4112 results about "Sight line" patented technology

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

Disclosed is a time series InSAR tropospheric delay correction method for relieving atmospheric seasonal oscillation, so as to relieve the seasonal oscillation deviation caused by tropospheric delay. This method is based on the temporal and spatial characteristics of atmospheric delay. Firstly, based on the ERA-5 atmospheric grid data covering the study area, the ZTD negative exponential vertical profile function model is established. Fourier series function is introduced to establish the time series model for β and ZTDr, Finally, according to the radar incidence angle parameters, the delay is changed from zenith direction to radar line of sight direction.

A wireless transmit / receive unit, WTRU, receives a positioning request message from another WTRU (1402). The positioning request message indicates a request associated with one or more of a sidelink positioning reference signals, SL-PRS, transmission or a SL-PRS reception and indicates a range of one or more parameters associated with the SL-PRS transmission or reception; these parameters comprise one or more of SL-RSRP, angle of departure AoD, angle of arrival AoA, and a line of sight LOS / NLOS metric. The WTRU measures the one or more parameters indicated in the positioning request message (1404) and performs the SL-PRS transmission (1410) or the SL-PRS reception (1410) on the condition that the measured parameters are within the range indicated in the positioning request message (1408).

The invention discloses an efficient 3D Gaussian scene reconstruction method based on multi-modal depth distribution supervision, and compared with the prior art, pixel-level depth dense point clouds are directly extracted in a Gaussian initialization stage, a multi-modal depth distribution map is generated, random initialization of dense Gaussian positions according to COLMAP sparse point clouds is effectively avoided, and the reconstruction efficiency is improved. The problems that under the condition of few views, point clouds obtained by COLMAP are too sparse, provided geometric constraints are insufficient, and meanwhile randomly initialized Gaussian blocks the point clouds to move to the correct position due to the local minimum value are solved. According to the method, an image segmentation algorithm based on edge detection is applied to the image frame, a large number of Gaussian primitives which are far away from edge points and are similar in depth are fused, the 3D Gaussian number is effectively pruned, and memory consumption of a GPU is saved. According to the method, extra isotropic regularization loss is introduced into global gauss, so that the problem of Gaussian distribution in which artifacts are generated along the line-of-sight direction and the height is lengthened is avoided.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention provides a cloud intelligent driving control system based on a multi-modal large model, and belongs to the technical field of automatic driving. The system comprises a cloud multi-modal large model analysis and control module, a real-time remote driving takeover module, an advanced prediction and risk avoidance module, a cloud intelligent driving collaboration and optimization module, a multi-modal fault diagnosis and remote repair module and a real-time communication and interaction enhancement module. The system realizes cloud remote monitoring and control of the autonomous vehicle through interaction and cooperation among different modules, and assists the autonomous vehicle through remote control, so that the autonomous vehicle runs more safely; the early prediction and risk avoidance module of the system sets the prediction of the sight line of the vehicle and the prediction of the sight line of the vehicle, and sets corresponding prediction methods for the vehicles in two different environments for prediction in a mode of extracting different matrix samples, so that the prediction accuracy of the cloud system and the safety of the automatic driving vehicle are improved.

The invention discloses an intelligent head-mounted device with a camera capable of rotating along with the camera, and the device comprises an eye movement tracking camera which is used for collecting and measuring the image data of the iris of the two eyes of a wearer; the IMU sensor is used for detecting head movement data of a wearer; the image acquisition and processing module is used for generating eyeball movement data according to the image data of the iris of the two eyes, and converting the eyeball movement data and the head movement data into a rotation instruction; the binocular camera motion control device executes the rotation instruction, drives the binocular camera to move, moves along with the iris and the eyeball, and collects image data in real time to realize line-of-sight tracking; and the image acquisition and processing module is also used for carrying out image and / or video fusion processing according to the received image acquired by the binocular camera in combination with the head movement data and the eyeball movement data. According to the invention, the problem of limitation in the aspects of acquiring depth information and expanding the visual field can be solved, and a more flexible camera tracking and adjusting mechanism is designed, so that wider and clearer image acquisition is realized.

The invention provides a BIM (Building Information Modeling) plane cutting analysis method based on Cesium, which comprises the following steps of: obtaining geometrical characteristics, topological relations and semantic attributes of components in a BIM, and calculating importance weights of the components according to a preset evaluation standard; obtaining the viewpoint position and the sight line direction of a user, calculating the visibility of the sub-region, and generating a clipping priority queue by combining the importance weight of the sub-region; dynamically scheduling a cutting task according to the cutting priority queue, and re-evaluating the cutting priority if the user viewpoint distance or the shielding relationship is remarkably changed; obtaining a component list of a to-be-cut sub-region, and calculating an influence weight of a component on a cutting effect according to the distance between the component and the viewpoint and the shielding relationship; and for the simplified and cut component, a progressive grid simplification algorithm is adopted, and the grid resolution is adjusted according to the geometric complexity and cutting precision requirements of the component.

The invention provides a water surface and underwater heterogeneous cooperative control method and device based on a preset time disturbance observer. The control method mainly comprises the steps that a water surface unmanned ship controller calculates an expected control torque according to a specified expected trajectory, the actual state information of an unmanned ship body and a compensation value obtained by the observer; the underwater unmanned vehicle controller calculates an expected control moment according to the expected trajectory obtained by the guidance method, the actual state information of the water surface unmanned ship, the actual state information of the underwater unmanned vehicle body and a compensation value obtained by the observer; and heterogeneous cooperative control of the water surface unmanned ship and the underwater unmanned vehicle is realized. According to the method, guidance of water surface and underwater heterogeneous cooperative control is completed by fusing vertical projection and a sight line method; according to the improved self-adaptive integral terminal sliding mode control method, the external interference is estimated by using the preset time interference observer and is compensated in the controller to improve the anti-interference capability of the system, and the robustness and rapidity of the system are improved while the tracking precision of the system is improved by the provided improved self-adaptive integral terminal sliding mode control method.

Sight systems comprise a sight receiver and a base attachable to and detachable from a sight receiver. A base carries a sighting component, such as an iron sight or optic sight. A sighting component may be attachably and detachably mounted to a base, or a sighting component may be made integrally with a base. A base may carry plural sighting components. Sight systems may include plural interchangeable sight receivers, bases, and / or sighting components. In some embodiments, a sight system may comprise a base deployed with an elastomeric or otherwise resilient mounting pad. In some embodiments, an elastomeric or otherwise resilient mounting pad may be proud of a front or rear face of the base, or a side face of the base, or both. In some embodiments, the pad may be proud of a bottom surface of the base.

The invention provides a driver fatigue monitoring technology using eye movement tracking, and relates to the field of auxiliary driving, and the technology comprises the steps: collecting a face image frame through a front-end camera, carrying out Medipe (Media Pipeline) detection, recording a head posture, and recognizing an iris center region; extracting continuous eye closing time and mouth state information of the driver through the face information; a PNP algorithm is used; a world coordinate system and a camera coordinate system are established, and the sight line vector and the external environment are located in the same space; modeling is carried out on the human eyes and the fixation area; coordinates of a fixation point in a three-dimensional space are obtained through calculation of the geometric model; real-time attention analysis is carried out through the fixation point, and an analysis result is determined; the attention change condition and the fatigue state of the driver are effectively recognized through a BP neural network in combination with a fixation point analysis result. And early warning measures are taken in time to remind a driver. The system is crucial for improving the driving safety, and necessary warning and intervention can be provided when the driver is distracted or fatigued.

The invention provides an interactive classroom student dynamic portrait generation method, a medium and a system, and belongs to the technical field of student dynamic portrays.The method comprises the steps that a student facial image sequence is collected, facial feature points are extracted to construct an expression feature matrix, and sight line focus data are obtained by combining an eye movement tracking technology to calculate an eye light concentration degree index; and establishing an individual association model, identifying specific expressions and analyzing attention fluctuation characteristics. And fusing multi-dimensional features such as an expression similarity vector and an eye light concentration index into a dynamic attention state score. Learning state evaluation is realized based on a multilayer bidirectional converter network, wherein multi-head attention mechanism parameters are determined by expression change frequency, an eye light concentration threshold and an attention fluctuation rate. And finally, generating a student dynamic portrait feature map, and forming a classroom interaction effect evaluation index through clustering analysis, thereby solving the technical problem that the dynamic change of the student classroom attention state is difficult to accurately evaluate based on a single feature.

The invention provides an unmanned ship path tracking control method based on lightweight self-adaptive sight guidance. The method comprises a self-adaptive sight guidance algorithm, a foresight distance is adjusted in real time according to the current position and path deviation of an unmanned ship, ideal course information is obtained, and the path convergence speed and algorithm calculation efficiency are improved. And in the virtual simulation platform, marine environment information is combined, and unmanned ship dynamic model parameters are obtained through experiments. And constructing an objective function taking the path tracking error and the rudder angle change as evaluation indexes, and optimizing PID controller parameters by adopting an improved sparrow search algorithm. According to the algorithm, a dynamic additive updating strategy is introduced, so that the global search capability and the convergence stability are improved, and the optimal PID parameter is ensured to be obtained. The method has the advantages of being high in response speed, small in overshoot and high in path tracking precision in the complex marine environment, the real-time control performance and the environment adaptability of the unmanned ship can be remarkably improved, and the method is suitable for the unmanned ship path tracking control requirements in various task scenes.

The invention relates to the technical field of image recognition, in particular to an unattended vision detection method and system based on image recognition, and the method comprises the following steps: collecting vision detection field image data, carrying out scene structure line detection and plane detection to obtain a line segment set and plane parameters, and establishing a basic geometric description element; and based on the basic geometric description element, combining the detected user sight line area coordinate and the vision detection screen boundary coordinate to carry out spatial integration, and obtaining the geometric structure information of the initial scene. According to the method, scene structure line detection and plane detection are carried out by collecting image data of a vision detection site, a line segment set and plane parameters are obtained, a basic geometric description element is established, and construction of an initial scene geometric structure is realized by combining spatial integration of a user sight area and a vision detection screen boundary; and the stability of on-site space structure analysis is improved.

The invention discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

The invention relates to a display screen direction dynamic adjusting system and method based on user behaviors, and the system comprises a movable robot platform which is configured to achieve indoor autonomous movement through a bottom drive mechanism which comprises a high-precision motion assembly suitable for various ground materials; the six-degree-of-freedom mechanical arm module is vertically mounted at the top of the robot platform, and a display screen mounting interface is arranged at the tail end; the intelligent sensing module, the integrated infrared sensor array, the nine-axis inertial measurement unit and the depth vision sensor are configured to capture user biological characteristics and environment space data in real time, and through the movable robot platform and the six-degree-of-freedom mechanical arm module, autonomous movement and three-dimensional pose accurate adjustment of a display screen are achieved; and in combination with a machine learning algorithm and a sight tracking technology of the adaptive control module, highly autonomous and accurate user interaction experience is provided.

The invention relates to the technical field of screen control, in particular to a computer screen control method and system. The method comprises the following steps: collecting a computer lens image to carry out scene modeling, generating a three-dimensional usage scene model, carrying out scene privacy evaluation and generating a privacy degree parameter, analyzing a peep-proof demand according to the privacy degree parameter, generating a screen peep-proof demand, carrying out portrait positioning when peep-proof is needed, generating a portrait positioning set, and carrying out image processing according to the portrait positioning set. And calculating a virtual contact distance based on the portrait positioning set, performing contact sight simulation to obtain a figure sight track, analyzing a screen visible range according to the sight track and the virtual contact distance, and dynamically adjusting the screen brightness to realize the peep-proof control method. According to the invention, a safer computer screen control method is realized.

The invention provides an unmanned aerial vehicle heterogeneous cluster collaborative navigation method and system oriented to complex terrains. The method comprises the following steps: acquiring communication link quality data between nodes in a cluster and position information relative to a terrain obstacle; dynamically reconstructing the radiation direction of the airborne antenna of the selected node according to the distribution of the communication attenuation area, and establishing a directional communication link around a terrain obstacle; associating the connection state of the directional link with the position information, and constructing a joint distribution feature of the cluster communication capability and the spatial configuration; optimizing cluster topology by using a distributed collaborative mechanism, and dynamically adjusting relative distance constraints among nodes according to a terrain sight shielding range and heterogeneous maneuverability of the unmanned aerial vehicle; and generating a collaborative navigation instruction set adapted to terrain and interference according to the optimized topology, and controlling the nodes to execute three-dimensional track correction. The navigation continuity of the unmanned aerial vehicle heterogeneous cluster in a complex terrain environment is improved.

The invention belongs to application of a virtual reality technology in the field of automobile exhibition, and particularly relates to an automobile exhibition interactive experience method based on virtual reality. Firstly, a virtual exhibition hall and a vehicle type three-dimensional model are constructed and optimized, so that a user checks the appearance and the internal structure of a vehicle in an immersive manner; recording browsing behaviors, and analyzing preferences through a recommendation algorithm to generate personalized vehicle model recommendation; after a user selects a vehicle, configuration selection and real-time rendering display can be carried out, test driving can be selected, and the system loads different driving scenes to simulate control experience; and finally, capturing a user attention area based on a line-of-sight tracking technology of a clustering algorithm to generate a visual heat map so as to identify preference key points, and providing data support for marketing and exhibition planning. According to the method, the reality sense of virtual display is improved through multiple technologies, interactivity is enhanced, test driving experience is enriched, user preferences are accurately captured, and the defects of an existing automobile exhibition display mode are effectively overcome.

The invention discloses an automatic tracking system based on XR space, and relates to the technical field of XR space, the system comprises a sight tracking module, a scene pairing module, an interactive navigation module and a feedback adjustment module; the technical key points are as follows: establishing a preset instruction library, accurately mapping recognized eyeball poses to a specific analysis instruction in an XR space, and obtaining a fixation area and a movement direction in the XR space; according to the method, rough and fine virtual scene division is carried out based on eye movement data, the virtual scene where the user is located is judged in real time, the immersion and participation sense of the user are greatly improved, then an initial rotation value and guide time are set, a preliminary calculation model is dynamically adjusted according to user feedback, guide simulation is optimized, and it is ensured that the user obtains vivid experience.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention relates to the technical field of image recognition and storage, and discloses a driving video recording method and system based on four-way monitoring. The method comprises the following steps: synchronously acquiring and processing the surrounding environment of a vehicle and the head posture of a driver through a video stream to obtain driving attention reference data; compressing and storing the sight blind area data according to the head posture features to obtain hierarchical video data; performing enhanced mapping on the graded video data to obtain an attention compensation image; performing threat identification and sorting on the compensation image to obtain threat level data; establishing a threat level index structure; and performing hierarchical storage on the data according to the attention coverage degree to obtain a storage data set. According to the method and the device, the technical problem that key scene data may be lost due to lack of consideration of driver attention distribution in a multi-channel video real-time recording process is solved.

A snakelike robot for internal inspection of small pipelines comprises a head machine body and a tail machine body which are connected through a cross universal joint. Three groups of crawling driving mechanisms are uniformly distributed and fixedly mounted on each of the head machine body and the tail machine body; a supporting shaft is fixedly installed at the front end of the head machine body, a rolling brush mechanism is rotationally installed on the supporting shaft, and a rolling brush driving mechanism is fixedly installed on one side of the supporting shaft. A sealed cabin is fixedly installed at the top end of a supporting shaft on the head machine body, and a camera assembly is fixedly installed in the sealed cabin. A cleaning mechanism for the sealed cabin is detachably and fixedly mounted on the rolling brush mechanism of the head machine body, and the cleaning mechanism is in rotating contact with the sealed cabin; according to the utility model, the problem that the camera assembly is blocked by foreign matters such as chippings in the pipeline is effectively avoided, and the shooting detection sight of the camera assembly is ensured; meanwhile, through the arrangement of the rolling brush mechanism, cleaning of the inner wall of the pipeline in the advancing process is achieved, and smooth crawling of the robot is guaranteed.

The invention belongs to the field of eye movement data tracking, particularly relates to a pilot eye movement tracking method, system and equipment based on multi-camera fusion and a storage medium, and aims to solve the problems that in the prior art, the visual angle of a traditional eye tracker is limited, and sight estimation errors are caused by head movement. The method comprises the following steps of: constructing a multi-view visual perception network by deploying multiple cameras and eye trackers in a cockpit; multi-source data space-time alignment is achieved through space coordinate system mapping and time synchronization, and head motion errors are compensated by combining dynamic cutting of eye interested areas and three-dimensional head posture estimation. The geometric path generates a physical driving vector based on iris fitting and an eyeball model, and the data path regresses the vector through a deep learning model; and converting to a world coordinate system through a head posture after fusion. High-precision data of the eye tracker is preferentially adopted in the central area, and field-of-view data of the system is expanded in the edge area. According to the invention, the tracking precision under field angle coverage and motion interference is obviously improved.

Provided is an optical sight with a non-optical rear sight element. The optical sight has a housing, an optical screen element, a light source that projects a sighting reticle image onto a surface of the screen element. A non-optical rear sight element is positioned on the housing forward of the screen element. The non-optical rear sight element may be selectively removable.

The invention discloses an autonomous cognitive algorithm of a patrol robot hidden area based on a 3D laser radar, and relates to the technical field of artificial intelligence. The invention provides an autonomous cognitive algorithm for a patrol robot hidden area based on a 3D laser radar. The autonomous cognitive algorithm comprises the following steps: acquiring a non-ground point cloud by adopting a ground point cloud filtering algorithm; for the obtained non-ground point clouds, dividing concentric circle regions according to Euclidean distances, and performing Euclidean clustering on the point clouds in the concentric circle regions to obtain clustered point cloud clusters; according to the point cloud cluster, calculating an axis alignment bounding box, and obtaining a hidden point coordinate; and according to the point cloud cluster, calculating a directional bounding box, and calculating coordinates of a shielding point in a sight line shielding mode, thereby automatically calculating and obtaining a shielding angle. Therefore, by the adoption of the method, the hidden area is reasonably selected according to the angle information in the patrol process through the 3D laser radar data, the hiding effect is achieved, the patrol task is better completed, and high robustness and practicability are achieved.

The invention discloses a vehicle driving assistance system and method based on AR glasses. The system comprises an environment sensing module, a driver state monitoring module, a danger early warning and avoiding module, an automatic driving and auxiliary driving module, a driving simulation and training module, an abnormal behavior recognition module, a road information and navigation module and a health and emergency response module. The environment perception module is used for solving the problem of poor sight in extreme weather and road perception in a complex environment; by means of sensors such as the laser radar and the camera, road obstacles, pedestrians and vehicles can be accurately recognized, highlighted marking in the AR visual field is achieved, accidents caused by sight blind areas or distraction are reduced, indexes such as the blinking frequency and the head posture of a driver can be monitored, immediate reminding is conducted when fatigue or distraction is found, and parking and resting are forcibly suggested when necessary; and when the driver does not respond in time, the system can automatically brake or adjust the direction to avoid collision.

A WTRU may be configured to receive first configuration information associated with a channel state information reference signal (CSI-RS) resource set, a set of transmission configuration index (TCI) states, a set of probability thresholds, and / or a set of time durations. The WTRU may be configured to estimate a line of sight (LOS) probability of the WTRU based on one or more CSI-RS measurements. The WTRU may be configured to determine whether the estimated LOS probability is greater than or equal to a probability threshold from the set of probability thresholds. In response to determining that the estimated LOS probability is greater than or equal to the probability threshold, the WTRU may be configured to determine a time duration from the set of time durations based on the estimated LOS probability. The WTRU may be configured to predict a future TCI state applicable to an associated time instance.

The invention discloses a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on a digital array radar, and the method specifically comprises the steps: setting radar system parameters, building a time-varying motion model of an unmanned aerial vehicle cluster, and obtaining the formation pose distribution of the unmanned aerial vehicle cluster under a global coordinate system; component decomposition is carried out on an unmanned aerial vehicle CAD model, shielding analysis is carried out under the radar sight line constraint condition, time-varying scattering center parameter extraction is carried out according to the dynamic electromagnetic effect between targets, scattering center space distribution of an unmanned aerial vehicle cluster is updated in a time sequence recursion mode, and construction of a time-varying scattering center model is completed; a radar system signal model is constructed according to digital array radar DAR parameters, multi-dimensional dynamic feature extraction and parameter mapping are carried out, digital array radar multichannel signal modulation and unmanned aerial vehicle cluster target radar echo construction are completed, and radar scattering electromagnetic characteristic modeling of a dynamic unmanned aerial vehicle cluster is realized. According to the method, the model description accuracy is improved, and the calculation process is more efficient.