1317 results about "Eye tracking on the ISS" patented technology

The invention discloses a semi-supervised medical image segmentation method based on eye movement guide mixed data enhancement. The method comprises the following steps: collecting fixation position information of a doctor when reading a medical image through eye movement tracking equipment; using a teacher-student network model and adopting a two-stage training mode to train the model; the method comprises the following steps: taking GazeMix as a data enhancement strategy in training, extracting a fixation area in a labeled or unlabeled image, and pasting the fixation area to the background of other unlabeled images to generate a mixed image data pair; in the first stage, a GazeMix data enhancement strategy is combined to pre-train a teacher network; in the second stage, a student network is trained, GazeMix generates diversified data, and meanwhile, a teacher network is pre-trained for pseudo tag generation; multi-scale gaze perception is adopted, and model perception information is extracted on different scales and used for perception loss calculation; and the optimizer iteratively optimizes the student network parameters, and updates the teacher network by using an index moving average method.

The invention provides an AR-based intelligent integrated production line special circuit control method and system, and relates to the technical field of production line control, and the method comprises the steps: collecting a real-time point cloud model of production line equipment through AR glasses, and carrying out the registration of the real-time point cloud model and a preset three-dimensional model, and obtaining the state deviation data of the equipment; and analyzing an equipment operation trend by using a time sequence convolutional network, and generating an operation state evaluation index and a control parameter correction amount. A to-be-adjusted circuit control node is projected and displayed in the AR glasses, a control instruction is generated according to eye movement tracking and gesture interaction operation of an operator, and a control model is formed by recording a corresponding relation between the instruction and an operation state evaluation index. And based on the model, an AR auxiliary control system is constructed by an edge calculation unit, a control instruction is generated through graph neural network reasoning according to a new operation state evaluation index, and operation guide information is displayed on an AR interface. And after the operator confirms, an instruction is issued to a production line controller, and an execution result is fed back to update the control model, so that closed-loop control is realized.

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

According to an aspect of the present invention, there is provided an electrical audio signal processing system and device, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

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 liquid crystal display drive control method and system, and the method comprises the steps: obtaining a page loaded in a memory of a liquid crystal computer, calculating the maximum image motion degree of the page loaded in the memory of the liquid crystal computer through the difference between continuous frames, and adjusting the refresh rate of a liquid crystal display screen; acquiring a fixation point position and fixation time of the user through an eye movement tracking technology, and adjusting the brightness of the liquid crystal display screen according to the fixation point position and the fixation time of the user; predicting future display contents and user behaviors by using a machine learning algorithm in combination with the use habits of the user and historical data of the display, and adjusting the brightness and refresh rate of the display based on a prediction result; the energy consumption data of the display are recorded in real time, the actual energy consumption and the expected energy consumption are compared and analyzed, and abnormity is found in time and feedback adjustment is carried out. Through the method and the corresponding system, the use energy consumption of the liquid crystal screen can be saved under the condition that the user experience is not influenced.

A video perspective method based on virtual reality relates to the technical field of virtual reality, and comprises: at least two cameras for capturing video information in real time, the cameras being provided with spatial positions and angles; performing geometric correction on the video information; the eye movement tracking module monitors the gazing direction of the eyes of the user in real time and adjusts the display position; the eye movement tracking module tracks eyeball movement; reconstructing a three-dimensional model of the surrounding environment of the user; based on the three-dimensional model, determining a relative position relationship between the virtual object and the real object; the head posture sensor obtains head movement information of a user; performing semantic segmentation on the video frame through a deep learning algorithm; and synthesizing the video information and the virtual content on the head-mounted display device, and outputting the synthesized video information and virtual content. The technical problems of visual discomfort and dizziness caused by position mismatching can be solved, and natural fusion of a real scene and a virtual scene is realized.

The embodiment of the invention provides a human eye attention positioning method and device based on a pre-trained neural network, and the method comprises the steps: obtaining human eye movement data in real time through an eye movement tracking device, extracting feature information related to a human eye fixation region through a pre-trained neural network model containing an attention mechanism based on the human eye movement data, and obtaining a human eye attention positioning result. Inputting the extracted feature information into a regression model, and calculating space coordinates of a human eye fixation area according to the feature information through the regression model; according to the invention, accurate positioning of the human eye fixation area can be realized through pre-training the neural network model, and fixation tracking precision and response speed in a complex scene are significantly improved.

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 a self-adaptive eye movement tracking method and device. The method comprises the steps that a calibration target position matrix and a historical center position are initialized; obtaining state information of the contact lenses, a calibration eye image sequence and a calibration sensing position sequence by using the intelligent head-mounted equipment, the intelligent contact lenses and the calibration target position matrix; processing the calibration target position matrix, the historical center position, the contact lens state information, the calibration eye image sequence and the calibration sensing position sequence to obtain a prediction target matrix, a prediction pupil center position matrix, a post-processing mark and an updated historical center position and calibration target position matrix; the post-processing mark is judged, and the calibration target position matrix is updated; and extracting the calibration target position matrix to obtain M predicted fixation point positions. Therefore, according to the method provided by the invention, the eye movement tracking mode can be adaptively selected, the defect of a single tracking method is overcome, and the accuracy and smoothness of eye movement tracking are improved.

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 belongs to the field of eye movement tracking, and particularly relates to an eye movement tracking system, method and equipment integrating electroencephalogram signals and a non-contact eye tracker. The objective of the invention is to solve the problems of limited acquisition range of a non-contact eye tracker and low spatial resolution of eye movement behavior deduction by EEG signals in the prior art. The method comprises the steps that eye movement data and electroencephalogram signals are synchronously collected, after calibration mapping and time sequence alignment preprocessing are conducted, an electroencephalogram modal behavior recognition model is supervised and trained through eye movement fixation point labels, and the incidence relation of the two modals is established. A dynamic fusion strategy is adopted in the online stage, when eye movement data are valid, a model prediction result and the eye movement data are fused to output a fixation point, when eye movement fails, the fixation point is calculated in combination with model prediction and historical data, enhanced compensation of eye movement tracking by electroencephalogram signals is achieved, the defect that the spatial resolution of a single EEG signal is low is effectively overcome, and the accuracy of eye movement tracking is improved. And the robustness and the tracking precision of the system in a complex scene are improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention discloses an eye movement tracking method, an eye movement tracking device, eye movement tracking equipment, computer equipment and a storage medium. The method comprises the following steps: calculating calibration gaze information and a calibration pupil position according to first event data collected by an event camera; determining a region of interest based on the calibrated pupil position; processing second event data collected by the event camera according to the region of interest to generate region of interest data, the collection time of the second event data being later than the collection time of the first event data; according to the region-of-interest data, calculating a current fixation position offset; according to the calibration gaze information and the current gaze position offset, the current gaze information is determined, the calibrated pupil position is updated based on the current gaze position offset, the gaze direction, the moving direction and the moving distance of the pupil are accurately captured in real time by utilizing the characteristics of high time resolution and the like of the event camera, and the eye movement tracking effect is effectively improved.

The invention discloses a personalized intelligent education textbook generation method and device based on a large model, and the method comprises the steps: obtaining a knowledge point set, learning data of a learner, a learning target, and eye movement tracking data; determining a cognitive style vector of the learner according to the learning data and the eye movement tracking data; according to the cognitive style vector and the learning target, a learning path diagram of the learner for the knowledge point set is constructed, and the learning path diagram comprises learning nodes and jump relations among the multiple learning nodes; determining cognitive matching parameters of the knowledge points based on the cognitive style vector and the knowledge point attribute vector of each knowledge point in the knowledge point set, and determining presentation forms of the knowledge points according to the cognitive matching parameters; and based on the learning path diagram and the presentation form, generating a personalized intelligent education textbook through a preset large model. The learning efficiency of students is improved.

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 provides an electroencephalogram eye tracker based on a brain-computer interface, a calibration method and device of the electroencephalogram eye tracker, computer equipment and a storage medium. According to the scheme, a stable stimulation source is provided for data acquisition by orderly displaying the calibration points, the eye movement data and the electroencephalogram signals are synchronously acquired, and the user state information is acquired from multiple dimensions. The attention index is quantified according to the electroencephalogram signal, the data quality is preliminarily controlled, the target calibration point is displayed again, the data is updated, and errors caused by inattention of the user are corrected. And finally, high-quality eye movement data is utilized to calibrate and compensate eye tracker system errors, individual differences and environmental influences, and the calibration robustness is greatly improved, so that the calibrated electroencephalogram eye tracker can more accurately detect the watching position of the user, and reliable data support is provided for subsequent research and application based on eye movement tracking.

The invention discloses an AR glasses control system and method, and belongs to the field of artificial intelligence and AR glasses application, and the method comprises the steps: capturing a user face image, combining a voice assistant, collecting user voiceprint data, and carrying out the matching through a recurrent neural network; constructing a three-dimensional environment model, positioning and marking obstacles and interactive objects, and identifying objects in the environment; the method comprises the following steps of: tracking a gesture, acquiring a key point coordinate by adopting an OpenPose algorithm, identifying a predefined gesture by utilizing a support vector machine, and mapping the gesture into a specific operation instruction; analyzing the user intention based on natural language processing, and calling a corresponding function module according to an analysis result; acquiring a gazing direction of a user by using an eye movement tracking sensor, calculating a screen focus position through an algorithm, and dynamically highlighting options or displaying information according to a gazing point position; through multi-modal data fusion, gestures, voices and fixation point input are analyzed in a unified manner. By accurately understanding the intention of the user, the interaction precision of the AR glasses is improved, and the user experience is optimized.

The invention relates to a pilot SA ability evaluation system based on eye movement tracking and space directional flight simulation, and the system comprises a space directional flight simulator which is used for simulating a real flight environment; the eye tracker is used for recording an eyeball movement track of a pilot in real time in a simulated real flight environment to obtain eye movement track time sequence data and shooting a flight training video; the microprocessor evaluation module comprises a time sequence data processing module and a Ti-SA model; the time sequence data processing module obtains corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training video and a preset AOI subdivision area; the Ti-SA model is used for predicting a current SA capability value of a pilot based on the eye movement trace time sequence data and the AOI fixation time sequence data and judging whether the predicted SA capability value is lower than a preset SA threshold value or not, and if the predicted SA capability value is lower than the preset SA threshold value, an early warning instruction is sent to the early warning and voice broadcasting module; and the early warning and voice broadcasting module is used for early warning when the SA capability value is lower than a preset SA threshold value.

Particular embodiments are directed to passthrough image generation for a mixed-reality experience. A device may determine, using an eye-tracking system of a head-mounted device, eye-tracking data associated with a user of the head-mounted device. The device may determine, based on the eye-tracking data, a desired scene depth for the user. The device may instruct a first autofocus camera of the head-mounted device to adjust a first focus distance based on the desired scene depth and capture a first image of a real-world environment of the user. The device may generate a first passthrough image based on the first image. The device may display the first passthrough image to a first eye of the user via a first display of the head-mounted device.

The invention provides a depression virtual reality auxiliary diagnosis and treatment method fused with multi-modal data, and the method comprises the steps: obtaining the multi-modal data, the multi-modal data comprises the peripheral physiological data, such as heart rate, body temperature and skin electricity, monitored by a flexible sensor based on epidermal electrons; and switching to a personalized interactive virtual scene according to the emotional state of the patient in real time, and in the interaction process, obtaining eyeball movement data by the eye movement tracking instrument, and knowing attention distribution, psychological state and micro facial expression change of the user. In combination with multi-modal data of peripheral physiological data, eye movement data and micro-expression change, key features are automatically extracted from multi-modal input data, and emotion analysis and recognition are performed through a multi-modal deep learning method with an attention mechanism. Data can be collected and analyzed in real time, the emotional state of a patient is objectively reflected through the combination of peripheral physiological data and micro-expression analysis, the requirements of the patient are met in a personalized mode, and the treatment effect is improved.

The invention provides a multi-mode user intention identification method and system based on eye movement tracking, the method identifies a gazing object based on an eye tracker, and combines modes such as text and image to estimate the user intention to execute a specific task, the eye movement tracking is that pupil positions are positioned through an event camera to obtain coordinates, and the gazing point of eyes is calculated; according to the method, the user intention is identified and estimated, then the gaze information graph is constructed through picture element segmentation, the user intention is identified and estimated, the robustness and accuracy of user intention identification are enhanced, the system can more intelligently adapt to complex requirements of different users in different scenes, and therefore more personalized service experience is provided.

The invention provides a subjective optometry prediction method combining wavefront aberration and optical signals. According to the method, eyeball wavefront aberration data are measured through a wavefront sensor, cornea, crystal and retina biological parameters are obtained by combining multi-view-field scanning of a galvanometer system, and after high-credibility data points are screened, a gradient refractive index eyeball three-dimensional model is constructed by adopting an NURBS algorithm; using finite element analysis to optimize cornea stress distribution, iteratively correcting the model through a deformation energy function, and dynamically compensating eyeball motion errors based on eye movement tracking data; and generating a multi-field-angle visual chart image through ray tracing simulation, and predicting a subjective optometry result in combination with an image quality evaluation algorithm. According to the method, high-precision modeling, dynamic error suppression and intelligent simulation technologies are fused, the optometry prediction accuracy, the personalized adaptation degree and the dynamic scene adaptability are remarkably improved, and an efficient and reliable solution is provided for refraction correction.

The invention discloses a part surface detection light source control method and system, a storage medium and a program product, and the method comprises the steps: receiving the real-time sight data, sent by eye movement tracking equipment, of a detector; a part surface picture is obtained through monitoring equipment, and a real-time observation range is obtained through calculation according to the part surface picture and the real-time sight line data; determining a current detection surface according to the real-time observation range and the part surface picture, and determining surface data corresponding to the current detection surface according to the part surface picture; inputting the surface data and the real-time line-of-sight data into a light source parameter calculation function to obtain light source adjustment parameters including light source coordinates, light source intensity and an irradiation angle; and controlling the light source equipment to start running and adjusting the running parameters of the light source equipment into the light source adjusting parameters. According to the method, the light source control time is shortened, the part surface detection time is shortened, the detection complexity is reduced, and the light source control efficiency is improved.

The invention discloses a surgical operation system with a training or auxiliary function, particularly relates to the field of intelligent surgical operation systems, and cooperatively works through five modules including multi-modal sensing, layered AI decision making, dynamic feedback, closed-loop learning and an expansion interface. MEMS pressure sensing and biological signal fusion sensing are adopted, strategic layer three-dimensional navigation and execution layer magneto-rheological damping control are combined, and an operation whole-process optimization system is constructed; a multi-source data persistent evolution model is integrated through federal learning, and precise man-machine interaction is realized by matching eye movement tracking AR and a variable stiffness touch glove. According to the system, the intraoperative risk identification accuracy is improved, the operation efficiency is improved, third-party equipment extension and doctor skill quantitative evaluation are supported, and the operation safety and the training standardization level are remarkably improved.

The invention provides a method and a system for dynamically evaluating competency of civil aviation flight trainees, and relates to the technical field of information collection and analysis systems.The method comprises the steps of firstly, obtaining various observation data including operation behavior data, physiological signal data, eye movement tracking data, face video data and voice dialogue data; a pre-trained multi-modal feature extraction model is utilized to mine the performance features of the trainees from different angles, and comprehensive feature representation is generated; secondly, a technical ability score, a psychological quality score, a team cooperation score and an emergency response ability score are obtained through a multi-task scoring model, a teacher score is obtained according to pre-constructed threat and error management classification evaluation indexes, then a comprehensive score comprehensively reflecting the ability of students is obtained through weighted fusion, and finally, the comprehensive score of the students is obtained. And combining the comprehensive score change rate and the score difference progressive value through a competency index calculation formula to obtain a competency index capable of dynamically reflecting a trainee ability development trajectory.

The invention is applicable to the field of intelligent audio adjustment, and provides an immersive audio-video follow-up adjustment method and system, and the method comprises the steps: constructing a multi-dimensional perception system, and collecting multi-source information in real time; carrying out fusion processing on the collected multi-source information based on a deep neural network model, mining a dynamic mapping relation between a user state and the video content through space-time correlation analysis, and identifying a user interaction intention and an emotional tone and a space scene attribute of the video content; according to a fusion processing result, calling a dynamic parameter adjustment engine, and generating an audio parameter adjustment scheme in real time; a user experience feedback closed loop is constructed, a visual attention area of a user is collected through eye movement tracking equipment, and personalized adjustment preference parameters are generated; according to the method and the device, the audio effect is accurately matched with the user state and the audio and video content, the naturalness and the adaptability of immersive experience are remarkably improved, universality and individual differences are considered, and the audio experience which is more suitable for scenes and needs of the user is brought to the user.

The embodiment of the invention provides a cognitive training method and system based on an eye movement tracking control technology, and relates to the field of eye movement tracking, and the method comprises the steps: obtaining test data, corresponding to a plurality of eye movement test tasks, of a tested person; wherein the various eye movement test tasks comprise eye movement test tasks corresponding to each cognitive ability to be evaluated, aiming at each cognitive ability, according to eye movement test data, related to the cognitive ability, of the testee, index values of various ability evaluation indexes, corresponding to the cognitive ability, of the testee are determined; and determining the ability score corresponding to the cognitive ability, and determining a matched target cognitive training game according to the ability scores corresponding to various cognitive abilities of the testee. According to the method, the cognitive competence is evaluated based on the eye movement data of the testee, and the matched game is selected according to the evaluation result to train the cognitive competence, so that the interestingness of cognitive training is enhanced, and the training effect of cognitive training is remarkably improved.

The invention belongs to the field of bionic product design, and discloses a ceramic product bionic design method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting a target plant image, and carrying out the adversarial training through an SA-StyleGAN2 model to generate a high-quality bionic sketch; converting the high-quality bionic sketch into a bionic rendering graph by using a stable diffusion model; performing feature learning on the bionic rendering graph by using a deep convolutional neural network to obtain an optimized bionic rendering graph; calculating and optimizing the similarity scores of the bionic rendering graph and the target plant as the first N design patterns; the first N design patterns are subjected to subjective and objective collaborative evaluation in combination with eye movement tracking data and an entropy weight evaluation method so as to determine a final optimal scheme, and the eye movement tracking data are obtained by obtaining user fixation hotspot distribution data through an eye movement experiment. According to the method, the problem of insufficient originality caused by low bionic design efficiency, insufficient bionic feature control and inaccurate user demand response of the existing ceramic product can be solved.