342 results about "Extended kalman filter algorithm" patented technology

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

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 the technical field of building engineering, and discloses a building robot space positioning method based on a BIM model, which comprises the following steps: S1, establishing the BIM model of a building, and in the construction process, integrating real-time sensor data of a building site, and dynamically updating the BIM model to reflect the real-time state of a construction environment; and S2, setting multiple reference points at the construction site, performing coordinate calibration on the reference points by using a total station or a GPS system, aligning a coordinate system of the BIM model with a coordinate system of an actual construction site, and matching a building structure in the BIM model with a building structure in an actual environment through the reference points. Through the fusion of detailed building geometric information and real-time sensor data in the BIM model, the system can realize high-precision space positioning, the pose of the robot is corrected in real time by using an extended Kalman filtering algorithm and a multi-source data fusion technology, and the operation efficiency and the construction precision are effectively improved.

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 mode switching control method and device for an air-sea amphibious unmanned aerial vehicle, and the method comprises the steps: monitoring the unmanned aerial vehicle through a sensor, and obtaining target collection data; on the basis of an extended Kalman filtering algorithm, state estimation is carried out on the unmanned aerial vehicle through the target acquisition data, and a state estimation result is obtained; monitoring the mode switching state of the unmanned aerial vehicle according to the state estimation result to obtain a state monitoring result; and executing a mode switching strategy according to the state monitoring result to realize mode switching control of the unmanned aerial vehicle. The method can improve the stability and accuracy of mode switching control of the air-sea amphibious unmanned aerial vehicle, and can be widely applied to the technical field of unmanned aerial vehicles.

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 coastal sound tomography data assimilation method based on stream function fitting. The method comprises the following steps: arranging a coastal sound tomography system, obtaining underwater sound data containing observation domain hydrological information, carrying out correlation peak extraction, and calculating to obtain a sound ray mutual return transmission time difference; scanning topographic information of an observation domain and temperature and profile data between stations, inputting the topographic information and the temperature and profile data into a sound velocity model for sound ray simulation, determining an actual propagation path of sound rays, and inverting path average flow; constructing a three-dimensional unstructured grid of the observation domain, and determining a projection relation between an actual propagation path of sound rays and the grid; establishing a state transition matrix by taking a sound ray path average flow as an observation vector of extended Kalman filtering and taking a coefficient matrix of a flow function least square fitting ocean numerical pattern calculation result as a state vector; and assimilating and updating by using an extended Kalman filtering algorithm, and obtaining an optimized flow field. According to the invention, the observation precision, the resolution and the spatial range of the coastal flow field can be obviously improved.

The invention discloses a sodium ion battery SOC estimation method based on extended Kalman filtering. The method comprises the following steps: acquiring charging and discharging voltage, current and open-circuit voltage OCV data of a sodium battery through an HPPC test and a capacity test; and fitting a function relationship between the OCV and the SOC based on the standing balanced voltage data. And constructing a three-order RC equivalent circuit model, and identifying ohmic resistance, polarization resistance and polarization capacitance parameters in the model through curve fitting. Based on the model, a state vector containing SOC and polarization voltage is defined, and a nonlinear state equation and a terminal voltage observation equation are established. And predicting the state vector and the covariance matrix at the current moment according to the state vector and the covariance matrix at the previous moment by using an extended Kalman filtering algorithm, calculating Kalman gain by combining the actually measured terminal voltage, correcting the prediction result, and updating the SOC estimation value. And through loop iteration, real-time high-precision dynamic estimation of the SOC of the sodium-ion battery is realized. The method provides an efficient and accurate SOC estimation solution for a sodium ion battery management system.

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 the technical field of batteries, and discloses a battery charge state estimation method and device, equipment and a storage medium. The method comprises the following steps: constructing a fractional order equivalent circuit model of a to-be-tested battery; constructing a first state equation set according to the fractional order equivalent circuit model, and discretizing the first state equation set to obtain a second state equation set; performing parameter identification on the fractional order equivalent circuit model by adopting a particle swarm optimization algorithm; and estimating the state of charge of the to-be-tested battery by adopting a self-adaptive extended Kalman filtering algorithm according to the second state equation set after the parameters are identified. According to the method, the equivalent circuit model is constructed by adopting the fractional order model, the fractional order model can better reflect the nonlinear characteristic of the battery, so that the precision of the circuit model is improved, and on the basis of the established equivalent circuit model, the state of charge is estimated in combination with the adaptive fractional order extended Kalman filtering algorithm, so that the accuracy of the circuit model is improved. Therefore, the accuracy of state-of-charge estimation can be improved.

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 relates to the technical field of network communication, provides a network self-healing device based on node state data, and aims to improve the stability and self-healing capability of network communication. Through real-time monitoring of the network state, state estimation and filtering are carried out in combination with an extended Kalman filtering algorithm, and fault prediction, path selection and resource allocation are carried out through an ant colony algorithm and a simulated annealing algorithm, so that prediction and rapid recovery of potential faults are realized. The device comprises a state monitoring module, a state estimation and filtering module, a fault prediction and prevention module, a fault recovery module and a resource redistribution module, forms a closed-loop optimization mechanism, and is particularly suitable for a complex large-scale network environment, such as a data center network.

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 relates to the technical field of data processing, in particular to a circuit breaker operation state monitoring method and system based on big data, and the method comprises the steps: obtaining an operation state vector; obtaining a gain influence at each acquisition moment by using a filtering algorithm, and determining a fluctuation characteristic value based on a curve fitting result in a data space constructed by the gain influence; determining the fluctuation degree of the monitoring data at the acquisition moment according to the influence degree of the data fluctuation at the data abnormal moment on the monitoring data at the acquisition moment and the fluctuation characteristic value, and completing the updating of the Kalman gain matrix at the acquisition moment; and obtaining a prediction result of the operation state vector based on the updated Kalman gain matrix by using an extended Kalman filtering algorithm, and obtaining a monitoring result of the operation state of the circuit breaker based on the prediction result. According to the invention, through more accurately predicting the operation state monitoring data of the circuit breaker, the accuracy of monitoring the operation state change of the circuit breaker is improved.

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 technical field of smart power grids, and discloses a hierarchical safety early warning method based on smart power grid analog simulation, and the method comprises the following steps: S1, collecting the operation data of a smart power grid, including the real-time monitoring data of voltage, current, power factor and temperature, and processing the collected operation data; s2, optimizing estimation of a power grid state by using an extended Kalman filtering algorithm; s3, establishing a nonlinear dynamic model of the smart power grid, and predicting the health state of the power grid according to the dynamic behavior of the power grid; and S4, constructing a Bayesian network model, evaluating the risk of the power grid through probabilistic reasoning, and judging the health condition of the power grid. According to the invention, through combination of intelligent power grid analog simulation, deep reinforcement learning and adaptive optimal control, intelligence, adaptive adjustment and real-time response capability of the power grid early warning system are realized, and stability and emergency response efficiency of the power grid in a complex environment are 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 relates to an electric vehicle air conditioner refrigerating circuit state estimation method based on data-driven modeling and an extended Kalman filtering algorithm, and belongs to the field of electric vehicle air conditioner system refrigerating circuit state monitoring. The method comprises the following steps: establishing a continuous state space equation based on a moving boundary method, a momentum law, a mass conservation law and an energy conservation law; collecting real vehicle data, and identifying system parameters of the system state equation based on a least square search method; discretizing the continuous state space equation; and estimating the refrigerant loop state by adopting an extended Kalman filtering method according to the discretized state-space equation and the parameters of the identification system. According to the method, the grey box model and the moving boundary method are adopted, the least square algorithm and the extended Kalman filtering are combined, and efficient modeling and accurate state monitoring of the refrigerant loop system are achieved. According to the method, modeling complexity is simplified, real-time performance and precision are improved, and important support is provided for performance improvement of an automobile air conditioning system.

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 discloses a power line inspection unmanned aerial vehicle pose estimation method and device based on multi-modal data fusion, and the method comprises the steps: obtaining detection data of a binocular vision sensor, an IMU and a TOF sensor, and obtaining multi-modal detection data; using TOF sensor data to initialize a flight height and performing scale alignment with a speedometer; determining a linear velocity reference value and an angular velocity reference value, and calculating adaptive weights of the binocular vision sensor and the IMU; according to the binocular vision sensor, the detection data of the IMU and the calculated adaptive weight, performing pose estimation based on an adaptive extended Kalman filtering algorithm to obtain a pose estimation result of the binocular IMU odometer; and fusing the pose estimation results of the monocular odometer and the binocular IMU odometer to obtain the final pose of the unmanned aerial vehicle. According to the method, the pose estimation precision, the environment adaptability and the robustness in the electric power line inspection process can be improved.

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 invention discloses a near-real-time reconstruction method, device and equipment for multispectral remote sensing time sequence images in the whole growth season of crops and a medium, and relates to the field of remote sensing data reconstruction. The method comprises the following steps: acquiring historical time sequence remote sensing image spectral data of a target ground feature type, constructing a spectral reference curve of the target ground feature type in a whole growing season by adopting a ground feature change detection algorithm and a harmonic function fitting algorithm, and further constructing a vegetation index reference curve of the target ground feature type in the whole growing season; according to the vegetation index reference curve and the near-real-time vegetation index data, utilizing an extended Kalman filtering algorithm to reconstruct a vegetation index to obtain a vegetation index reconstruction result; and according to the spectrum reference curve, the near real-time remote sensing image spectrum data and the vegetation index reconstruction result, performing remote sensing image spectrum data reconstruction by using an extended Kalman filtering algorithm to obtain a remote sensing image spectrum data reconstruction result. According to the invention, reconstruction of near-real-time remote sensing image spectral data is realized.