing feature extraction on the historical energy spectrum data set based on a wavelet transform method and a least square method; constructing a baseline initial fitting curve according to the corrected low-frequency coefficient features and the reconstructed high-frequency coefficient features; optimizing the baseline initial fitting curve based on a difference operation method and a preset Kalman filtering algorithm; carrying out median filtering processing on the real-time energy spectrum data set based on a median filtering method and a preset sliding window length; performing iterative optimization on the median filtering data set based on a preset particle swarm optimization algorithm and the baseline optimization fitting curve; according to the scheme, multiple algorithms are fused, baseline features are deeply mined, high and low frequency signals are separated, drift is corrected, spectral peak features are reserved, accuracy is improved, and the method is suitable for complex scenes and assists high-precision application of the germanium spectrometer.

The invention discloses a three-dimensional in-vivo radiotherapy dose monitoring method and system, and the method comprises the steps: collecting an image sequence {In} containing time dimensions at a theta t time interval through CBCT scanning equipment in a radiotherapy process; calculating target area displacement of adjacent frames of images in the image sequence {In} by using a bidirectional optical flow estimation method; state estimation and prediction are carried out on the displacement data based on an adaptive Kalman filtering algorithm, a dynamic displacement rule of the target area in the respiratory cycle is generated, and a respiratory movement prediction result is output; acquiring radiotherapy plan parameters from a radiotherapy plan system, and calculating planned dose distribution without considering respiratory movement; dynamically adjusting a ray transmission path and energy deposition according to a respiratory movement prediction result, and calculating real-time dose distribution of each respiratory time phase; mapping the dose distribution of each time phase to a reference time phase coordinate system to generate accumulated dose distribution; and comparing and verifying the accumulated dose distribution and the planned dose distribution.

The invention discloses a multi-sensor data fusion method for an intelligent transfer device of power equipment, and the method comprises the steps: carrying out the dynamic weight fusion of multi-source data through building a measurement model of a plurality of visual sensors, and generating a high-precision measurement sequence; in combination with an improved adaptive Kalman filtering algorithm, process noise and a measurement noise covariance matrix are estimated in real time, and the stability and the anti-interference capability of state estimation are optimized. And based on the fused high-precision state information, a real-time path planning instruction is generated, and the transfer device is controlled to complete obstacle avoidance and millimeter-level docking of the cabinet body. The problems that traditional manual transfer efficiency is low, the positioning error is large, and a single sensor is prone to electromagnetic interference are solved, the navigation precision and operation safety of heavy equipment in the complex electromagnetic environment are remarkably improved, and the method is suitable for intelligent transfer scenes of equipment such as transformer substation circuit breaker handcarts and the like.

The invention relates to the technical field of computer data processing and artificial intelligence, in particular to a data processing method and device for aircraft atmospheric turbulence early warning and a medium, and the method comprises the steps: carrying out the offline training of a first-scale meteorological risk assessment model and a second-scale turbulence disturbance model; in the flight process of the aircraft, time sequence flight state data and corresponding first-scale meteorological data are obtained in real time, and first-scale meteorological features and second-scale turbulence features are extracted; based on an ensemble Kalman filtering algorithm, taking the output of the first-scale meteorological risk assessment model as a background trend term, generating a nonlinear correction term from real-time data based on a key indication factor determined by the second-scale turbulence disturbance model, and fusing the background trend term and the nonlinear correction term to generate a forecast result of atmospheric turbulence intensity; and generating a turbulence early warning signal according to the forecast result. According to the invention, through multi-scale information fusion, accurate and early warning of atmospheric turbulence is realized, and the flight safety is effectively improved.

The invention relates to the technical field of agricultural non-point source pollution load evaluation, and provides an agricultural non-point source pollution real-time simulation method, device and equipment for an event rainfall. The method comprises the following steps: acquiring preset basic data of a target area, corresponding forecast rainfall of each hydrological response unit under a time scale, and initial conditions of each hydrological response unit before a rainfall event occurs; according to the forecast rainfall capacity, preset basic data and initial conditions of the model, prediction is carried out in combination with an agricultural non-point source pollution model of the rainfall event, and the flow and nitrogen and phosphorus load simulation values at the hydrological monitoring node at the current time point are obtained; and real-time correction simulation of the session event process is carried out based on an ensemble Kalman filtering algorithm and real-time monitored and transmitted flow and nitrogen and phosphorus load observation values. According to the method, the precision of the initial condition of the agricultural non-point source pollution model of the session rainfall event can be effectively improved, and real-time simulation of the process of the agricultural non-point source pollution model of the session rainfall event is realized.

A set Kalman filtering turbulence model assimilation method based on full field similarity criterion is disclosed, which comprises the following steps: sensitivity analysis is performed on the coefficients of the candidate turbulence model to obtain sensitive coefficients and their sampling range for Latin hypercube sampling, i.e., sampling interval; model constant sample combinations are extracted from the sampling interval by using a directional Latin hypercube sampling method, and target flow field results are calculated; a data assimilation algorithm based on the set Kalman filtering algorithm is used for the target flow field results and the model constant sample combinations, and after obtaining the optimized model constant combination, the model constant combination is updated and verified in cycles to obtain the optimal model parameter combination; finally, the improved turbulence model is used to simulate the internal flow field of the fuel assembly in the core of the pressurized water reactor with high similarity. The present application can provide an objective evaluation standard for the full physical field similarity of the data assimilation method, significantly improve the convergence speed of the algorithm, strengthen the robustness of the algorithm, and reduce the calculation cost of the algorithm.

The invention relates to a gamma radiation field reconstruction method based on Lagrange particle model parameter assimilation, and the method comprises the steps: determining the spatial positions of a plurality of radioactive aerosol particles based on a Lagrange particle model; constructing state parameters based on the plurality of spatial positions and the source intensity of each aerosol particle; forming a background state vector by using the state parameters, and dynamically updating the background state vector by adopting an ensemble Kalman filtering algorithm to obtain a corrected assimilation state; determining an optimized transportation track based on the spatial position of the radioactive aerosol particles in the corrected assimilation state; constructing a local concentration field corresponding to the radioactive aerosol particles by adopting a Gaussian kernel function; and determining a gamma radiation field corresponding to the radioactive aerosol particles. The three-dimensional radiation field reconstruction is carried out based on the background state vector and the real-time monitoring data by taking the parameters of the radioactive aerosol particles as the core, and the purposes of correcting the background state variable, obtaining the corrected assimilation variable and improving the accuracy of the three-dimensional radiation field reconstruction are achieved.

The invention discloses a data and model fusion driving-based space stack operation parameter correction and inversion method, which comprises the following steps of: 1, analyzing the influence degree of empirical constants in a space stack simulation model on an operation parameter prediction result, and determining empirical constants needing to be corrected and operation parameters; 2, predicting N groups of operation conditions containing empirical constant error disturbance by using the simulation model, obtaining N groups of simulation model estimation vectors, and forming an initial prediction matrix; 3, summarizing on-orbit actual operation data or ground test actual measurement data acquired by a space stack sensor, and providing an actual measurement data matrix for correcting space stack operation parameters; 4, fusing the characteristics of the prediction data and the actual measurement data of the simulation model by using an ensemble Kalman filtering algorithm to obtain a corrected empirical constant; 5, updating the empirical constants in the space stack simulation model into the corrected empirical constants to realize the correction of the simulation model; and 6, using the corrected simulation model to carry out operation condition analysis, and realizing operation parameter inversion calculation. The method is high in prediction efficiency and accurate in result.

The application discloses a kind of based on multimodal data fusion pumped storage unit fault diagnosis method, the voiceprint signal of pumped storage unit, infrared thermal image and historical operation data are collected, improved unscented Kalman filtering algorithm is used to denoising processing voiceprint set, the voiceprint signal after denoising and historical operation data are trained using COMRes+Model, future voiceprint signal is predicted, improved Deeplabv3+Model is used to train image set, and the features of infrared thermal image are extracted;Voiceprint feature, historical operation data text feature and image feature are input into improved CentralNet model for multimodal data fusion, and the fault category is output through classification identification module, so as to complete the diagnosis of pumped storage unit fault;The method can effectively improve the accuracy and efficiency of fault diagnosis by the fusion of multimodal data and the optimization of deep learning model, and provide strong guarantee for the safe operation of pumped storage unit.

The invention discloses dual-communication intelligent fire fighting equipment with a high-precision indoor positioning function in the field of intelligent distress systems. The dual-communication intelligent fire fighting equipment comprises a microprocessor, a power supply, an interaction function module, a positioning function module, an acquisition function module, an audio function module, a communication function module and an alarm function module. The system has the functions of accurate positioning and efficient communication, provides accurate indoor and outdoor positioning capability by combining the ATGM336H positioning module and the UWB positioning technology, and ensures that firefighters can be accurately positioned in real time no matter the firefighters are in the indoor environment, the underground environment or the outdoor environment. For indoor positioning, especially indoor positioning, initial position calculation is realized through PDOA and DS-TWR algorithms, and then an adaptive Kalman filtering algorithm with memory attenuation is used as a post-processing link to realize indoor centimeter-level high-precision positioning.

The invention provides a communication antenna steering control system and method, and relates to the technical field of antenna control, and the system comprises a parameter configuration module which is used for obtaining the initial state of a communication antenna, and collecting hardware state information in real time; the signal capturing and tracking module is used for configuring the initial frequency of the numerical control oscillator, starting the parallel correlator group to scan the carrier frequency and calculating frequency domain power spectrum data; and the angle calculation module is used for mapping the resampling phase difference and the baseline parameter into an azimuth angle based on a quaternion solution, and superposing IMU angular velocity data for dynamic compensation. According to the invention, a multi-stage phase-locked loop architecture, a dynamic threshold adaptive algorithm, cascade frequency division coefficient compensation and an FIR comb filter are adopted, so that high-sensitivity acquisition of signals in a broadband is realized; an extended Kalman filtering algorithm and a mechanical lag compensation algorithm are fused, so that the phase lag of the steering engine system is greatly reduced; and in combination with real-time fusion of the IMU angular velocity, the system can still keep relatively high pointing precision in a dynamic scene.

The invention discloses a permanent magnet synchronous motor turn-to-turn short circuit fault multivariate state monitoring method based on counter electromotive force, which comprises the following steps of: obtaining dq-axis voltage and dq-axis current according to direct current bus voltage, three-phase current and electrical angle of a permanent magnet synchronous motor, and further obtaining dq-axis counter electromotive force and fault component thereof; performing negative sequence rotation transformation on the fault component of the dq-axis counter electromotive force to obtain a dq-axis counter electromotive force negative sequence component, and extracting a normalized dq-axis counter electromotive force negative sequence direct current component to obtain the amplitude and phase of the counter electromotive force negative sequence component; judging a motor fault state according to the amplitude to obtain a fault indication, and obtaining a fault phase according to the phase; the counter electromotive force, the fault indication and the fault phase are used as input, and the fault severity is estimated through an extended Kalman filtering algorithm. According to the method, extra hardware is not needed, the turn-to-turn short circuit fault detection, positioning and fault severity estimation of the permanent magnet synchronous motor servo system can be realized, and the method has relatively high robustness for the operation condition of the motor.

The application discloses a vehicle speed fusion estimation method and device based on GPS, IMU and wheel speed sensor signals, introduces other system estimated road longitudinal slope values, compensates and filters vehicle longitudinal acceleration, adopts steering compensation, maximum and minimum wheel speed method and improved dynamic slope method to construct a three-layer architecture wheel speed processing algorithm to realize high robustness processing of wheel speed, based on kinematics principle, takes the vehicle speed output by the wheel speed processing algorithm as a measurement value of IMU Kalman filtering, reasonably models the measurement noise, realizes adaptive estimation of the vehicle speed, converts the signal under the navigation coordinate system to the vehicle body coordinate system through coordinate conversion, models the noise, and then processes the signal through the Kalman filtering algorithm, and finally, based on the optimal fusion theory of multi-sensor Kalman filtering, optimally fuses the vehicle speeds output by the GPS Kalman filtering and the IMU Kalman filtering, and realizes higher precision and stronger robustness of the vehicle speed estimation.

This invention discloses a cable cutting accuracy method and apparatus with error compensation function, relating to the field of power cable processing technology. The method includes: generating a cable axis bending curve based on cross-sectional radial offset fitting; calculating the straightening roller feed amount based on the bending curve using fuzzy inference; calibrating the cable center to a preset cutting center based on the straightening roller feed amount; setting an initial cutting amount and critical cutting distance based on the cable dielectric layer; solving the tool-copper core real-time distance based on the real-time signal of the copper core during the cutting process; using a Kalman filter algorithm to perform error fusion compensation on the initial cutting amount and the tool-copper core real-time distance, calculating the corrected cutting amount and corrected real-time distance; and determining the tool stop position for circumferential cutting based on the corrected real-time distance and the critical cutting distance. This invention addresses the problems of existing technologies that cannot adaptively straighten cables during cutting and do not correct cutting accuracy deviations through error compensation.

The present disclosure relates to an intelligent induction type cable quick connector plugging method, comprising: removing noise and errors in dust particle data through a signal processing module, combining the processed dust particle data with ambient air pressure data, using a Kalman filter algorithm to construct a first data set, and storing the data in a predefined format; obtaining the output results of a particle swarm optimization model, analyzing the movement trend and concentration change of dust, monitoring the insulation state of the cable joint in real time through an insulation state sensor of a dust removal instrument, and constructing a second data set based on the movement trend, concentration change of dust particles and the insulation state of the cable joint; obtaining the working intensity of the dust removal instrument through a fuzzy logic controller in the dust removal instrument; judging whether the insulation performance of the cable joint meets the safety standard based on the insulation state of the cable joint; collecting real-time plugging data during the plugging process, and evaluating the accuracy and safety of the plugging operation by analyzing the real-time plugging data.

The invention discloses an anti-alternating magnetic field roll angle calculation method based on a geomagnetic sensor, and belongs to the technical field of attitude measurement. The method comprises the following steps: firstly, calculating the direction of a geomagnetic field vector in a quasi-projectile coordinate system based on a small attack angle hypothesis by utilizing speed information measured by a satellite receiver arranged on a two-dimensional correction assembly; and then a two-axis geomagnetic sensor installed in the two-dimensional correction assembly is used for measuring the magnetic field intensity in the axial direction of the projectile body coordinate system. And finally, taking the roll angle phase, the roll angle rate, the roll angle acceleration rate and the geomagnetic signal amplitude as state quantities of extended Kalman filtering, taking the magnetic field intensity in the axial direction of the projectile body coordinate system as a measured value, and performing an extended Kalman filtering algorithm to obtain an optimal estimated value of the state quantities. According to the method, an attitude reference is constructed through satellite receiver information, and a roll angle measurement model is constructed by using extended Kalman filter (EKF), so that high-precision roll angle estimation under alternating magnetic field interference is realized.

The application discloses a kind of basins water quality variable-frequency sampling method and device for runoff process, belong to hydrology and water environment monitoring field, the method is: obtaining the historical runoff observation data of the basin to be monitored, precipitation data and the hydrological observation data of current time;Through the preset runoff prediction model, runoff forecast data are predicted in combination with hydrological observation data and precipitation data;Runoff prediction model is obtained by coupling long short-term memory network model and natural resources conservation service curve number model;Hydrological observation data is assimilated to runoff forecast data by ensemble Kalman filtering algorithm;By piecewise sampling method, the sampling time position of several different runoff stages in the corresponding precipitation period of runoff forecast data is determined after assimilation;When the water level of the basin to be monitored reaches the preset trigger water level, the basin to be monitored is sampled according to the sampling time position.Therefore, the application can improve the sampling efficiency and sampling result accuracy in runoff process.

The invention relates to the technical field of data processing, and discloses a wind turbine generator self-adaptive protection method based on high-precision storm surge numerical forecasting. The method comprises the steps that a dynamic resolution forecasting system is established by improving a spherical coordinate system Arakawa C-type grid, high-precision storm surge forecasting is generated by fusing data of a Jason-3 satellite and a tide station and adopting an ensemble Kalman filtering algorithm, a storm surge intensity-fan action mapping table is established based on forecasting data, edge computing nodes execute millisecond-level protection control, and a high-precision storm surge model is established. The multi-parameter sensor evaluates the protection effect, and protection strategy parameters are adaptively optimized according to the evaluation result. The technical problems that in an existing wind turbine generator protection technology, storm surge forecasting precision is insufficient, a protection decision lacks dynamic adaptability, and a protection strategy cannot be optimized autonomously are solved.