Vision detection method, system, equipment and medium

Abstract

The invention relates to a vision detection method, system and device and a medium, and belongs to the technical field of vision detection.The vision detection method comprises the steps that identity feature information of a subject is obtained, the type of a vision chart is determined, and the corresponding vision chart is generated and displayed; according to a preset rule, sequentially selecting one sighting mark image as a test sighting mark image for highlighting, and collecting a response signal made by the subject to the test sighting mark image selected each time until a preset test frequency is reached; according to the correct direction of the test sighting mark image selected each time, the response signal made by the subject each time is verified in sequence, and a response verification result is obtained; obtaining response time corresponding to a response signal made by the subject in each test and test sighting mark image difficulty; and based on the test sighting mark image difficulty of each test, the response verification result and the corresponding response time, calculating a vision index, and obtaining a vision detection result of the subject. The accuracy of the vision detection result can be improved.

Description

Technical Field

[0001] The present application relates to the field of vision detection technology, and in particular to a vision detection method, system, device and medium. Background Art

[0002] Vision is one of the important indicators of eye health. Regular vision examinations not only help detect eye diseases at an early stage, but also help adjust the degree of glasses or contact lenses in time to ensure visual quality. Vision testing methods are usually conducted using standard eye charts (such as the Snellen eye chart), which contain a series of letters or numbers of different sizes and require the subjects to give the characters they can recognize.

[0003] At present, in the process of vision testing, the vision test chart is mostly a static vision chart. The subject needs to report the characters or patterns he recognizes in order from top to bottom and from large to small, and the subject's vision condition can be determined based on the test results. However, the common vision testing method does not consider the special needs of different subjects to make personalized adjustments to the chart, nor does it combine multi-dimensional test data to conduct a comprehensive assessment of the subjects, resulting in inaccurate test results. Summary of the invention

[0004] In order to improve the accuracy of vision detection results, the present application provides a vision detection method, system, device and medium.

[0005] In a first aspect, the present application provides a method for visual acuity detection, which adopts the following technical solution: A vision detection method, the vision detection method comprising: Obtaining identification information of the subject; Determining a corresponding vision chart type according to the identity feature information; Based on the vision chart type, a corresponding vision chart is generated and displayed; wherein the vision chart includes a plurality of sight mark images with random directions and different sizes; Selecting one optotype image in turn as a test optotype image for highlighting according to a preset rule, and collecting a response signal made by the subject to each selected test optotype image until a preset number of tests is reached; wherein the response signal includes a gesture response signal or a voice response signal; Verifying the response signal made by the subject each time in sequence according to the correct direction of the test sight mark image selected each time, and obtaining a response verification result for each test; The response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight image in each test are obtained; based on the difficulty of the test sight image in each test, the response verification result and the corresponding response time, the vision index is calculated to obtain the vision test result of the subject.

[0006] By adopting the above technical solutions, based on the selection of personalized vision charts, multiple tests of sight mark images of different difficulty levels, the collection and verification of real-time response signals, and the consideration of response time, the special needs of different subjects can be met. At the same time, comprehensive evaluation of multi-dimensional test data can be carried out to achieve accurate evaluation of the subjects' vision, ensure the scientific nature and reliability of the test results, enhance the comprehensiveness and accuracy of vision evaluation, and provide strong support for daily vision care and professional vision testing.

[0007] Optionally, the step of determining a corresponding vision chart type according to the identity feature information includes: Determining the age group of the subject according to the identity characteristic information; wherein the age group includes children, adults and the elderly; Based on a preset chart library, a corresponding vision chart type is determined according to the age group.

[0008] By adopting the above technical solution, the vision chart is customized according to the specific characteristics of the subjects, which reflects the careful consideration of the needs of different groups of people, ensures the accuracy and effectiveness of the vision testing process, and also improves the user experience of the vision testing system.

[0009] Optionally, the step of verifying the response signal made by the subject each time in turn according to the correct direction of the test sight mark image selected each time to obtain the response verification result of each test includes: Identify the response signal made by the subject each time and analyze it to obtain the corresponding response meaning; Determining whether the correct direction of the test sight mark image selected each time is consistent with the corresponding response meaning; If it is judged to be consistent, the output response verification result is the correct response; If it is judged to be inconsistent, the output response verification result is an error response.

[0010] By adopting the above technical solution, based on the recognition and analysis of multimodal signals (such as gestures and voice), combined with the correct direction information of the sight mark image to verify the subject's response, it can be applied to a variety of visual function testing scenarios. Through this solution, the system can automatically complete the test of vision, visual sensitivity, etc., greatly improving the efficiency and accuracy of the test, while improving the experience of the subjects and the comfort of the test.

[0011] Optionally, the step of calculating the vision index based on the difficulty of the test sight image, the response verification result and the corresponding response time of each test to obtain the vision test result of the subject includes: Based on the test sight image difficulty, the response verification result and the corresponding response time of each test, the test result of each test is obtained; Classify the test sight image according to its difficulty, count the number of correct responses and the number of incorrect responses in the response verification result corresponding to each difficulty category, and determine the accuracy rate corresponding to each difficulty category; Compare the accuracy rate corresponding to each difficulty category with the preset accuracy rate requirement, and take the highest difficulty category that meets the preset accuracy rate requirement as the target difficulty category; Obtaining a preliminary vision index range of the subject according to the vision index range corresponding to the target difficulty category; Calculate the average response time according to the response time of each test corresponding to the target difficulty category; Calculating a response time ratio based on the average response time and the preset ideal response time; Based on a preset mapping function, the vision index value is calculated according to the response time ratio and the preliminary vision index range to obtain the vision test result of the subject.

[0012] By adopting the above technical solution, the visual condition of the subject is comprehensively evaluated based on the difficulty of the test sight mark image, the response verification result and the response time of each test. First, the accuracy of each difficulty category is determined by statistics and classification, and the category that meets the preset accuracy requirements and has the highest difficulty is selected as the target difficulty category. Then, the preliminary visual acuity index range is obtained according to the target difficulty category, and the response time ratio is calculated by combining the average response time and the ideal response time. Finally, the specific visual acuity index value is obtained through the preset mapping function; this calculation method not only takes into account the accuracy of the subject, but also combines the influence of the response time, so as to more comprehensively and accurately evaluate the subject's visual acuity level.

[0013] Optionally, the calculation formula of the preset mapping function is: In the above formula, f(x) is the vision index value, mid is the middle value of the preliminary vision index range, x is the response time ratio, and min and max are the minimum and maximum values ​​of the preliminary vision index range, respectively.

[0014] Optionally, after the step of obtaining the visual acuity test result of the subject, the step further includes: Based on a preset index mapping table, determine the index interval according to the vision index in the vision test result, and determine the corresponding vision level according to the index interval; Determining whether the vision level is lower than a preset level threshold; If so, an eye protection recommendation strategy is generated according to the vision level and sent to the user terminal of the subject.

[0015] By adopting the above technical solution, the visual index value is converted into an easy-to-understand visual acuity level using a preset index mapping table, making the visual acuity test results more intuitive. By setting the visual acuity level threshold to identify individuals whose visual conditions may require special attention, the system will automatically generate targeted eye care suggestions and pass them to the subjects through the user terminal device. This series of steps not only helps to accurately assess the visual health status of the subjects, but also provides effective intervention measures in a timely manner to promote the visual health of the subjects, reflecting the practical application value of this application in preventing and managing vision problems.

[0016] In a second aspect, the present application provides a vision detection system, which adopts the following technical solution: A vision detection system, the vision detection system comprising: A first acquisition module is used to obtain the identity feature information of the subject; A vision chart type determination module, used to determine a corresponding vision chart type according to the identity feature information; A vision chart display module, used to generate and display a corresponding vision chart based on the vision chart type; wherein the vision chart includes a plurality of sight mark images with random directions and different sizes; The vision chart display module is also used to select one sight mark image in turn as a test sight mark image for highlighting according to a preset rule; A response signal acquisition module, used to acquire the response signal made by the subject to the test sight mark image selected each time until a preset number of tests is reached; wherein the response signal includes a gesture response signal or a voice response signal; A response verification module, used to verify the response signal made by the subject each time in turn according to the correct direction of the test sight mark image selected each time, and obtain a response verification result for each test; The second acquisition module is used to acquire the response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight mark image in each test; The vision test result generation module is used to calculate the vision index based on the difficulty of the test target image, the response verification result and the corresponding response time of each test to obtain the vision test result of the subject.

[0017] Optionally, the detection system further includes: A vision level determination module, configured to determine an index interval according to the vision index in the vision test result based on a preset index mapping table, and determine a corresponding vision level according to the index interval; A judgment module is used to judge whether the vision level is lower than a preset level threshold; if so, output an abnormal judgment result; an eye protection suggestion module is used to respond to the abnormal judgment result, generate an eye protection suggestion strategy according to the vision level and send it to the user terminal of the subject.

[0018] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: based on the selection of personalized vision charts, multiple tests of sight mark images of different difficulty levels, the collection and verification of real-time response signals, and the consideration of response time, it can meet the special needs of different subjects, and at the same time, through comprehensive evaluation of multi-dimensional test data, it can achieve accurate evaluation of the subject's vision, ensure the scientificity and reliability of the test results, enhance the comprehensiveness and accuracy of vision evaluation, and provide strong support for daily vision care and professional vision testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a first flow chart of a vision detection method according to one embodiment of the present application.

[0022] Figure 2 It is a second flow chart of a vision detection method according to one embodiment of the present application.

[0023] Figure 3 This is a third flow chart of a vision detection method according to one embodiment of the present application.

[0024] Figure 4 This is a fourth flow chart of a vision detection method according to one embodiment of the present application.

[0025] Figure 5 This is a fifth flow chart of a vision detection method according to one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-5 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] The embodiment of the present application discloses a vision detection method.

[0028] Reference Figure 1 , a vision detection method, comprising: Step S101, obtaining the identity information of the subject; In some embodiments, by obtaining the subject's identity characteristic information (such as age, gender, occupation, etc.), the system can customize appropriate testing methods and vision charts for specific populations based on their characteristics, making vision tests more personalized and helping to adapt to the needs of different populations, thereby avoiding errors caused by generalized testing.

[0029] Step S102, determining a corresponding vision chart type according to the identity feature information; Among them, according to the identity characteristic information of the subject, the system will select or generate a vision chart type suitable for the subject through a preset rule or algorithm; in some embodiments, the vision chart type may include a standard E-letter chart, a character chart, etc., or a chart customized according to the characteristics of the subject; For example, based on the subject's age, vision history and other factors, the system will select the most suitable vision chart type for the subject. The size and type of sight marks suitable for people of different age groups may be different, so it is important to choose the right chart type. For example, for children, the pattern on the vision chart can be adjusted to a cartoon image to improve children's attention; for the elderly, a vision chart with a relatively large image can be selected.

[0030] It can be understood that through the selection of chart types, the test can better meet the actual vision level and recognition ability of the subjects, reduce errors or difficulties caused by inappropriate testing, and improve the accuracy of the test and the applicability of diagnosis.

[0031] Step S103, based on the vision chart type, generating a corresponding vision chart and displaying it; Among them, the visual acuity chart includes multiple sight mark images with random directions and different sizes; Specifically, the system creates a corresponding vision chart according to the selected vision chart type and displays it on the screen for the subject to use in the test.

[0032] In some embodiments, the vision chart contains sight mark images in different directions and sizes, which is designed to test the subject's vision more comprehensively and ensure that the content presented to the subject during the vision test is of reasonable difficulty and clarity, thereby evaluating the vision level at multiple levels.

[0033] Step S104, selecting one optotype image in turn as a test optotype image for highlighting according to a preset rule, and collecting a response signal made by the subject to each selected test optotype image until a preset number of tests is reached; Wherein, the response signal includes a gesture response signal or a voice response signal; Specifically, the system can select a sight mark image according to predefined rules (such as random selection, preset order, gradually increasing difficulty, etc.), and highlight it as a test sight mark image in the chart to start a specific vision test. The system collects the subject's response signal through sensors (such as cameras, microphones, etc.) to ensure the authenticity and statistical validity of the test results. At the same time, the highlighted form also ensures that the subject's attention is focused on the current test picture.

[0034] It is understandable that the response signal may be a gesture or voice, etc. The diverse response methods make the test more natural and diverse, and can adapt to different test environments and specific needs of the subjects.

[0035] It should be noted that the preset number of tests can be pre-configured and adjusted according to actual conditions. A different test visual target image is selected in turn during each test. After the subject responds or after a preset time, the test is completed and the next test visual target image can be replaced.

[0036] As some implementation methods of the preset rules, the random selection rule is to randomly select a sight mark image from the current vision chart for highlighting; the preset order rule is to select sight mark images in sequence according to a preset order for highlighting, and this order can be fixed or dynamically adjusted based on a certain logic; the gradually increasing difficulty rule is to gradually increase the difficulty of the selected sight mark; the preset rules can be configured according to actual conditions, so as to ensure the randomness and fairness of the test and make personalized adjustments according to the actual conditions of the subjects.

[0037] Step S105, verifying the response signal made by the subject each time in turn according to the correct direction of the test sight image selected each time, and obtaining the response verification result of each test; The system compares the correct direction of the selected test sight mark image with the subject's response signal to determine whether it is accurate. This process requires processing each response signal and performing corresponding verification to accurately evaluate whether the subject has identified the correct direction of the sight mark image, thereby laying the foundation for calculating the visual acuity index.

[0038] Step S106, obtaining the response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight mark image in each test; Among them, when the subject sends a response signal, the system records the time period from the highlighting of the sight mark image to the response signal, which is the response time; the difficulty of test sight mark images of different sizes is also different. The smaller the sight mark image, the greater the corresponding difficulty.

[0039] It can be understood that the response time can provide information about the subject's reaction speed. Combined with the accuracy of the response verification results and the difficulty of the test target image, the response time data can more carefully and comprehensively reflect the subject's visual acuity and neural reflex speed.

[0040] Step S107, based on the difficulty of the test sight image, the response verification result and the corresponding response time of each test, the vision index is calculated to obtain the vision test result of the subject.

[0041] Among them, the difficulty of the sight mark image is the preset difficulty value corresponding to different sight mark images, the response verification result includes a correct response or an incorrect response, and the response time is the time period from the highlighting of the test sight mark image to the receipt of the subject's response signal; specifically, by combining the difficulty, accuracy and response time of each test, the system can calculate a quantitative vision index, which is like the vision value in a general vision chart, and is used to describe the subject's vision level, making the test results intuitive and easy to understand, which is convenient for doctors or users to judge and track vision health status.

[0042] In the above implementation, based on the selection of personalized vision charts, multiple tests of sight mark images of different difficulty levels, the collection and verification of real-time response signals, and the consideration of response time, it is possible to meet the special needs of different subjects. At the same time, through comprehensive evaluation of multi-dimensional test data, an accurate evaluation of the subject's vision is achieved, ensuring the scientificity and reliability of the test results, enhancing the comprehensiveness and accuracy of vision evaluation, and providing strong support for daily vision care and professional vision testing.

[0043] Currently, most vision testing methods lack personalized adjustments for specific groups of people (such as children and the elderly). For example, adult vision charts usually consist of text, numbers or standard symbols, which may be too abstract for young children. Children's lack of concentration and poor comprehension during the test may affect the accuracy of the measurement. Similarly, for groups with gradually declining vision, such as the elderly, current vision testing methods do not fully take into account their special needs.

[0044] Therefore, refer to Figure 2 As an implementation of step S102, the step of determining the corresponding vision chart type according to the identity feature information includes: Step S201, determining the age group of the subject according to the identity characteristic information; Among them, age groups include children, adults, and senior citizens; Step S202, based on the preset chart library, determine the corresponding vision chart type according to the age group.

[0045] Among them, the children's group can use a vision chart with cartoon images, the adult group can use a standard E-chart or letter chart, and the elderly group can use a vision chart with relatively large sight image.

[0046] Specifically, children generally refer to individuals from infancy to early adolescence, such as from birth to about 12 years old. During this stage, the visual system is still developing and improving, and children may prefer colorful and interesting patterns, so vision charts can use cartoon characters or other design elements that attract children's attention. Adults generally refer to individuals from late adolescence to old age, such as from 13 to about 60 years old. The vision charts of adults can use standard E-charts or letter charts, because this group of people are generally familiar with traditional vision testing methods, and their visual systems are mature and stable. The elderly generally refer to people over 60 years old. As they age, the elderly may experience vision loss, so vision charts should use larger sight marks for easier identification.

[0047] In the above implementation, the vision chart is customized according to the specific characteristics of the subject, which reflects the careful consideration of the needs of different groups of people, ensures the accuracy and effectiveness of the vision testing process, and also improves the user experience of the vision testing system.

[0048] Reference Figure 3As an implementation of step S105, the step of verifying the response signal made by the subject each time in turn according to the correct direction of the test sight image selected each time, and obtaining the response verification result of each test includes: step S301, identifying the response signal made by the subject each time, and parsing to obtain the corresponding response meaning; Specifically, for gesture response signals, the system can detect the subject's hand posture or movement through image recognition technology, which requires a series of preprocessing steps, such as background subtraction, edge detection, and hand position recognition, and then analyze the shape or movement of the gesture through a feature extraction algorithm (such as a CNN convolutional neural network) to determine the corresponding response meaning (for example, directional indications such as "up", "down", "left", and "right").

[0049] For voice response signals, the system can use speech recognition technology to convert voice signals into text. Speech recognition involves sound collection, feature extraction (such as MFCC, PLP, etc.), and then uses acoustic models and language models to match the most likely sentences or keywords, and then combines natural language processing technology to parse the response meaning, for example, recognize directional instructions in voice.

[0050] In some embodiments, assuming that the subject makes a right-pointing gesture, the system will recognize it as the "right" direction through image recognition technology; if the subject says the word "right", the speech recognition system can convert it into text and recognize it as the "right" direction after sound comparison.

[0051] Step S302, respectively judging whether the correct direction of each selected test sight mark image is consistent with the corresponding response meaning; if it is judged to be consistent, jumping to step S303; if it is judged to be inconsistent, jumping to step S304; Specifically, the system needs to determine whether the correct direction of the test sight mark image selected each time is consistent with the meaning of the subject's response derived from the analysis, so as to evaluate the subject's vision level.

[0052] Step S303, outputting the response verification result as a correct response; Step S304: outputting a response verification result as an error response.

[0053] The output verification results are not only instant feedback on the subjects' behavior, but also provide a data basis for subsequent analysis and recording, which can help further optimize the subjects' training process and design feedback mechanisms.

[0054] In the above implementation, based on the recognition and analysis of multimodal signals (such as gestures and voices), the subject's response is verified in combination with the correct direction information of the sight mark image, which can be applied to a variety of visual function detection scenarios. Through this solution, the system can automatically complete the test of vision, visual sensitivity, etc., greatly improving the detection efficiency and accuracy, while improving the subject's experience and the comfort of the test.

[0055] Reference Figure 4 , as an implementation of step S107, the step of calculating the visual acuity index based on the difficulty of the test sight image, the response verification result and the corresponding response time of each test, and obtaining the visual acuity test result of the subject includes: step S401, obtaining the test result of each test based on the difficulty of the test sight image, the response verification result and the corresponding response time of each test; Specifically, each test generates a set of data including the difficulty of the test sight image, the test subject's response verification result (correct or incorrect), and the test subject's response time.

[0056] Step S402, classifying the test sight image according to its difficulty, counting the number of correct responses and the number of incorrect responses in the response verification results corresponding to each difficulty category, and determining the accuracy rate corresponding to each difficulty category; Specifically, through statistics and classification, the accuracy rates of different difficulty levels can be obtained, providing a basis for the subsequent selection of the appropriate visual acuity index range; Step S403, comparing the accuracy corresponding to each difficulty category with the preset accuracy requirement, and taking the difficulty category that meets the preset accuracy requirement and has the highest accuracy requirement as the target difficulty category; Among them, among all difficulty categories that meet the preset accuracy requirements, the highest difficulty category is selected as the target difficulty category to ensure that the visual acuity index is based on the difficulty level that is closest to the actual ability of the subject.

[0057] Step S404, obtaining a preliminary vision index range of the subject according to the vision index range corresponding to the target difficulty category; The vision index range corresponding to each difficulty category is pre-set based on historical data or actual experience, and the selected target difficulty category corresponds to a specific vision index range, thereby providing a preliminary range for determining the vision index value in the next step.

[0058] Step S405, calculating an average response time according to the response time of each test corresponding to the target difficulty category; wherein the response time of all tests under the target difficulty category is extracted, and the average value of these response times is calculated to provide a response time indicator for subsequent adjustment of the vision index; Step S406, calculating a response time ratio according to the average response time and the preset ideal response time; Among them, an ideal response time is pre-set according to historical experimental data or standards, and the response time ratio is the ratio of the average response time to the ideal response time, through which the degree of the subject's response speed relative to the ideal state can be reflected; Step S407, based on a preset mapping function, according to the response time ratio and the preliminary vision index range, the vision index value is calculated to obtain the vision test result of the subject.

[0059] It should be noted that the goal of the preset mapping function is to map a specific vision index value according to the ratio of response time within the preliminary vision index range; the mapping function needs to ensure that the shorter the response time is than the ideal response time, the faster the subject reacts, and the higher the vision index; the farther the response time is from the ideal response time, the slower the subject reacts, and the lower the vision index.

[0060] In the above implementation, the visual condition of the subject is comprehensively evaluated based on the difficulty of the test sight mark image, the response verification result and the response time of each test. First, the accuracy of each difficulty category is determined by statistics and classification, and the category that meets the preset accuracy requirements and has the highest difficulty is selected as the target difficulty category. Then, the preliminary visual acuity index range is obtained according to the target difficulty category, and the response time ratio is calculated by combining the average response time and the ideal response time, and finally the specific visual acuity index value is obtained by a preset mapping function; this calculation method not only takes into account the accuracy of the subject, but also combines the influence of the response time, so as to more comprehensively and accurately evaluate the visual acuity level of the subject.

[0061] In the embodiment of the present application, as an implementation of the preset mapping function, the calculation formula of the preset mapping function is: In the above formula, f(x) is the visual acuity index value, mid is the middle value of the preliminary visual acuity index range, x is the response time ratio, and min and max are the minimum and maximum values ​​of the preliminary visual acuity index range, respectively.

[0062] Specifically, for the response time ratio x, when x=1, it means that the average response time is equal to the ideal response time, and the vision index should be in the middle value of the preliminary vision range; when x>1, it means that the average response time is greater than the ideal response time, and the vision index should gradually decrease; when x<1, it means that the average response time is less than the ideal response time, and the vision index should gradually increase.

[0063] Therefore, for the mapping function f(x), when x=1, f(x)=mid, that is, the vision index is the maximum value of the initial vision range; when x>1, as x increases, the vision index gradually decreases; when x<1, as x decreases, the vision index gradually increases.

[0064] Exemplarily, assuming that the initial vision range is [0.6, 0.8] and the ideal response time is 100ms, if the average response time is 123.3ms, the ratio x is 1.233, and the corresponding vision index value calculated according to the preset exponential function is 0.6767; if the ratio x is 1, the vision index value is the middle value of the initial vision range, 0.7; if the ratio x<1, for example, x is 0.8, the vision index value is 0.72, which is slightly higher than the middle value.

[0065] Reference Figure 5 As a further implementation of the vision detection method, after the step of obtaining the vision detection result of the subject, the method further includes: Step S501, based on a preset index mapping table, determining the index interval according to the vision index in the vision test result, and determining the corresponding vision level according to the index interval; The preset index mapping table includes mapping relationships between multiple groups of index intervals and vision levels, converting continuous vision index values ​​into discrete vision levels, such as grade A, grade B, grade C, etc., to facilitate understanding of the results of vision testing; Step S502, determining whether the vision level is lower than a preset level threshold; if so, jumping to step S503; if not, not performing any operation; Among them, the subjects who need further attention or measures can be screened by pre-setting the vision level threshold; Step S503: Generate an eye protection recommendation strategy according to the vision level and send it to the user terminal of the subject.

[0066] Specifically, a personalized eye care recommendation strategy is provided for subjects with poor vision to help improve their vision. In some embodiments, the eye care recommendation strategy includes, for example, resting the eyes for at least 20 minutes a day, keeping the screen distance at more than 50 centimeters, regularly engaging in outdoor activities, etc.

[0067] In the above implementation, a preset index mapping table is used to convert the vision index value into an easy-to-understand vision level, making the vision test results more intuitive. By setting the vision level threshold to identify individuals whose vision conditions may require special attention, the system will automatically generate targeted eye protection suggestions and pass them to the subjects through the user terminal device. This series of steps not only helps to accurately assess the vision health status of the subjects, but also provides effective intervention measures in a timely manner to promote the vision care of the subjects, reflecting the practical application value of this application in preventing and managing vision problems.

[0068] In actual application, the technical solution of this application not only simplifies the vision testing process and improves the user experience, but also reduces human errors through automation, enhances the scientificity and objectivity of vision testing, and has important practical significance for early detection of vision problems, tracking vision change trends, and formulating personalized eye care plans.

[0069] The embodiment of the present application also discloses a vision detection system.

[0070] A vision detection system, comprising: A first acquisition module is used to obtain the identity feature information of the subject; A vision chart type determination module, used to determine the corresponding vision chart type according to the identity feature information; A vision chart display module, used to generate and display a corresponding vision chart based on the vision chart type; wherein the vision chart includes a plurality of sight mark images with random directions and different sizes; The vision chart display module is also used to select one sight mark image in turn as a test sight mark image for highlighting according to a preset rule; A response signal acquisition module is used to collect the response signal made by the subject to each selected test sight mark image until a preset number of tests is reached; wherein the response signal includes a gesture response signal or a voice response signal; A response verification module is used to verify the response signal made by the subject each time in turn according to the correct direction of the test sight mark image selected each time, and obtain the response verification result of each test; The second acquisition module is used to acquire the response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight mark image in each test; The vision test result generation module is used to calculate the vision index based on the difficulty of the test target image, the response verification result and the corresponding response time of each test to obtain the vision test result of the subject.

[0071] In the above implementation, by integrating modules such as personalized vision chart generation, intelligent visual mark image highlighting, and multimodal response signal acquisition and verification, an efficient, accurate and humanized vision detection solution is provided. The system can determine the most suitable type of vision chart based on the subject's identity feature information, such as age, which helps to improve the pertinence and effectiveness of the test; then, the system generates a vision chart containing multiple visual marks of different sizes in random directions, and highlights these visual marks one by one according to preset rules to test the visual sensitivity of the subject under different conditions. At the same time, the response signal acquisition module can receive feedback from the subject through gestures or voice, making the vision detection process more convenient and diversified. The response verification module verifies each answer of the subject according to the correct visual mark direction and records the results of each test. In addition, the system can also record the response time and visual mark image difficulty of each test to facilitate a comprehensive assessment of the subject's vision level. Finally, all this information is integrated to calculate a comprehensive vision index, thereby obtaining accurate and reliable vision detection results.

[0072] As a further embodiment of the detection system, the detection system further includes: A vision level determination module, for determining an index interval according to the vision index in the vision test result based on a preset index mapping table, and determining a corresponding vision level according to the index interval; A judgment module is used to judge whether the vision level is lower than a preset level threshold, and if so, output an abnormal judgment result; The eye protection suggestion module is used to generate an eye protection suggestion strategy according to the vision level in response to the abnormal judgment result and send it to the user terminal of the subject.

[0073] In the above implementation, while accurately assessing the visual health status of the subject, effective suggestions and strategies can be provided in a timely manner to promote the visual health of the subject.

[0074] The vision detection system of the embodiment of the present application can implement any of the above-mentioned vision detection methods, and the specific working process of each module in the vision detection system can refer to the corresponding process in the above-mentioned method embodiment.

[0075] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0076] The embodiment of the present application also discloses a computer device.

[0077] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the vision detection method as described above is implemented.

[0078] The embodiment of the present application also discloses a computer-readable storage medium.

[0079] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the vision detection methods described above.

[0080] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0081] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for visual acuity detection, characterized in that: The vision detection method comprises: Obtaining identification information of the subject; Determining a corresponding vision chart type according to the identity feature information; Based on the vision chart type, a corresponding vision chart is generated and displayed; wherein the vision chart includes a plurality of sight mark images with random directions and different sizes; Selecting one optotype image in turn as a test optotype image for highlighting according to a preset rule, and collecting a response signal made by the subject to each selected test optotype image until a preset number of tests is reached; wherein the response signal includes a gesture response signal or a voice response signal; Verifying the response signal made by the subject each time in sequence according to the correct direction of the test sight mark image selected each time, and obtaining a response verification result for each test; The response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight image in each test are obtained; based on the difficulty of the test sight image in each test, the response verification result and the corresponding response time, the vision index is calculated to obtain the vision test result of the subject.

2. A method for visual acuity detection according to claim 1, characterized in that: The step of determining the corresponding vision chart type according to the identity feature information comprises: Determining the age group of the subject according to the identity characteristic information; wherein the age group includes children, adults and the elderly; Based on a preset chart library, a corresponding vision chart type is determined according to the age group.

3. A method for visual acuity detection according to claim 1, characterized in that: The step of sequentially verifying the response signal made by the subject each time according to the correct direction of the test sight mark image selected each time to obtain the response verification result of each test includes: Identify the response signal made by the subject each time and analyze it to obtain the corresponding response meaning; Determining whether the correct direction of the test sight mark image selected each time is consistent with the corresponding response meaning; If it is judged to be consistent, the output response verification result is the correct response; If it is judged to be inconsistent, the output response verification result is an error response.

4. A method for visual acuity detection according to claim 3, characterized in that: The step of calculating the visual index based on the difficulty of the test sight image, the response verification result and the corresponding response time of each test to obtain the visual acuity test result of the subject comprises: Based on the test sight image difficulty, the response verification result and the corresponding response time of each test, the test result of each test is obtained; Classify the test sight image according to its difficulty, count the number of correct responses and the number of incorrect responses in the response verification result corresponding to each difficulty category, and determine the accuracy rate corresponding to each difficulty category; Compare the accuracy rate corresponding to each difficulty category with the preset accuracy rate requirement, and take the highest difficulty category that meets the preset accuracy rate requirement as the target difficulty category; Obtaining a preliminary vision index range of the subject according to the vision index range corresponding to the target difficulty category; Calculate the average response time according to the response time of each test corresponding to the target difficulty category; Calculating a response time ratio based on the average response time and the preset ideal response time; Based on a preset mapping function, the vision index value is calculated according to the response time ratio and the preliminary vision index range to obtain the vision test result of the subject.

5. A method for visual acuity detection according to claim 4, characterized in that: The calculation formula of the preset mapping function is: In the above formula, f(x) is the vision index value, mid is the middle value of the preliminary vision index range, x is the response time ratio, and min and max are the minimum and maximum values ​​of the preliminary vision index range, respectively.

6. A method for visual acuity detection according to any one of claims 1 to 5, characterized in that: After the step of obtaining the visual acuity test result of the subject, the method further includes: Based on a preset index mapping table, determine the index interval according to the vision index in the vision test result, and determine the corresponding vision level according to the index interval; Determining whether the vision level is lower than a preset level threshold; If so, an eye protection recommendation strategy is generated according to the vision level and sent to the user terminal of the subject.

7. A vision detection system, characterized in that: The vision detection system comprises: A first acquisition module is used to obtain the identity feature information of the subject; A vision chart type determination module, used to determine a corresponding vision chart type according to the identity feature information; A vision chart display module, used to generate and display a corresponding vision chart based on the vision chart type; wherein the vision chart includes a plurality of sight mark images with random directions and different sizes; The vision chart display module is also used to select one sight mark image in turn as a test sight mark image for highlighting according to a preset rule; A response signal acquisition module, used to acquire the response signal made by the subject to the test sight mark image selected each time until a preset number of tests is reached; wherein the response signal includes a gesture response signal or a voice response signal; A response verification module, used to verify the response signal made by the subject each time in turn according to the correct direction of the test sight mark image selected each time, and obtain a response verification result for each test; The second acquisition module is used to acquire the response time corresponding to the response signal made by the subject in each test and the difficulty of the test sight mark image in each test; The vision test result generation module is used to calculate the vision index based on the difficulty of the test target image, the response verification result and the corresponding response time of each test to obtain the vision test result of the subject.

8. A vision detection system according to claim 7, characterized in that: The detection system also includes: A vision level determination module, configured to determine an index interval according to the vision index in the vision test result based on a preset index mapping table, and determine a corresponding vision level according to the index interval; A judgment module, used to judge whether the vision level is lower than a preset level threshold; if so, output an abnormal judgment result; The eye protection suggestion module is used to generate an eye protection suggestion strategy according to the vision level in response to the abnormal judgment result and send it to the user terminal of the subject.

9. A computer device, characterized in that: The method comprises 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 method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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