1188 results about "Kaiman filter" patented technology

The invention discloses a multi-unmanned aerial vehicle combined flight control system coping with wind power change, and relates to unmanned aerial vehicle flight control, and the system comprises a flight data collection module which is used for collecting unmanned aerial vehicle flight data in real time, and the unmanned aerial vehicle flight data comprises wind power data, attitude angle data and height data of each unmanned aerial vehicle; the data receiving and processing module is electrically connected with the flight data acquisition module, and the data receiving and processing module is used for receiving unmanned aerial vehicle flight data acquired in real time and performing noise removal on the acquired data. According to the multi-unmanned aerial vehicle combined flight control system provided by the invention, the wind field prediction model is constructed by combining the LSTM neural network with the time attention mechanism, wind power periodic change characteristics are accurately captured, real-time correction is performed in cooperation with the Kalman filter, the prediction precision and timeliness are both optimized, and the system is suitable for large-scale popularization and application. A multi-unmanned aerial vehicle cooperative kinetic model is constructed based on a prediction result, so that the formation trajectory tracking error is reduced.

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention relates to the technical field of battery intelligent monitoring, and discloses an intelligent monitoring system and method for a new energy automobile battery. The method comprises the following steps: acquiring a target battery operation data set of a new energy automobile battery, and extracting time domain differential characteristics and frequency domain energy loss characteristics in a battery charging and discharging process to obtain a multi-dimensional state characteristic set; performing electrochemical characteristic analysis on the new energy automobile battery based on the multi-dimensional state feature set to obtain a micro degradation state judgment result; and dynamically adjusting the parameter configuration of a hybrid Kalman filter according to the micro degradation state judgment result, and generating a battery state-of-charge estimation value. According to the method, the problems of insufficient estimation precision and accumulative errors in a traditional method are solved, and the use safety and reliability of the battery are improved.

The invention belongs to the technical field of unmanned aerial vehicle autonomous navigation, and relates to an intelligent unmanned aerial vehicle navigation method and system based on bimodal obstacle feature extraction. Aligning the information of the multi-modal sensor in time and space through a time-space alignment network; real-time pose information of the unmanned aerial vehicle is obtained through a Kalman filter fused with a positioning failure adaptive strategy; a double-flow obstacle feature extraction network is adopted to extract a composite environment vector containing dynamic obstacle features and static obstacle features from the multi-modal sensor data; training the SAC model based on the maximum entropy by adopting an improved reinforcement learning training method based on curriculum learning to obtain an SAC strategy network; and inputting the composite environment vector and the real-time pose information of the unmanned aerial vehicle into an SAC strategy network, and obtaining the optimal control of the unmanned aerial vehicle in an end-to-end manner, thereby having good path planning performance, and effectively improving the task execution capability and safety of the unmanned aerial vehicle in a complex environment.

The invention discloses a power distribution network planning method and system considering distributed energy uncertainty, and relates to the technical field of power grid planning, and the method comprises the steps: collecting distributed energy node data, compensating space-time migration in combination with meteorological data, and carrying out the time sequence alignment through a dynamic time warping algorithm; constructing an improved Wasserstein generative adversarial network to generate a conventional scene, and injecting Gaussian noise through potential spatial disturbance to generate an extreme scene deviating from training distribution; inputting the mixed scene set into a mixed integer nonlinear programming model, and adopting a graph neural network to establish a topology-power flow agent model to accelerate solution; updating line impedance parameters through a Kalman filter, and collecting and checking actual output; and decomposing the corrected planning scheme into cloud global optimization and edge local control, and carrying out cloud-edge collaboration. According to the method, the adaptability of a power distribution network planning scheme in a complex and uncertain environment is improved by combining spatial-temporal feature alignment, adversarial network scene enhancement, a graph neural network and cloud edge collaborative optimization.

The invention discloses an anesthesia early warning method based on multi-source signal fusion. The anesthesia early warning method comprises the following steps that physiological signals in the anesthesia process are collected in real time and preprocessed; a multi-modal Transform attention network is adopted to extract correlation features in the time sequence physiological signals, and multi-modal fusion features are obtained; predicting the fusion feature at the next moment by using a long-short-term memory neural network to obtain a prediction error; updating a noise and observation covariance matrix of the Kalman filter according to the prediction error; the dynamically adjusted Kalman filter calibrates the multi-modal fusion features in real time; according to the calibration characteristics, anesthesia depth and consciousness state prediction values are calculated in real time, and early warning is output. The real-time performance, the stability and the accuracy of anesthesia state monitoring are improved.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention discloses a thermal management strategy management method and system based on an energy storage battery system, and relates to the technical field of battery thermal management, and the method comprises the steps: collecting temperature data, carrying out the preprocessing, generating and recording a measurement result and a convective heat transfer coefficient, taking the measurement result and the convective heat transfer coefficient as the input of a Kalman filter, and outputting an optimal estimation result of a battery. The temperature data refer to the environment temperature and the core temperature of the single battery, based on the optimal estimation result of the battery, establishing a fuzzy rule base for iteration and updating, outputting a dynamic temperature difference threshold value, constructing a three-dimensional thermodynamic potential function, calculating the cosine similarity of a cooling driving force vector and a cooling mode, and correcting the cosine similarity to obtain a cooling driving force vector. The mode switching list is generated, the cooling equipment decision mode is generated, starting judgment and optimization of the cooling mode are carried out, and the intelligent level, safety and energy efficiency ratio of thermal management of the energy storage battery system are remarkably improved.

The invention relates to the technical field of organic display data processing, in particular to a touch data processing method based on an organic display, which comprises the following steps of: monitoring a flexible deformation acceleration component and an environmental parameter change rate in real time through a dynamic trigger function, and triggering high-priority baseline calibration when a threshold value exceeds a limit; and generating a self-adaptive compensation amount matched with the current touch signal noise spectrum. And constructing a dielectric constant offset prediction model by using a long short-term memory neural network or a Kalman filter, deducing the dynamic change trend of the dielectric property of the material, and correcting a touch signal compensation coefficient in a grading manner according to the predicted offset. And a closed-loop error correction mechanism is formed based on the spatial distribution entropy reverse optimization model weight parameter and the trigger threshold boundary of the contact coordinate residual matrix. According to the method, the problem of time domain mismatch of fixed period calibration and organic material nonlinear offset is solved, the accumulation of touch coordinate analysis errors in a high-curvature deformation scene is effectively inhibited, and the touch positioning precision and the interaction response real-time performance are improved.

The invention relates to the technical field of book position retrieval, in particular to an intelligent library book retrieval method based on the Internet of Things, which comprises the following steps of: deploying an RFID (Radio Frequency Identification Device) dense read-write array on a bookshelf layer, arranging a UWB (Ultra Wideband) positioning base station in a library area channel, and embedding a composite label integrated with various sensors into a book cover; a dynamic position discrimination algorithm is introduced to identify the moving state of the book, an extended Kalman filter is utilized to fuse data to establish a motion trail prediction model, a hierarchical position display mechanism is adopted to provide detailed position information, and for temporarily placed books and overtime non-return books, the accuracy of positioning and tracking of the books is improved. And special processing and alarm prompting are respectively carried out, so that the problems of dynamic position updating lagging and inaccurate retrieval result in traditional library book retrieval are effectively solved, and the book retrieval efficiency of readers is remarkably improved.

The invention discloses a real-time multi-target detection system and method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting a video stream and an image sequence of a to-be-detected scene, and carrying out the preprocessing of the video stream and the image sequence; multi-scale feature extraction is carried out on the preprocessed input data by using a deep learning model, a multi-scale feature pyramid is constructed, and each layer of feature map represents target information of different scales; fusing the feature maps of different scales, introducing an attention mechanism, and performing weighting processing on the features of the target area; using an improved YOLOv5 target detection model to carry out target detection on the fused feature map, and outputting a bounding box and a category probability of a target; performing real-time tracking on the detected target by adopting a Kalman filter algorithm to generate a motion track of the target; carrying out post-processing on the detection result, wherein the post-processing comprises non-maximum suppression, target association and trajectory smoothing; and finally outputting a detection result and a tracking trajectory of the target.

The invention belongs to the field of aquaculture, and particularly relates to an underwater fish multi-target identification, tracking and motion analysis method, the method is based on a multi-target attitude tracking framework based on direction perception, and the framework comprises an improved YOLOv11 model (YOT model) and a directional bounding box attitude tracking module (OPBT module). The YOT model integrates directional bounding box detection and attitude estimation, adopts a task-aligned dynamic detection head structure, and combines a multi-task loss function to realize efficient direction perception detection and key point positioning. The OPBT module is based on an unscented Kalman filter, integrates rotation IoU, angle similarity and body length similarity, and improves identity continuity and occlusion recovery capability in multi-target tracking. The framework is suitable for complex scenes such as high-density fish schools, the real-time processing performance can be kept, meanwhile, the detection and tracking precision can be remarkably improved, and stable and reliable technical support is provided for individual behavior analysis.

The invention relates to a rigid-flexible trans-mass self-adaptive robot force position and rigidity collaborative optimization method and system, and relates to the technical field of robot control, and the method comprises the steps: firstly, carrying out the linearization processing of a robot dynamic model through a semi-implicit integral method, and obtaining a robot dynamic model; and in combination with an extended Kalman filter, stiffness parameters in the contact operation process are estimated in real time, a dynamic stiffness sensing model is constructed, and the system response capability is effectively improved. Then, a rigidity parameter is embedded into a Hertz contact model, the dependence of a traditional model on prior information of a contact surface is broken through, a dynamic interaction model between the robot and an operation object is established, and high-precision real-time sensing of normal force and friction force is achieved. And finally, under a nonlinear model predictive control framework, multi-dimensional physical constraints are constructed based on the contact force, the position and the contact rigidity, an optimal control model is obtained, control input is dynamically and adaptively updated through rolling optimization, and the stability and the control precision of the robot in rigid-flexible heterogeneous contact operation are improved.

The invention discloses an aircraft target tracking method and system based on compensation prediction, which are used for improving the target tracking precision in an image transmission delay scene. The method comprises the following steps: firstly, acquiring an image frame sequence of a target aircraft by using an airborne monocular camera, extracting a target center coordinate through a small target detection algorithm, and constructing a position sequence; the method comprises the following steps: extracting current high-frequency I MU data aiming at the condition that an image frame has transmission delay, inputting the current high-frequency I MU data into an LSTM-DKF model constructed by fusing LSTM and a delay Kalman filter, and predicting and generating a process noise and observation noise covariance matrix; and initializing a delay Kalman filter by using the matrix, and recursively predicting the target position during the delay period. And when the delayed image frame is received, backtracking and updating the state of the filter, recurring to the current moment again, and outputting the compensated target position. And finally, pixel deviation is calculated according to the compensation position, an aircraft tracking control instruction is generated, and high-precision target tracking is realized.

The invention relates to the technical field of flight simulation control and precision measurement, provides a precise positioning method, system and device for a three-dimensional adjustable connecting piece of a flight simulator and a storage medium, and solves the problems of flight simulation control and precision measurement. The method comprises the following steps: acquiring the real-time stroke length of a motion control rod, rotation angle change data and a connecting piece vibration image, outputting a displacement prediction parameter through a platform kinematics model, and generating three-dimensional point cloud data; performing sub-pixel identification and distortion compensation on the image through a mark point identification algorithm to generate a three-dimensional visual coordinate; fusing the point cloud and the visual data, and processing through a Kalman filter to obtain a space pose offset; finally, a displacement compensation instruction is generated, the pose of the connecting piece is adjusted through a closed-loop controller, and accurate positioning of the connecting piece relative to a flight target reference coordinate system is achieved. According to the invention, the accuracy and stability of the pose control of the connecting piece in the dynamic vibration environment are improved.

A method for estimation of state of health of a rechargeable battery includes: obtaining input data of a set of predetermined battery features that jointly indicates State of Health of the battery; applying a plurality of machine learning algorithms to conduct state of health estimation of the battery, wherein each machine learning algorithm, based on obtained input data from the battery features, calculates an estimation of state of health of the battery, as well as quantitative estimation of a confidence interval / value of the state of health estimation of the battery; and applying a Kalman filter based fusion algorithm for combining the state of health estimations from all of said plurality of machine learning algorithms, for providing a fused state of health estimation.

The invention relates to the technical field of test bed operation monitoring, in particular to a fuel pump test bed operation monitoring method and system. The method comprises the following steps: acquiring monitoring time sequence data of operation of a fuel pump test bed, performing feature extraction, constructing a coupling prediction model of a space-time diagram attention network-unscented Kalman filter, and predicting a process noise covariance matrix and a measurement noise covariance matrix according to the coupling prediction model, and performing state estimation by using an unscented Kalman filter to obtain vector posterior probability distribution, constructing a fault evolution trajectory manifold, calculating a mahalanobis distance between the vector posterior probability distribution and the fault evolution trajectory manifold, and taking the mahalanobis distance as a monitoring index. According to the scheme of the invention, the deep features which can better reflect the inherent nonlinear and complex dynamic characteristics of the system can be extracted from the multi-source data, the estimation accuracy of the filter on the potential health state of the system is improved, and the defects that model parameters are fixed and gradual change faults are difficult to capture in a traditional method are overcome.

The invention belongs to the technical field of equipment energy-saving control, and particularly discloses and provides an energy-saving control system based on a direct-current bus system, which comprises a direct-current bus framework module, an equipment state acquisition module, an equipment state analysis module and an energy flow control module. According to the method, the direct current bus voltage and the multi-dimensional operation data of the equipment motor are collected in real time, the motor power state space model is constructed by means of the Kalman filter, and the power sudden change probability of the future preset time period is accurately predicted. And meanwhile, an energy distribution strategy is dynamically adjusted in combination with the total power demand generated by the production schedule and according to the braking type and the power sudden change probability. The defect that dynamic power difference cannot be tracked through fixed threshold value feed-forward compensation in the prior art is effectively overcome, so that bus energy deposition is avoided, braking energy is distributed according to needs, meanwhile, it can be ensured that the braking energy can be recycled timely and correctly, and energy waste caused by misjudgment is reduced.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention discloses a shielding target tracking method based on dynamic size attenuation perception, and belongs to the technical field of computer vision and target tracking. The method comprises the following steps: continuously tracking a target by using a baseline tracker to obtain state information of the target in each frame; judging whether the target is lost or not according to the state information, if not, performing a conventional tracking mode, and directly outputting a target position through a baseline tracker; and otherwise, if the target is lost, judging whether the target is shielded, and if the target is shielded, performing motion state estimation and trajectory prediction according to the shielding starting moment and a Kalman filter to obtain a target prediction position. According to the invention, the real motion state of the target can be estimated more accurately, and the track prediction precision of the target during shielding is improved, so that the capture probability during target reproduction can be improved.

The invention discloses a high-dynamic mobile ad hoc network communication and sensing integrated routing method, device and equipment and a storage medium, an OFDM (Orthogonal Frequency Division Multiplexing) sensing system is adopted to actively acquire feature information of neighbor nodes, and meanwhile, neighbor features passively acquired based on communication feedback are matched and fused, so that the sensing precision and efficiency in an ad hoc network are remarkably improved. In order to reduce noise interference of a wireless communication environment and realize prediction of a future state of a network, a Kalman filter is adopted to carry out dynamic filtering and state estimation on neighbor feature information. And on the basis of the processed sensing data, the node further calculates stability and effectiveness indexes of the link, inputs the stability and effectiveness indexes into a link quality comprehensive evaluation model constructed by fuzzy logic, and finally generates predictive link quality parameters to provide priori knowledge for following routing selection. And the routing efficiency and the resource utilization are optimized through an on-demand routing normal form and a multipath routing mechanism. And meanwhile, through a node early warning mechanism and a fault pre-detection mechanism, the control overhead is remarkably reduced, and the network stability is improved.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention discloses a multi-modal measurement data fusion method for an unmanned aerial vehicle. The method comprises the following steps: synchronously acquiring multi-modal measurement data through a plurality of sensors carried on the unmanned aerial vehicle; performing preprocessing including time synchronization and space alignment on the data; based on the environmental reliability evaluation model, dynamically analyzing reliability parameters of each sensor in the current environment, and generating a dynamic weight for each sensor data; and utilizing a fusion module to carry out adaptive weighted fusion on the preprocessed data according to the dynamic weight, and generating a fusion result containing unmanned aerial vehicle global pose estimation and an environment map. The device comprises functional modules corresponding to the steps. The core of the method is that through dynamic reliability evaluation and weight generation, the weight is introduced into the Kalman filter as an observation noise covariance adjustment factor, adaptive optimization of the fusion strategy to the environment is realized, and the pose estimation precision, the system robustness and the long-term operation stability of the unmanned aerial vehicle in a complex scene are remarkably improved.

A detection device, such as an unmanned vehicle, is adapted to traverse a search area and generate sensor data associated with objects that may be present in the search area. The generated sensor data is used by a system including object detection inference models configured to receive the sensor data and output object data, a local object tracker configured to track detected objects in a local map, and a global object tracker configured to track detected objects on a global map. The local object tracker is configured to fuse object detections from the object detection inference models to identify locally tracked objects, and a Kalman filter processes frames of fused object data to resolve duplicates and / or invalid object detections. The global object tracker includes a pose manager, configured to track global objects in the global map and update the pose based on a map optimization process. User-in-the-loop processing includes a user interface for displaying and manual editing of detected object data.

The invention relates to a combined attitude determination technology, in particular to a high-precision combined attitude determination method based on a multi-antenna GNSS (Global Navigation Satellite System) and an INS (Inertial Navigation Satellite System), which comprises the following steps of: S1, fixing a GNSS receiver and the INS on a carrier; s2, calculating carrier position information and carrier speed information, and establishing a double-difference carrier phase observation model; s3, calculating a baseline vector predicted value between the antennas; s4, an LAMBDA algorithm model based on INS attitude information constraint is constructed; resolving the IC-LAMBDA algorithm model by using a least square method to obtain an optimal integer solution of the integer ambiguity; s5, calculating a baseline vector between the antennas; carrier attitude information is solved; and S6, fusing the GNSS navigation information and the INS navigation information by using an extended Kalman filter. The method solves the problems of low attitude measurement precision and poor scene adaptability of the existing integrated navigation technology, and is suitable for the fields of unmanned aerial vehicle control, unmanned vehicle driving, unmanned agricultural machinery seeding, marine navigation surveying and mapping and the like.

The invention discloses a ship-based radar and AIS data fusion method and device based on the satellite internet, and relates to the technical field of ship information. The method comprises the following steps: acquiring self-motion, AIS and radar data recorded by a plurality of ships in real time through a low-orbit satellite, and performing target trajectory tracking and data filtering by adopting a first-order linear ship motion model and a self-adaptive extended Kalman filter; the method comprises the following steps: establishing an error model containing a radial error coefficient, an azimuth angle error and an effective detection distance for dynamic system errors of a shipborne radar, and realizing radar-AIS target matching and error correction based on an adaptive differential evolution algorithm and a Hungary algorithm; and finally, performing fusion processing on unknown targets sensed by multiple nodes through a multi-hypothesis tracking method to form a globally unified target observation result. According to the method, shipborne radar data and AIS data can be effectively associated, real-time monitoring of ship targets in a wide-area sea area is achieved, and reliable technical support is provided for marine supervision, safety early warning and track backtracking.

The invention relates to a target tracking system based on multi-modal deep feature proxy cross attention guidance, and the method combines and uses a visible light image and a thermal infrared image, and improves the performance of a tracking algorithm under a complex illumination condition through an innovative proxy cross attention mechanism. The system comprises a multi-modal detector and a data associator, wherein the detector consists of a double-branch feature extraction network, a proxy cross attention feature enhancement module and a feature pyramid sharing convolution module; the data correlator comprises a neural Kalman filter and a low-confidence-coefficient-based detection box reuse strategy, the challenge of a complex motion scene is solved by dynamically adjusting process noise and observation noise parameters, and the long-term tracking performance is improved. According to the method, multi-modal data fusion is carried out on a deep feature level, so that target detection and tracking are optimized, and the tracking precision and stability in a complex environment are improved.

The invention provides a satellite compass installation error adaptive correction method, device and equipment and a storage medium, and the method comprises the steps: obtaining the operation data of a ship, and carrying out the preprocessing of the operation data; then, whether the navigation state meets a correction condition or not is evaluated according to the preprocessed operation data; when it is judged that the sailing state meets the correction condition, a multi-dimensional reliability factor is calculated according to the ship speed, the course change rate and the attitude change degree, a time weighting coefficient is calculated according to the accumulated operation time of the system, and the observation noise variance of a Kalman filter is jointly adjusted based on the multi-dimensional reliability factor and the time weighting coefficient; and performing Kalman filtering adaptive state prediction and updating based on the observation noise variance so as to extract an estimated value of the installation error of the satellite compass, and applying the estimated value of the installation error of the satellite compass to course data output by the satellite compass by adopting a progressive strategy. The installation error of the satellite compass is automatically corrected, so that the navigation requirement of a ship in long-term navigation is met.

The invention provides a multi-target tracking method and device based on a large model. The method provided by the invention comprises the following steps: acquiring a current frame image of a video, detecting a target in the current frame image through a detector, and generating a target detection frame and a corresponding confidence score; based on a dynamic equation of a Kalman filter, predicting the position of a track fragment in the current frame of image according to the track fragment of the previous frame of image; when the target detection frame is an effective detection frame, associating the target detection frame with the predicted trajectory fragment based on a feature coordinate matching method, and updating the position of the trajectory fragment through an observation equation after successful association; when the target detection frame is not the effective detection frame, generating a mask fragment of the target based on a mask fragment updating method, associating the mask fragment with the predicted trajectory, and updating the position of the trajectory fragment; and outputting the tracking result of the current frame, repeatedly executing the step of outputting the tracking result of each frame, integrating the tracking results of all single frames, and generating a complete tracking trajectory of all targets in the video.

The invention relates to the technical field of electric scooters, in particular to a vehicle battery energy-saving control system of a high-strength impact-resistant scooter, which comprises an impact-resistant battery bin, a battery energy-saving control system and a battery energy-saving control system, wherein the impact-resistant battery bin comprises a multi-layer composite shell, a vibration sensor and a filling layer; the energy-saving control module comprises an intelligent detection unit, a central controller and a power optimizer; the power optimizer is used for adjusting the PWM duty ratio of the motor driving circuit according to the power mapping table; the energy recovery module is used for receiving brake force, a slope angle, a motor rotating speed and a vehicle mass estimation value, calculating an optimal recovery current through a Kalman filter, and injecting the recovery current into a battery pack by adopting a bidirectional Buck-Boost circuit; and the safety cooperative controller executes a hierarchical protection strategy, and a data bus is connected with each module to realize data interaction. Therefore, the problems that in the prior art, an electric scooter is insufficient in battery endurance, low in energy recovery efficiency, weak in impact resistance, poor in collaboration and the like are solved.