Learning difficulty training system based on visual function detection

Abstract

The invention discloses a learning difficulty training system based on visual function detection, and relates to the technical field of artificial intelligence, and the system comprises a personal file recording unit, a visual function detection unit, a grade field positioning unit, a personalized training unit, and a training data uploading unit. The visual function detection unit is used for obtaining a visual function index according to visual function detection, and associating the visual function index with a personal visual function file to obtain visual function data; the level field positioning unit is used for obtaining a visual ability cluster to which the user belongs, and predicting a learning difficulty risk level and a problem field of the user based on a learning difficulty risk assessment model; the personalized training unit is used for calculating an arrangement sequence of visual function data needing to be trained, selecting training items from a training item bank, and calculating a training time length distribution value and a training frequency corresponding to each training item; and the training data uploading unit is used for generating an analysis report according to the historical training result data of the user.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a learning difficulties training system based on visual function detection. Background Art

[0002] As the vision of children and adolescents is receiving more and more attention, routine vision tests are not enough to reflect the overall visual function, so visual function tests are needed. Visual function tests are an important means to evaluate the overall visual function of the eyes. They include evaluations of color vision, contrast sensitivity, visual field, stereoscopic vision and other aspects. These tests can comprehensively reflect the state of visual function. Scientific visual function training based on the results of visual function tests can effectively solve symptoms such as visual fatigue, eye soreness, eye swelling, difficulty seeing near, drowsiness and other symptoms caused by abnormal binocular vision function, as well as learning difficulties caused by some symptoms.

[0003] At present, the industry has developed some improvement methods to solve the problems of efficiency, management, and experience in visual function inspection and training. For example, the introduction of automated inspection equipment and software, the establishment of an electronic file management system, the development of visual training apps and games, and the use of virtual reality technology to simulate real scenes. However, these methods still have some obvious defects.

[0004] First, most existing automated inspection programs have limited accuracy and applicability, and still require manual operation and interpretation, which cannot give full play to the advantages of intelligence and achieve large-scale application. Secondly, the equipment and systems in each link are relatively independent, the efficiency of data transmission and processing is not high, and there is a lack of information integration and optimization of the entire process. Furthermore, the content of visual function training is single, and the form is not interesting enough, which makes it difficult to truly mobilize the initiative and enthusiasm of patients, and the training compliance and efficacy are poor. In addition, various types of inspection and training data are scattered, lacking system integration and big data analysis, and cannot achieve accurate evaluation and personalized program optimization.

[0005] Therefore, an innovative technical solution is urgently needed to build a comprehensive and intelligent visual function inspection and training management platform. Summary of the invention

[0006] The present application aims to solve one of the technical problems in the related art at least to some extent. To this end, one purpose of the present application is to propose a learning difficulties training system based on visual function detection, which realizes an intelligent visual function inspection training management platform.

[0007] One aspect of the present application provides a learning difficulty training system based on visual function detection, comprising: Personal profile recording unit, visual function detection unit, grade field positioning unit, personalized training unit, training data uploading unit; The personal profile recording unit is used for user registration and login, to improve the user's personal information indicators, and to establish a personal visual function profile; The visual function detection unit is used to prompt the user to complete the visual function detection, obtain the user's visual function index according to the visual function detection, and associate the visual function index with the personal visual function file to obtain visual function data; The level domain positioning unit is used to classify the user based on the visual function data of the user and the trained visual ability clustering cluster, obtain the visual ability clustering cluster to which the current user belongs, and predict the learning difficulty risk level and problem domain of the user's learning difficulty based on the pre-established learning difficulty risk assessment model; The personalized training unit is used to calculate the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; The training data uploading unit is used to upload the training result data of the user after completing the training project to the data server, calculate the score improvement and time reduction of the user on each training project according to the historical training result data of the user, calculate the score difference and time difference of the user compared with other users on each training project according to the historical training result data of other users in the same visual ability cluster, and generate a training result analysis report; The user registration and login, improvement of user personal information indicators, and establishment of personal visual function files specifically include: Step S110: The user registers using the mobile phone number, and a unique identifier is generated for each user; Step S120: The user fills in personal information indicators; Step S130: storing the user's personal information indicators in a data server to establish a personal visual function profile; The prompting of the user to complete the visual function test, obtaining the user's visual function index according to the visual function test, associating the visual function index with the personal visual function file, and obtaining the visual function data specifically includes: Step S201: Check the user's inhibition level and obtain the number of questions the user answered correctly. Total number of questions The percentage of the user's suppression index SI is obtained; Step S202: Check the user's perceptual eye position and obtain the coordinates of the position where the user clicks each time and the actual center coordinates of the blue circle , calculate the user's perceived eye position deviation PEP; Step S203: Check the convergence and divergence range of the user to obtain the position of the rupture point marked in the divergence step and the locations of the rupture points marked in the collection step , get the maximum displacement of the vector diagram , calculate the user's gathering and dispersion range ; Step S204: Perform a stereoscopic inspection on the user to determine the pattern selected by the user and the correct stereoscopic view Whether they are consistent, if they are consistent, the stereoscopic accuracy is 1, otherwise it is 0; Step S205: Check the user's adjustment sensitivity and calculate the number of E-table directions correctly identified by the user. Total number of E-table renderings The percentage of adjustment sensitivity is obtained ; Step S206: Perform binocular visual balance check on the user and measure the left eye accommodation sensitivity of the user , Right eye adjustment sensitivity , binocular accommodation sensitivity , get the user's binocular balance ; Step S207: Perform a color vision test on the user, count the number of patterns correctly identified by the user, and calculate the number of patterns correctly identified by the user. Total number of patterns The ratio of the user's color vision completeness percentage ; Step S208: Check the signal-to-noise ratio of the user, and obtain the average amplitude of the signal according to the different signal-to-noise ratios of the pictures under different transparency levels. The average amplitude of the sum noise , calculate the signal-to-noise ratio of the image that the user distinguishes ; Step S209: Check the user's scanning ability and count the user's scanning time. Number of word directions that can be correctly identified , calculate the user's scanning ability score ; Step S210: Check the user's follow-up ability and count the user's time during the check. The number of directions of moving characters that can be correctly identified , calculate the user's followability score ; Step S211: generating visual function indicators from the inspection results obtained from the visual function test, wherein the visual function indicators include: inhibition index, perceived eye position deviation, vergence range, stereoscopic accuracy, accommodation sensitivity, binocular balance, color vision integrity percentage, signal-to-noise ratio, scanning ability score, and tracking ability score; Step S212: Associating the visual function index with the personal visual function profile, collectively referred to as the user's visual function data; The method for obtaining the visual ability clustering cluster is: Step S301: normalizing the visual function data of N users; The normalized calculation formula is: ,in, represents the normalized visual function data of the i-th user, represents the original value of the jth visual function data of the ith user, represents the mean value of the jth visual function data, represents the standard deviation of the j-th visual function data; Step S302: taking the normalized visual function data of N users as data points, clustering the data points using a K-means clustering algorithm to obtain K visual ability clusters; The specific method for obtaining K visual ability clusters is: Step S3021: randomly select K data points as initial cluster centers; Step S3022: For each data point , calculate the distance from the data point to each cluster center, and assign it to the cluster with the nearest cluster center; Step S3023: for each cluster, recalculate the mean vector of all data points in the cluster as the new cluster center of the cluster; Step S3024: repeating steps S3022 to S3023 until the cluster center of each cluster and the data points in the cluster no longer change, thereby obtaining K visual ability clusters; The method for establishing the learning difficulty risk assessment model is: Collect data on users with diagnosed learning difficulties, including personal information indicators and visual function indicators, as well as learning difficulty risk levels and problem areas assessed by professionals; For each sample of users with learning difficulties collected, the risk level of learning difficulties and whether there are obstacles in each problem area are marked according to the professional assessment results. The risk level of learning difficulties includes low, medium and high. The problem areas include but are not limited to reading, writing and mathematics; The visual function data of the learning difficulty user samples are constructed based on the personal information indicators and visual function indicators. The learning difficulty user samples are clustered based on the visual ability clustering clusters. The visual ability clustering cluster to which each learning difficulty user sample belongs is determined. The learning difficulty risk score is calculated based on the visual function data and the mean and standard deviation of the visual ability clustering cluster to which it belongs. ; Design a learning difficulty risk classification model based on the learning difficulty risk score, set the first threshold and the second threshold of the learning difficulty risk score, when the learning difficulty risk score is less than or equal to the first threshold, the learning difficulty risk level is low, when the learning difficulty risk score is greater than the first threshold and less than or equal to the second threshold, the learning difficulty risk level is medium, when the learning difficulty risk score is greater than the second threshold, the learning difficulty risk level is high; Design a multi-task learning model, the multi-task learning model includes a shared feature extraction layer, a task-specific output layer and a loss function, use samples of users with learning difficulties that are marked with whether there are obstacles in each problem field as training samples, use visual function data of the samples of users with learning difficulties as input data, and use binary classification results of whether the samples of users with learning difficulties have obstacles in each problem field as output data, train the multi-task learning model, and obtain a trained multi-task learning model; The learning difficulty risk classification model and the multi-task learning model are combined to obtain a learning difficulty risk assessment model; The shared feature extraction layer receives visual function data, extracts multimodal input features of different visual function data and performs feature splicing as input to the shared feature extraction layer, uses a deep neural network to perform hierarchical abstraction and transformation on the multimodal input features, extracts deep features and outputs them; maps the output of the deep features to a low-dimensional latent space to obtain common representations between classification tasks in different problem domains; The low-dimensional latent space is realized by a fully connected layer, and its output dimension is smaller than the input dimension, thereby achieving the effect of dimensionality reduction and compression.

[0008] The task feature output layer designs an output subnetwork for each problem domain classification task. The problem domain classification task is a binary classification task of whether there is an obstacle in each problem domain. Each subnetwork takes the commonality representation as input and outputs the probability distribution of whether there is an obstacle in the corresponding problem domain classification task. The sub-network uses a convolutional neural network, and its output layer generates a probability prediction value of whether there is an obstacle for the corresponding task through a fully connected layer and an activation function; The loss function is composed of the independent loss functions of each problem domain classification task, the independent loss function of each problem domain classification task selects the binary cross entropy loss, and then the independent loss functions are weighted summed to obtain the loss function of the multi-task learning model, wherein the weight coefficient of each independent loss function is set by a technician in this field based on experience; The calculation of the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, selecting training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, and calculating the corresponding training time allocation value and training frequency for each training item, and recording the actual training time and training score of each training item specifically include: Step S401: When the user is classified into the kth visual ability cluster, obtain each visual function feature in the cluster, the visual function feature is the mean value of the visual function data and standard deviation , according to the preset visual function characteristic benchmark value , calculate the deviation degree of each visual function data in the cluster ; Step S402: sorting the visual function data by priority from large to small according to the degree of deviation, and selecting the arrangement order of the first S visual function features as the arrangement order of the visual function data for training of the visual ability cluster; Step S403: the system presets a training item question bank, each training item is targeted at specific visual function data, problem areas and learning difficulty risk levels; Step S404: randomly selecting S training items from the training item question bank in order according to the learning difficulty risk level and problem area of ​​the user and the priority of the visual function data; Step S405: Calculate the training time allocation value for each training item corresponding to each visual function data based on the deviation degree and the user's learning difficulty risk score and training frequency ; Step S406: Allocate a value to each training item according to the calculated training duration and training frequency The system recommends and conducts training for users, and records the actual training time and training score of each training item; The training result data of the user after completing the training project is uploaded to the data server, and the score improvement and time reduction of the user on each training project are calculated based on the historical training result data of the user, and the score difference and time difference of the user compared with other users on each training project are calculated based on the historical training result data of other users in the same visual ability cluster, and the training result analysis report is generated, which specifically includes: Step S501: After the user completes each training item, the system records the user's score on the training item and obtains the user's training result data, which includes: training item number, training date, actual training time, and training score; Step S502: Uploading the user's training result data to the data server to update the user's personal visual function profile; Step S503: extract the historical training result data of the user from the data server, compare and analyze the current training result data with the historical training result data, and calculate the score improvement of the user on the sth training item. and shortened duration ; Step S504: extract the training result data of other users in the same visual ability cluster as the user from the data server, and calculate the score difference of the user in the kth visual ability cluster in the sth training item relative to other users in the same visual ability cluster. and duration gap ; Step S505: The training result analysis report includes a longitudinal training result analysis report and a transverse training result analysis report. The longitudinal training result analysis report of the user is generated by the score improvement and duration reduction of each training item of the user, and the transverse training result analysis report of the user is generated by the score difference and duration difference between the user and other users in the same visual ability cluster.

[0009] One aspect of the present application provides a learning difficulty training method based on visual function detection, comprising: User registration and login, improve user personal information indicators, and establish personal visual function files; Prompt the user to complete a visual function test, obtain the user's visual function index according to the visual function test, and associate the visual function index with the personal visual function file to obtain visual function data; According to the user's visual function data, the user is classified based on the trained visual ability clustering cluster to obtain the visual ability clustering cluster to which the current user belongs, and the learning difficulty risk level and problem area of ​​the user's learning difficulty are predicted based on the pre-established learning difficulty risk assessment model; Calculate the arrangement order of visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; The training result data of the user after completing the training project is uploaded to the data server. The score improvement and time reduction of the user on each training project are calculated based on the user's historical training result data. Based on the historical training result data of other users in the same visual ability cluster, the score gap and time gap between the user and other users on each training project are calculated, and a training result analysis report is generated.

[0010] One aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a learning difficulty training method based on visual function detection are implemented.

[0011] One aspect of the present application provides a readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor to execute steps in a learning difficulty training method based on visual function detection.

[0012] Compared with the prior art, the learning difficulties training system based on visual function detection proposed in this application has the following advantages: This application takes into account multiple visual function indicators and constructs a relatively comprehensive visual function detection and evaluation system, which can more accurately evaluate the user's visual ability level.

[0013] This application adopts a clustering algorithm to cluster users based on multi-dimensional visual function indicators. Users with similar visual abilities can be automatically classified into the same cluster, making it easier to provide personalized training plans for users in different clusters.

[0014] This application establishes a learning difficulty risk assessment model, which integrates personal information and visual function indicators to predict the risk level of learning difficulties and the learning areas where obstacles may exist, which helps to identify the risk of learning difficulties at an early stage and achieve timely intervention.

[0015] This application intelligently sorts the visual functions that need to be trained based on the visual function characteristics of the cluster to which the user belongs, and matches the corresponding training items, so as to provide a personalized and targeted visual training program. The system can also dynamically and adaptively adjust the training duration and frequency according to the degree of visual deviation and the risk of learning difficulties.

[0016] This application automatically records training data. On the one hand, it analyzes the user's training effectiveness improvement vertically, and on the other hand, it compares the gap between the user and other users in the same cluster horizontally, generating an intuitive training result analysis report to help users understand their own progress and the gap with similar people, thereby enhancing training motivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A functional module diagram of a learning difficulties training system based on visual function detection provided in this application; Figure 2 Schematic diagram of the visual function examination training management platform provided for this application; Figure 3 A schematic diagram of a method for obtaining a training result analysis report provided for this application; Figure 4 A method flow chart of a learning difficulties training method based on visual function testing provided in this application; Figure 5 A schematic diagram of the structure of an electronic device provided in this application; Figure 6 It is a schematic diagram of the structure of a readable storage medium provided by this application. DETAILED DESCRIPTION

[0018] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0019] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in this application, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0020] It should also be understood that expressions such as "including", "comprising", "having", "containing" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0021] Unless otherwise defined, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that unless there is a clear explanation in this application, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0022] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] Example 1 like Figure 1 As shown, a learning difficulty training system based on visual function detection provided by the present application includes: Personal profile recording unit, visual function detection unit, grade field positioning unit, personalized training unit, training data uploading unit; The personal profile recording unit is used for user registration and login, to improve the user's personal information indicators, and to establish a personal visual function profile; The visual function detection unit is used to prompt the user to complete the visual function detection, obtain the user's visual function index according to the visual function detection, and associate the visual function index with the personal visual function file to obtain visual function data; The level domain positioning unit is used to classify the user based on the visual function data of the user and the trained visual ability clustering cluster, obtain the visual ability clustering cluster to which the current user belongs, and predict the learning difficulty risk level and problem domain of the user's learning difficulty based on the pre-established learning difficulty risk assessment model; The personalized training unit is used to calculate the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; The training data uploading unit is used to upload the training result data of the user after completing the training project to the data server, calculate the score improvement and time reduction of the user on each training project according to the historical training result data of the user, calculate the score difference and time difference of the user compared with other users on each training project according to the historical training result data of other users in the same visual ability cluster, and generate a training result analysis report; like Figure 2 As shown, it is a schematic diagram of the visual function examination training management platform provided by this application; The user registration and login, improvement of user personal information indicators, and establishment of personal visual function files specifically include: Step S110: The user registers using the mobile phone number, and a unique identifier is generated for each user; Step S120: The user fills in personal information indicators; Step S130: storing the user's personal information indicators in a data server to establish a personal visual function profile; The personal information indicators include: the user's daily reading time, daily study time, daily electronic device use time, and daily outdoor exercise time; The visual function test includes: inhibition test, perceptual eye position test, vergence range test, stereoscopic vision test, accommodation sensitivity test, binocular balance test, color vision test, signal-to-noise ratio test, scanning ability test, and tracking ability test; The inhibition check refers to displaying the four points of Worth on the display screen, and the user wears red and green glasses to watch, and selects the answer according to the number of points seen. When the selected answer is correct, the number of questions correctly answered by the user increases by one; The perceptual eye position test refers to displaying a red cross and a blue circle on a display screen, and the user wears red-green glasses to watch. The user places the red cross in the middle of the blue circle and clicks it three times, and obtains the coordinates of each click position and the actual center coordinates of the blue circle; The convergence range check refers to the red and green variable vector Figure 1 The comparison is restored on the display screen, and the user wears red and green glasses to watch. The inspection is divided into two steps: the first step is to gather, and the second step is to disperse. In each step, when the user sees the vector diagram separated, click to mark the rupture point, and record the position of the rupture point marked in the dispersed step and the position of the rupture point marked in the gathered step respectively; The stereoscopic vision detection refers to displaying the stereoscopic vision test image on the display screen, and the user wears red and green glasses to watch and select the pattern he sees; The adjustment sensitivity check refers to displaying a random E-table on a display screen, and the user uses a positive and negative mirror to assist in the check, and selects the direction of the E-table to be viewed. Each time the E-table is selected, it is randomly rendered again for one minute. The check is divided into three times for the left eye, the right eye, and both eyes, and the number of correctly identified E-table directions is recorded; The binocular visual balance test refers to displaying a random E-chart on a display screen. The user does not need to wear glasses. The user chooses the direction of the E-chart to watch. The test is divided into three times: left eye, right eye, and both eyes. The user's left eye accommodation sensitivity, right eye accommodation sensitivity, and binocular accommodation sensitivity are measured; The color vision test refers to displaying the color vision test pattern on the display screen, and the user does not need to wear glasses. If the user can distinguish the graphics in the pattern, the user proceeds to identify the next one. If the user cannot distinguish, the user clicks to end the test, and the number of patterns correctly identified by the user is recorded; The signal-to-noise ratio check refers to displaying a signal-to-noise ratio check pattern on a display screen, and the user wears red and green glasses to watch it. If the user recognizes it correctly, the transparency of the signal-to-noise ratio picture is adjusted to check again. Different signal-to-noise ratios will be obtained according to pictures with different transparency. If it cannot be distinguished, click to end the check.

[0024] The scanning ability test refers to displaying the test logo in random directions and random positions on a display screen. The user does not need to wear glasses. The user selects a direction according to the direction of the logo he or she is viewing. The process is repeated within the test time, and the number of logo directions correctly identified by the user is counted.

[0025] The following ability test refers to displaying the test word logo in random directions and irregularly on the display screen. The user does not need to wear glasses. The user chooses the direction according to the moving word logo he or she is watching and repeats the process within the test time. The user needs to complete the monocular and binocular tests and count the number of moving word logo directions correctly identified by the user.

[0026] The prompting of the user to complete the visual function test, obtaining the user's visual function index according to the visual function test, associating the visual function index with the personal visual function file, and obtaining the visual function data specifically includes: Step S201: Check the user's inhibition level and obtain the number of questions the user answered correctly. Total number of questions The percentage of the user's suppression index SI is obtained; The calculation formula of the user's suppression index is: ; Step S202: Check the user's perceptual eye position and obtain the coordinates of the position where the user clicks each time and the actual center coordinates of the blue circle , calculate the user's perceived eye position deviation PEP; The calculation formula of the perceived eye position deviation is: ; Step S203: Check the convergence and divergence range of the user to obtain the position of the rupture point marked in the divergence step and the locations of the rupture points marked in the collection step , get the maximum displacement of the vector diagram , calculate the user's gathering and dispersion range ; The calculation formula of the convergence range is: ; Step S204: Perform a stereoscopic inspection on the user to determine the pattern selected by the user and the correct stereoscopic view Whether they are consistent, if they are consistent, the stereoscopic accuracy is 1, otherwise it is 0; The calculation formula of the stereoscopic accuracy is: ; Step S205: Check the user's adjustment sensitivity and calculate the number of E-table directions correctly identified by the user. Total number of E-table renderings The percentage of adjustment sensitivity is obtained ; The calculation formula of the adjustment sensitivity is: ; Step S206: Perform binocular visual balance check on the user and measure the left eye accommodation sensitivity of the user , Right eye adjustment sensitivity , binocular accommodation sensitivity , get the user's binocular balance ; The calculation formula for binocular balance is: ; Step S207: Perform a color vision test on the user, count the number of patterns correctly identified by the user, and calculate the number of patterns correctly identified by the user. Total number of patterns The ratio of the user's color vision completeness percentage ; The calculation formula for the color vision integrity percentage is: ; Step S208: Check the signal-to-noise ratio of the user, and obtain the average amplitude of the signal according to the different signal-to-noise ratios of the pictures under different transparency levels. The average amplitude of the sum noise , calculate the signal-to-noise ratio of the image that the user distinguishes ; The calculation formula of the signal-to-noise ratio is: ; Step S209: Check the user's scanning ability and count the user's scanning time. Number of word directions that can be correctly identified , calculate the user's scanning ability score ; The calculation formula of the scanning ability score is: ; Step S210: Check the user's follow-up ability and count the user's time during the check. The number of directions of moving characters that can be correctly identified , calculate the user's followability score ; The calculation formula of the following ability score is: ; Step S211: generating visual function indicators from the inspection results obtained from the visual function test, wherein the visual function indicators include: inhibition index, perceived eye position deviation, vergence range, stereoscopic accuracy, accommodation sensitivity, binocular balance, color vision integrity percentage, signal-to-noise ratio, scanning ability score, and tracking ability score; Step S212: Associating the visual function index with the personal visual function profile, collectively referred to as the user's visual function data; The method for obtaining the visual ability clustering cluster is: Step S301: normalizing the visual function data of N users; The normalized calculation formula is: ,in, represents the normalized visual function data of the i-th user, represents the original value of the jth visual function data of the ith user, represents the mean value of the jth visual function data, represents the standard deviation of the j-th visual function data; Step S302: taking the normalized visual function data of N users as data points, clustering the data points using a K-means clustering algorithm to obtain K visual ability clusters; The specific method for obtaining K visual ability clusters is: Step S3021: randomly select K data points as initial cluster centers; Step S3022: For each data point , calculate the distance from the data point to each cluster center, and assign it to the cluster with the nearest cluster center; Step S3023: for each cluster, recalculate the mean vector of all data points in the cluster as the new cluster center of the cluster; Step S3024: repeating steps S3022 to S3023 until the cluster center of each cluster and the data points in the cluster no longer change, thereby obtaining K visual ability clusters; The method for establishing the learning difficulty risk assessment model is: Collect data on users with diagnosed learning difficulties, including personal information indicators and visual function indicators, as well as learning difficulty risk levels and problem areas assessed by professionals; For each sample of users with learning difficulties collected, the risk level of learning difficulties and whether there are obstacles in each problem area are marked according to the professional assessment results. The risk level of learning difficulties includes low, medium and high. The problem areas include but are not limited to reading, writing and mathematics; The visual function data of the learning difficulty user samples are constructed based on the personal information indicators and visual function indicators. The learning difficulty user samples are clustered based on the visual ability clustering clusters. The visual ability clustering cluster to which each learning difficulty user sample belongs is determined. The learning difficulty risk score is calculated based on the visual function data and the mean and standard deviation of the visual ability clustering cluster to which it belongs. ; The calculation formula for the learning difficulty risk score is: ,in, Indicates Visual function data, is the weight coefficient of the jth visual function data, and They represent the mean and standard deviation of the jth visual function data in the kth visual ability cluster where the user is located, and M represents the total number of indicators of the visual function data; The value of the weight coefficient of the visual function data is determined by expert knowledge; Design a learning difficulty risk classification model based on the learning difficulty risk score, set the first threshold and the second threshold of the learning difficulty risk score, when the learning difficulty risk score is less than or equal to the first threshold, the learning difficulty risk level is low, when the learning difficulty risk score is greater than the first threshold and less than or equal to the second threshold, the learning difficulty risk level is medium, when the learning difficulty risk score is greater than the second threshold, the learning difficulty risk level is high; Preferably, the first threshold is 0.3 and the second threshold is 0.7; The learning difficulty risk classification model classifies the learning difficulty risk level of the user according to the calculated learning difficulty risk score value and the first threshold and the second threshold; Design a multi-task learning model, the multi-task learning model includes a shared feature extraction layer, a task-specific output layer and a loss function, use samples of users with learning difficulties that are marked with whether there are obstacles in each problem field as training samples, use visual function data of the samples of users with learning difficulties as input data, and use binary classification results of whether the samples of users with learning difficulties have obstacles in each problem field as output data, train the multi-task learning model, and obtain a trained multi-task learning model; The learning difficulty risk classification model and the multi-task learning model are combined to obtain a learning difficulty risk assessment model; The shared feature extraction layer receives visual function data, extracts multimodal input features of different visual function data and performs feature splicing as input to the shared feature extraction layer, uses a deep neural network to perform hierarchical abstraction and transformation on the multimodal input features, extracts deep features and outputs them; maps the output of the deep features to a low-dimensional latent space to obtain common representations between classification tasks in different problem domains; The low-dimensional latent space is realized by a fully connected layer, and its output dimension is smaller than the input dimension, thereby achieving the effect of dimensionality reduction and compression.

[0027] The task feature output layer designs an output subnetwork for each problem domain classification task. The problem domain classification task is a binary classification task of whether there is an obstacle in each problem domain. Each subnetwork takes the commonality representation as input and outputs the probability distribution of whether there is an obstacle in the corresponding problem domain classification task. The sub-network uses a convolutional neural network, and its output layer generates a probability prediction value of whether there is an obstacle for the corresponding task through a fully connected layer and an activation function; The loss function is composed of the independent loss functions of each problem domain classification task. The independent loss function of each problem domain classification task selects binary cross entropy loss, and then the independent loss functions are weighted summed to obtain the loss function of the multi-task learning model, wherein the weight coefficient of each independent loss function is set by technicians in this field based on experience.

[0028] The calculation of the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, selecting training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, and calculating the corresponding training time allocation value and training frequency for each training item, and recording the actual training time and training score of each training item specifically include: Step S401: When the user is classified into the kth visual ability cluster, obtain each visual function feature in the cluster, the visual function feature is the mean value of the visual function data and standard deviation , according to the preset visual function characteristic benchmark value , calculate the deviation degree of each visual function data in the cluster; The calculation formula for the degree of deviation is: ,in, is the visual function characteristic benchmark value of the j-th visual function data in the k-th visual ability cluster; The visual function characteristic reference value is set by those skilled in the art according to actual needs; Step S402: sorting the visual function data by priority from large to small according to the degree of deviation, and selecting the arrangement order of the first S visual function features as the arrangement order of the visual function data for training of the visual ability cluster; Step S403: the system presets a training item question bank, each training item is targeted at specific visual function data, problem areas and learning difficulty risk levels; Step S404: randomly selecting S training items from the training item question bank in order according to the learning difficulty risk level and problem area of ​​the user and the priority of the visual function data; Step S405: Calculate the training time allocation value for each training item corresponding to each visual function data based on the deviation degree and the user's learning difficulty risk score and training frequency ; The calculation formula of the training duration allocation value is: ,in, The duration of basic training, is the maximum value among all deviation values, Score the user's risk of learning difficulties, and are the influence coefficients of the deviation value item and the learning difficulty risk score item on the training time allocation value; The training time allocation value is the training time of the training item allocated by the system to the user, and its value is not necessarily equal to the value of the training time actually consumed by the user; The weight coefficients of the deviation value item and the learning difficulty risk score item are set by those skilled in the art based on experience; The calculation formula of the training frequency is: ,in, Indicates the frequency of basic training, , are the influence coefficients of the deviation value item and the learning difficulty risk score item on the training frequency; The influence coefficients of the deviation value item and the learning difficulty risk score item on the training frequency are set by those skilled in the art based on experience; Step S406: Allocate a value to each training item according to the calculated training duration and training frequency The system recommends and conducts training for users, and records the actual training time and training score of each training item; The training result data of the user after completing the training project is uploaded to the data server, and the score improvement and time reduction of the user on each training project are calculated based on the historical training result data of the user, and the score difference and time difference of the user compared with other users on each training project are calculated based on the historical training result data of other users in the same visual ability cluster, and the training result analysis report is generated, which specifically includes: Step S501: After the user completes each training item, the system records the user's score on the training item and obtains the user's training result data, which includes: training item number, training date, actual training time, and training score; Step S502: Uploading the user's training result data to the data server to update the user's personal visual function profile; Step S503: extract the historical training result data of the user from the data server, compare and analyze the current training result data with the historical training result data, and calculate the score improvement of the user on the sth training item. and shortened duration ; The calculation formula for the score improvement is: ,in, represents the current training score of the sth training item, represents the bth historical training score of the sth training item, Indicates the total number of historical training; The calculation formula for shortening the duration is: ,in, represents the current actual training time of the sth training item, represents the bth historical actual training time of the sth training item; Step S504: extracting training result data of other users in the same visual ability cluster as the user from the data server, and calculating the score difference and duration difference of the user in the kth visual ability cluster in the sth training item relative to other users in the same visual ability cluster; The calculation formula of the score gap is: ,in, represents the total number of users in the kth visual ability cluster, represents the average training score of the g-th other user in the k-th visual ability cluster on the s-th training item; The calculation formula of the time difference is: ,in, represents the average actual training time of the g-th other user in the k-th visual ability cluster on the s-th training item; Step S505: The training result analysis report includes a longitudinal training result analysis report and a transverse training result analysis report. The longitudinal training result analysis report of the user is generated by the score improvement and duration reduction of each training item of the user, and the transverse training result analysis report of the user is generated by the score difference and duration difference between the user and other users in the same visual ability cluster.

[0029] like Figure 3 , which is a schematic diagram of a method for obtaining a training result analysis report provided by the present application; Exemplarily, taking the training score of training item s as an example, the longitudinal training result analysis report is: First training score: 60 points; Second training score: 70 points, improvement value 10 points, improvement rate 16.7%; The third training score: 75 points, improvement value 5 points, improvement rate 7.1%; … Cumulative score improvement: 15 points, average improvement rate: 11.9%; … For example, taking the score difference of the user in the training item s in the kth visual ability cluster as an example, the horizontal training result analysis report is as follows: The difference between the user's score and other user 1: 5 points; The difference between the user's score and other user 2: -4 points; … User score ranking: 5th, top 10%; … The above steps comprehensively evaluate the user's training effect through the combination of vertical and horizontal reports, understand their progress over time, and their strengths and weaknesses compared with similar users, which helps to improve the user's training motivation and confidence.

[0030] Example 2 like Figure 4 As shown, a learning difficulty training method based on visual function detection provided by the present application includes: Step S100: User registration and login, completing user personal information indicators, and establishing a personal visual function profile; Step S200: prompting the user to complete a visual function test, obtaining the user's visual function index according to the visual function test, and associating the visual function index with the personal visual function profile to obtain visual function data; Step S300: According to the visual function data of the user, the user is classified based on the trained visual ability clustering cluster to obtain the visual ability clustering cluster to which the current user belongs, and the learning difficulty risk level and problem area of ​​the user's learning difficulty are predicted based on the pre-established learning difficulty risk assessment model; Step S400: Calculate the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; Step S500: Upload the training result data of the user after completing the training project to the data server, calculate the user's score improvement and time reduction on each training project based on the user's historical training result data, calculate the user's score difference and time difference on each training project compared with other users based on the historical training result data of other users in the same visual ability cluster, and generate a training result analysis report.

[0031] Example 3 Figure 5 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 5 As shown, according to another aspect of the present application, an electronic device is also provided. The electronic device may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are executed by one or more processors, a learning difficulty training method based on visual function detection as described above may be executed.

[0032] The method or system according to the embodiment of the present application can also be used by Figure 5 The electronic device architecture shown in FIG. Figure 5As shown, the electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, and the like. A storage device in the electronic device, such as a ROM or a hard disk, may store a learning difficulty training method based on visual function detection provided in the present application. A learning difficulty training method based on visual function detection may, for example, include: user registration and login, improving user personal information indicators, and establishing a personal visual function profile; prompting the user to complete a visual function test, obtaining the user's visual function indicators based on the visual function test, associating the visual function indicators with the personal visual function profile to obtain visual function data; based on the user's visual function data, classifying the user based on the trained visual ability clustering cluster to obtain the visual ability clustering cluster to which the current user belongs, and predicting the learning difficulty risk level and problem area of ​​the user's learning difficulty based on a pre-established learning difficulty risk assessment model; calculating the visual function that needs to be trained based on the visual ability clustering cluster to which the user belongs. According to the arrangement order of the visual function data, the training items are selected from the training item question bank according to the learning difficulty risk level and problem areas of the user's learning difficulties and the arrangement order of the visual function data, and the corresponding training time allocation value and training frequency of each training item are calculated, and the actual training time and training score of each training item are recorded; the training result data of the user after completing the training item is uploaded to the data server, and the score improvement and time reduction of the user on each training item are calculated according to the user's historical training result data, and the score gap and time gap of the user compared with other users on each training item are calculated according to the historical training result data of other users in the same visual ability cluster, and a training result analysis report is generated. Furthermore, the electronic device may also include a user interface. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 5 One or more components of an electronic device are shown.

[0033] Example 4 Figure 6 Schematic diagram of a readable storage medium structure provided by an embodiment of the present application. Figure 6 As shown, it is a readable storage medium according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by the processor, a learning difficulty training method based on visual function detection according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0034] In addition, according to the implementation mode of the present application, the process described in the above reference flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided by the present application, such as: user registration and login, improving user personal information indicators, and establishing a personal visual function profile; prompting the user to complete a visual function test, obtaining the user's visual function indicators based on the visual function test, associating the visual function indicators with the personal visual function profile to obtain visual function data; according to the user's visual function data, classifying the user based on the trained visual ability clustering cluster, obtaining the visual ability clustering cluster to which the current user belongs, and predicting the user's learning difficulty risk based on a pre-established learning difficulty risk assessment model, etc. level and problem domain; calculate the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, and calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score of each training item; upload the training result data after the user completes the training item to the data server, calculate the score improvement and time reduction of the user on each training item based on the user's historical training result data, calculate the score difference and time difference of the user compared with other users on each training item based on the historical training result data of other users in the same visual ability cluster, and generate a training result analysis report. When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of this application are executed.

[0035] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0036] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0037] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A learning difficulties training system based on visual function detection, characterized in that: include: Personal profile recording unit, visual function detection unit, grade field positioning unit, personalized training unit, training data uploading unit; The personal profile recording unit is used for user registration and login, to improve the user's personal information indicators, and to establish a personal visual function profile; The visual function detection unit is used to prompt the user to complete the visual function detection, obtain the user's visual function index according to the visual function detection, and associate the visual function index with the personal visual function file to obtain visual function data; The level domain positioning unit is used to classify the user based on the visual function data of the user and the trained visual ability clustering cluster, obtain the visual ability clustering cluster to which the current user belongs, and predict the learning difficulty risk level and problem domain of the user's learning difficulty based on the pre-established learning difficulty risk assessment model; The personalized training unit is used to calculate the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; The training data uploading unit is used to upload the training result data of the user after completing the training project to the data server, calculate the score improvement and time reduction of the user on each training project based on the user's historical training result data, calculate the score difference and time difference of the user compared with other users on each training project based on the historical training result data of other users in the same visual ability cluster, and generate a training result analysis report.

2. A learning difficulties training system based on visual function testing as claimed in claim 1, characterized in that: The prompting of the user to complete the visual function test, obtaining the user's visual function index according to the visual function test, associating the visual function index with the personal visual function file, and obtaining the visual function data specifically includes: Perform a suppression check on the user and obtain the number of questions the user answered correctly Total number of questions The percentage of the user's suppression index SI is obtained; Perform a perceptual eye position check on the user to obtain the coordinates of each click position and the actual center coordinates of the blue circle , calculate the user's perceived eye position deviation PEP; Check the convergence range of the user and obtain the location of the rupture point marked in the divergence step and the locations of the rupture points marked in the collection step , get the maximum displacement of the vector diagram , calculate the user's gathering and dispersion range ; Perform stereoscopic vision check on the user to determine the pattern selected by the user and the correct stereoscopic view Whether they are consistent, if they are consistent, the stereoscopic accuracy is 1, otherwise it is 0; Check the user's adjustment sensitivity and count the number of E-table directions that the user correctly identifies Total number of E-table renderings The percentage of adjustment sensitivity is obtained ; Perform binocular balance check on the user and measure the left eye accommodation sensitivity , Right eye adjustment sensitivity , binocular accommodation sensitivity , get the user's binocular balance ; Perform color vision tests on users, count the number of patterns that users correctly identify, and calculate the number of patterns that users correctly identify Total number of patterns The ratio of the user's color vision completeness percentage ; Perform a signal-to-noise ratio check on the user, and obtain the average amplitude of the signal based on the different signal-to-noise ratios of the images at different transparency levels. The average amplitude of the sum noise , calculate the signal-to-noise ratio of the image that the user distinguishes ; Check the user's scanning ability and count the time the user spends checking Number of word directions that can be correctly identified , calculate the user's scanning ability score ; Check the user's follow-up ability and count the user's check time The number of directions of moving characters that can be correctly identified , calculate the user's followability score ; The visual function index is generated from the test results obtained from the visual function test, and the visual function index includes: inhibition index, perceived eye position deviation, convergence range, stereoscopic accuracy, accommodation sensitivity, binocular balance, color vision integrity percentage, signal-to-noise ratio, scanning ability score, and tracking ability score; The visual function indicators are associated with the personal visual function profile, collectively referred to as the user's visual function data.

3. A learning difficulties training system based on visual function testing as claimed in claim 2, characterized in that: The method for obtaining the visual ability cluster is as follows: Normalize the visual function data of N users; The normalized calculation formula is: ,in, represents the normalized visual function data of the i-th user, represents the original value of the jth visual function data of the ith user, represents the mean value of the jth visual function data, represents the standard deviation of the j-th visual function data; The normalized visual function data of N users are used as data points, and the data points are clustered using a K-means clustering algorithm to obtain K visual ability clusters; The specific method for obtaining K visual ability clusters is: Step S3021: randomly select K data points as initial cluster centers; Step S3022: For each data point , calculate the distance from the data point to each cluster center, and assign it to the cluster with the nearest cluster center; Step S3023: for each cluster, recalculate the mean vector of all data points in the cluster as the new cluster center of the cluster; Step S3024: Repeat steps S3022 to S3023 until the cluster center of each cluster and the data points within the cluster no longer change, thereby obtaining K visual ability clusters.

4. A learning difficulties training system based on visual function testing as claimed in claim 3, characterized in that: The method for establishing the learning difficulty risk assessment model is: Collect data on users with diagnosed learning difficulties, including personal information indicators and visual function indicators, as well as learning difficulty risk levels and problem areas assessed by professionals; For each sample of users with learning difficulties collected, the risk level of learning difficulties and whether there are obstacles in each problem area are marked according to the professional assessment results. The risk level of learning difficulties includes low, medium and high. The problem areas include but are not limited to reading, writing and mathematics. The visual function data of the learning difficulty user samples are constructed based on the personal information indicators and visual function indicators. The learning difficulty user samples are clustered based on the visual ability clustering clusters. The visual ability clustering cluster to which each learning difficulty user sample belongs is determined. The learning difficulty risk score is calculated based on the visual function data and the mean and standard deviation of the visual ability clustering cluster to which it belongs. ; Design a learning difficulty risk classification model based on the learning difficulty risk score, set the first threshold and the second threshold of the learning difficulty risk score, when the learning difficulty risk score is less than or equal to the first threshold, the learning difficulty risk level is low, when the learning difficulty risk score is greater than the first threshold and less than or equal to the second threshold, the learning difficulty risk level is medium, when the learning difficulty risk score is greater than the second threshold, the learning difficulty risk level is high; Design a multi-task learning model, the multi-task learning model includes a shared feature extraction layer, a task-specific output layer and a loss function, use samples of users with learning difficulties that are marked with whether there are obstacles in each problem field as training samples, use visual function data of the samples of users with learning difficulties as input data, and use binary classification results of whether the samples of users with learning difficulties have obstacles in each problem field as output data, train the multi-task learning model, and obtain a trained multi-task learning model; The learning difficulty risk classification model and the multi-task learning model are combined to obtain a learning difficulty risk assessment model.

5. A learning difficulties training system based on visual function testing as claimed in claim 4, characterized in that: The shared feature extraction layer receives visual function data, extracts multimodal input features of different visual function data and performs feature splicing as input to the shared feature extraction layer, uses a deep neural network to perform hierarchical abstraction and transformation on the multimodal input features, extracts deep features and outputs them; maps the output of the deep features to a low-dimensional latent space to obtain common representations between classification tasks in different problem domains.

6. A learning difficulties training system based on visual function testing as claimed in claim 5, characterized in that: The calculation of the arrangement order of the visual function data required for training based on the visual ability cluster to which the user belongs, selecting training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of the visual function data, and calculating the corresponding training time allocation value and training frequency for each training item, and recording the actual training time and training score of each training item specifically include: When the user is classified into the kth visual ability cluster, the visual function features in the cluster are obtained, and the visual function features are the mean values ​​of the visual function data. and standard deviation , according to the preset visual function characteristic benchmark value , calculate the deviation degree of each visual function data in the cluster ; Prioritize each visual function data according to the degree of deviation from large to small, and select the arrangement order of the first S visual function features as the arrangement order of the visual function data for the visual ability clustering cluster for training; The system has a preset training project question bank, each of which targets specific visual function data, problem areas, and learning difficulty risk levels; According to the user's learning difficulty risk level and problem area, as well as the priority of visual function data, S training items are randomly selected from the training item question bank in sequence; Calculate the training time allocation value for each training item corresponding to each visual function data based on the degree of deviation and the user's learning difficulty risk score and training frequency ; Each training item is assigned a value according to the calculated training duration and training frequency The system recommends and conducts training for users, and records the actual training time and training score of each training item for users.

7. A learning difficulties training system based on visual function testing as claimed in claim 6, characterized in that: The training result data of the user after completing the training project is uploaded to the data server, and the score improvement and time reduction of the user on each training project are calculated based on the historical training result data of the user, and the score difference and time difference of the user compared with other users on each training project are calculated based on the historical training result data of other users in the same visual ability cluster, and the training result analysis report is generated, which specifically includes: After the user completes each training item, the system records the user's score on the training item and obtains the user's training result data, which includes: training item number, training date, actual training time, and training score; Upload the user's training result data to the data server and update the user's personal visual function profile; Extract the user's historical training result data from the data server, compare and analyze the current training result data with the historical training result data, and calculate the user's score improvement on the sth training item. and shortened duration ; Extract the training result data of other users in the same visual ability cluster as the user from the data server, and calculate the score difference of the user in the kth visual ability cluster on the sth training item relative to other users in the same visual ability cluster and duration gap ; The training result analysis report includes a longitudinal training result analysis report and a transverse training result analysis report. The longitudinal training result analysis report of the user is generated by the score improvement and duration reduction of each training item of the user, and the transverse training result analysis report of the user is generated by the score difference and duration difference between the user and other users in the same visual ability cluster.

8. A learning difficulties training method based on visual function testing, characterized in that: include: User registration and login, improve user personal information indicators, and establish personal visual function files; Prompt the user to complete a visual function test, obtain the user's visual function index according to the visual function test, and associate the visual function index with the personal visual function file to obtain visual function data; According to the user's visual function data, the user is classified based on the trained visual ability clustering cluster to obtain the visual ability clustering cluster to which the current user belongs, and the learning difficulty risk level and problem area of ​​the user's learning difficulty are predicted based on the pre-established learning difficulty risk assessment model; Calculate the arrangement order of visual function data required for training based on the visual ability cluster to which the user belongs, select training items from the training item question bank according to the learning difficulty risk level and problem domain of the user's learning difficulty and the arrangement order of visual function data, calculate the corresponding training time allocation value and training frequency for each training item, and record the actual training time and training score for each training item; The training result data of the user after completing the training project is uploaded to the data server. The score improvement and time reduction of the user on each training project are calculated based on the user's historical training result data. Based on the historical training result data of other users in the same visual ability cluster, the score gap and time gap between the user and other users on each training project are calculated, and a training result analysis report is generated.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the learning difficulty training method based on visual function detection as claimed in any one of claim 8 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps in the learning difficulty training method based on visual function detection as described in any one of claim 8.

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