3466 results about "Sight line" patented technology

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.

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 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 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.

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.

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.

Disclosed herein are system, method, and device embodiments for determining the mental state of a driver using gaze tracking and object detection. Specifically, an Advanced Driver Assistance System may determine if the driver's gaze is associated with spatial locations of objects of interest in the surrounding environment. The surrounding environment may be represented with a 360-degree, three-dimensional model generated by the external-facing cameras and other sensors. The driver's gaze direction may be represented by a heatmap derived based on the driver's eye movements. Whether or not the driver's gaze is associated with a spatial location of an object may be used as evidence about the driver's mental state. The system may perform appropriate actions based on the mental state including generating alerts, engaging mechanical systems, and adjusting parameters in ancillary safety systems to increase or decrease tolerance. The mental state may be tracked throughout a driving session and stored in a driver profile.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

Various aspects of the present disclosure relate to a use equipment (UE) configured with multiple artificial intelligence (AI) models each of which has been configured (e.g., trained) based on training data sets corresponding to one or more of different conditions, such as location of the UE, orientation of the UE, whether the UE is indoors or outdoors, whether the UE is line-of-sight or non-line-of-sight with a base station, and so forth. A network entity (e.g., a gNB) configures the UE with a set of reference signals for measurement of at least one quantity. The UE generates the measurement report based at least in part on the set of reference signals and one of the multiple AI models, and transmits the measurement report to the network entity (e.g., gNB).

Extended Reality (XR) applications may require high throughput and very low latency. New Radio (NR) Wireless receiver / transmitter units (WTRUs) running XR applications (XR-WTRUs) may be confronted with link failures when these applications are used in dense urban or indoor environments because of these devices operating in the millimeter band. Methods and devices are provided therefore for performing cell selection and beam management proactively to avoid link failure due to objects being in line of sight between the XR-WTRU and a network node with which it has established a connection. XR-WTRUs may therefore perform visual sensing that may enable visualizing current and future radio environment and using this information to prevent link failures by handing the XR-WTRU over to an alternative network node in line of sight of the XR-WTRU before the XR-WTRU is no longer in line of sight of the network node with which it has established a connection.

The invention relates to the technical field of computer aided design, in particular to a BIM-based garden landscape optimization method and system, and the method comprises the following steps: extracting a spatial data model in a BIM based on the spatial feature data of garden landscape elements, carrying out the sight tracking of garden viewing nodes, capturing the visual data of different angles, and carrying out the calculation of the visual data. And analyzing the visual area range and the visual focus attraction to obtain the garden landscape sight line access coverage rate. According to the invention, through landscape data acquisition, sight tracking and illumination analysis, improvement of visual accessibility identification, quantitative evaluation of footpath environment factors, improvement of path experience judgment precision, path structure combination intersection relation and substitutability optimization, improvement of connectivity and viewing continuity, and facility layout fusion interference identification and function coordination; the space partition reasonability is improved, path and element matching is adjusted, the space response capability is enhanced, conversion from static analysis to dynamic scheduling is achieved, and the layout adaptability and the touring integrating degree are improved.

In various examples, a line of sight (LOS) filter may be used to determine whether or not a detected object represents a potential collision risk, filter out detected objects that are deemed not to be a collision risk, and provide a representation of the remaining detected objects for evaluation by one or more downstream automatic emergency braking (AEB), collision mitigation braking systems (CMBS), and / or other modules of an ego-machine. For example, the LOS filter may use an estimated position of a detected object to derive a corresponding LOS angle between the heading of the ego-machine and the detected object, and apply one or more thresholds based on the LOS angle to identify detected objects that are non-collision risks. As such, the LOS filter may remove non-collision risks from consideration by the downstream AEB, CMBS, and / or other module(s).

The invention belongs to the technical field of automatic driving safety, and relates to a rainfall condition highway interleaving area automatic driving sight distance safety evaluation method based on conflict point position estimation, which comprises the following steps: acquiring laser radar related information, rainfall condition information, interleaving area scene information and automatic driving vehicle parameter information; target vehicle laser radar point cloud numbers under different distances and different relative course angles are obtained, then a laser radar sight line database is constructed, and a laser radar point number-distance regression function is obtained; selecting a laser radar point number threshold value based on the laser radar point cloud data set, and obtaining an effective identification distance by using a laser radar point number-distance regression function; extracting vehicle lane changing track characteristics of the interleaving area based on the vehicle track data set of the interleaving area, and obtaining vehicle track form distribution of the interleaving area by using K-means clustering; and predicting a target vehicle trajectory cluster, obtaining an interleaving region conflict point set and an available sight distance, establishing a sight distance failure performance function, and evaluating the automatic driving sight distance safety of each interleaving region scene.

The invention is suitable for the technical field of radar signal processing, and provides a space target SFM three-dimensional reconstruction method based on a multi-frame ISAR image, and the method comprises the steps: carrying out the preprocessing and imaging of large-angle inverse synthetic aperture radar ISAR echo data of a space target, and obtaining a multi-frame ISAR image sequence; extracting scattering points in each frame of ISAR image sequence according to an orthogonal matching pursuit algorithm, and performing feature matching on adjacent frames of ISAR image sequences to obtain a scattering point set; obtaining instantaneous radar sight line information, and constructing a projection matrix from a space target scattering center three-dimensional coordinate to an ISAR two-dimensional pixel plane according to the instantaneous radar sight line information; and obtaining a three-dimensional reconstruction result according to the scattering point set and the projection matrix in combination with a triangulation principle in an incremental motion recovery structure SFM method. According to the method, the robustness and precision of three-dimensional reconstruction can be improved.

The invention provides a high-dynamic deformation measurement system and method based on laser section contour differential scanning. The measurement system is composed of a section contour measurement unit, a calibration unit, an error compensation unit and a data processing unit, wherein the section contour measurement unit comprises a multi-sight-line structured light sensor module, a speed sensing module and a distance measurement module; the calibration unit comprises a synchronous control module, an adjustment module and a common tooth-shaped calibration target; the error compensation unit comprises a vibration detection module, a posture detection module and a temperature detection module; the data processing unit is a device with programming and computing functions. The measuring method comprises the following steps: (1) calibrating a laser plane and an image plane; (2) unifying a global coordinate system; (3) obtaining point cloud data; (4) error compensation; (5) constructing a deformation time-history model; and (6) carrying out maximum deformation global optimization. According to the measurement system and method, accurate expression and maximum value global optimization of a pavement micro-deformation three-dimensional space under the load effect can be realized.

The invention provides an exhibition hall content self-adaptive central control display method and system. The method comprises the following steps: acquiring and preprocessing exhibition hall display data; based on the exhibition hall display data obtained through preprocessing, generating feature vectors of the exhibition hall display data by adopting a multi-layer pulse neural network; based on the feature vector of the exhibition hall display data, outputting a content theme association matrix through a content association analysis model; acquiring real-time user behavior data, wherein the real-time user behavior data comprises user position information, sight focus information and stay time information; based on the content theme incidence matrix and the user behavior data, constructing a display strategy optimization model by adopting a space-time orthogonal propagation network so as to generate a self-adaptive display scheme through the display strategy optimization model; and encoding the self-adaptive display scheme by using a multicast addressing encoding scheme, and transmitting the self-adaptive display scheme to an exhibition hall display device for display.

The invention relates to the technical field of tunnel lighting, in particular to a tunnel portal lighting control method and system, and the method comprises the steps: S1, obtaining a first image in a short exposure time mode; obtaining a second image in a long exposure mode; s2, synthesizing the second image and the first image into a high-dynamic-range image, and dividing the high-dynamic-range image into a low-brightness area, a transition area and a high-brightness area; s3, generating a brightness transition curve of the transition area; s4, determining a brightness adjusting curve of the transition area according to the maximum brightness of the tunnel portal and the light and shade adaptive pupil area change rate; s5, determining the target brightness of each part of the transition area according to the brightness transition curve and the brightness adjustment curve; and S6, adjusting the brightness of the illumination lamp in the transition area to enable the brightness of each part of the transition area to reach the target brightness. The target brightness is determined according to the brightness transition curve in combination with the brightness adjustment curve, the influence of the brightness adaptive pupil area change rate on the sight line is fully considered, and the change of the brightness of the tunnel portal is more adaptive to the function of human eyes.

A method in a network node and a network is provided to determine line of sight, LOS, base stations for a user equipment (UE) is provided. A request is provided to at least one of the UE and a plurality of base stations to measure and report LOS detection measurements, wherein the plurality of base stations includes base stations of a serving cell of the UE. The LOS detection measurements are received from the at least one of the UE and the plurality of base stations. LOS base stations for the UE are determined based on the LOS detection measurements. An indication of the LOS base stations is transmitted to the UE.

The invention relates to the technical field of vehicles, in particular to an intelligent cabin man-machine cooperative control method and device, a vehicle, a medium and a product. Performing fusion processing on the voice signal data, the visual signal data and the tactile signal data of the current driver by using a preset dynamic weight distribution model to obtain a multi-modal fusion control instruction; and under the condition that the multi-mode fusion control instruction meets a preset sight-voice dual-mode condition, synchronously issuing the multi-mode fusion control instruction to a plurality of target execution devices in the intelligent cabin so as to drive each target execution device to perform cooperative control. Therefore, by constructing a cooperative control mechanism of multi-mode sensing and dynamic weight distribution, the problems that the false wake-up rate is high, the voice and graphical interface cooperative efficiency is low and the interaction mode cannot adapt to a complex driving environment due to a single or separated interaction mode in the related technology are effectively solved.

The embodiment of the invention discloses a visual field tracking method and device, and the method achieves the differential weight fusion of an image through a first infrared image set, greatly reduces the influence of a tear film, a lens scratch, a cosmetic contact lens line and other interference factors, improves the recognition accuracy of a cornea reflection point, and improves the sight tracking precision. The combined methods of FFT confidence analysis, RANSAC algorithm verification, Kalman filtering, median filtering and the like are adopted, interference of pseudo spots and dynamic noise is effectively reduced, and the stability and reliability of cornea reflection point detection are remarkably improved. The self-adaptive enhancement technology and the ROI accurate extraction method are adopted, so that the method can better adapt to eye feature differences of different users and various complex actual environments and illumination conditions, and the universality of the visual field tracking device is enhanced. Differential fusion, feature recognition, confidence verification and filtering processing are automatically carried out in the whole process, and manual intervention is not needed.

The invention discloses an intelligent cockpit implementation method and system based on single-model multi-task reasoning, and belongs to the field of automotive electronics, and the method comprises the steps: inputting shared depth features into a detection head and a first classification head after original image data in an intelligent cockpit is preprocessed; the output of the detection head is subjected to detection post-processing to obtain a target detection anchor frame and position information, and the output of the first classification head is subjected to classification post-processing to obtain distraction identification classification; after the target detection anchor frame and the position information are preprocessed, human body area sub-images are input into a safety belt recognition and classification model; inputting the face region sub-images into a fatigue recognition classification model, a face ID matching model, a fixation point estimation model, a sight line estimation model and a head posture estimation model in parallel; and a face image sequence and a voice sequence in the continuous video stream are collected, the face image sequence and the voice sequence are respectively input into the lip language recognition model and the voice recognition model, a recognition result is input into the large language model to be processed to generate an interaction instruction, and a response is output through the voice synthesis module.

The invention discloses a high-altitude falling simulation system based on VR equipment, and relates to the technical field of virtual reality simulation, and the system comprises a data collection module which collects the pupil distance and initial sight line data of a user; the visual disturbance module is used for dynamically adjusting local visual disturbance of a corresponding area in the high-altitude virtual scene based on the gaze data to form a visual disturbance effect; the psychological index calculation module is used for calculating an anxiety index and a focus drift speed by adopting a time sequence convolutional neural network according to the visual disturbance effect and the eye movement behavior of the user to obtain an anxiety index and a focus drift speed index; and the disturbance adjustment module adaptively adjusts the frequency and amplitude of the visual disturbance effect by adopting a method of combining fuzzy logic control and adaptive gain adjustment to obtain disturbance rhythm parameters. According to the method, the personalized matching ability and training effectiveness in psychological behavior intervention and virtual high-altitude adaptation training are improved.

The invention is applicable to the technical field of information interaction, and provides an asymptomatic patient interaction method based on an eye-controlled staring trigger communication interface, which comprises the following steps: sight tracking: acquiring facial image data of a patient through a camera, extracting facial and eye mark points, performing image processing on an eye area, and judging a staring direction; staring triggering: presetting a coordinate area of an interaction control, judging whether a fixation point of a patient falls into the control area in real time, and if the fixation point is continuously kept for preset time, triggering corresponding operation; executing a compensation mechanism: compensating head movement and illumination change in real time, and correcting a staring point coordinate; and interface design: designing an interactive interface with a daily communication function, and broadcasting the content of the control through voice. The invention provides an efficient, convenient and easy-to-use eye-controlled staring triggering communication interface for the patient with the asymptomatic disease, aims to reduce the learning cost and the operation complexity of the patient, and has great significance in improving the life quality of the patient with the asymptomatic disease.

The invention discloses a far-field imaging system, a metasurface grating and an imaging terminal, and relates to the technical field of image generation. Dynamic environment compensation and imaging quality optimization are realized through cooperation of multiple modules of the system: the environment temperature is read in real time, the refractive index of the metasurface grating is corrected according to a pre-stored mapping relation, and a target wavefront function is constructed and dispersed to obtain an initial phase distribution matrix; dividing a focusing area in combination with a user sight line, and compensating and regulating a phase distribution matrix by voltage gradient; based on the divided buffer and background regions, a multi-region sampling rate is configured to generate a mask, and the collection efficiency of the key region is improved; monitoring far-field information change in real time, and selecting an interpolation or total variation strategy to update the image; by means of image definition feedback, a secondary phase correction term is superposed to optimize a phase distribution matrix; the problems of traditional far-field imaging in the aspects of environment adaptation, dynamic focusing, data acquisition and imaging quality are solved, and the method is suitable for augmented reality glasses and the like.