663 results about "Motion prediction" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

The invention provides an anti-unmanned aerial vehicle intelligent identification and tracking system based on multi-source data fusion, and relates to the technical field of anti-unmanned aerial vehicle detection, and the system comprises a multi-source data preprocessing module which is used for outputting preprocessed multi-source data; the target detection module is used for carrying out unmanned aerial vehicle target detection on visual data in the preprocessed multi-source data and outputting a detection result containing a bounding box position, confidence and morphological characteristics; the target tracking module is used for performing unmanned aerial vehicle target tracking based on the target detection result and outputting a tracking result; and the fusion decision module is used for confirming the target identity based on the tracking result and the preprocessed multi-source data and outputting a final recognition result. The technical problems of low detection precision of small targets, difficulty in distinguishing similar targets, inaccurate 3D motion prediction, difficulty in re-identification after long-time shielding and the like in the prior art can be solved, and accurate identification, stable tracking and intelligent decision making of the unmanned aerial vehicle target are realized.

The invention relates to the technical field of image recognition, and provides an anti-falling recognition early warning method based on image recognition and related equipment thereof. The method comprises the following steps: carrying out multi-modal preprocessing on a real-time image acquisition data set to obtain an RGB-D data stream, carrying out target detection and three-dimensional attitude modeling on the RGB-D data stream to obtain a target personnel label set and a personnel attitude parameter set, and carrying out trajectory prediction on the personnel attitude parameter set through a physical kinematics model to obtain predicted motion trajectory data. Acquiring an inertial monitoring data set in real time according to the target person label set, performing multi-modal fusion in combination with the predicted motion trajectory data to obtain a confidence evaluation value, and performing risk quantification on the confidence evaluation value and the predicted motion trajectory data to obtain a graded early warning instruction. And performing protocol coding and signal conversion on the graded early warning instruction to obtain a control signal. According to the invention, through image identification, motion prediction, multi-modal data fusion and fine processing, the accuracy of anti-falling monitoring in a complex scene is improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an operation safety distance management method based on laser radar point cloud data, and the method comprises the steps: generating a dangerous point cloud set based on an initial three-dimensional point cloud model of a complete field environment; performing coordinate transformation on the point cloud data currently collected by the three-dimensional laser radar equipment based on the rigid body transformation matrix to obtain current point cloud data; registering the current point cloud data with the initial three-dimensional point cloud model and identifying all independent dynamic targets; determining a danger approaching trend of each independent dynamic target based on the motion prediction trajectory of each independent dynamic target; determining a dynamic safety distance threshold value of each dangerous dynamic target based on the danger approaching trend and the response delay of each independent dynamic target and the additional safety distance of the corresponding dangerous source area; triggering a corresponding alarm mechanism based on the dynamic safety distance threshold and the distance between the point cloud set of each independent dynamic target and the dangerous point cloud set; and the overall safety of a working site is improved.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The present disclosure provides an ice floe motion prediction method, a device, a storage medium, and an electronic device. In the method, the electronic device is configured to acquire a historical motion information sequence of ice floe in a to-be-navigated region, wherein the historical motion information sequence includes multiple pieces of historical motion information of the ice floe at different time points; invoke a motion prediction model to analyze a spatiotemporal relationship of the multiple pieces of historical motion information; and obtain motion prediction information of the ice floe in the to-be-navigated region.

The invention discloses a target detection method and device based on an aerial view, equipment, a medium and a product. The method comprises the following steps: extracting multi-scale features for an input image of a multi-view-angle surround-view camera; converting the point cloud data of the laser radar into a sparse voxel structure and generating BEV features for voxel features; fusing the multi-scale features and the BEV features to obtain fused BEV features; road topology and semantic information are extracted according to the fused BEV features, and a map is generated; and determining a three-dimensional bounding box and a motion state of the traffic participant according to the fused BEV features and a prediction result of the position of the traffic participant in the map. According to the technical scheme, multi-scale geometric features, BEV features and semantic information are fused, time sequence features of dynamic target detection and static map segmentation are synergetic, the reliability of target detection based on aerial view is improved, and the method is suitable for multi-task BEV perception of three-dimensional object detection, high-precision map segmentation and motion prediction.

The invention provides a class-independent motion prediction network system and method combining space-time and frequency domain features, equipment and a medium, and belongs to the field of automatic driving. The problem that the space-time dependency relationship and the dynamic characteristics cannot be fully captured in a complex dynamic scene in the moving target prediction is solved; comprising construction of a feature coding module, a hierarchical space-time integration module, a frequency-space fusion module and a feature decoding module, and the HTSIM fuses space-time information step by step through a multi-level space-time feature interactive learning mechanism, and can capture a long-term space-time dependency relationship and local dynamic change, so that a complex motion mode is modeled more accurately; fSFM combines the characteristics of a frequency domain and a space domain, multiband decomposition is carried out on input characteristics through two-dimensional discrete wavelet transform, and the capturing capability of dynamic characteristics is enhanced through deep fusion of the characteristics of the frequency domain and the space domain; the method is suitable for real-time track prediction tasks in intelligent traffic systems such as automatic driving and the like.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

The invention discloses an automatic obstacle avoidance method and system for an unmanned ship based on Beidou artificial intelligence, and relates to the field of computer vision, and the method comprises the steps: collecting a visible light image and a near-infrared image of a water surface environment, and carrying out the reflection suppression processing; performing feature fusion on the image to generate an environment perception graph; obstacle features are extracted through a cascade cavity convolution structure, and an obstacle pixel-level semantic segmentation result is obtained; constructing an obstacle motion prediction model, and obtaining a predicted obstacle motion track; acquiring motion track data of the unmanned ship based on Beidou positioning and electronic chart data; establishing a space-time collision detection model, calculating collision parameters, and generating a collision data set; an obstacle avoidance path is generated through a dynamic situation field method, and a rudder angle instruction and a propeller rotating speed instruction are obtained. The unmanned ship has the advantages that accurate sensing, motion prediction and intelligent obstacle avoidance of the unmanned ship on water surface obstacles are realized through Beidou positioning and technology, and the sailing safety and intelligent level are improved.

The invention discloses a real-time rendering optimization method based on dynamic rendering and multi-hardware acceleration cooperation, and relates to the technical field of AR and VR real-time rendering, and the method comprises the following specific steps: dividing a user field angle into three-level resolution regions through an eye movement tracking module, and dynamically adjusting the resolution and anti-aliasing strategy of each region; stage processing is realized through an asynchronous time warping hardware acceleration module; the asynchronous time warping hardware acceleration module is combined with the optical flow compensation module to cooperatively optimize anti-aliasing compensation, and dynamic key points are extracted through a sparse optical flow algorithm; a multi-hardware acceleration collaborative architecture is adopted, and GPU rendering pipelines are divided according to a multi-viewport parallel rendering module; and the motion prediction module synchronizes IMU data in real time through a special AXI bus, so that optimization model updating is realized, and a final rendering frame is obtained. Through cooperation of dynamic rendering and multi-hardware acceleration, the bottleneck of a traditional scheme in adaptability, energy efficiency and real-time performance is solved, and the rendering quality and cruising ability of AR / VR equipment are remarkably improved.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

The invention provides a laser radar odometer and static map construction method and system, and relates to the technical field of positioning and navigation.The method comprises the steps that semantic reasoning is used for obtaining point-by-point semantic tags for subsequent true dynamic point detection, and semantic constraints are provided for ICP registration; distortion removal is carried out based on motion prediction of a constant-speed motion model; performing dynamic point detection according to the shielding relation of the potential dynamic points; according to the method, multi-target tracking is carried out on a potential dynamic object to obtain state estimation, cross validation is carried out on the state estimation and a dynamic point detection result, instance-level dynamic objects based on priori pose estimation are accurately removed, unstable dynamic points are filtered out in the pre-registration stage, and the positioning accuracy and robustness of an odometer are improved. Semantic weights are introduced into ICP data association, pose estimation is obtained through robust optimization, and a static map is constructed. And high-precision and robust laser radar odometer and static map building is realized.

The invention discloses a mobile robot control method and system, and relates to the technical field of mobile robot intelligent motion control, and the method comprises the steps: collecting environment feature data and motion state data; performing preprocessing and feature fusion to generate standardized state data; constructing a reinforcement learning training environment in an offline simulation environment, and obtaining a convergent reinforcement learning strategy model; inputting the current robot standardized state data into the reinforcement learning strategy model, and obtaining and outputting a preliminary motion control instruction and a reference trajectory; a prediction control model MPC is established, current robot standardized state data are input, short-time motion prediction and optimization are carried out, and a final control instruction meeting constraint conditions is obtained through solving; instructions are distributed to the four Mecanum wheel drivers, and the rotating speed and the direction of each wheel are adjusted. According to the invention, high-precision trajectory tracking, adaptive obstacle avoidance and energy efficiency optimization of the mobile robot in a complex dynamic environment are realized, and the system stability and the intelligent level are improved.

The invention discloses a real-time respiratory movement prediction and compensation method for thoracic and abdominal radiotherapy. A respiratory signal acquisition module, a respiratory movement prediction module, a body surface mark point position monitoring module, a body surface mark point displacement prediction module, a body surface mark point coordinate conversion module, a tumor target center point prediction module and a tumor target visualization module are included. The invention discloses a respiratory movement prediction system based on a causal convolutional network, and the system achieves the precise prediction of the position of a tumor target region in the future by collecting a respiratory waveform signal and a body surface mark point movement track of a patient in real time, extracting a periodic movement mode, and predicting the three-dimensional displacement of the tumor target region in 0.5-2 seconds in the future.

The invention discloses a visual labeling mechanism device and an automatic labeling method thereof, and the method comprises the steps: obtaining a workpiece surface image in motion in real time through a visual assembly, extracting a pose parameter of a workpiece in combination with an image processing algorithm, generating a pose data set containing a coordinate parameter and a deflection angle, and inputting a motion prediction model. Calculating a future movement track of the workpiece in combination with the speed parameter of the conveying line, and generating an adjustment instruction set of the labeling head; the movement track of the multi-axis mechanical arm is controlled based on the adjusting instruction set, the attaching pressure of the label contact face is adjusted through the pressure feedback module, and labeling operation of the curved surface of the workpiece is completed; a line scanning camera module is used for scanning the geometric position of the labeled label, a real-time detection result is generated by comparing the geometric position with a preset quality standard, a compensation signal is triggered if position deviation exists, and compensation operation is conducted through a compensation pressure head so as to ensure that the labeling quality meets the preset standard; and through cooperative control of visual identification, track prediction and compensation, the stability and the fitting precision of dynamic labeling are effectively improved.

The present invention provides a vehicle control device capable of ensuring safety and ride comfort. The device includes: a self-location estimation processing unit 306 estimating self location (absolute position information on a map) of a vehicle by matching sensor information obtained from external sensors 2 to 5 mounted in the vehicle with map information including future information as a point group; a solid object movement predicting unit 307 predicting movement of an object as a factor of obstructing the matching with the map information; and a driving movement candidate generating unit (driving movement planning unit) 309 making a driving movement plan of the vehicle on the basis of a result of predicting movement of the obstructive factor object, a result of presuming a road situation at future time, and an estimation result of a position error of the vehicle at future time.

The invention relates to a multi-modal perception fusion target following method and system. The method comprises the following steps: adopting an improved KCF algorithm to realize a closed-loop process of target tracking, multi-scale space construction, feature fusion, response calculation, optimal scale decision and model updating; designing a four-level shielding processing mechanism fusing motion prediction and depth verification, and realizing tracking recovery in a shielding scene through shielding judgment, motion prediction, fine search and template protection; constructing a target distance mapping function fusing geometric distortion correction and attitude compensation, and realizing high-precision distance estimation based on monocular vision; laser radar point cloud information is integrated, an obstacle threat degree model is constructed, and cooperative path planning of following and obstacle avoidance is realized in combination with an improved TEB algorithm; and designing a linear velocity control law and an angular velocity control law based on the distance deviation and the azimuth angle deviation, and driving the robot to complete target following motion. According to the invention, high-precision and robust following of the robot to the target can be realized.

The invention relates to a collision early warning method based on a laser radar in a non-structural environment, belongs to the technical field of vehicle driving safety, and solves the technical problem of low accuracy and effectiveness of collision early warning in the non-structural environment. Comprising the following steps: in a vehicle body coordinate system, collecting point cloud data in a non-structural environment by using a laser radar, estimating a vehicle motion pose by using the point cloud data, and obtaining a real-time pose and speed of a vehicle; generating a vehicle motion prediction trajectory based on the motion pose and speed of the vehicle, and performing ground point cloud filtering and obstacle clustering detection on the point cloud data in the vehicle prediction driving area to obtain an obstacle clustering result; carrying out obstacle geometric collision check based on an obstacle clustering result, determining an obstacle vertex closest to the vehicle in a vehicle prediction trajectory range area, estimating the movement speed of the obstacle vertex, and calculating an RSS distance; if the RSS distance is smaller than the preset safety distance threshold value, collision early warning is triggered, and accurate early warning in the non-structural environment is achieved.

The invention relates to the technical field of navigation assistance, in particular to a dynamic obstacle prediction and obstacle avoidance path planning method based on multi-sensor fusion, which realizes omnibearing perception of a dynamic obstacle through multiple sensors, provides a rich and reliable data basis for subsequent processing, and improves the accuracy of the dynamic obstacle prediction and obstacle avoidance path planning. Meanwhile, motion prediction is carried out on a dynamic obstacle through data processing fusion and model prediction, the motion inertia of the obstacle is fully considered, track prediction of the obstacle is closer to a real physical rule, a global path search problem is converted into a local but large enough space search problem through a mode of delimiting a final obstacle avoidance target area, and the search efficiency is improved. According to the method, the number of grids needing to be processed and the search calculation amount are remarkably reduced, the harsh requirement of the mobile device for the real-time performance is met, the area possibly occupied by the obstacle in the future is avoided in real time in the mode of constructing the cost function, then the obstacle avoidance planning path is generated, and the path safety is improved.

The invention belongs to the field of ocean robots, and particularly relates to an underwater acousto-optic fusion sensing method and system based on multi-scale space-time alignment. According to the invention, environment parameter data, a target distance and a low-resolution image are acquired through the data acquisition layer; establishing an acoustic effective distance model and an optical effective distance model, and generating acoustic features, optical features and a distance-confidence mapping function; adjusting the focal length and the wavelength and generating a 4K image; carrying out acousto-optic feature fusion through the acoustic features, the optical features and a confidence coefficient function, and outputting fusion features; performing observation updating by using space-time asynchronous compensation and motion prediction; according to the method, starting point and detection are utilized, system parameters are updated in real time through a multi-target algorithm, a sensing-optimization-feedback-iteration closed loop is formed, and the full-distance environment adaptability is improved. According to the invention, the problem of data fusion fault caused by inconsistent sensing distances of acoustic and optical sensors is solved, and high-precision target identification and positioning with full-distance coverage are realized.

Systems and methods are disclosed for motion forecasting in autonomous vehicles using a machine learning model trained with cycle consistency loss. Some machine learning models are trained to predict future object motion based on past observed motion, using ground truth knowledge of future object motion. In practice, such models are often inaccurate and thus unsuitable for safety-critical operations. Disclosed herein is an improved training mechanism for an object prediction model, which training mechanism utilizes cycle consistency loss. This loss can be calculated using an inverted motion prediction-that is, given observed motion and a predicted future motion, how likely the predicted future motion, if passed through the model as if it were historical data, would result in a prediction of the observed motion. Training based on inverted or backward motion prediction can improve an ability of a machine learning model to accurately predict future motion based on observed motion.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention relates to the technical field of robot control, in particular to a complex distribution network operation environment-oriented mechanical arm track obstacle avoidance control method, which comprises the following steps of: acquiring geometric information of an operation environment, a mechanical arm body posture and a grabbed target object through a multi-modal sensor; performing time synchronization and coordinate system calibration on the information to obtain environment data under a unified reference system; on the basis of the environment data, discretizing a work space into a three-dimensional voxel grid, calculating the occupancy probability of each voxel, and generating a probability occupancy model in combination with dynamic obstacle recognition and motion prediction; according to the method, through multi-modal sensing and unified reference system modeling, the problem that dynamic obstacle avoidance fails due to the fact that most of traditional mechanical arm track control methods adopt static environment hypothesis and cannot sense dynamic environment changes in real time and cannot unify multi-source information reference is solved.

The invention discloses a multi-level and multi-mode target identification and dynamic tracking method based on an unmanned aerial vehicle, and relates to the technical field of target identification and tracking, and the method comprises the steps: obtaining target information in the tracking process of the unmanned aerial vehicle, processing visual features and semantic features through a double-flow heterogeneous network, and generating a fusion feature map; recognizing a search object in the fused feature map, locking a tracking target based on a target evaluation and motion prediction result, and starting real-time dynamic tracking of the unmanned aerial vehicle; and periodically calculating the coordinate position of the tracking target relative to the unmanned aerial vehicle, adaptively adjusting the calculation frequency through regional warning monitoring, and continuously tracking the tracking target. According to the method, different levels of target identification are established, the object range is defined through the target contour, the target to be tracked is determined through the color, the vehicle mark, the animal hair color, the dressing and vehicle color and the like, effective combination of target identification and a dynamic tracking technology is realized, and automatic searching and automatic tracking of the target needing to be searched can be completed.

The invention discloses a dynamic submarine cable response extremum prediction method and device fusing a physical mechanism and a dynamic time sequence characteristic, and the method comprises the steps: deeply fusing a dynamic submarine cable control equation and data driving training through a physical information neural network, and converting the mechanical mechanism of a dynamic submarine cable into a residual constraint term; the defect of poor generalization of a pure data driving method is overcome; amplitude characteristics of six-degree-of-freedom motion of the floating body are extracted through an LSTM time sequence prediction model, and a long time sequence dynamic coupling effect is compressed into physical interpretable characteristics; a structure type judgment module is designed, so that two types of mainstream submarine cable structures can be covered without reconstructing the model, and the engineering applicability is greatly enhanced; dynamically balancing data fitting loss, physical residual loss and motion prediction loss by adopting a time-varying weight strategy, accelerating convergence and ensuring that a prediction result accords with a physical rule; dynamic submarine cable strength failure early warning is provided for offshore wind power by outputting key node response extreme values and motion amplitude characteristics in real time, and the operation and maintenance cost of the offshore wind power is greatly reduced.