73 results about "Time map" patented technology

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 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 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 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.

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.

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.

The invention provides an online high-precision map construction method and a model construction method thereof, and the model construction method comprises the steps: sequentially constructing a backbone network, a view conversion network, a hierarchical query embedded network and a model optimization network based on a MapTr model architecture, and obtaining an initial model; training the initial model to obtain a map construction model; and carrying out model integration on the plurality of map construction models to obtain an online high-precision map construction model. According to the method, efficient and accurate high-precision map construction can be realized through a stronger backbone network, an effective view conversion and data enhancement technology and a model integration strategy. The method has excellent performance in the aspect of multi-category semantic information extraction, and is suitable for real-time map construction tasks in an automatic driving system.

The invention belongs to the technical field of robot navigation, and relates to a precise docking motion control method and system based on a local real-time map, and the method comprises the following steps: 1, obtaining the position and angle of a current point odometer of a mobile charging robot, and then obtaining the position and angle of a target point; 2, judging whether the construction of a local map coordinate system is completed, if the construction of the local map coordinate system is not completed, constructing a local region coordinate system, and if the construction of the local region coordinate system is completed, turning to step 3; 3, converting the current point to a local map; 4, calculating the position and angle deviation of the current point and the target point under the local coordinates; 5, calculating an angular velocity and a linear velocity according to the position and the angular deviation; and 6, releasing the angular velocity and the linear velocity. The method has the effect of constructing the local map according to the target point detected by the starting point and the current odometer data under the condition of not depending on the existing map and not changing the map characteristics.

The invention relates to the field of image processing, and provides a hand rehabilitation evaluation method, system and device based on gesture scores, and the method comprises the steps: obtaining spatial input features and time domain input features, and carrying out the spatial graph convolution and time graph convolution, and obtaining the spatial convolution output and time convolution output; carrying out average pooling and full connection to obtain space-time attention output; stage division is carried out, stage output is obtained, multi-scale residual image convolution and global average pooling are carried out, and an identification image is obtained; obtaining hand three-dimensional data, calculating a joint angle, calculating a thumb pair distance, and calculating a finger function score according to the joint angle and the thumb pair distance; calculating a hand activity range score and a gesture speed score, calculating a gesture accuracy score, and calculating a gesture score according to the hand activity range score, the gesture accuracy score and the gesture speed score; and performing weighted summation to obtain a hand activity score, and evaluating the hand rehabilitation degree according to the hand activity score.

A computer implemented method for obtaining a real-time map, h(x), of a distribution in a D-dimensional space of a physical quantity, ƒ(x), describing a true phenomenon characterizing a complex uncertain system, wherein said physical quantity, ƒ(x), is inaccessible to direct measurements. The method can be utilized to obtain real time maps characterizing complex uncertain systems from scarce and indirect measurements, using non-invasive techniques.

This document describes the techniques and systems associated with a radar system using a machine learning model for stationary object detection. The radar system includes a processor capable of receiving radar data in the form of time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy, which is then input to the machine learning model. The machine learning model is capable of receiving features corresponding to stationary objects extracted from the range-time map of multiple range intervals at each time-series frame as input. In this manner, the described radar system and techniques are able to accurately detect stationary objects of various sizes and extract key features corresponding to the stationary objects.

The present disclosure provides a real-time mapping method and device combined with a navigation map, electronic equipment and a storage medium. In the present disclosure, the real-time mapping method combined with the navigation map comprises: acquiring navigation map data of a vehicle and point cloud data collected by a laser radar on the vehicle; obtaining BEV features of a region around the vehicle according to the navigation map data and the point cloud data through an encoding model; generating a driving region classification map of the BEV by using the BEV features through a first classification model, the driving region classification map containing driving category information of the region around the vehicle; and generating a road structure classification map of the BEV by using the BEV features through a second classification model, the road structure classification map containing road category information of the region around the vehicle. The present disclosure can obtain a real-time map that can be directly used for path planning in combination with a navigation map without using a high-definition map.

A method and system for sea surface target recognition based on range-time images, belonging to the field of measurement image recognition and processing, firstly maps the actually observed sea surface RT image into sea surface RT image data with amplitude following a standard Rayleigh distribution. The covariance matrix of the mapped sea clutter data is constructed. A binary hypothesis testing method is used on the mapped sea clutter data to construct a generalized likelihood ratio test statistic. The optimal estimate is substituted into the log-likelihood ratio expression to obtain the standard form of the mapped generalized likelihood ratio detector. The detector is then used to identify whether a target exists in the range cell to be detected. This invention can convert arbitrary non-Gaussian sea surface RT image data into RT image data with amplitude following a standard Rayleigh distribution, thereby achieving unified processing in complex sea clutter environments and effectively improving the accuracy of target recognition under complex non-Gaussian sea clutter backgrounds.

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 automobile multi-source data time alignment and fusion method and system. The method comprises the steps of obtaining first data and second data with non-uniform time references, and performing standardization processing on time; in the second data, window data are extracted from each candidate position through sliding window search, time coverage is judged, the window data are resampled to a first data time point, and a similarity distance is calculated by adopting a dynamic time warping algorithm; selecting the position with the minimum distance to establish a time mapping relation; and performing data fusion to generate fused data covering the complete or partial time range of the second data. The system comprises a data acquisition and preprocessing module, a sliding window matching module based on dynamic time warping, a time alignment relationship establishment module, a data fusion module and a data output module. According to the method, the relation between the time type and the sampling frequency of the data is not limited, universality is achieved through self-adaptive resampling, and the problems that in the prior art, the requirement for data characteristics is harsh, and the application range is narrow are solved.

The present application relates to the technical field of engineering safety control, in particular to a three-dimensional real-time map construction method for power transmission and transformation engineering, which sequentially collects substation site clouds, determines initial voxel size according to target volume of drawings, creates voxel grid for thinning, retains any point in voxel space, constructs a three-dimensional map model in sequence according to regions, periodically detects the completeness of model features, if the accuracy is unqualified, determines the reason according to the variance of the difference between target size and drawings, and corrects the thinning mode, thinning parameters and data compression rate; if it is qualified, a three-dimensional real-time map is generated, the target live state, voltage level and safety distance are updated, and a safety operation virtual fence is constructed. The construction method can accurately thin the point cloud data according to the state of the point cloud data and the actual operation demand, thereby efficiently constructing a high-precision three-dimensional map, and providing more accurate and reliable safety protection for nearby live operation.

The invention discloses a time sequence classification method based on an attention mechanism and dynamic graph modeling. The method comprises the following steps: 1) extracting multi-scale time sequence features; 2) performing dynamic time sequence dependence modeling; 3) constructing a dynamic graph structure; 4) modeling a dynamic graph neural network; 5) double attention enhancement; 6) pooling the time graph; according to the multi-scale time sequence feature classification method, through combined modeling of the dynamic relation between the multi-scale time sequence features and the variables, stable feature characterization and effective distinguishing of multivariate time sequence data are achieved, so that the classification accuracy and reliability of multivariate time sequences under the complex space-time condition are improved, and the multi-scale time sequence feature classification method is suitable for classification application scenes of multi-source sensor data.

This application provides a map updating method, apparatus, device, storage medium, and program product. The method includes: acquiring driving trajectories reported by crowdsourced vehicles and the corresponding road perception elements; determining the map structure to be updated based on the driving trajectories, and determining the matching relationship between the road perception elements and map elements in the map structure to be updated; based on the matching relationship, statistically determining the element heat corresponding to the road perception elements and map elements respectively; and performing crowdsourced updating of the map structure to be updated based on the element heat and road perception elements. This application updates electronic maps through crowdsourced map updating technology, enabling low-cost, fast, and accurate updates to obtain electronic maps reflecting the latest state of real-world roads, meeting the real-time map requirements of autonomous driving.

The present disclosure relates to an information processing method, an information processing apparatus, a manufacturing system, and an article manufacturing method. The information processing method includes acquiring, in response to an input from a user, a content of an operation of an apparatus and definition information defining a time dependency of the operation, and generating a time chart of the operation based on the content and the definition information.

This invention relates to the field of IoT intelligent control technology and discloses an intelligent system for mosquito control equipment, including a time synchronization and control module, a dual-count fusion module, a multi-dimensional environmental perception module, a prediction and control module, a health assessment module, a regional collaborative optimization module, and a visualization management module. The time synchronization and control module obtains device control commands based on the fusion of BeiDou and GPS dual-channel time signals; the dual-count fusion module combines a weighing sensor and image recognition algorithm to obtain the accurate number of mosquitoes captured; the multi-dimensional environmental perception module collects environmental parameters and performs noise reduction processing; the prediction and control module uses transfer learning and incremental learning to build an activity prediction model; the health assessment module performs local prediction and optimizes the data upload strategy; the regional collaborative optimization module uses weighted Kriging interpolation and particle swarm optimization algorithms; and the visualization management module constructs a real-time map display. This invention enables intelligent management and precise control of mosquito control.

The application discloses an object detection method and system based on adaptive time window and time threshold, and the method comprises the following steps: acquiring event camera data, acquiring an event set in an optimal time window through an adaptive time window method; eliminating a part of events generated by self-motion of the event camera from the event set in the optimal time window through an adaptive compensation algorithm, and acquiring a compensated event set; constructing an event frame image through an adaptive threshold method according to the acquired optimal time window and the compensated event set; and performing iterative image matrix fitting on the constructed event frame image to acquire a final detection result. The application provides an object detection method based on adaptive time window and time threshold, realizes detection of fast-moving objects through an adaptive time window, self-motion compensation of a camera, a threshold of a time image and an iterative image matrix fitting method, thereby realizing the requirement of obstacle avoidance of fast-moving obstacles in the environment and improving the obstacle avoidance performance of the unmanned aerial vehicle.

The invention relates to the technical field of computer vision and positioning, in particular to a tight coupling method for vision relative positioning and absolute positioning. The technical problems that in the application process of a visual positioning method in the prior art, the visual positioning method depends on a holder, information utilization is insufficient, and the adaptive capacity is poor are solved. According to the technical scheme, the tight coupling method for visual relative positioning and absolute positioning comprises a visual relative positioning step, a reference image obtaining step, a real-time image preprocessing step, a feature matching step and an optimization step, in the feature matching step, feature matching is conducted on a preprocessed real-time image and a reference image, a homography matrix is calculated, and the real-time image and the reference image are optimized; mapping feature points on the real-time image to a world coordinate system in the visual relative positioning step; according to the method, visual absolute positioning and relative positioning are tightly coupled under a factor graph framework, and meanwhile, position, attitude and scale parameters are optimized, so that high-precision and high-robustness positioning is realized.

The present application relates to the field of image processing, and provide a kind of hand rehabilitation assessment method, system and equipment based on gesture score, including obtaining spatial input feature and time domain input feature, spatial graph convolution and time graph convolution are carried out to obtain spatial convolution output and time convolution output;Average pooling and full connection are carried out to obtain space-time attention output;Phase division is carried out to obtain stage output, multi-scale residual graph convolution and global average pooling are carried out to obtain identification image;Hand three-dimensional data is obtained, joint angle is calculated, thumb finger pair distance is calculated, and finger function score is calculated according to joint angle and thumb finger pair distance;Hand range of motion score and gesture speed score are calculated, gesture accuracy score is calculated, gesture score is calculated according to hand range of motion score, gesture accuracy score and gesture speed score;Weighted sum is obtained to obtain hand activity score, and the degree of hand rehabilitation is evaluated according to hand activity score.