245 results about "Unscented kalman filtering" patented technology

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention discloses a traveling wave fault positioning system and method based on Beidou synchronization and intelligent filtering, and belongs to the technical field of power system fault detection. The system comprises a Beidou synchronization module used for acquiring a high-precision time service signal and realizing multi-terminal clock consistency; the traveling wave sampling module is used for synchronously collecting multi-point electric power signals and filtering the multi-point electric power signals; the wave head extraction module realizes wave head identification through a sliding window and dynamic threshold judgment; the time coding module constructs a time difference vector and maps a position relation in combination with a power grid topology; the fault positioning module estimates an initial position based on a nonlinear propagation model, and the intelligent filtering module carries out dynamic correction through state prediction and unscented Kalman filtering. The method realizes rapid and accurate positioning of fault points, has the advantages of high synchronization precision, strong anti-interference capability, small positioning error and the like, and is suitable for traveling wave fault positioning requirements in various electric power scenes.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention discloses a ship target tracking method based on radar imaging in a shielding environment, belongs to the technical field of target tracking, is used for ship target tracking, and comprises the following steps: obtaining multiple frames of echo images of a time sequence, obtaining a potential target on each frame by using a ship image feature detection method, and obtaining a time accumulation space diagram; initial tracks are extracted from the time accumulation space diagram, and a tracking ID is distributed to each initial track; a frame of echo image is read, the position of a potential target is detected, and the position of the potential target is associated and matched with the tracking ID; and updating the position of a potential target which is not normally associated and matched by using a ship target tracking method combining unscented Kalman filtering and LSTM. According to the method, more potential targets are extracted, and a local matching result is optimized; more and more accurate target information is provided for the VTS system, so that the VTS system can have more prior information of early warning, and safe passage of a main channel is ensured.

The invention relates to the technical field of wind resistance design of bridge structures, discloses a bridge tower aerodynamic damping determination method and device based on unscented Kalman filtering, and aims to solve the technical problem that existing aerodynamic damping identification is not accurate enough. The scheme mainly comprises the following steps: determining an initial nonlinear aerodynamic damping model comprising a plurality of unknown model parameters of the bridge tower, and constructing a state equation comprising the model parameters; obtaining a vortex-induced vibration response time history of the bridge tower model by using a wind tunnel test, and constructing an observation equation; inputting the state equation and the observation equation into an unscented Kalman filter, and determining values of model parameters by using the unscented Kalman filter; and substituting the values of the model parameters into the initial nonlinear aerodynamic damping model to obtain a nonlinear aerodynamic damping model, and determining aerodynamic damping by using the nonlinear aerodynamic damping model. Accurate identification of aerodynamic damping is achieved, the requirement for the actual measurement response time history is lowered, and the method is particularly suitable for accurately evaluating the wind-induced response of the structure.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and application, and relates to the technical field of unmanned aerial vehicle control and navigation, and the method comprises the steps: constructing a three-dimensional visual simulation platform, including dynamic environment modeling, sensor data simulation and virtual-real combination verification; identifying and tracking a target through a multi-sensor data fusion and deep learning algorithm of the three-dimensional visual simulation platform; based on a multi-unmanned aerial vehicle cooperative positioning algorithm, realizing real-time estimation of the position and state of the target through geometric modeling and interactive multi-model unscented Kalman filtering; information fusion and collaborative decision among multiple unmanned aerial vehicles are realized through a wireless communication network; and optimizing the flight path, task allocation and target identification strategy of the unmanned aerial vehicle based on simulation feedback. The problems that a traditional unmanned aerial vehicle cluster is low in positioning precision and poor in target recognition and tracking capacity in a complex environment can be solved.

The invention discloses a CAN bus intrusion detection method based on adaptive unscented Kalman filtering, and the method comprises the steps: obtaining real-time message data, and carrying out the preprocessing of the real-time message data, and obtaining a time sequence feature vector; constructing a nonlinear state space model based on the time sequence feature vector; performing unscented Kalman filtering state prediction based on the nonlinear state space model; inputting the time sequence feature vector as an actual observation value, calculating a Kalman gain to correct an unscented Kalman filtering state prediction result, and outputting a state estimation residual error; dynamically updating a process noise covariance matrix through exponentially weighted moving average based on the state estimation residual, and adjusting a measurement noise covariance matrix according to the measurement innovation sequence; calculating the mahalanobis distance of the state estimation residual error, comparing the mahalanobis distance with a self-adaptive anomaly detection threshold value, and judging whether an intrusion behavior occurs or not; and if the abnormal score exceeds a threshold value, triggering a multi-level alarm mechanism, recording a suspicious message and executing a safety protection operation.

The invention discloses a perception fusion method of an unmanned driving system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining a laser point cloud, image data and millimeter wave radar data; a Zhang's calibration method is adopted to obtain internal parameters of a camera, and a first pose transformation relation of a camera coordinate system relative to a laser radar coordinate system is solved; a second pose transformation relation of the millimeter wave radar relative to the laser radar is obtained by using the millimeter wave reflector; a Point RCNN method is adopted to obtain first target information; obtaining target information II by adopting a YOLOv8 method; obtaining target information III by adopting a clustering algorithm; and carrying out coordinate unification on the target information by utilizing a pose transformation relation, fusing the multi-sensor target information under a unified coordinate system by utilizing an unscented Kalman filtering algorithm to obtain target state estimation, and generating an unmanned driving control instruction. The target sensing precision is improved aiming at the low target sensing precision of multiple sensors under the unmanned driving scene of the well industry and mining in the prior art.

The embodiment of the invention provides an aircraft motion state determination method based on Lie group unscented Kalman filtering, and the method comprises the steps: building a state model of a target aircraft, and a measurement model of the target aircraft; obtaining a final Lie group state estimation value of the target aircraft through iterative calculation according to the state model and the measurement model; and determining the motion state of the target aircraft according to the final Lie group state estimation value. According to the technical scheme, a Lie group unscented Kalman filtering navigation framework based on external measurement information is provided, specific forms of sigma point error propagation on Lie algebra and state updating on the Lie group are given, and a self-adaptive updating method of a state noise covariance matrix and a measurement noise covariance matrix is designed. Therefore, the self-adaptive Lie group unscented Kalman filtering method is provided, and the multi-sensor multi-beacon target state estimation precision is greatly improved by means of the self-adaptive Lie group unscented Kalman filtering method.

The embodiment of the invention provides a parameter acquisition method and device, a medium and a vehicle, and relates to the technical field of vehicles. According to the parameter acquisition method provided by the embodiment of the invention, the driving state information such as the three-axis acceleration, the three-axis angular velocity, the real-time vehicle speed and the output torque is acquired through the sensor equipped on the vehicle, and the algorithm such as the unscented Kalman filtering and the recursive least square with the forgetting factor is combined; the mass of the vehicle and the road gradient can be obtained with low cost, high real-time performance and high precision. The algorithms have advantages in processing nonlinear systems and dynamic changes, and can better adapt to complex working conditions, reduce interference of noise and errors and improve the vehicle quality and the acquisition precision and real-time performance of road gradient parameters especially under the complex working conditions of rapid acceleration, rapid deceleration, up and down of abrupt slopes and the like.

The invention provides a multi-source data fusion self-positioning method and system. The method comprises the following steps: acquiring GPS positioning data, visual image data and laser radar point cloud data of an unmanned aerial vehicle; carrying out noise reduction preprocessing on the GPS positioning data by adopting an unscented Kalman filtering algorithm; performing sensor joint calibration based on a visual image and a laser radar point cloud, and establishing a geometric mapping relation between a camera coordinate system and a world coordinate system by retrieving a preset high-precision tower ledger library and solving a PnP problem; inputting the image target detection data, the GPS state estimation value and the geometric mapping data into a pre-trained auto-encoder regression network; anti-interference potential features are extracted through an encoder of the network, and a target position estimation value of the target space position of the unmanned aerial vehicle is output through a regression head. According to the invention, the problem of positioning drift caused by strong electromagnetic interference and complex landform in electric power inspection is effectively solved.

The invention discloses a transformer fault diagnosis method and system based on multi-information feature fusion, and the method comprises the steps: collecting transformer operation information and a transformer voiceprint signal, and carrying out the filtering processing of the transformer voiceprint signal through a self-adaptive unscented Kalman filtering algorithm; a penalty factor C of the SVM is optimized through an improved snake egret optimization algorithm, and an ISBOA-SVM model is constructed; inputting the data into the constructed ISBOA-SVM model, and obtaining the probabilities of various fault types; and fusing the probability of the fault type output by each ISBOA-SVM model by adopting a D-S evidence theory, and determining the fault type. According to the method provided by the invention, the transformer fault diagnosis precision can be remarkably improved, and the generalization capability is higher.

The invention relates to the field of autonomous navigation and robots, in particular to an outdoor mobile robot positioning method based on unscented Kalman filtering, which comprises the following steps: 1) data acquisition and initialization; 2) based on an unscented Kalman filter, calculating a new mean value and covariance to update a system state; 3) substituting the new sigma point set into the observation equation to obtain a predicted observation quantity; obtaining a new mean value and a new covariance by using a weighting mode, and calculating a Kalman gain; updating the system state and the covariance; 4) when the AGV is shielded, adding a 3D laser radar point cloud inter-frame registration result as an observation value into a trace Kalman filter to replace RTK data; multiplexing an unscented Kalman filter, searching an initial position on the priori point cloud map, and completing the initialization of the filter; and 5) carrying out inter-frame matching on the laser radar point cloud, and updating a system state by combining a position value predicted by IMU data. According to the branch and bound search method based on RTK, the initialization problem in an outdoor large scene is effectively solved.

The invention discloses an improved unscented Kalman filter long baseline positioning method based on a variable forgetting factor, belongs to the technical field of aircraft measurement, and particularly relates to the improved unscented Kalman filter long baseline positioning method based on the variable forgetting factor. The objective of the invention is to solve the problem of low positioning accuracy of an existing long-baseline underwater acoustic positioning method. According to the invention, a forgetting factor valuing method based on recursive least squares is deduced, so that the forgetting factor can realize self-adaptive valuing in unscented Kalman filtering prediction updating iteration, and the adaptability and updating efficiency of the algorithm for an actual positioning problem are improved; and compared with an original adaptive unscented Kalman filter long baseline positioning algorithm, the positioning accuracy is improved. Meanwhile, the problem of reference value of the combined positioning system for current data and historical data is solved by adding the self-adaptive forgetting factor, the memory length of the unscented Kalman filter is limited in a self-adaptive mode, accumulative errors occurring in the calculation process are reduced, and the positioning accuracy is improved.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

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

The invention provides an energy storage system electric quantity state estimation platform and method based on improved unscented Kalman filtering, and relates to the technical field of electric quantity state estimation, and the method comprises the steps: carrying out the reference calibration through recognizing a standing condition, and matching a standing terminal voltage with an OCV-SOC curve to generate a three-dimensional calibration vector; performing space-time correlation on the real-time current, the voltage and the battery temperature with the reference points, predicting an SOC value by using unscented Kalman filtering, calculating a time attenuation weight and a space distance weight of each candidate reference point, and constructing a composite weight coefficient to form an effective reference set; and dynamically adjusting the covariance matrix of the process noise and the observation noise of the UKF based on the composite weight coefficient of the effective reference set, and updating the SOC estimated value. And establishing a dynamic safety evaluation domain on the SOC-temperature plane, and triggering early warning when an estimated value continuously exceeds a confidence interval for three times. According to the invention, the accuracy of electric quantity state estimation and the system security are improved.

The invention relates to the technical field of high-precision positioning in water surface construction, and discloses a Beidou positioning-based cofferdam positioning method for bridge construction, which comprises the following steps: S1, initializing Beidou RTK, setting a base station, acquiring an initial position of a cofferdam, and realizing centimeter-level positioning by using differential correction; s2, fluid-solid coupling modeling is carried out, a dynamic model of the cofferdam under the action of water flow, tide and wind power is established based on a computational fluid mechanics method, and stress and deformation of the cofferdam are calculated in combination with finite element analysis; and S3, estimating an unscented Kalman filtering state, and calculating the current position, speed and error range of the cofferdam based on the historical position information of the cofferdam and fluid-solid coupling modeling prediction data. According to the method, the timeliness of error correction is ensured through an error loop feedback mechanism and a real-time alarm function, the robustness and stability of the system are improved, and the positioning precision and reliability of the cofferdam in the construction process are remarkably improved.

The invention relates to the technical field of new energy batteries, in particular to a working performance detection method for a power storage battery of a new energy automobile, and the method comprises the steps: obtaining a voltage residual sequence at a current sampling moment according to terminal voltage collection values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment; obtaining a first optimization factor and a second optimization factor for optimizing a process noise covariance matrix in an unscented Kalman filtering algorithm according to the voltage residual sequence; obtaining an optimized process noise covariance matrix by using the first optimization factor and the second optimization factor; according to the terminal voltage collection value at the current sampling moment and the optimized process noise covariance matrix, the SOC estimation value of the power storage battery at the current sampling moment is obtained and used for detecting the working performance of the power storage battery, and the estimation accuracy of the SOC value of the power storage battery is achieved by adjusting the process noise covariance matrix online.

The invention discloses a road adhesion coefficient estimation method based on adaptive unscented Kalman filtering, and the method comprises the steps: obtaining the static parameters of a vehicle and the driving parameters of the vehicle in the driving process, and carrying out the modeling of the vehicle based on a seven-degree-of-freedom dynamic model, modeling a vehicle tire based on a Dugoff tire model on the basis of the seven-degree-of-freedom dynamic model to obtain a nonlinear system of the vehicle; estimating a road adhesion coefficient in the Dugoff tire model based on an adaptive unscented Kalman filter algorithm and a seven-degree-of-freedom dynamic model to obtain a road adhesion coefficient estimation value; according to the adaptive unscented Kalman filtering algorithm, a proportion correction coefficient is introduced in an unbiased transformation link, and an adaptive noise coefficient is introduced in a standard UKF. According to the method, a proportion correction coefficient and an adaptive coefficient are introduced into an adaptive unscented Kalman filtering algorithm, so that the response capability and estimation precision of a sudden change state are enhanced.

The invention discloses an intelligent pet health monitoring and intelligent evaluation system, which comprises a plurality of integrated sensors, and carries out real-time processing on multi-source data by adopting an improved unscented Kalman filtering and deep fusion algorithm. The system integrates a convolutional neural network, a long-short-term memory network and a Transform model at the same time, and realizes adaptive behavior recognition, health state evaluation and personalized health suggestion generation through online learning and incremental learning. The edge device is responsible for data acquisition and primary processing, including wavelet denoising, filtering and feature extraction, and performs data interaction and model updating with the cloud server through a standardized interface, such as MQTT or RESTful API. And the cloud server uses large-scale data to train a depth model, and continuously feeds back and optimizes an edge end algorithm. Through detailed parameter setting, interface design between modules and an early warning strategy, multi-dimensional data fusion, dynamic health assessment and multi-level early warning are realized, and an accurate, real-time and customized health management scheme is provided for pets.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention discloses a power lithium battery SOH and SOC joint estimation method based on data and model fusion, and the method comprises the steps: carrying out the modeling of a second-order RC equivalent circuit model of a lithium battery, so as to simulate the dynamic behaviors of the lithium battery under different charging and discharging conditions; the method comprises the following steps of: constructing a hybrid model fusing a convolutional neural network and a transformer by combining a multi-head differential attention mechanism under a macro scale, and inputting a plurality of extracted battery health factors into the model to obtain an SOH estimated value; and under the microscopic scale, based on a battery equivalent model, SOC estimation is carried out by using a weighted multi-information adaptive unscented Kalman filtering algorithm based on dynamic noise, and the SOC estimation process is corrected through the obtained SOH estimation value, so that an SOC estimation value is obtained. According to the method, the coupling relation between the SOH and the SOC is considered, and multi-time scale estimation is adopted, so that the calculation amount of the BMS can be effectively reduced, and the accuracy and robustness of the joint estimation of the SOH and the SOC of the power lithium battery can be improved.

The invention relates to a self-adaptive longitudinal vehicle speed estimation method, which comprises the following steps of: establishing detailed vehicle dynamic models, including a vehicle longitudinal dynamic model and a wheel speed dynamic model; a Sage-Husa self-adaptive unscented Kalman filtering algorithm is combined with a vehicle dynamics model, a state equation and an observation equation are designed, the longitudinal vehicle speed of a vehicle serves as a state variable, and observation vectors are constructed through data collected by a wheel speed sensor, an acceleration sensor and the like; divergence calculation is introduced for detecting and correcting divergence phenomena in the filtering process; and on the basis of a self-adaptive mechanism of a Sage-Husa algorithm, the filtering gain is adjusted in real time, and the vehicle speed estimation process is optimized. The improved Sage-Husa adaptive filter can dynamically adjust the filter parameters according to the change of the system state and the difference of noise, detects transient disturbance in combination with divergence calculation, reduces the influence of the transient disturbance on longitudinal vehicle speed estimation, and effectively improves the estimation precision.

The invention belongs to the technical field of battery management systems, and particularly relates to a battery state-of-charge estimation method for resisting a voltage sensor fault, which comprises the following steps: S1, data acquisition and system initialization; s2, state prediction; s3, measurement updating and online compensation are carried out; and S4, recursive circulation is carried out. According to the method, the direct-current bias quantity generated by a voltage sensor due to zero drift is widened into a state variable of a battery model, and the state variable, ohm internal resistance, polarization internal resistance, polarization voltage and charge state of a battery jointly form an expansion state vector; and performing real-time recursive estimation on the extended state vector by adopting an unscented Kalman filtering algorithm, and simultaneously obtaining an estimated value of the offset of the sensor and an estimated value of the SOC of the battery subjected to deviation compensation. Through combination of state extension and unscented Kalman filtering, fault bias of the voltage sensor can be identified and compensated online, non-Gaussian noise interference is effectively suppressed, and accuracy and robustness of battery SOC estimation in a complex industrial environment are remarkably improved.

The invention provides a road adhesion coefficient estimation method based on an adaptive slip speed tire model. The method comprises the following steps: establishing a vehicle dynamics model; estimating a tire force based on vehicle dynamics; estimating the vehicle speed by using an unscented Kalman filtering algorithm based on singular value decomposition; a tire model based on the slip speed is constructed, and normalized tire force is calculated according to the tire model; estimating a road adhesion coefficient by using an unscented Kalman filtering algorithm based on singular value decomposition; according to the self-adaptive slip speed tire model provided by the invention, the problem of denominator singularity of a current slip rate-based tire model at a low speed and the limitation of a fixed stiffness parameter are overcome, so that the estimation precision and the response speed of the road adhesion coefficient are improved.

The invention discloses a fuel cell health state estimation method, device and equipment, a medium and a product, and relates to the field of fuel cells, the method comprises the following steps: determining an operation condition of a fuel cell at a current moment according to current attenuation data, and determining a speed state based on the operation condition; determining a running time parameter value of the fuel cell at the current moment according to the speed state; according to the start-stop state of the fuel cell at the current moment, obtaining a health state vector of the fuel cell at the next moment and a covariance matrix of the fuel cell at the next moment by adopting an unscented Kalman filtering algorithm according to the running time parameter value, the health state vector and the covariance matrix at the current moment; when the start-stop phenomenon exists and the start-stop phenomenon does not exist, observation equations in the unscented Kalman filtering algorithm are different; and obtaining the health state estimation value of the fuel cell at the next moment according to the value of the health index in the health state vector of the fuel cell at the next moment. The method and the device can improve the accuracy of a health state estimation result.

A moving target trajectory prediction method based on deep learning optimizes input image data through image equalization and normalization processing, and uses a YOLO network for training to extract image features and classify the image features. And after the training is completed, inputting the data set test set image into the YOLO network for detection, and evaluating whether the trained YOLO network meets the detection requirement or not. And matching the detection target of the current frame with the prediction target at the previous moment through a KM matching algorithm. And then, predicting the position of each moving target at the next moment by adopting a self-adaptive unscented Kalman filtering algorithm. And the system constructs a trajectory prediction model of the moving target through the dynamic observation equation, continuously optimizes the filtering process, and finally outputs a trajectory prediction result of the moving target. According to the method, under the requirements of real-time performance and high precision, recognition and prediction of moving objects such as balls and athletes are achieved, and improvement of training scientificity, prevention of injuries and diseases, optimization of competition strategies, promotion of technical innovation and the like are facilitated.