37 results about "Kalman filtering algorithm" patented technology

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 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 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 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 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 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 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 embodiment of the application provides a multi-source error real-time separation and compensation method in torque measurement, relates to the technical field of mechanical measurement and precision instruments, and comprises the following steps: a multi-source error mathematical model is established, which comprises a temperature compensation submodel, a friction loss submodel, a nonlinear correction submodel and a misalignment correction submodel; temperature sensors, rotation speed sensors, acceleration sensors and torque sensors are arranged to obtain real-time measurement data; an adaptive Kalman filtering algorithm is used to perform online recursive estimation on error characteristic parameters, so that real-time separation of each error component is realized; and the original torque measurement value is compensated according to the separated error components and the correction result is output. The application has the effect of significantly improving the dynamic precision of torque measurement under variable working conditions.

The invention discloses an intelligent regulation and control method and system for water transportation, distribution and back supplement in a mining area. The method comprises the steps that pipe network operation data of a transportation and distribution pipe network and current multi-source geological and hydrological data are obtained; determining an optimal state estimation value of the pipe network by using a real-time hydraulic model and an ensemble Kalman filtering algorithm, and calculating a pressure residual error of each monitoring node; when the residual represents an abnormal working condition, determining a leakage result by using a leakage positioning algorithm; generating an emergency regulation and control decision by using an optimization scheduler based on the leakage result; when the residual represents a normal working condition, a scheduler is used for executing multi-time-scale predictive control through a real-time hydraulic model based on pipe network operation data to generate a transmission and distribution scheduling decision, and the pipe network is regulated and controlled to realize back-replenishing water transmission and distribution; performing multi-physics field coupling real-time simulation on the whole diffusion and migration process of the back-replenishing water by using the geologic structure digital twinborn model to obtain a back-replenishing prediction result; and evaluating the risk of the candidate supplementation scheme based on a reliability theory, and solving by using a multi-objective optimization algorithm to obtain a target supplementation scheme.

The application discloses a satellite-based augmentation positioning device and a rapid starting method. After starting, the power supply is started by a button, and then the power inspection reference station PPP mode is automatically started. Satellite observation data of GNSS multi-system is received. It is judged whether the root mean square (RMS) of the precision index of the GNSS receiver of the user end is greater than a first set value. When RMS>the first set value, the fixed solution based on the PPP-AR / RTK algorithm continues to converge the error source of the satellite observation data, or when RMS≤the first set value, the station coordinates are solved based on the fuzzy adaptive Kalman filtering algorithm. The reference station coordinates of the first time and the Nth time are recorded. The reference station coordinates obtained through N times are used for average value calculation once again, accurate station coordinate data is obtained, and the differential data with the station coordinate message and the constellation frequency point message are sent to the corresponding signal receiving end, so that the differential data acquisition and broadcasting of high-precision absolute coordinates are realized, and the starting speed of the satellite-based augmentation positioning device is improved.

The invention discloses a novel gravity measurement method based on the combination of an atomic gravimeter and a classical accelerometer. The method comprises the following steps: S1, carrying out synchronous measurement by using the atomic gravimeter and the classical accelerometer; s2, on the basis of a quantum-classical hybrid method, dynamic gravity measurement is carried out in combination with a vertical output signal of a classical accelerometer; and S3, a variational Bayesian adaptive unscented Kalman filter (VBAUKF) algorithm is adopted. According to the method, a feasible technical scheme is provided for high-precision ocean gravity measurement, a new thought is developed for research in related fields, and the method has important theoretical value and engineering application significance; by dynamically combining the advantages of the atomic gravimeter and the classical accelerometer, large-range, high-sampling-rate, high-precision and zero-drift-free absolute gravity value measurement is realized, and the measurement precision and reliability of the system are remarkably improved.

The application discloses a kind of structural dynamic displacement monitoring methods based on double-channel visual signal interaction and fusion, first, the motion video of structure is recorded in real time using industrial camera, and Gaussian filter denoising and gray processing are carried out frame by frame;Second, wavelet amplitude screening and corner response factor screening are comprehensively used, pixel set with measurement robustness is established, and monitoring point is selected;Then, template matching algorithm is used to measure the displacement of integral pixel accuracy, as the displacement signal of visual channel one, and the signal is used as dynamic window size reference value and visual channel two interaction, optical flow algorithm is used to measure the speed of subpixel accuracy, as the speed signal of visual channel two;Finally, based on Kalman filtering algorithm, the displacement signal of visual channel one and the speed signal of visual channel two are data fusion, so as to obtain the structural dynamic displacement signal of subpixel accuracy.The application effectively combines the advantages of double-channel visual signal, can realize high-precision non-contact structural dynamic displacement monitoring, and does not need to spray artificial mark on the surface of structure, and has good practicability.

The invention discloses a data processing method of a wave control module, and belongs to the field of radar data processing, and the method comprises the steps: collecting a space electromagnetic signal, constructing an original data sequence of the space electromagnetic signal, sampling the original data, and obtaining the standardized data of a sampling point; gaussian white noise and pulse interference are dynamically suppressed by using an improved adaptive Kalman filtering algorithm, and a denoised target useful signal is output; extracting three-dimensional features of a time domain, a frequency domain and a space domain to obtain weight fusion features of the sampling points; an LSTM-Attention hybrid model is constructed, and core parameters of the wave control module are predicted; and correcting the core parameters, and correcting the data processing process of the wave control module by using the corrected core parameters as the core parameters output by the wave control module in the future. According to the invention, the anti-interference capability of the wave control module in the data processing process is effectively improved, the environment adaptability is enhanced, and the method is suitable for the data processing process of the wave control module with high precision, low delay and strong anti-interference characteristics in a complex electromagnetic environment.

The invention provides a stability control method and system for an off-road vehicle driven by a drive-by-wire chassis hub motor, and belongs to the field of stability control of drive-by-wire chassis hubs. The problems that during stability control of an existing drive-by-wire chassis, process noise disturbance exists, control precision is poor, and the existing drive-by-wire chassis is not suitable for off-road vehicles are solved. A pull pressure sensor, a position sensor, a rotating speed sensor, a GPS signal and an inertial navigation sensor are adopted to obtain real-time information of a vehicle, a vehicle state quantity calculated by combining a vehicle model is used, an extended Kalman filtering algorithm is used for data fusion, and the yaw velocity is increased; according to the method, the matching degree of the vehicle body slip angle parameter and the actual state of the vehicle is determined, the road adhesion coefficient is calculated according to the matching degree, stability control is carried out, road adhesion coefficient identification is carried out on the parameters obtained in real time through unscented Kalman filtering, and the obtained road adhesion coefficient is used for stability control. The method can reflect the actual state of the vehicle more accurately, and is suitable for the intense driving environment of the off-road vehicle.

The invention relates to the technical field of charging pile management, in particular to a charging pile fire early warning method and system based on dynamic risk assessment, and the method comprises the steps: deploying temperature sensors in a charging module internal region, a battery interface contact region, a heat dissipation air channel and a surrounding environment region of a charging pile; the method comprises the following steps: acquiring temperature data of different types of sensors in the same area to form a multi-area temperature sensing network, receiving original temperature data acquired by all sensors in the multi-area temperature sensing network, filtering the original temperature data by adopting a self-adaptive Kalman filtering algorithm, and performing weighted fusion on the temperature data of different types of sensors in the same area to obtain temperature data; based on the processed data, carrying out dynamic risk assessment from two dimensions of temperature deviation degree and temperature change rate, and carrying out multi-level fire risk level judgment; and performing grading response operation according to the judged fire risk grade. The false alarm rate and the missing report rate are reduced, and the problem of response lag is solved.

The invention relates to a distance measuring method, system and device for man-gun distance measurement and a medium, and belongs to the technical field of data processing, the distance measuring method comprises the following steps: synchronously collecting data from an inertial measurement unit and an environment sensor, resolving the current posture of a target bracelet, identifying an environment scene, and predicting IMU reckoning displacement by combining the position at the previous moment; calculating an initial distance between the gun tag and the target bracelet by using UWB bidirectional distance measurement, obtaining a dual-antenna signal phase difference of the target bracelet, and calculating a signal incident angle; running an extended Kalman filtering algorithm, fusing the initial distance, the IMU reckoning displacement and the signal incident angle, and obtaining a relative position vector and a fusion distance of the gun label; calculating the approaching speed of the target bracelet, and calculating a comprehensive risk value in combination with the environment scene and the non-line-of-sight state; and determining an early warning level according to the comprehensive risk value, and executing a corresponding early warning action. The method has the beneficial effect of reducing the ranging error.

The invention discloses an urban flood simulation and prediction method, which comprises the steps of generating rainfall forecast data based on a numerical weather forecast mode, and obtaining actual rainfall data; according to the actual rainfall data, predicting future rainfall trend data through a time sequence prediction method; performing data assimilation through an ensemble Kalman filtering algorithm according to the rainfall forecast data, the live rainfall data and the future rainfall trend data to obtain assimilated rainfall forecast data; constructing a hydrological hydrodynamic model according to the basic geographic data and the historical flood data of the forecast area; respectively driving a hydrological hydrodynamic model according to the rainfall forecast data and the assimilated rainfall forecast data, and carrying out flood routing simulation by adopting a GPU parallel acceleration technology to obtain a simulation forecast result; and repeatedly executing the steps according to the updating of the rainfall forecast data to realize rolling simulation forecast. According to the invention, dynamic rolling simulation prediction of the urban flood evolution process is realized.

The invention relates to the technical field of vehicle navigation, in particular to an inertial navigation precision improving method and device based on vehicle satellite signal disconnection, and the method comprises the following steps: obtaining vehicle satellite navigation data and vehicle inertial navigation data; the method comprises the following steps: constructing a non-integrity constraint condition of vehicle motion based on vehicle inertial navigation data, and obtaining a corrected inertial navigation trajectory based on a pre-constructed Kalman filtering algorithm according to the non-integrity constraint condition and the vehicle inertial navigation data. The problem that the navigation precision is low after the vehicle satellite signal is disconnected due to the fact that no satellite serves as auxiliary information to correct the output result of inertial navigation in the prior art is solved, and the navigation precision after the vehicle satellite signal is disconnected is improved.

The present application relates to the technical field of computer data processing and artificial intelligence, in particular to a data processing method, device and medium for aircraft atmospheric turbulence early warning, the method comprising: offline training a first scale meteorological risk assessment model and a second scale turbulence disturbance model; in the process of aircraft flight, real-time acquisition of time series flight state data and corresponding first scale meteorological data, and extraction of first scale meteorological features and second scale turbulence features; based on the ensemble Kalman filtering algorithm, the output of the first scale meteorological risk assessment model is taken as the background trend item, and based on the key indicator factor determined by the second scale turbulence disturbance model, a nonlinear correction item is generated from real-time data, and the two are fused to generate a prediction result of atmospheric turbulence intensity; generating a turbulence early warning signal according to the prediction result. Through multi-scale information fusion, the present application realizes accurate and early warning of atmospheric turbulence, effectively improving flight safety.

The application discloses an infrared and visible light target tracking correlation method and system based on bimodal fusion modeling, and the method comprises the following steps: target detection networks are used to detect an infrared image and a corresponding visible light image respectively to obtain a detection frame set; Kalman filtering algorithm is used to obtain two prediction frame sets; visible light motion similarity, infrared gradient structural similarity and bimodal appearance similarity are calculated respectively between the detection frames in the detection frame set of the visible light image and the prediction frames in the prediction frame set; three indexes are fused by weighting to obtain a comprehensive correlation score, and a one-to-one matching result of infrared and visible light target tracking correlation is obtained based on the Hungarian algorithm. The application aims to solve the problems that the correlation basis is single in the existing infrared and visible light bimodal target tracking, and the identity switching and mis-matching are prone to occur in complex scenes, and improve the reliability of target correlation and the stability of subsequent state updating in complex scenes.

The application provides an unmanned helicopter magnetic interference real-time compensation method and system based on an improved Kalman filter, and relates to the technical field of unmanned aerial vehicle navigation and control, comprising: a response processing of a trigger instruction received by an unmanned helicopter flight control system to form a compensation start signal, wherein the response processing comprises flight control program initialization, attitude parameter loading and sensor self-checking operation; and a time synchronization processing of magnetic field joint sensor data collected during unmanned helicopter flight to form a multi-source flight data set, wherein the time synchronization processing adopts a unified timestamp calibration mechanism. The unmanned helicopter magnetic interference real-time compensation method and system based on the improved Kalman filter compensate the magnetic interference of the unmanned helicopter in real time by adopting the improved Kalman filter algorithm. Through state estimation and dynamic weight adjustment, the dynamic model of the unmanned helicopter and the magnetic field distribution model are combined to realize magnetic field error correction during flight and ensure accurate control of the flight attitude.

The invention relates to the technical field of servo car machines, and discloses a servo car machine monitoring method and system based on double-encoder redundancy check, a servo motor encoder is used for monitoring motor shaft motion parameters, and a friction belt type encoder is attached to a piston rod to collect linear motion parameters of the piston rod in real time; carrying out filtering processing on the acquired data by adopting a self-adaptive Kalman filtering algorithm, and dynamically adjusting filtering parameters to remove noise interference; establishing a kinematic model of a servo vehicle engine transmission system, constructing a mapping relation between motor shaft motion parameters and piston rod theoretical motion parameters according to the transmission ratio of a transmission mechanism, and calculating piston rod theoretical motion data through the motor shaft parameters; the data processed by the double encoders and the theoretical motion data of the piston rod serve as DBN network input, an early warning mechanism is triggered when abnormity is judged, and the fault type and position are output based on abnormal data characteristics through a fault tree algorithm; according to the invention, the safety and the stability are improved.

The invention discloses a radar pulse sequence denoising method based on a state space model, and belongs to the technical field of radar electronic reconnaissance. The method comprises the steps of obtaining a pulse sequence through a receiver; establishing a state space model for representing the pulse sequence; estimating dimensions of unknown parameters and state variables in the state space model in a self-adaptive manner by using a Burger algorithm; and carrying out optimal estimation on PRI parameters of the pulse sequence by adopting a Sagger-pizza adaptive Kalman filtering algorithm so as to realize the denoising of the pulse sequence. According to the method, the state space model of the pulse sequence is constructed, the model parameters and the state variable dimensions are adaptively estimated by adopting the Burger algorithm, and the PRI parameters are optimally estimated in combination with the Sagger-Pensa adaptive Kalman filtering algorithm, so that the problem of measurement errors caused by unstable work of the reconnaissance receiver is solved; the quality of radar pulse sequence data can be improved.

The invention discloses an intelligent steel billet taking method based on a multi-modal signal, and relates to the technical field of metallurgical industry automation, and the method comprises the following steps: extracting production takt data, and calculating and optimizing an execution time sequence of steel taking operation by analyzing the production takt data; a cooperative control model of the unmanned vehicle and the steel taking equipment is established, motion parameters of the steel taking equipment are adjusted through a cooperative control algorithm, and cooperative control parameters are generated; a fault-tolerant compensation system is switched to a laser point cloud matching mode in combination with the cooperative control parameters and the real-time working condition information, and multi-modal data are fused by using a federated Kalman filtering algorithm to generate a comprehensive signal; judging whether the steel taking task is completed or not; and a target position and a target posture are determined according to the comprehensive signal, and an actuator is driven by a control unit to execute steel taking operation. According to the invention, the comprehensiveness and accuracy of data are enhanced, and the acquisition parameters can be adaptively adjusted in a complex production environment, so that stable operation under different illumination and dust conditions is ensured.