141 results about "Time map" patented technology

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention provides a task priority and equipment state linkage path adjustment method and related equipment, and relates to the technical field of intelligent distribution center systems. Task queue information, production system feedback information and equipment state information are obtained in real time, the dynamic priority score of each task is calculated based on the information, the urgency of a production critical path and the equipment suitability are integrated in the scores, and therefore it is ensured that priority calculation focuses on the core target of avoiding production interruption. Then, the system ranks the tasks according to the dynamic priority scores, and adopts a strategy of'high priority priority planning + path reservation '; the path of the high-priority task is preferentially planned and reserved on the space-time map, and path conflicts among multiple devices are avoided from the source. The low priority tasks are then directed to avoid these reserved areas. Finally, the planned conflict-free path is issued to the robot equipment for execution, and the overall operation efficiency and stability of the intelligent distribution center system are comprehensively improved.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and system for a power grid line, and particularly relates to the technical field of power grid routing inspection, and the method comprises the steps: 1, dynamically fusing a multi-source sensor and prior data, and constructing and updating a comprehensive threat field with a time attenuation mechanism and a passability map in real time; 2, decomposing an inspection line into subtask sections, and generating a reference route of each section by combining static safety cost; 3, on the basis of a real-time map in the flight of the unmanned aerial vehicle, a local optimal track is solved online through rolling window optimization of adaptive weight adjustment; and step 4, realizing multi-machine collaboration and automatic handover of blocked tasks through a block chain type distributed task state account book and an intelligent contract rule. According to the invention, the real-time avoidance capability, the environmental adaptability and the task continuity of the unmanned aerial vehicle to dynamic obstacles are remarkably enhanced, and the safety and the efficiency of power grid inspection operation are effectively guaranteed.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent path planning and control system for an automatic driving carrying robot, and the system is operated through the following method: collecting environment data through a multi-mode sensor, and processing a laser radar point cloud and a camera image; fusing multi-source information to generate a real-time map, and obtaining a three-dimensional environment model containing dynamic obstacle positions; according to the three-dimensional environment model, obstacle features are extracted, an improved neural network model is adopted to identify a plurality of current obstacles, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; the objective of the invention is to solve the problem that accurate sensing, real-time decision making and efficient obstacle avoidance are difficult to realize in a complex dynamic environment in the prior art.

The invention discloses a high-precision satellite attitude correction method, system and equipment for a maximum initial positioning error, and belongs to the technical field of machine vision image matching. A proper reference region is selected to prepare a first reference image and a second reference image, and feature descriptors of the reference images are extracted offline; performing down-sampling on a remote sensing image acquired by a satellite remote sensing camera, and performing image matching on a real-time image after down-sampling and the first reference image; calculating the optimal window position of the ground calibration point of the reference area in the real-time image according to the matching result; cutting the real-time image according to the window position to obtain a high-resolution calibration point real-time image; performing image matching on the high-resolution calibration point real-time image and the second reference image; and calculating latitude and longitude coordinates of the center of the real-time image according to a matching result for satellite attitude correction. The position error of the target area is gradually reduced in a two-stage remote sensing image matching mode, the longitude and latitude coordinates of the center of the real-time image are accurately positioned, and high-precision satellite attitude correction is realized.

The invention provides an artificial intelligence-based lumen lesion feature recognition method and system, and the method comprises the steps: obtaining an original image frame sequence collected by an electronic endoscope and a collection timestamp of each frame, and forming a pseudo-time image sequence set; inputting the pseudo time image sequence set into a pre-constructed candidate region extraction model, and outputting a focus candidate region set; performing vascular structure enhancement on the lesion candidate region set, constructing a graph neural network, extracting structural features through the graph neural network, outputting a lesion confidence score in combination with the region features, and finally generating an enhanced structured candidate region set in combination with the lesion candidate region set, the structural features and the lesion confidence score; performing trajectory aggregation on the structured candidate region set based on structural feature similarity and time continuity to form a cross-frame focus trajectory set; and calculating a space center position and a time range of a focus track according to the cross-frame focus track set, and generating a structured report.

The application provides an underground inspection path planning method for an inspection robot, and relates to the technical field of robot path planning.The method comprises the following steps: acquiring basic map data, robot basic data and an inspection plan; acquiring real-time map data and robot real-time data; determining an inspection node; determining a selectable inspection path; determining an inspection consumption coefficient; determining a preset inspection path according to the robot basic data, the real-time map data, the inspection consumption coefficient and the basic map data; determining whether the preset inspection path needs to be changed according to the basic map data, the robot basic data and the robot real-time data; in the case that it is determined that the preset inspection path needs to be changed, determining a changed inspection path according to the robot basic data, the basic map data and the robot real-time data, and performing inspection according to the changed inspection path. According to the application, the rationality of underground inspection path planning can be improved.

The invention discloses a port operation vehicle scheduling method and system based on map data, and particularly relates to the technical field of scheduling optimization. Real-time map state information of a port channel network is generated by collecting real-time map data of the port channel network and analyzing the real-time occupation state of a port channel road section. And analyzing the local change trend of the occupation state of the port channel road section and the priority change of the vehicle task based on the real-time map state information, outputting dynamically updated vehicle task priority information and local area map data updating information, identifying a priority propagation abnormal area, and generating priority inversion risk early warning information. By identifying the vehicle path conflict points causing the traffic jam, generating the path conflict resolution strategy information, adjusting the vehicle path planning and executing the vehicle scheduling, the collaboration, responsiveness and traffic efficiency of the operation vehicle scheduling in the port environment are effectively improved.

The invention discloses an ultrasonic guided wave damage ellipse imaging positioning method and equipment for a layer decreasing composite material laminated plate. The method comprises the following steps: S1, acquiring thickness change direction information of the laminated plate and position coordinates of an excitation transducer and a receiving transducer; s2, according to the material characteristic parameters, simulating and calculating the propagation time of ultrasonic guided waves transmitted from an excitation transducer to any point to generate a first theoretical propagation time map, and simulating and calculating the propagation time of the ultrasonic guided waves transmitted from any point to a receiving transducer to generate a second theoretical propagation time map to generate a second theoretical propagation time map; coupling influence of thickness change and material anisotropy on guided wave propagation is considered in the simulation process; s3, transmitting an ultrasonic guided wave signal, and extracting the actual flight time of a damaged wave packet; s4, calculating theoretical total propagation time, and determining a candidate area where damage exists; and S5, on the basis of repeated operation of the multiple groups of transducer pairs, taking a plurality of candidate region intersections to determine a final damage position. The objective of the invention is to solve the positioning error problem caused by the structural characteristics of a laminated plate in a traditional algorithm, and to realize more accurate damage detection and positioning.

The invention discloses a millimeter wave radar gesture recognition method and system based on gating collaborative fusion, and belongs to the technical field of artificial intelligence and man-machine interaction. The method comprises the following steps: firstly, acquiring and preprocessing original data through a millimeter wave radar sensor; secondly, extracting a global spatial feature map (such as a two-dimensional point cloud map) capable of representing the overall motion track of the gesture and a plurality of local dynamic feature maps (such as a distance-time map, a Doppler-time map and the like) capable of representing dynamic information of the gesture in parallel; and then, the extracted multi-modal features are processed through the gated collaborative fusion network designed by the invention. The core of the method is that the global spatial feature map is processed through a first neural network and a global average pooling layer to generate a static global feature vector; dynamically modulating a local feature sequence processed by a second neural network through a gating unit by using the static global feature vector; and finally, fusing all modulated sequences, inputting the fused sequences into a time sequence processing module for analysis, and outputting a final gesture category. According to the method, through an innovative gating fusion mechanism, dynamic cooperation of multi-modal features is realized, the problem that a traditional fusion method cannot adapt to different gesture feature changes is effectively solved, and the accuracy and robustness of gesture recognition are remarkably improved.

The application discloses a CSI-based position-independent human activity continuous learning recognition method, which comprises the following steps: 1, collecting CSI action sample data; 2, pre-processing the CSI action sample data; 3, constructing positive samples by randomly scaling the pre-processed samples in the time dimension; 4, constructing a multivariate time graph neural network and extracting CSI action sample features; 5, calculating the similarity between the sample feature values and the positive samples and the feature values of the remaining samples, obtaining a comparison loss, and optimizing the feature extraction network; 6, freezing the feature extraction network, sending the features obtained from the input samples into a classifier for training to obtain a classification model. When the application continuously learns new action categories, the user does not need to retrain the feature extraction network, and the new and old action recognition in any position in the room can be realized by providing limited position new category samples to train the classifier, and the practicability is relatively high.

The invention provides an electric field operation safety management and control method and system, and relates to the technical field of power plant operation data processing, an electric field operation site is divided into a plurality of sub-regions, laser point cloud data are collected, attribute parameters of the laser point cloud data are analyzed, optimization is carried out if problems of shielding, insufficient density and the like exist, and finally a refined three-dimensional point cloud model is constructed. The modeling mode can effectively improve the scene space reduction degree and the risk area identification precision, provide high-quality basic data for subsequent operation, receive the position signal of the target entity and map the position signal to the three-dimensional point cloud model in real time, and dynamically adjust the signal mapping precision by monitoring the mapping process parameters of the position signal and dynamically adjust the signal mapping precision. Therefore, the spatial position of the entity in the operation site is accurately obtained, the position information of the entity is compared with the risk area, whether the entity enters the preset safety control threshold value or not is judged, graded early warning management and control are achieved, the site safety response efficiency is practically improved, misinformation and missing report are reduced, and it is guaranteed that the operation process is safe and efficient.

The application provides a kind of robot interactive local map construction method and system based on depth camera, it is related to robot real-time map construction technical field. Including: obtaining depth image and color image by depth camera, obtaining IMU data by IMU;Color image, IMU data are input into global odometer module, and the pose of robot under global coordinate system is estimated in real time;According to global pose, depth image is constructed to the elevation grid map with robot as center, the terrain feature and the passage cost of each grid are calculated, and the local passability map is constructed;The region to be modified selected by operator in color image is obtained, the map grid corresponding to the region to be modified in color image is obtained based on image alignment module, and the passage cost of corresponding grid is modified, to realize the interactive modification of local passability map.The application combines the data of depth camera and the operation intention of human operator, realizes the interactive construction and modification of map.

The invention belongs to the technical field of insurance data prediction and analysis, and particularly provides an insurance product combination linkage prediction method and device, and the method comprises the steps: S1, carrying out the modeling of an economic relation, and forming an economic basic information model; s2, adopting a time trend capture module comprising expansion causal convolution to perform hierarchical capture of multi-scale trend characteristics on the model; s3, taking a future event as a forward supervision signal, injecting the future event into a time graph neural network prediction module, and updating economic basic information model parameters through an error feedback mechanism and dynamic loss calculation; and S4, according to the updated economic basic information model, outputting a prediction result of purchase intentions of the user for different insurance products in a period of time in the future. According to the method, a more scientific and reasonable strategy is made for an insurance company, decision errors caused by one-sided information are avoided, meanwhile, more personalized and precise insurance product recommendation and service are provided for the user, the satisfaction and loyalty of the user are improved, and the market competitiveness is enhanced.

This invention relates to time series prediction of UAV faults based on entropy weight fusion and a time-graph convolutional network (T-GCN). It acquires data on the UAV's acceleration, angular velocity, and angle in the x, y, and z directions using sensors; combines the data into a matrix and calculates the entropy weight fusion value; determines the threshold value for fault occurrence based on the chi-square distribution; and constructs graph data for input into a T-GCN model based on the relationships between the sensor data. n historical time data points are segmented from the graph data and input into the T-GCN model. After inputting n historical time data points, the T-GCN model can predict data at time T in the future. After model training, it captures the spatiotemporal correlation of the data obtained from the UAV's sensors and predicts the graph data based on the time series. The prediction results are accurate and reliable, facilitating timely maintenance of quadcopter UAVs.

This invention discloses a local SLAM construction method, a global SLAM construction method, and a construction device. In this embodiment, a cloud map and a real-time map are compared, and an object classification model is used to obtain the object classification of the current scene. Based on the object classification, prediction updates are performed, and a sub-map is obtained based on the prediction updates. The updated sub-maps obtained by the local SLAM construction method are then fused to obtain the global map. By classifying objects into dynamic and static objects, and predicting the motion of dynamic objects, a sub-map is obtained based on the prediction results, enabling real-time identification of dynamic objects.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a limited space unmanned aerial vehicle autonomous homeward voyage method based on real-time mapping and path evaluation. The method comprises the steps that when the unmanned aerial vehicle flies in a limited space without a GPS, real-time positioning is carried out through a laser radar SLAM technology, a three-dimensional grid map is constructed synchronously, and meanwhile a flight path is recorded in real time in a key point mode. When the electric quantity is insufficient or communication is lost to trigger return flight, the system generates two candidate paths in parallel, one path is a historical path which is backtracked in a reverse order, and the other path is a new path which is searched on a real-time map by using an A * algorithm. Then, the two paths are evaluated through a comprehensive scoring function, and scoring standards comprise path smoothness, path safety and path length; and finally, selecting a path with a relatively high score as an execution path, and performing safe return by combining a local obstacle avoidance algorithm. According to the invention, the limitation of pure path backtracking is overcome, and safer, more efficient and more intelligent autonomous homeward voyage is realized.

Systems and methods for real-time map data coverage optimization are disclosed. A system includes one or more processors configured to determine a real-time map data collection coverage in an interested region, wherein real-time map data are collected by one or more consumer vehicles at locations of the consumer vehicles, determine whether one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, in response to determining that the one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, generate a route comprising the one or more uncovered locations based on a map of the interested region, and send an instruction to cause operation of a data-collecting vehicle to follow the route for real-time map data collection.

The invention discloses an automatic operation and maintenance platform operation and maintenance method and system based on big data. Establishing an anomaly detection model through a topological dependency relationship between space layer modeling equipment in the ST-GNN space-time diagram neural network and utilizing a time layer to capture time sequence features of monitoring indexes; based on the anomaly detection model, combining a GAN confrontation generation network to generate confrontation sample data for training, and obtaining a target anomaly detection model; and inputting the fusion feature data into the target anomaly detection model, outputting a potential fault event and a confidence coefficient, and generating an optimal operation and maintenance strategy according to the potential fault event and the confidence coefficient based on an RL reinforcement learning framework. The long-term operation and maintenance stability is improved, and the management efficiency of large-scale complex operation and maintenance scenes is improved.

The invention discloses an intelligent field patrol method, device, equipment, system, medium and product, which are applied to the technical field of intelligent agriculture, and the method comprises the following steps: obtaining real-time attribute data of each preset area in a to-be-patrolled farmland from a pre-constructed digital real-time map of the to-be-patrolled farmland; inputting the real-time attribute data into a trained prediction model, and outputting patrol urgency scores of the preset areas by adopting the trained prediction model; determining a to-be-inspected area set according to each inspection urgency score, and generating a field inspection path covering the to-be-inspected area set according to a preset field inspection strategy by adopting a preset path planning algorithm; the preset field patrol strategy comprises preferentially patrolling a to-be-patrolled area with a higher urgency score; and controlling the field patrol equipment to carry out field patrol operation according to the field patrol path. The method can respond to the sudden event in time, inspect the area where the sudden event occurs, improve the field inspection efficiency, guarantee the field inspection effect, and avoid the expansion of a small-range problem.

The invention relates to the technical field of power system design, in particular to a three-dimensional GIS (geographic information system)-based power transmission line tension pay-off analogue simulation system, which comprises a three-dimensional terrain acquisition result set generated by acquiring lead, terrain and construction information and merging matched data, and a slope direction energy response data set generated by reconstructing terrain based on elevation and gradient. Connecting the control points according to a potential energy distribution sequence to form a continuous line track, analyzing stress and redistributing tensile force, generating a line tension distribution data set, loading tension data to simulate a pay-off process, and outputting a power transmission line tension pay-off analogue simulation result. According to the method, terrain potential energy, slope direction and tension are fused in a space coordinate system, terrain and line stress are coordinated, a path extends along a potential energy gradient, node tension is balanced after reconstruction and time sequence correction, mapping of potential energy response, slope direction extension and tension transmission is formed, pay-off mechanics is mapped into a stable path in real time, it is guaranteed that the line is consistent with the terrain, and the method has the advantages of being simple in structure and convenient to operate. And the coordination, the precision and the reliability are improved.

The invention provides a terahertz signal aliasing elimination method and system based on deconvolution, and relates to the technical field of detection, and the method comprises the steps: collecting time domain waveforms of a reference region and a detection region, and obtaining geometric and material parameters; calculating a point spread function based on the reference waveform and geometric prior; calculating a time axis standardization mapping function based on geometric prior, and carrying out standardization processing on the observation waveform; performing deconvolution processing on the normalized time axis to obtain a restored pulse train; and generating a flight time chart based on the recovered pulse train, and executing curvature correction in combination with geometric priori to obtain a defect distribution diagram. According to the method, by introducing reference region prior and time standardization, point spread function drift caused by curvature and refractive index gradual change is effectively overcome, noise amplification in a high-absorption medium is inhibited, signal aliasing can be stably removed in a polyethylene hot melting joint scene, and accurate defect positioning can be achieved.

A method and apparatus evaluates a map for autonomous driving. The method acquires pixel points of lane lines of a road in a static map; acquires pixel points of lane lines of the road in a real-time map; forms first fitted curves of the lane lines in the static map by fitting the acquired pixel points of the lane lines in the static map respectively; forms second fitted curves of the lane lines in the real-time map by fitting the acquired pixel points of the lane lines in the real-time map respectively; calculates fitted-curve-similarities between the first fitted curves and the corresponding second fitted curves; and evaluates the consistency of the static map and the real-time map based on the fitted-curve-similarities.

This invention discloses a deep learning-based method for detecting and tracking small unmanned aerial vehicles (UAVs). The method includes: 1) acquiring historical motion video data of the small UAV; 2) inputting the video into a salient target detection network to learn the UAV's features; 3) modeling the UAV's temporal and spatial maps using the features and position information of the same UAV at different times; 4) inputting the spatiotemporal maps into a trajectory tracking network based on long short-term memory (LSM) recurrent neural networks to track the UAV's trajectory; and 5) performing distance error analysis based on the tracked UAV trajectory. This invention provides a novel and practical method for tracking the motion trajectory of small UAVs. By using a salient target detection network to obtain the UAV's spatiotemporal information from historical motion videos, and by using a LSM recurrent neural network to model the UAV's trajectory features in both time and space dimensions, it achieves accurate detection and tracking of the small UAV's motion.

The present application relates to a robot, a method, apparatus, computer equipment, storage medium, and computer program product for estimating the motion state of a dynamic obstacle. The robot obtains point cloud data and positions of an obstacle at different times; based on the point cloud data and positions at different times, it plots a time image and a static probability image of the obstacle in an environmental map; based on the time image and the static probability image, it determines whether the obstacle is a dynamic obstacle; if the obstacle is a dynamic obstacle, it generates a dynamic obstacle time image based on the time image and the static probability image; and based on the dynamic obstacle time image, it determines the motion state of the dynamic obstacle. When the obstacle is in a dynamic motion state, the segmentation accuracy of the target point cloud and the background point cloud is high, resulting in a more accurate estimation of the target state.

The application discloses a traffic speed prediction method based on bidirectional interaction fusion space-time graph prompt, and belongs to the field of traffic speed prediction. First, the road network data and the historical traffic speed observation data of a target region are used to construct traffic space data and a historical traffic speed sequence, and a traffic speed prediction data set is generated by using a sliding time window; a multi-scale time graph prompt is obtained by constructing a multi-scale time subgraph from the historical traffic speed sequence; and a multi-relation space graph prompt is obtained by constructing a multi-relation space subgraph from the traffic space data and the historical traffic speed sequence, the multi-relation space subgraph containing physical neighborhoods and semantic neighborhoods. The two types of prompts are subjected to feature alignment, bidirectional attention interaction and gate fusion to generate a bidirectional interaction fusion space-time graph prompt, a traffic speed prediction backbone model is input with a historical input sequence, and a future traffic speed prediction result is output. The method improves prediction accuracy and generalization ability.

This application provides a real-time trajectory data processing method, apparatus, and system. The method includes: acquiring trajectory data of a moving object and performing denoising processing on the trajectory data to obtain valid trajectory point data; acquiring M candidate road network points of the current trajectory point from the valid trajectory point data; where M is a positive integer; determining a variable sliding window corresponding to the current trajectory point; wherein the length of the variable sliding window is K, the variable sliding window includes N first transition probabilities of the previous trajectory point and a first candidate road network point combination corresponding to each first transition probability, each first candidate road network point combination including K historical candidate road network points, where N and K are positive integers; and performing map matching processing on the M candidate road network points based on the variable sliding window to obtain the road network points of the current trajectory point. This achieves high-precision and low-latency real-time map matching of trajectory data.

The invention relates to computer security, and specifically to a method and system for creating and storing a time-based graph of all application behaviors in an enterprise or organization. For example, according to one aspect, multiple application behavioral metric definitions are expressed for expected application interactions and state on a temporal basis, these metrics are each part of a behavior that needs to be evaluated individually or in combination with other metrics. At least some of the behavioral metrics are expressed based on an association with an application profile or common entity. The Temporal Profile Graph make associating and lookup of application behaviors efficient and cheap on compute and memory requirements. The graph node relationship is built using a time-based key that enables quick association of all application behavior metrics to a specific period in time within the enterprise or organization operating environment.

The invention discloses a slope radar precision correction monitoring system and method based on a corner reflector, and the method comprises the steps: constructing an initial atlas between the corner reflector and a radar scanning angle, and constructing an initial slope state atlas between the displacement / deformation rate of each monitoring point in a monitored region and the radar scanning angle; when the side slope radar is in a precision correction mode, the side slope radar sequentially obtains each monitored corner reflector based on a set rotation angle and forms a corner reflector real-time atlas, and displacement deviation existing in the side slope radar is determined based on the position difference between the initial atlas and the corner reflector real-time atlas; when the side slope radar is in a dynamic monitoring mode, forming a real-time side slope state map of the monitored area, regenerating a real-time side slope state map after angle compensation, and performing point-to-point comparison on the real-time side slope state map after compensation correction and the initial side slope state map to determine the change of the monitored side slope; according to the invention, the monitoring precision of the mine pit slope is improved.

A low-altitude 3D radio map reconstruction method based on scene guidance and sparse measurement is proposed. This method constructs and trains a reconstruction network offline, comprising a sparse data converter module, a radio map data feature extraction module, a multi-scale convolutional pyramid module, and a layer-by-layer radio map reconstruction module. In the online phase, the trained reconstruction network performs real-time map reconstruction. This invention utilizes a deep neural network architecture to fuse environmental scene understanding and sparse measurement data, achieving high-precision, physically consistent 3D reference signal received power (RSRP) field reconstruction, significantly reducing measurement overhead and improving reconstruction quality. By fusing environmental building distribution with sparse RSRP measurements, capturing environmental patterns using feature pyramids, and maintaining vertical dependence through a layered reconstruction strategy, high-precision 3D radio map reconstruction is achieved.