39 results about "Blink frequency" patented technology

The invention relates to the technical field of screen display adjustment and visual health protection, and discloses a screen color temperature intelligent adjustment method and system, and the method comprises the steps: obtaining a user face image sequence and ambient light perception data; according to the face image sequence, eye features are extracted, a dynamic change trend is analyzed, blinking frequency and eyelid closing duration data are calculated, and a visual fatigue evaluation value is obtained; according to the ambient light sensing data and the visual fatigue evaluation value, correcting the fatigue degree and classifying fatigue stages to obtain color temperature and brightness adjustment parameters; and transmitting the adjustment parameter to a screen display driver for gradual change processing to obtain a final screen display configuration. According to the method, personalized accurate adjustment of the screen parameters is realized, visual fatigue is effectively relieved, and the use experience and the visual health protection effect are improved.

The invention belongs to the technical field of driving safety, and provides a driver fatigue risk dynamic assessment method, system and device and a medium, and the method comprises the steps: obtaining an infrared image of a driver at a current moment, and carrying out the detection to obtain a current face key point; based on a historical face key point set in a historical time period including the current face key points, obtaining a basic fatigue risk at the current moment; the average eye closing time ratio, the blinking frequency and the yawning frequency in the historical time period are counted, and fatigue risk excitation is obtained through calculation according to the historical average eye closing time ratio, the blinking frequency and the yawning frequency; and carrying out weighted fusion on the basic fatigue risk and the fatigue risk excitation to obtain a comprehensive fatigue risk. The method does not need to increase too much equipment cost to collect information such as the driving track and the steering wheel correction frequency, does not need to recognize sensing information such as weather conditions, road states and motion situations with high requirements for computing power, saves a large amount of time cost and hardware cost, and is high in efficiency.

The invention discloses a Parkinson's disease patient video analysis and evaluation system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal data set; extracting face key point data in the multi-modal data set by using a Mediappe face detection algorithm, and analyzing blink frequency, eyelid movement symmetry and face muscle tension change; extracting a hand joint motion track through an OpenPose model, and calculating tremor frequency, motion completion time and motion smoothness; detecting step length, step width, step speed and coordination of left and right limbs in the gait of the human body based on a YOLOv8 target detection algorithm; dynamically weighting the facial tremor features, the limb movement features and the gait dynamics features through a multi-head attention mechanism; and outputting the symptom evaluation index of the Parkinson's disease patient through the LSTM network. And the efficiency and accuracy of illness state assessment are improved.

The invention discloses an LED display wireless charger based on an AI blinking algorithm and a control method, blinking parameters including the average blinking frequency, the single blinking duration and the eye closing interval are preset in an AI blinking algorithm module, the AI blinking algorithm module carries out randomization processing on the blinking parameters based on a linear congruent random number algorithm, and the random processing is carried out on the blinking parameters according to the linear congruent random number algorithm. Adjusting the fluctuation time length of each blinking interval and the blinking time length, setting parameter constraint logic, and generating a blinking control signal representing a natural blinking effect; the main MCU module is used for controlling the charging state of the circular charging coil and outputting a control instruction to the LED eye module according to the blinking control signal, so that the on-off time sequence of an LED lamp in the LED eye module is controlled, and the natural blinking effect of human eyes is simulated. Compared with the prior art, the non-coil empty idle area can be fully utilized, the natural blinking effect of human eyes can be achieved, the structure is simple, cost is low, and application requirements are well met.

The invention relates to a data evaluation method and device based on artificial intelligence, computer equipment and a medium, and is suitable for smart televisions, laptops, projectors, tablet personal computers and the like, and the method comprises the following steps: collecting video streams related to a user, and obtaining environment illumination intensity; performing face key point detection on the video stream to obtain key point information; performing blink detection and frequency calculation based on the key point information to obtain a blink frequency; performing head posture estimation on the key point information, and calculating continuous fixation duration based on a posture estimation result; generating a watching distance of the user to the screen of the target equipment; integrating the environment illumination intensity, the continuous fixation duration, the blinking frequency and the watching distance to obtain index data; evaluating the face image of the user, and obtaining a target index threshold value based on the target age; and performing data evaluation on the index data based on the target index threshold to generate a health evaluation result. According to the invention, the accuracy of health assessment processing of eye using behaviors is improved.

The present invention provides a system for assessing the state of consciousness of injured persons based on facial recognition, which relates to the field of health assessment technology. It includes a facial information extraction module, a facial feature analysis module, a state of consciousness calculation module, an anomaly detection module, and a state of consciousness assessment module. In the present invention, an infrared camera is used to carefully capture the subtle movements of the muscles around the eyes, corners of the mouth, and forehead, thereby improving the accuracy and comprehensiveness of data collection. By evaluating the blinking frequency, displacement of the corners of the mouth, and stretching amplitude of the forehead muscles, the accuracy and dimension of the assessment are improved. By extracting deformation features and calculating the gradient of motion amplitude, combined with time series analysis, the assessment of the state of consciousness becomes more sensitive and accurate, and is able to detect potential changes in consciousness in a timely manner, thereby responding quickly. The density and distribution feature analysis of mutation points provide rich data interpretation for diagnosis and monitoring, so that the state of consciousness of the injured person can be quickly and accurately assessed without contacting the injured person.

The invention discloses a public work mechanical vehicle operation and driver behavior monitoring system and method, and aims at solving the problem that in the operation process of a public work mechanical vehicle, the eye closing time is prolonged and the blinking frequency is reduced due to fatigue of a driver, and consequently misoperation is caused. A driver face video stream is collected; the embedded edge computing terminal is connected with the vehicle-mounted camera, performs eye key point detection on each frame of image and calculates an eye height ratio to obtain a fatigue trigger signal, and encapsulates a video stream and vehicle operation data into an event packet; the vehicle-mounted loudspeaker is connected with the embedded edge computing terminal and outputs voice warning in a cab according to the fatigue triggering signal; and the wireless communication module uploads the event packet to a ground server, and automatically enters a breakpoint resuming mode when the signal is interrupted.

The embodiment of the invention provides a transaction risk identification method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology and the field of artificial intelligence. The transaction risk identification method comprises the steps that multiple pieces of feature data of the eyes of a user under a current transaction are collected, the multiple pieces of feature data comprise the tear secretion amount, the blinking frequency and the variable quantity of eyeball fixation point coordinates, and the risk value of the current transaction is determined based on the multiple pieces of feature data; updating the risk value of the current transaction according to the risk level of the previous transaction, and determining the risk level of the current transaction according to the updated risk value of the current transaction; according to the method, the accuracy of transaction risk identification is improved, and the risk level of the current transaction of the user is effectively identified.

The invention belongs to the technical field of airport security check anomaly detection, and discloses an airport security check behavior pattern analysis and abnormal state detection method, which comprises the following steps of: adding meaning labels to data such as human faces, certificates, behaviors and equipment states, and establishing cross-modal association logic; scattered data such as certificate anti-counterfeiting characteristics, passenger micro-expressions and behavior time sequences can be serially connected; for example, when a holographic pattern of a certificate is slightly blurred, if the blinking frequency of a passenger is suddenly increased and the behavior of showing the certificate is deliberately delayed, the system can accurately identify the hidden risk that the single data does not reach a threshold value, avoids the situation that the false certificate is missed to be judged along with the abnormal behavior like a traditional scheme, enables the security check to capture the risk more comprehensively, and improves the security check efficiency. Potential safety hazards are reduced practically; 24 types of composite scenes including passenger flow peak, old passengers, equipment failure and disabled passengers are divided, and the normal behavior threshold value can be dynamically adjusted according to the real-time scene.

Provided in the embodiments of the present application are a blinking guidance method, a method for updating a screen display parameter, and an electronic device. In the blinking guidance method, after the current operating state of an electronic device is acquired, when the current operating state of the electronic device satisfies a first predetermined state, the electronic device performs blinking measurement on a user who uses the electronic device, so as to obtain a blinking frequency of the user; and when the blinking frequency of the user is less than or equal to a blinking threshold value, the electronic device performs blinking guidance on the user, such that when the blinking frequency of the user is relatively low, the user is prompted to appropriately blink, thereby improving the stability of a tear film, and relieving dry eyes and visual discomfort.

The invention discloses learning equipment based on artificial intelligence control, and relates to the technical field of learning equipment, and the equipment comprises a multi-modal data acquisition module, an artificial intelligence analysis module, an interest-driven learning guide module, an eyesight protection intelligent intervention module, a personalized learning and eye protection scheme generation module and a man-machine interaction module. By collecting and analyzing data such as facial expressions and blinking frequencies of students in real time, calculating interest indexes and accurately judging interest points of the students, learning content is accurately pushed according to interest preferences of the students, eye using states of the students are monitored by utilizing an asthenopia evaluation formula, and an intelligent intervention module for eyesight protection is combined, so that the interests of the students are accurately judged. When the students have asthenopia symptoms, learning tasks are paused in time, eye protection activities are pushed, learning content modes are converted, personalized eye use suggestions and learning environment adjustment schemes are generated according to vision changes, the vision health of the students is comprehensively protected, and scientific and healthy learning experience is created for the students.

The invention relates to the technical field of intelligent health equipment, in particular to multi-mode asthenopia quantification and light-nerve regulation sleep optimization equipment which comprises an asthenopia quantification module, a light regulation module, a nerve regulation module and a central control module. The visual fatigue state is accurately quantified from multiple dimensions such as blink frequency, tear secretion, eye myoelectricity and a visual regulation function through multiple monitoring units, compared with single-index evaluation, the accuracy and comprehensiveness are greatly improved, light regulation and control and nerve regulation and control technologies are combined, and the visual fatigue state is accurately quantified. Through the synergistic effect of external visual environment optimization and internal nerve regulation, the vicious circle of asthenopia and insomnia is broken through, the intervention effect is more remarkable, personalized and intelligent regulation of light regulation and nerve regulation is achieved based on an asthenopia quantification result, the requirements of different users are met, and the user experience and the intervention effect are improved.

The invention discloses a pilot visual fatigue detection system and method based on artificial intelligence, and belongs to the technical field of flight safety monitoring, and the method comprises the steps: obtaining a pilot face video through an image sensor, carrying out the face positioning and tracking through a neural network, extracting a blinking frequency and a critical flash fusion frequency from an eye region, and carrying out the detection of the visual fatigue of a pilot. The head posture movement is analyzed; fusing the multi-dimensional physiological data with the behavior features, and inputting the fused data into a pre-trained visual fatigue evaluation model to obtain an initial fatigue index; performing dynamic calibration on the initial fatigue index in combination with the historical fatigue data of the pilot individual and the current flight mission load, and generating an optimized visual fatigue score; and finally, grading early warning is started according to the grading grade, personalized and foresight fatigue state intervention is realized, and the flight safety is effectively improved.

The invention discloses a blink recognition evaluation system and method, and relates to the technical field of blink recognition, and the method comprises the steps: selecting upper and lower eyelid mark points, collecting a preprocessed video clip, located in front of a computer screen, of a subject, counting stable part trend features of interval periods between different time points of each blinking of the subject and every two adjacent different blinking time points, and extracting stable part trend features from change trends of upper and lower eyelid mark point movement data tracked under the condition of blinking time features in the preprocessed video; the method comprises the steps of preprocessing a video to select a first training image set and a second training image set from preprocessed segmented images obtained by segmenting the preprocessed video, selecting a test image set according to the first training image set, and counting the blinking frequency and the incomplete blinking proportion of a subject according to the first training image set, the second training image set and the test image set. The method can enhance real-time performance, automation and noninvasive performance, gets rid of dependence on special hardware, and can be applied to ophthalmic clinic.

The invention discloses a control method of an eye protection displayer, the eye protection displayer and electronic equipment, and relates to the technical field of displayers, and the method comprises the steps that change data of the pupil diameter of a user is collected in real time; acquiring user blinking frequency and user blood oxygen saturation; acquiring a user head micro-motion signal; determining a visual fatigue index according to the change data of the pupil diameter of the user, the blinking frequency of the user, the oxyhemoglobin saturation of the user and the head micro-motion signal of the user; and adjusting display parameters of the eye protection display according to the visual fatigue index. The intelligent degree of the displayer is greatly improved, the displayer does not depend on an ambient light sensor any more, closed-loop control over physiological signals and display parameters is established, and the eye protection effect is improved.

The application provides a high beam adaptive control method, system, device and medium, relates to the technical field of vehicle lighting control, and the method comprises the following steps: acquiring the driving environment of a target vehicle, and generating a first light scheme of a high beam; in combination with the first vehicle feature of the target vehicle and the second vehicle feature of an oncoming vehicle, and the distance between the target vehicle and the oncoming vehicle, generating an adjustment index of the high beam, adjusting the first light scheme according to the adjustment index, and generating a second light scheme; in combination with the driving time length of the driver of the target vehicle, the blink frequency of the driver and the pupil change rate, generating a visual fatigue parameter of the driver, calculating the light sensitivity coefficient of the driver according to the visual fatigue parameter; in combination with the visual fatigue parameter and the driving environment, generating a driving risk coefficient, and in combination with the driving risk coefficient and the light sensitivity coefficient, adjusting the second light scheme to generate a target light scheme. The application has the technical effect that the safety of night driving is ensured.

The invention discloses a multi-feature fusion fatigue driving monitoring method and system based on an infrared band. The method comprises the following steps: acquiring a face image; marking key points in the face image through a face detection model; performing eye aspect ratio calculation, eye data filtering and eye adaptive threshold correction according to the key points of the eyes to realize blink frequency calculation; calculating the aspect ratio of the mouth according to the key points of the mouth, filtering the data of the mouth, and correcting a self-adaptive threshold value to realize the judgment of yawning; pulse signals are extracted according to the key points of the face, noise reduction processing is conducted on the pulse signals, and heart rate calculation is conducted; and performing multiple-set canonical correlation analysis according to the blinking frequency, the yawning possibility and the heart rate to judge the fatigue driving degree. According to the method, three indexes including the heart rate, the blinking frequency and the yawning are fused into one parameter capable of reflecting the fatigue degree of the driver through multi-set canonical correlation analysis, and then the accuracy of fatigue driving judgment is improved.

The bus driver fatigue parameter compensation and double-track parallel detection method comprises the following steps: S1, labeling the eye blinking and yawning image data of the driver; S2, calculating the frame number of the bus driver closing eyes and opening mouth in the front face state by using a face key point algorithm, and comparing with the current data preprocessing method, and compensating according to the ratio; S3, double-track division of the fatigue state time sequence; after the image frame is detected according to the time sequence, the detection results of each area are output, and the complete time segment results are divided into double-track time sequence according to the vehicle speed and vehicle condition; S4, setting the double-track early warning mechanism of the fatigue state time sequence; the analysis data includes the eye blinking frame number, eye blinking frequency, mouth opening frame number and yawning frequency; the perclos eye The ratio of the number of closed eye frames to the total number of frames in a calculation unit can reflect the ratio of the eye closure duration to the detection time and the fatigue state of the driver.

The invention discloses a blink fatigue detection method and system based on deep learning, and the method comprises the steps: obtaining an eye image data set with an eye opening and closing state label, carrying out the preprocessing, and dividing a training set, a verification set, and a calibration data set; performing training and performance verification on the convolutional neural network model by using the training set and the verification set to obtain a blink detection model; the calibration data set is used for conducting INT8 quantification processing on the blink detection model, and the blink detection model is converted into a light-weight blink detection model and then deployed to an embedded device; taking a continuous video frame sequence which is acquired by a camera in real time and contains a face image as input data of a lightweight blink detection model for detection, and obtaining an effective blink frequency; the number of effective blinking times is counted, the blinking frequency in unit time is calculated, the blinking frequency is compared with a preset multi-level fatigue level threshold value level by level to determine the corresponding fatigue level, and whether the user is in a fatigue state or not is judged. According to the invention, blink fatigue detection can be efficiently and accurately carried out on low-power-consumption equipment.

The embodiment of the invention provides a blink guiding method, a screen display parameter updating method and electronic device.In the blink guiding method, after the current running state of the electronic device is obtained, when the current running state of the electronic device conforms to a first preset state, the electronic device carries out blink detection on a user using the electronic device, and the screen display parameter updating method is used for updating the screen display parameter. And obtaining the blinking frequency of the user. When the blinking frequency of the user is smaller than or equal to the blinking threshold value, the electronic equipment guides blinking of the user, so that when the blinking frequency of the user is low, the user is prompted to blink properly, the tear film stability is improved, and eye dryness and visual discomfort are relieved.

The invention relates to the technical field of vision detection, in particular to novel medical equipment for vision detection and a control method thereof. Determining that the to-be-detected child is located at a designated position, and triggering a detection program to start according to the age of the to-be-detected child Displaying a three-color sphere based on preset parameters, controlling the sphere to move based on a vision value locking algorithm, determining eyeball following times, and obtaining a vision value; on the basis of an amblyopia qualitative detection algorithm, controlling the sphere to move to obtain retinal imaging response delay time and child blinking frequency; based on the vision value, the retinal imaging response delay time and the child blinking frequency, a detection result is obtained through a child amblyopia detection algorithm model. According to the method, a vision value locking algorithm, an amblyopia qualitative detection algorithm and a child amblyopia detection algorithm model are adopted, the improved E word table quantitative logic and the improved OKN qualitative logic are fused through algorithms, quantitative and qualitative synchronous detection of child amblyopia is achieved, the detection efficiency is 2.7 times that of a traditional method, and the detection accuracy rate reaches 95% or above.

The application discloses a multi-modal fusion physiological fatigue recognition method and device, and the method comprises the following steps: constructing a standardized speech data set and a physiological information data set; the physiological information data set comprises physiological index information and biochemical index information; the physiological index information comprises heart rate information, heart rate variability information, skin electricity information, eye movement information and blink frequency information; the biochemical index information comprises saliva information, urine information and fingertip blood sample information; processing the standardized speech data set and the physiological information data set to obtain multi-dimensional physiological fatigue feature information; training a preset fatigue recognition model by using the multi-dimensional physiological fatigue feature information to obtain an optimized fatigue recognition model; and processing the multi-dimensional physiological fatigue feature information to be processed by using the optimized fatigue recognition model to obtain a multi-modal fusion physiological fatigue recognition result. The multi-modal fusion physiological fatigue recognition method constructed by the application has higher accuracy, and the robustness and generalization of model migration are better.

The application discloses a kind of pilot visual fatigue detection system and method based on artificial intelligence, belong to flight safety monitoring technical field, first by image sensor obtains pilot face video, face positioning and tracking are carried out by neural network, and then blink frequency and critical flicker fusion frequency are extracted from eye area, and head posture motion is analyzed;After the fusion of multidimensional physiological data and behavior characteristics, the initial fatigue index is obtained by inputting the pre-trained visual fatigue evaluation model;Then, in combination with the individual historical fatigue data of the pilot and the current flight task load, the initial fatigue index is dynamically calibrated to generate an optimized visual fatigue score;Finally, according to the score level, a graded warning is started, a personalized and forward-looking fatigue state intervention is realized, and flight safety is effectively improved.

The present invention relates to the technical field of operation monitoring, and specifically discloses a railway station liaison operation monitoring method and system based on intelligent identification, comprising the following steps: step S1: selecting a sample of station liaison officers, and collecting action video samples of the station liaison officers when using communication equipment; extracting common limb movement features of all samples through a limb analysis model; collecting facial images of the samples when performing tasks, and calculating the blinking frequency of each sample; generating a unified frequency reference value based on all blinking frequencies; step S2: determining the liaison monitoring duration, and after real-time monitoring of key moments of train status changes, defining a liaison monitoring time period based on the monitoring duration; obtaining real-time video data of the station liaison officers, extracting their limb movement features through a limb analysis model, and calculating the similarity with the common features; collecting facial images of the station liaison officers in real time, and analyzing the current blinking frequency; and step S3: generating a work status index by combining a limb index and a concentration index.

The invention discloses a screen brightness adjusting method and system based on double-stage blink frequency calibration, and the method comprises the steps: carrying out the initial calibration, monitoring the initial blink frequency of a user under the conditions of first use and the like, and generating a personalized basic baseline through statistical processing and coefficient compensation; dynamically running, periodically updating the dynamic baseline based on historical data in the rolling time window, and monitoring the current blinking frequency in real time; adjusting triggering, and when the current blinking frequency is continuously lower than a threshold value set by the dynamic baseline, controlling the screen brightness to smoothly and gradually change to a target value; the system comprises an image acquisition and feature extraction module, a dual-stage calibration and decision module, a secure storage module and a brightness adjustment execution module. According to the method, the personalized dynamic baseline is constructed through double-stage calibration, the problem of misjudgment caused by a fixed threshold value is solved, self-adaptive visual fatigue intervention is realized, and the privacy security of a user is guaranteed.

The application discloses a kind of pilot dangerous driving behavior monitoring methods based on multi-modal data fusion, belong to multi-modal data fusion monitoring field, including using multivariate sensor obtains the physiological data of pilot under the state of take-off and landing, judge the fatigue state of pilot, utilize eye tracker and camera to the pilot under the state of take-off and landing carry out data acquisition, obtain pilot blink frequency, eyelid closure, pupil change, yawn frequency, nodding frequency, joystick average moving speed, the behavior data of moving direction, judge the fatigue state of pilot;Network based on the multi-modal data fusion of Transform is constructed, and it is trained;The driving behavior of pilot is monitored using the multi-modal data fusion network based on the Transform trained;Through multi-channel interactive technology improves the comprehensiveness and accuracy of information, reduce the influence of environment, individual difference, enhance stability and robustness.

The invention provides a real object identification method based on AI big data and an intelligent wearable device, and the method comprises the steps: carrying out the detection of collected continuous environment images, obtaining an initial real object candidate region, carrying out the correlation analysis of a rotation angle and a pupil center position based on a spatial position, and obtaining an initial real object candidate region; determining three-dimensional space coordinates of the sight line pointing point of the user in the environment at each moment; performing spatial conversion based on imaging parameters of the image sensor and the pixel coordinates of each initial object candidate region to obtain a three-dimensional space region, and performing trajectory fitting in the time dimension based on the three-dimensional space coordinates to obtain a sight line movement trajectory; matching is carried out based on the blinking frequency and the sight line moving track in combination with the three-dimensional space area of each initial object candidate area, and a target object candidate area is determined; and performing feature matching based on the first object feature of the target object candidate region and the second object feature of the AI big data to obtain an object identification result. According to the invention, the efficiency and accuracy of object identification are improved.

The invention relates to the technical field of data processing, and provides an attention guiding method and system based on multi-sensory feedback, the attention guiding method is used for guiding a user to concentrate attention in a remote classroom, a remote conference or remote training, and the attention guiding method comprises the following steps: collecting behavior characteristic information of the user in real time through a camera and a sensor, the behavior characteristic information at least comprises the sight direction, the head posture and the blinking frequency of the user; analyzing and judging the attention state of the user according to the behavior characteristic information; if it is monitored that the attention of the user is reduced, intervention at least including tactile vibration is triggered, and the user is guided to concentrate attention. According to the attention guiding method based on multi-sensory feedback, attention monitoring and regulation serve as the core, the attention state of the user is analyzed by capturing the behavior characteristic information of the user, and therefore the user is intervened through the vibration signals, and the user is prompted or guided to concentrate attention.

This invention proposes a video-based ocular surface monitoring method and system, belonging to the field of ocular surface monitoring technology. The method includes: acquiring ocular surface video sequences; using a geometrically constrained cascaded localization strategy to identify anatomical structures in each frame of the image, obtaining the location information of key anatomical structures, and calculating the palpebral fissure height; calculating the average tear meniscus height and the standard coefficient of variation of the tear meniscus height based on the palpebral margin position; calculating the corneal exposed area; detecting blinking events based on the temporal changes in palpebral fissure height from consecutive frames of the video sequence, and calculating the blinking frequency and the proportion of incomplete blinks; constructing a tear fluid dynamic balance model, substituting the average tear meniscus height, the standard coefficient of variation of the tear meniscus height, the corneal exposed area, the blinking frequency, and the proportion of incomplete blinks into the tear fluid dynamic balance model to calculate the tear fluid dynamic balance index, and performing a graded assessment of ocular surface health status based on this index. This invention enables a quantitative and comprehensive assessment of ocular surface health status.

This invention relates to a method and system for evaluating the online reading quality of students. The method includes: Q1. During online reading, collecting data on the user's viewing distance, blink frequency, and heart rate at the same moment, and performing normalization processing to obtain normalized data on the user's viewing distance, blink frequency, and heart rate at the same moment. Then, using an ELMAN neural network regression prediction algorithm based on the sardine optimization algorithm, the method predicts the user's viewing distance, blink frequency, and heart rate at the next moment, obtaining data on the user's viewing distance, blink frequency, and heart rate at the next moment. This invention not only evaluates the user's reading quality from multiple aspects, ensuring the accuracy of the reading quality assessment, but also assesses and reminds users of their reading quality in advance, thereby improving the user's enthusiasm and stability in reading.