288 results about "Extended kalman filter algorithm" patented technology

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention discloses a graph optimization-based UWB / IMU / GNSS multi-unmanned vehicle cooperative positioning method, which comprises a global satellite navigation system GNSS, an ultra wide band (UWB) system, an inertial measurement unit IMU and a robot motion control system, and is characterized in that the UWB system comprises a UWB label and three UWB base stations, the UWB label is deployed on a target unmanned vehicle to be positioned, and the UWB label is deployed on the target unmanned vehicle to be positioned; and the three UWB base stations are respectively arranged on the other three unmanned vehicles as mobile base station unmanned vehicles. The GNSS module and the IMU module are deployed on the unmanned vehicle to serve as a movable UWB base station, and GNSS absolute position information and IMU relative motion information are fused by adopting an extended Kalman filtering algorithm; and according to the determined position of the unmanned vehicle in the movable base station, a distance observation value between the label and the base station is obtained based on a single-side bidirectional distance measurement method, a factor graph model fusing UWB distance measurement constraint and IMU motion constraint is constructed, and a factor graph optimization algorithm is adopted to optimize the position of the target unmanned vehicle. According to the method, the cost of a traditional fixed base station is reduced through the deployment of a mobile base station unmanned vehicle, and the precision and robustness of UWB system positioning in a complex environment are improved in combination with a multi-source sensor data fusion strategy.

The invention discloses a wide-range weak feature scene measurement system based on multi-sensor fusion and structured light scanning, and relates to the technical field of wide-range scene scanning, in the system, an extended Kalman filtering algorithm is adopted, fusion is carried out based on positioning data provided by an inertial measurement module and a positioning sensing module, and a wide-range weak feature scene is obtained. And the distance measurement information provided by the laser distance measurement module is combined to correct the fused positioning data, so that the precision and robustness of subsequent point cloud registration are ensured. Then, according to position information and attitude information of the binocular structured light scanning camera during scene scanning, calculating a relative rotation matrix and a relative displacement matrix of each frame of scanning point cloud data relative to the first frame of scanning point cloud data, and performing coarse registration; in the coarse registration process, all frames of point clouds can be converted into a unified coordinate system by directly utilizing global pose information, and the efficiency of coarse registration is greatly improved. And finally, extracting laser marking point clouds projected by the non-contact laser marking device in each frame of scanning point cloud data, and performing iterative precise registration on each frame of scanning point cloud data by using color features and geometric distribution rules of the extracted laser marking point clouds. In conclusion, high-precision, high-efficiency and high-robustness point cloud registration in a large-scale weak feature scene is realized, and the method can be used for realizing high-precision non-contact measurement.

The invention relates to the technical field of photovoltaic inspection, in particular to an unmanned aerial vehicle photovoltaic inspection positioning method and system based on extended Kalman filtering, and the method comprises the steps: obtaining the GPS positioning data, attitude data and photovoltaic panel image data of an unmanned aerial vehicle in real time based on a sensor carried by the unmanned aerial vehicle; according to the image data of the photovoltaic panel, fault features of the fault photovoltaic panel are identified, and pixel coordinates of the fault photovoltaic panel are extracted; converting the pixel coordinates of the fault photovoltaic panel into initial position information under a geographic coordinate system; constructing an extended Kalman filtering model, inputting the positioning data, attitude data and initial position information of the unmanned aerial vehicle into the extended Kalman filtering model, and establishing a state vector and an observation vector; and iteratively calculating an optimal estimation value through an extended Kalman filtering algorithm according to the state vector, and outputting geographic coordinates of the fault photovoltaic panel. According to the invention, the method achieves the quick and precise positioning of the fault photovoltaic panel, improves the operation and maintenance efficiency of a photovoltaic power station, and reduces the operation and maintenance cost.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

According to the mechanical arm control method based on multi-scale dynamic state estimation and error compensation, high-frequency and high-precision estimation of the state of a mechanical arm system is achieved through multi-sensor data fusion and an extended Kalman filtering algorithm, and the accuracy and reliability of state estimation are remarkably improved; on the basis, a multi-scale error analysis method combining a time domain and a frequency domain is provided, and an autoregressive moving average model is utilized to dynamically predict system errors, so that the sensing and pre-judging capability of the system on the errors is enhanced; a comprehensive control strategy including multiple links of feedforward, self-adaption and feedback is designed, a dynamic compensation and adjustment mechanism of errors is introduced, meanwhile, a real-time execution framework based on three-layer task scheduling is constructed, cooperative operation of all functional modules is coordinated, and the real-time performance and high efficiency of a control system are ensured. The method is suitable for multiple fields of industrial manufacturing, intelligent robots and the like, and provides technical support for precise control of mechanical arms in complex scenes.

The invention provides a multi-target tracking method based on EKF-ANA and multi-distance trajectory matching, relates to the technical field of multi-target tracking, and provides an adaptive extended Kalman filter algorithm for a trajectory prediction stage and a multi-distance trajectory matching method for a trajectory matching stage based on a two-stage multi-target tracking framework. And a multi-target tracking function under a complex background is realized. According to the method, a two-stage detection-based multi-target tracking mode is used, firstly, the position, the category and the confidence coefficient of a detected target are obtained through a detection algorithm, and then a track is predicted through a self-adaptive extended Kalman filtering algorithm. And finally, performing association operation between the target and the track by using a Hungary matching algorithm based on a multi-distance weighted data association cost matrix. According to the method, the accuracy of target tracking can be improved, the problem of partial ID switching caused by shielding is reduced, and certain real-time performance is met while a multi-target tracking task is completed.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention provides a lithium battery internal short circuit diagnosis method based on interpretable machine learning, and the method comprises the steps: collecting voltage, current and time data of a battery in a constant-current charging process under different internal short circuit degrees, and building an experiment data set; preprocessing and filtering the voltage data, calculating the ratio of the voltage increment to the capacity increment, and obtaining the IC curve characteristics of the capacity increment; extracting a battery state-of-charge-open-circuit voltage relationship, and establishing a battery equivalent circuit model for a constant current stage; estimating the state of charge of the battery through an extended Kalman filtering algorithm and battery charging data; extracting characteristic parameters from the IC curve, wherein the characteristic parameters comprise a peak value, a peak position and an area between peaks; time sequence features are extracted based on a state of charge estimation result, and a multi-dimensional feature space is constructed; carrying out standardization processing on the extracted features, and constructing a classification model; and a Shapril additive feature interpretation method is introduced to realize interpretability analysis of a classification model decision process.

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention relates to the field of mechanical arms, and discloses a multi-sensor fusion mechanical arm joint module force control system and a debugging method, and the multi-sensor fusion mechanical arm joint module force control system comprises a sensing module which is used for collecting state data of a mechanical arm joint module in real time through multiple sensors; the processing module is used for preprocessing the state data by adopting a federated learning algorithm and fusing the preprocessed state data by utilizing an extended Kalman filtering algorithm to obtain processed data; and the decision module is used for constructing a multi-objective optimization function based on a mechanical arm dynamic model according to the processing data. The method comprises the following steps: acquiring multi-dimensional data through multiple sensors, preprocessing the data by utilizing federated learning, fusing and outputting an accurate state estimation value in combination with extended Kalman filtering, constructing a multi-objective optimization function based on fused data, identifying kinetic parameters in real time through a recursive least square method, and dynamically adjusting a weighting factor by utilizing a particle swarm optimization algorithm.

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

The invention discloses a short message communication method and system for FTU (feeder terminal unit) equipment of a power distribution network. The method comprises the following steps: acquiring a power state data stream, an equipment fault data stream and an equipment health state data stream in the FTU equipment of the power distribution network; each data stream is preprocessed; calculating the priority of each data stream by using a dynamic priority sorting algorithm based on each preprocessed data stream, and classifying the data streams into a high-priority data stream and a low-priority data stream; monitoring the real-time quality data of the communication link based on the Beidou No.3 satellite system, and predicting the health state of the satellite link by using an extended Kalman filtering algorithm to obtain a link health state prediction result; the real-time quality data comprises link delay, bandwidth and packet loss rate; and calculating a quality score of each link by using a nonlinear programming algorithm based on a link health state prediction result, and selecting the link according to the score to carry out data stream transmission. The reliability and the transmission efficiency of the system can be improved.

The invention provides a lithium iron phosphate battery thermal runaway trend prediction method and system. The method comprises the steps that real-time operation data of a to-be-predicted lithium iron phosphate battery, initial estimated values of all parameters of an equivalent circuit model, a basic process noise covariance matrix and a basic measurement noise covariance matrix within a preset duration are acquired; determining an adjustment factor, and determining an adjusted process noise covariance matrix and an adjusted basic measurement noise covariance matrix based on the adjustment factor; optimizing the initial estimated value of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery by using an extended Kalman filtering algorithm to obtain optimized ohm internal resistance, first-stage polarization internal resistance and second-stage polarization internal resistance; determining the temperature rise rate of the to-be-predicted lithium iron phosphate battery at the current moment t and the predicted temperature of the to-be-predicted lithium iron phosphate battery at the moment t + 1; and performing thermal runaway trend prediction on the lithium iron phosphate battery. According to the technical scheme provided by the invention, the reliability, the sensitivity and the timeliness of the prediction result are improved.

The invention relates to the technical field of digital control switching power supplies, and particularly discloses a switching power supply dynamic response adjusting system based on digital control, which comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, a self-adaptive control law adjusting module, a pulse width modulation driving module and a stability monitoring module. On-line identification of key parameters of a system is realized by constructing a nonlinear state space model and combining an extended Kalman filtering algorithm, dynamic compensation is carried out for non-minimum phase characteristics such as a right half plane zero point, and a self-adaptive control law adjustment module optimizes a control strategy in real time according to an updated model. A Lyapunov stability criterion and a predictive control mechanism are introduced, the robustness and response speed of the system are improved, and a stability monitoring module identifies an abnormal state through sliding window variance analysis and peak detection and triggers a fault-tolerant mechanism to guarantee safe operation of the system.

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

The present invention discloses a mobile robot positioning method based on CycleGAN network and two-dimensional code, including: calibrating a camera and an inertial measurement unit; acquiring a sequence of images; determining whether there is a two-dimensional code in the application scenario. If there is no two-dimensional code, input the sequence of images into the CycleGAN network to obtain an image with consistent brightness and extract key points, then perform optical flow tracking on the key points and remove mis-matched key points, and estimate the pose of the camera by using an extended Kalman filter algorithm according to the data collected by the inertial measurement unit to obtain the pose of the mobile robot; if there is a two-dimensional code, calculate the rotation matrix and translation matrix between the two-dimensional code and the camera based on the PNP algorithm according to the two-dimensional code, estimate the pose of the camera, and obtain the pose of the mobile robot according to the pose of the camera. This method helps to ensure that the mobile robot can work normally in a dark environment or an overexposed environment, and eliminates the cumulative error after long-term movement, ensuring the working stability and accuracy.

Disclosed in the present application are a method and apparatus for estimating the state of charge of a battery, and a device and a storage medium. The method comprises: by means of a Thevenin equivalent circuit model, constructing a Thevenin equivalent circuit state space model for a lithium battery; on the basis of a preset high-order polynomial, performing fitting on the state of charge and open-circuit voltage of the lithium battery, and determining a non-linear relationship curve between the state of charge and the open-circuit voltage; collecting real-time operating state data of the lithium battery, and on the basis of the real-time operating state data, using an extended Kalman filter algorithm to perform online parameter identification on the Thevenin equivalent circuit state space model, so as to obtain an estimated state variable value; and constructing an adaptive double-power sliding mode observer, and inputting the non-linear relationship curve and the estimated state variable value into the adaptive double-power sliding mode observer, so that the adaptive double-power sliding mode observer calculates an estimated state-of-charge value of the lithium battery in real time. Compared with the prior art, the technical solution of the present application can improve the accuracy of estimating the state of charge of a lithium battery.

The invention discloses a fusion positioning method and system, a storage medium and a program product, and the method comprises the steps: collecting original point cloud data of target equipment through a laser radar, carrying out the preprocessing of the collected original point cloud data, and obtaining effective point cloud data; analyzing the preprocessed effective point cloud data fused with inertial measurement data collected by an inertial measurement unit by using a laser radar mileage calculation method to obtain a first estimated pose of the target equipment; performing preset part feature extraction on the effective point cloud data, and matching the extracted preset part point cloud features with the known preset part distribution map of the current site by using an adaptive Monte Carlo positioning method to obtain a second estimated pose; and fusing the first estimated pose and the second estimated pose by using an extended Kalman filtering algorithm, and performing real-time output to obtain a final pose of the target equipment.

The invention discloses a self-adaptive brushless motor control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting the original data of the operation state of a motor through a sensor group, and carrying out the preprocessing; time-varying parameter identification is completed through combination of an extended Kalman filtering algorithm and a radial basis function neural network, an evaluation index system is constructed based on an analytic hierarchy process to obtain a comprehensive evaluation value, and a related trend is predicted through a long and short-term memory neural network; constructing a multi-modal control strategy library, determining an adaptive strategy, optimizing core parameters by using an improved particle swarm optimization algorithm, generating a control instruction, and outputting a corresponding current through a power driving module; and monitoring motor parameters in real time, comparing with a control target value, calculating deviation, correcting an identification result, adjusting a strategy threshold value, and updating and optimizing an objective function. The method has the advantages that by accurately sensing the state of the motor, dynamically adapting the control strategy and optimizing parameters in real time, it is ensured that the motor stably and efficiently operates under the complex working condition, and the characteristics of energy conservation and long service life are achieved.

The invention discloses a low-altitude large-speed small-radius maneuvering control method for an unmanned aerial vehicle. The method comprises the steps of data acquisition and preprocessing, establishment of an accurate unmanned aerial vehicle model, design of a model prediction controller, sensor fusion, real-time control and the like. Flight state data is collected and preprocessed through various sensors, dynamics and kinematics models considering aerodynamic nonlinearity are established, a model prediction controller is designed by using a recurrent neural network and a particle swarm optimization algorithm, sensor fusion is performed by using an extended Kalman filtering algorithm, and a model prediction model is established. Accurate control of the attitude and track of the unmanned aerial vehicle is realized. Meanwhile, the functions of environment perception and obstacle avoidance, fault diagnosis and fault-tolerant control, real-time controller parameter adjustment, target optimization and the like are added, the maneuvering performance, adaptability and safety of the unmanned aerial vehicle are improved, and the unmanned aerial vehicle can be widely applied to the fields of military reconnaissance, logistics distribution, film and television shooting and the like.

The invention relates to the field of intelligent positioning, and provides an IMU (Inertial Measurement Unit) and UWB (Ultra Wideband) combined positioning method based on extended Kalman filtering, which comprises the following steps: acquiring IMU and UWB data, establishing a combined positioning model, carrying out preliminary distance measurement according to the combined positioning model, carrying out consistency check, and carrying out autonomous detection and gross error identification on UWB measurement. According to the method, measured values which are geometrically inconsistent are suppressed by using information, when detection exceeds a threshold, UWB data at the moment are not adopted, only an IMU prediction result is reserved, and when detection passes, standardized information square of each channel is taken as a statistical index and mapped to a basic probability distribution function in a DS evidence theory; according to the method, the UWB measurement value is subjected to UWB measurement, multi-channel measurement evidences are fused through a DS evidence combination rule, a fusion result is converted into measurement noise covariance for adaptive adjustment, filtering updating of the UWB measurement value in an extended Kalman filtering algorithm is achieved, and the positioning precision and stability of IMU and UWB combined positioning in a complex indoor environment are improved.

The invention discloses a SiC MOSFET service life prediction method and device, equipment and a medium, and relates to the technical field of health detection of power electronic devices. The method comprises the following steps: acquiring an acquisition signal set acquired by a multi-channel sensor; the collected signals are preprocessed, smoothing and fusion estimation are carried out by adopting an extended Kalman filtering algorithm, and multi-dimensional degradation features including drain-source conduction voltage drift rate and thermal resistance change rate are extracted through feature engineering. And carrying out global optimization on the key hyper-parameters and the initial weight of the LSTM network by adopting an improved artificial fish swarm algorithm. The algorithm comprises an adaptive optimization strategy, a mixed local search operator and a boundary constraint processing mechanism. And training the optimized LSTM network by using the multi-dimensional degradation characteristics, establishing a mapping relationship between the multi-dimensional degradation characteristics and the device life, and obtaining a trained prediction model. And acquiring a to-be-predicted acquisition signal set to extract multi-dimensional degradation features and input the multi-dimensional degradation features into the prediction model to obtain a residual life prediction result.

The invention relates to the technical field of mobile robot attitude control, and provides a mobile robot attitude control method, device and equipment and a medium, which can detect whether the load of a mobile robot changes in real time and perform reconfirmation based on a robot kinetic equation and real-time motion and attitude data, thereby improving the detection accuracy. Newly-added load modeling is carried out by utilizing an extended Kalman filtering algorithm and real-time motion and attitude data, and centroid reconstruction is carried out according to a newly-added load mass estimation value and a newly-added load position estimation value, so that the whole processing process does not need to stop and does not need to rearrange a sensor, and the compatibility is improved; and the model prediction control model is called, and the mobile robot is controlled to carry out attitude adjustment according to the reconstruction result and the real-time motion and attitude data, so that dynamic compensation for gravity center change can be realized, instability, slipping or falling caused by mass center drift is effectively prevented, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved.

The invention discloses a vehicle transverse anti-interference control method, device and equipment and a storage medium, and the method comprises the steps: employing an extended Kalman filtering algorithm to process the collected yaw velocity, longitudinal velocity and transverse acceleration of a vehicle, so as to estimate a road side slope angle value; and calculating a feed-forward compensation amount based on the road side slope angle value so as to carry out feed-forward compensation on vehicle transverse control and actively counteract interference of transverse displacement. According to the method, the transverse control precision can be remarkably improved, the control robustness is enhanced, the method adapts to complex road conditions, and the safety, comfort and driving stability of the vehicle are improved.

The invention discloses a photovoltaic power ultra-short-term prediction method, system and device and a storage medium, and relates to the technical field of new energy prediction. The method comprises the following steps: constructing an improved ensemble empirical mode decomposition algorithm to decompose original photovoltaic power data, and distinguishing and reconstructing a plurality of decomposed intrinsic mode functions to obtain reconstruction sequences of high, medium and low frequency components; for high-frequency and intermediate-frequency components, carrying out dynamic denoising by adopting an extended Kalman filtering algorithm; and inputting the original data, the low-frequency component, the denoised high-frequency component and the denoised intermediate-frequency component into a WTConv1d module, extracting multi-scale features through a wavelet filter and convolution operation, and inputting a multi-dimensional input matrix constructed by the multi-scale features and meteorological data into a Transform model for prediction. According to the method, noise interference can be effectively suppressed, multi-time scale feature expression is enhanced, and a self-attention mechanism is fully utilized to capture a cross-scale dependency relationship, so that the prediction precision and stability are improved.

The invention provides a goods shelf storage location calibration method and system for an unmanned forklift and related equipment. The method comprises the steps that a scheduling instruction is responded, a target storage location driving path is obtained, and the optimal pose parameter of the unmanned forklift is determined based on a goods shelf model containing the goods shelf standard size and the topological relation; the unmanned forklift is controlled to run to a target garage location, forklift movement data and field environment data are collected through a fork arm laser radar, a top laser radar, a visual sensor and an auxiliary measuring device, and the current actual pose of the unmanned forklift is calculated through an extended Kalman filtering algorithm; based on the current actual pose and the optimal pose parameter, calculating to obtain a pose deviation set; and when the deviation index of the pose deviation set exceeds the standard, automatically updating the corresponding storage location parameters in the storage map according to the pose deviation set. According to the method, the multi-source data fusion framework is constructed, and the extended Kalman filtering algorithm is utilized to realize automatic positioning and calibration, so that the problem that time and labor are consumed due to manual calibration traditionally is solved.

The invention relates to the technical field of data processing, and discloses a self-adaptive anti-shake control method and system for a mechanical arm of a neurosurgical robot. The method comprises the steps that tissue contact force, intracranial pressure variation and vascular pulsation frequency are collected, jitter feature vectors are obtained through fusion of a neurosurgery special extended Kalman filtering algorithm, compensation strategy parameters are generated through a neurosurgery scene self-adaptive prediction model, a tissue protective super-distortion sliding mode controller outputs a control signal, and the tissue protective super-distortion sliding mode controller is used for controlling the tissue protective super-distortion sliding mode controller. And the intracranial constraint flexible execution mechanism is driven to realize self-adaptive anti-shake control. According to the method, by constructing the extended Kalman filtering algorithm special for neurosurgery based on multi-modal sensor fusion and the LSTM prediction model, and combining with the tissue protective super-distortion sliding mode controller, the jitter suppression precision and the adaptive adjustment capability of the neurosurgical robot in a complex intracranial environment are remarkably improved.