75 results about "Trajectory segmentation" patented technology

The invention relates to a welding track control method, device and equipment and a storage medium, and the method comprises the following steps: carrying out imaging scanning and welding contour extraction on the surface of a to-be-welded workpiece through a multispectral camera to obtain a welding contour feature sequence; based on the welding contour feature sequence, deep reconstruction is conducted on a welding area of a to-be-welded workpiece, welding three-dimensional point cloud data are obtained, and a welding track space curve is extracted from the welding three-dimensional point cloud data; track segmentation planning is conducted on a preset welding gun on the basis of the welding track space curve, and a segmented motion track sequence is obtained; based on the segmented motion trail sequence, pose mapping is conducted on an end effector of the welding gun, and a joint space motion sequence is obtained; and on the basis of the joint space motion sequence, the welding gun is controlled to conduct welding operation on the surface of the welding workpiece, and the technical problem of precise motion control of the robot welding gun under the complex space posture is solved.

The invention discloses a motion track construction method based on a visual large model, and relates to the technical field of computer vision, and the method comprises the steps: employing a target tracking algorithm based on Kalman filtering, and generating a smooth time sequence synchronization track from an original laser radar point cloud and an original video stream; dividing the trajectory into a series of candidate kinematic segments which are complete in kinematics and semantics by applying a hybrid method combining a minimum description length principle and visual language model semantic verification; performing preliminary semantic annotation on the segments by utilizing a visual language model to generate an initial annotation track; performing logic consistency refining on the initial labeling track until the initial labeling track is converged into a refined labeling track; and formatting the refined trajectory into a standard structured semantic trajectory representation character string. According to the method, a semantic gap between low-dimensional physical observation and high-dimensional driving intention is bridged, and ideal input is provided for understanding and prediction tasks of downstream complex scenes.

The invention discloses an agricultural machinery field and road track classification method fusing spatial-temporal clustering and semantic segmentation. The requirements for farmland track and road track classification precision and operation efficiency in an actual agricultural machinery operation scene are met at the same time. The method mainly comprises three stages: firstly, track preprocessing; and removing abnormal track points by using four preprocessing operations of null value filling, attribute filtering, speed cleaning and linear interpolation, and complementing missing tracks. And 2, track clustering. A space-time neighbor track segment clustering method based on track position, time sequence and direction information is adopted to form a track group on the basis of removing a long-distance road driving track. And 3, track segmentation. And combining a channel and space mixed attention mechanism and a focus loss function to construct a trajectory diagram segmentation model based on U-Net so as to realize segmentation of a road driving trajectory, a drifting trajectory and a farmland trajectory close to a farmland plot in each trajectory group. Experiments show that the method provided by the invention has the highest classification precision in a real agricultural machine operation scene data set, and the average reasoning time is superior to that of a method with the highest precision in latest classification methods.

The invention discloses a multi-scene-oriented agricultural machine operation area calculation method based on a GNSS trajectory, and belongs to the field of agricultural machine operation area calculation methods. The method comprises the steps of obtaining an agricultural machine operation trajectory; the agricultural machine moving track is preprocessed; preliminarily segmenting the trajectory by using an agricultural machinery clustering method based on space-time density to form a trajectory cluster; using an improved YOLOv8-based field road trajectory segmentation model to correct wrong segmentation trajectories in the trajectory cluster, and obtaining farmland trajectory points; dividing the continuous farmland track points into a plurality of farmland plots, extracting a single operation line track, and generating a smooth continuous track by using a cubic spline interpolation algorithm; and constructing a track buffer area, performing intersection and merging, then converting the track buffer area into a UTM coordinate system, and calculating the operation area through a polygon area algorithm. Compared with an existing algorithm, the method has the advantages that the two stages of field road track segmentation and area calculation are creatively fused and expanded to various operation scenes, and the precision of agricultural machine track segmentation and operation area calculation is improved.

The invention discloses a truck trip chain identification method based on trajectory data, which comprises the following steps: S1, acquiring GPS trajectory data of a truck, and preprocessing the GPS trajectory data, including data cleaning, noise removal and trajectory segmentation; s2, by setting a speed threshold value and a time threshold value, the stop point of the truck is recognized; s3, spatial matching is carried out on the stay point and road network data, and the accurate position of the stay point is determined; s4, segmenting the track of the truck based on the time sequence and the space distribution of the stop points to form a plurality of travel segments; s5, analyzing the driving characteristics and the stop characteristics of each travel section, and removing stop points related to non-freight; and S6, identifying key staying points related to loading and unloading activities, and connecting the key staying points according to a time sequence to form a truck travel chain. According to the method, the actual running path and the logistics movable chain of the truck can be clearly restored, and high-precision technical support is provided for freight path analysis and industrial chain space structure recognition.

The invention relates to a ship arrival time high-precision prediction method and device based on a long-short-term neural network, and a storage medium, and the method comprises the steps: firstly carrying out the aggregation of an AIS and a navigation log according to hours, carrying out the space matching in a sliding window, and generating a fusion navigational speed; and then inverse distance weighted interpolation is carried out on meteorological data, wind wave parameters are complemented, and ship static information codes are unified. And then the three types of data are synchronized to a unified time sequence axis, after trajectory segmentation and Kalman filtering smoothing, an optimal feature subset is screened through recursive feature elimination and cross validation, the optimal feature subset is used as input to train a bidirectional long-short-term memory network model, and finally accurate prediction of the ship arrival time is achieved. Compared with the prior art, the method has the advantages that the long short-term memory network (LSTM) and the attention mechanism (Attention) are comprehensively utilized, high-precision and real-time prediction of the ETA of the ship is achieved, and the method can be widely applied to scenes such as port scheduling, shipping management and intelligent logistics.

The invention discloses a trajectory segmentation method and system based on key point recognition, and relates to the field of trajectory analysis and pattern recognition, and the method comprises the steps: S1, obtaining multi-source trajectory data, and generating time sequence trajectory information; s2, calculating the acceleration corresponding to the track point according to the track information, further calculating the corresponding curvature, carrying out curvature related information statistics through the curvature of the track, and calculating an optimized curvature first-order derivative corresponding to the track point through the curvature information and the statistical information of the curvature; s3, marking acceleration key points, optimizing curvature first-order derivative key points and circling area key points; and S4, integrating information of the acceleration key point, the optimized curvature first-order derivative key point and the circling area key point, and generating a track segmentation result. According to the method, the key points in the trajectory can be accurately recognized, particularly, high performance can be kept in complex trajectories and high-noise data, and the trajectory analysis efficiency and reliability are effectively improved.

The invention provides a method and system for supervising fishing operation behaviors in a marine ecological protection red line, and the method comprises the steps: obtaining AIS data, and extracting key data from the AIS data; wherein the key data comprises MMSI, position, navigational speed, navigational direction and navigational time; sub-track segments are divided through MMSI; ship trajectory segmentation is carried out according to the sub-trajectory segments; based on ship trajectory segmentation, judging whether a fishing boat fishing behavior exists or not; if the fishing boat catching behavior exists, judging whether the period is a no-fishing period or not; if the ship is not in the no-fishing period, ship AIS data are fully utilized, an integrated judgment system of the track, the navigational speed, the navigational time and the like is established, under the navigational speed and navigational time limiting conditions, through track feature judgment based on a direction distribution algorithm, the ship with the marine trawl fishing behavior can be more accurately and rapidly positioned, and the positioning accuracy is improved. The efficiency of managing man-made activities on the sea in the marine ecological protection red line is improved, and functional departments can conveniently make management and control decisions in time.

This invention relates to a rapid robot motion planning method. First, it models example actions using a sequence of trajectory points that characterizes task features. Then, it extracts key points from the example trajectory using a trajectory segmentation method based on a first-order difference metric. Finally, it uses PCA to obtain the motion pattern features of the key points of the example trajectory, determines prediction constraints, and achieves rapid motion prediction under a given robot state by solving a quadratic programming problem under these constraints, thus completing the planning. This invention also provides a rapid robot motion planning system. This invention improves the efficiency of robot motion generation in industrial environments and enhances the robot's adaptability to real-time customized tasks.

The invention provides a track segmentation method, system and device based on space-time perception and adaptive learning, and belongs to the technical field of artificial intelligence and track data processing. The track segmentation method comprises the following steps: preprocessing original track data, and dividing an unlabeled data set and a labeled data set; the method comprises the following steps: constructing an encoder-decoder model based on Transform, and carrying out self-supervision pre-training through mask reconstruction and a contrast learning task; introducing a segmentation decision head, and performing supervised fine tuning on the pre-training model by using a small amount of labeled data; after model deployment, continuous adaptive learning is realized by constructing a dynamic database and regularly performing online mixing loss fine adjustment. The method effectively reduces the dependence of the model on labeled data, improves the generalization ability, long-term stability and decision interpretability in a complex scene, and is suitable for air traffic control, target intention recognition, trajectory prediction and other scenes.

The invention discloses a flight trajectory prediction method and system based on airspace information perception, and the method comprises the steps: obtaining historical aircraft trajectory information, and carrying out the multi-scale flight trajectory segmentation based on downsampling; carrying out aircraft flight mode correlation graph calculation on the aircraft trajectory information of different scales; performing high-order tensor graph neural network construction based on the flight mode correlation graph, and fusing trajectory information of different airplanes in the same airspace; acquiring historical wind speed and wind direction information, and fusing the wind speed and wind direction information with the aircraft trajectory information; and fusing the fusion results according to different scales, and outputting a predicted future aircraft trajectory. According to the method, sufficient information interaction of different airplanes in the same airspace can be ensured, and meanwhile, extra peripheral wind speed and direction information is fused as auxiliary features, so that the flight path is accurately predicted.

Systems and methods, implemented as computer programs on one or more computers for training a consistency model for use in generating a frame of data, such as a frame of image data, and methods of using a trained consistency model to generate a frame of data. A consistency model is used to generate a frame of data by predicting a succession of de-noised frames starting with an initial, noisy frame at an initial time and ending with a final frame, without noise, at a final time. The consistency model is trained to generate self-consistent predictions over a trajectory of predicted frames corresponding to these times. Implementations of the described techniques divide the trajectory into segments and only require the model to generate self-consistent predictions over each segment. This can facilitate the rapid generation of high quality frames of data.

The embodiment of the invention relates to a processing method and device for track extraction and labeling based on roadside data. The method comprises the following steps: receiving a roadside sensing data set; abnormal track cleaning, static track smoothing, abnormal inflection track repairing and course angle correction are carried out on the roadside sensing data set; performing lane feature addition, track behavior type segmentation identification and behavior fragment track segmentation on all tracks of the roadside perception data set, and constructing a labeling record set based on all fragment tracks; identifying a motion game relationship between every two target tracks in the labeling record set to obtain a game relationship matrix; and generating a multi-target game scene according to the game relation matrix and the annotation record set. According to the invention, a large number of complex game scenes can be constructed based on the road side real perception data.

The invention provides a track segmentation labeling method and related equipment, and relates to the technical field of track data processing. Original track data of a moving target is acquired; clustering the original trajectory data by adopting a space-time clustering method to obtain a plurality of clusters, and segmenting trajectories in all the clusters in combination with an adaptive sliding window to obtain a segmentation result; carrying out manual annotation on the segmentation result to obtain a manual annotation result, taking the manual annotation result as priori knowledge, and carrying out feature extraction on the segmentation result to obtain multi-dimensional spatial-temporal features; performing automatic label inference on unlabeled segments in the segmentation result based on the multi-dimensional spatio-temporal features to obtain an inference result, and calculating the confidence of the inference result; the inference result is optimized according to the confidence coefficient, and a trajectory labeling result of the moving target is obtained; and the labeling efficiency is remarkably improved while the labeling accuracy is ensured.

The application discloses a kind of filtering and remaining area spray trajectory replanning method and system across station has been sprayed area, belong to industrial robot vision and automatic spraying technical field.This system is composed of mobile chassis, six-axis robot arm, terminal RGB-D camera, spray gun, station visual label and supporting function module;Robot generates standard spraying trajectory by visual segmentation, point cloud processing in first station and records sprayed area data, after moving across station, relies on visual label to complete multi-frame fusion relocation, calculates inter-station coordinate transformation matrix, maps historical sprayed area to current coordinate system and eliminates from real-time point cloud, re-plans serpentine spraying trajectory for remaining to be sprayed area, and accurately controls spray gun on-off in combination with trajectory segmentation logic.The application solves the problem that traditional mobile spraying robot is prone to repeated spraying, uneven paint film and repeated teaching when working across station, realizes multi-station continuous automatic spraying, and the trajectory runs smoothly with high spraying consistency.

A kind of agricultural machinery motion mode recognition method combining road network map and agricultural machinery trajectory clustering algorithm belongs to the technical field of map matching, the steps of the method include: obtaining agricultural machinery positioning trajectory original data;Original agricultural machinery positioning trajectory data is cleaned;Road network map in corresponding target area is obtained using agricultural machinery positioning trajectory data;The mapping deviation between agricultural machinery positioning trajectory and road of road network map is corrected using HFTMM algorithm;Determine agricultural machinery road driving motion mode according to the high-precision mapping relationship between agricultural machinery positioning trajectory and road of road network;Determine agricultural machinery field operation and field transfer motion mode using DBSCAN density clustering algorithm.The road scene of road network map and density clustering algorithm are fused in the present application to improve the accuracy and efficiency of agricultural machinery positioning trajectory segmentation, and more accurate agricultural machinery motion mode recognition results are obtained.

The present invention belongs to the technical field related to grinding and polishing, and discloses a segmented DMP robot grinding and polishing skill learning method based on trajectory segmentation. It includes the following steps: S1 collects the posture and normal force corresponding to each sampling point of the robot end during the grinding process, and calculates the combined speed of each sampling point during the grinding process; S2 calculates the normal force segmentation threshold and the speed segmentation threshold, constructs the trajectory segmentation condition, traverses all sampling points, and divides the motion trajectory of the robot end into three stages: approach-grinding-withdrawal; S3 learns the motion trajectories of the three stages respectively, and uses different regular systems for modeling; for the grinding stage, uses the arc length-probabilistic motion primitive to establish the grinding and polishing force model of the grinding stage, thereby realizing the robot grinding and polishing skill learning process. Through the present invention, the different stages of the teaching trajectory are segmented according to the trajectory data and force data, ensuring the accuracy of the trajectory segmentation and improving the efficiency of trajectory and grinding and polishing force learning.

The invention belongs to the technical field of vehicle trajectory prediction, and relates to a fine-grained highway automatic driving vehicle lane changing trajectory prediction method, which comprises the following steps: step 1, a data processing stage: data acquisition and screening, lane changing process segmentation and data same-dimensional processing are included; 2, a lane changing track prediction scheme: designing a heuristic attention-assisted encoder, determining an optimal structure of each stage of lane changing, finally constructing a network and verifying the network, and performing error analysis; 3, vehicle kinematics data prediction: analyzing the vehicle kinematics data, and selecting a prediction method to predict the kinematics data; according to the lane changing track segmentation and sampling algorithm, the lane changing process is divided into four stages, a data and behavior analysis basis is provided for a track prediction model and other related researches of lane changing, and the lane changing process can be deeply understood.

The invention discloses a mechanical arm track segmentation method based on Z-axis state transition and speed troughs. According to the method, a Z-axis height sequence and a speed vector sequence are extracted from a motion trail position sequence of an end effector of the mechanical arm. Firstly, features of a Z-axis height sequence are counted, a relative threshold value is set, Z-axis height is subjected to state division, and state conversion points are searched; and the moment of conversion from the high-order state or the intermediate state to the low-order state is paired with the nearest moment of conversion from the low-order state to the high-order state or the intermediate state to form a candidate interval. Then selecting an interval with the longest time span from all candidate intervals as a rough candidate interval; and then speed trough detection is conducted on the speed size sequence in the rough candidate interval, speed minimum value points closest to the starting point and the ending point of the interval are found to serve as boundary candidate points of starting and ending, a boundary point pair based on speed troughs is constructed, a mechanical arm track is segmented, and an action track of task execution is obtained.

The application discloses a trajectory compression method. In view of the deficiency of the prior art, the solution idea of the application is that, based on the global optimization idea, the feature trajectory sub-chain obtained by simplifying and compressing a line segment is globally optimized, and multiple influence factors such as an offset angle, a perpendicular Euclidean distance, a trajectory segmentation number and the like are comprehensively considered, an evaluation index capable of comprehensively reflecting space and time characteristics of a compression result is established, and therefore the contradiction between compression efficiency and effective information reservation and between the original trajectory and the possibility of being as similar as possible is well balanced. The application also discloses a trajectory compression device. Compared with the prior art, the application can realize offline compression of all trajectories containing more effective information and having higher accuracy, and the overall operation accuracy and efficiency of trajectory compression is improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a trajectory planning method and device for a machine tool, equipment and a medium. The method comprises the following steps: analyzing a G code track file of the numerical control machine tool to generate a middle track instruction; in a Cartesian space trajectory represented by the intermediate trajectory instruction, calculating a direction included angle and a theoretical speed variation according to the pose parameter and the motion parameter; according to the direction included angle and the theoretical speed variation, dividing the trajectory points into segmentation points or merging points so as to perform trajectory segmentation and merging processing on the Cartesian space trajectory; based on the Cartesian space trajectory subjected to trajectory segmentation and merging processing, a joint space trajectory is constructed through discrete and inverse solution processing; and performing smooth transition processing and speed planning processing on the joint space trajectory to generate a machine tool control instruction. Compared with the prior art, the problems that a track planning method is low in machining efficiency, poor in precision and large in vibration in machine tool application are solved.

The invention discloses a super real-time simulation and cooperative control integration method based on track segmentation calculation. The method comprises the following steps: for a plurality of missiles which the same target arrives at at the same time, acquiring real-time positions of the missiles in a flying process; performing super-real-time simulation according to the real-time position, and determining the remaining flight time of each missile; determining expected time according to the remaining flight time; and the time constraint guidance rate of each missile is determined according to the expected time, and then the path of the corresponding missile is adjusted. According to the invention, accurate prediction of the residual flight time is realized, closed-loop control of flight time error and lateral overload is established, and thus high-precision cooperation of multi-missile attack time is realized.

The invention provides a tethered robot anchor point detection and trajectory segmentation method based on tether dynamics, and belongs to the technical field of mobile robot navigation and positioning. The method is based on tether dynamic analysis, combines a speedometer of a robot and observation data of a tether, and preliminarily determines the position of an anchor point by generating rays corresponding to the direction of the tether and calculating ray intersection points at different moments; the sensitivity and effectiveness of anchor point detection are ensured by adopting a clustering algorithm of preliminary abnormal value elimination and abnormal value rejection; taking the clustering center point as a final anchor point estimation value; and analyzing the accuracy and integrity of track segmentation according to index transfer and track continuity. The innovative technology can provide a more accurate anchor point prior value for a robot positioning algorithm such as a graph optimization algorithm so as to enhance the positioning capability of the robot in a global map.

The application discloses a space-based space target trajectory segmentation fitting method based on infrared radiation characteristics, and comprises the following steps: (1) acquiring initial positioning information of a space target in a ground-fixed coordinate system through double-star joint observation; (2) monitoring the infrared radiation intensity of the space target in real time through an infrared sensor, identifying the classification points of the space target in different flight stages according to the infrared radiation intensity and signal-to-noise ratio change, and completing target trajectory segmentation; (3) for different flight stages, the least square method is introduced through an adaptive factor, the trajectory optimization fitting of the space target in the space-based scene is completed, high-precision positioning and tracking are realized, and the motion characteristics of the target are acquired. By using the application, the overfitting or underfitting problems in the traditional method can be reduced, so that the precision and reliability of space-based space target detection and tracking are improved, and the application is suitable for a complex space target observation environment.

The present invention provides a lower limb exoskeleton gait learning planning method and system based on segmented DMP, which relates to the field of lower limb robot gait planning. The method divides the trajectory of a complete gait cycle into several segments, performs teaching learning on each sub-trajectory, and sequentially activates the generalization process of the sub-trajectory in a periodic activation sequence, thereby generating a periodic anthropomorphic gait trajectory that meets the gait requirements, and the sub-trajectory target point can be changed as needed before the trajectory generalization activation, so as to realize the variable gait trajectory planning within the cycle. The segmented DMP in the embodiment of the present invention has better generalization ability, can generate a new trajectory that accurately passes through the expected intermediate point and maintains the shape of the original trajectory, avoids the difficulty of forcing the construction of the term when the starting point and the end point of the trajectory are similar or equal in a certain dimension; there will be no deformation of the trajectory shape; after the DMP generalizes the trajectory, a compensation algorithm based on the end point position error is added to ensure that each segment of the trajectory can reach the set end point.

The application provides a trajectory segmentation labeling method and related equipment, and relates to the technical field of trajectory data processing. The original trajectory data of a moving target is obtained. A space-time clustering method is used to cluster the original trajectory data, a plurality of clustering clusters are obtained, and the trajectories in all the clustering clusters are segmented by combining an adaptive sliding window to obtain a segmentation result. The segmentation result is manually labeled to obtain a manual labeling result. The manual labeling result is used as prior knowledge to extract features from the segmentation result to obtain multi-dimensional space-time features. Unlabeled segments in the segmentation result are automatically labeled based on the multi-dimensional space-time features to obtain an inference result, and the confidence of the inference result is calculated. The inference result is optimized according to the confidence to obtain the trajectory labeling result of the moving target. The labeling accuracy is ensured while the labeling efficiency is significantly improved.

This invention relates to the field of image enhancement technology, specifically to a method for segmenting rotary tillage operation trajectory images based on the gray-level co-occurrence matrix. The method includes: adaptively remapping and quantizing the original farmland image to obtain a quantized enhanced grayscale image; combining spectral components and performing mask convolution to obtain a spectral feature enhancement image; extracting the second-order probability distribution statistics of the multi-directional gray-level co-occurrence matrix to construct a multi-dimensional feature space vector map; quantizing the inverse smoothness using the inverse differential moment operator to obtain a texture feature gradient map; and determining physical faults through tensor fusion and morphological evolution to obtain a binarized trajectory segmentation image. This invention utilizes texture statistical characteristics to characterize differences in surface fragmentation, eliminating interference from uneven lighting and stubble occlusion, and achieving accurate extraction of operation trajectory boundaries in complex farmland environments.

The present invention provides a method for accelerating neutron transport calculation of a three-dimensional characteristic line of a domestic DCU architecture, and belongs to the field of high-performance computing technology. The method includes: constructing uniform / non-uniform coarse-mesh finite difference grid geometric modeling according to the reactor geometric modeling and domain decomposition structure; in the trajectory generation and trajectory segmentation stage, recording the relationship between the flat source area and the coarse-mesh grid and the coarse-mesh grid and the communication domain, storing the trajectory segments reaching the vertices, edges and faces of the coarse-mesh grid, storing the trajectory segments located at the domain boundary, recording the trajectory connection sequence between adjacent communication domains, and the coarse-mesh grid surface adjacency relationship; in the transport scanning stage, according to the domain decomposition structure, sending the calculation task to the device end, storing the neutron flow on the surface of the coarse-mesh grid to which the trajectory segment belongs according to the coarse-mesh grid points, edges and faces to which the trajectory segment belongs, and storing the neutron flow at the communication domain boundary that needs information transmission; transmitting the transport solution calculation result to the coarse-mesh finite difference solution module, constructing a coarse-mesh finite difference equation group and solving it using the successive super-relaxation iteration method, updating the flux of each flat source area according to the calculation result and calculating the effective neutron multiplication factor in the current iteration.