A vision detection method, system, device, and medium

Abstract

The application relates to a vision detection method, system, device and medium, and belongs to the technical field of vision detection. The vision detection method comprises the following steps: acquiring identity feature information of a subject, determining a vision chart type, generating a corresponding vision chart and displaying the vision chart; according to a preset rule, selecting a test target image one by one as a test target image for highlighting display, and collecting a response signal of the subject to each selected test target image until a preset test number is reached; verifying the response signal of the subject each time according to the correct direction of each selected test target image to obtain a response verification result; acquiring a response time corresponding to the response signal of the subject each time and a test target image difficulty; and calculating a vision index based on the test target image difficulty, the response verification result and the corresponding response time of each test to obtain a vision detection result of the subject. The application can improve the accuracy of the vision detection result.

Description

Technical Field

[0001] This application relates to the field of vision testing technology, and in particular to a vision testing method, system, device and medium. Background Technology

[0002] Vision is a crucial indicator of eye health. Regular eye exams not only help detect eye diseases early but also allow for timely adjustments to eyeglasses or contact lenses, ensuring optimal visual quality. Vision tests typically use standard eye charts (such as the Snellen chart), which contain a series of letters or numbers of varying sizes, requiring test-takers to identify the characters they can recognize.

[0003] Currently, most vision tests use static charts where subjects must sequentially report the characters or patterns they recognize from top to bottom and from largest to smallest. The test results then determine the subject's visual acuity. However, common vision testing methods do not consider the specific needs of different subjects to personalize the charts, nor do they incorporate multi-dimensional test data for comprehensive evaluation, leading to inaccurate results. Summary of the Invention

[0004] To improve the accuracy of vision test results, this application provides a vision test method, system, device, and medium.

[0005] Firstly, this application provides a vision testing method, which adopts the following technical solution:

[0006] A vision testing method, the vision testing method comprising:

[0007] Obtain the subject's identity information;

[0008] The corresponding vision chart type is determined based on the aforementioned identity feature information;

[0009] Based on the aforementioned visual acuity chart type, a corresponding visual acuity chart is generated and displayed; wherein, the visual acuity chart includes multiple optotype images of different sizes and random orientations;

[0010] According to preset rules, a visual target image is selected sequentially as the test visual target image and highlighted. The response signal of the subject to the selected test visual target image is collected until the preset number of tests is reached. The response signal includes a gesture response signal or a voice response signal.

[0011] The response signal made by the subject each time is verified according to the correct orientation of the selected test target image each time, so as to obtain the response verification result of each test;

[0012] The response time corresponding to the response signal made by the subject in each test and the difficulty of the test target image in each test are obtained; based on the difficulty of the test target image, the response verification result and the corresponding response time in each test, the visual acuity index is calculated to obtain the visual acuity test result of the subject.

[0013] By adopting the above technical solutions, based on the selection of personalized vision charts, testing with multiple optotype images of varying difficulty, real-time acquisition and verification of response signals, and consideration of response time, the special needs of different subjects can be met. At the same time, through comprehensive evaluation of multi-dimensional test data, accurate assessment of the subjects' vision is achieved, ensuring the scientificity and reliability of the test results, enhancing the comprehensiveness and accuracy of vision assessment, and providing strong support for daily vision care and professional vision testing.

[0014] Optionally, the step of determining the corresponding vision chart type based on the identity feature information includes:

[0015] The age group of the subject is determined based on the identity characteristics information; wherein, the age group includes children, adults, and the elderly;

[0016] Based on a preset chart library, the corresponding vision chart type is determined according to the age group.

[0017] By adopting the above technical solution, the vision chart is customized according to the specific characteristics of the subjects, which reflects the meticulous consideration of the needs of different groups of people, ensures the accuracy and effectiveness of the vision test process, and also improves the user experience of the vision test system.

[0018] Optionally, the step of verifying the subject's response signal each time according to the correct orientation of the selected test target image to obtain the response verification result for each test includes:

[0019] The corresponding response meaning is obtained by identifying and analyzing the response signals made by the subject each time.

[0020] Each time, determine whether the correct orientation of the selected test target image is consistent with the corresponding response meaning;

[0021] If the result is consistent, the output response verification result is a correct response;

[0022] If the result is determined to be inconsistent, the output response verification result will be an error response.

[0023] By adopting the above technical solution, based on the recognition and analysis of multimodal signals (such as gestures and voice), and combined with the correct orientation information of the optotype image to verify the subject's response, it can be applied to various visual function testing scenarios. Through this solution, the system can automatically complete tests on visual acuity, visual sensitivity, etc., greatly improving testing efficiency and accuracy, while also enhancing the subject's experience and testing comfort.

[0024] Optionally, the step of calculating the visual acuity index based on the difficulty of the test optotype image, the response verification result, and the corresponding response time for each test, to obtain the visual acuity test result of the subject, includes:

[0025] The test results for each test are obtained based on the difficulty of the test target image, the response verification results, and the corresponding response time.

[0026] The test target images are classified according to their difficulty. The number of correct responses and the number of incorrect responses in the response verification results corresponding to each difficulty category are counted to determine the accuracy rate for each difficulty category.

[0027] The accuracy rate for each difficulty category is compared with the preset accuracy rate requirement, and the difficulty category that meets the preset accuracy rate requirement and has the highest accuracy rate is taken as the target difficulty category.

[0028] Based on the visual acuity index range corresponding to the target difficulty category, the preliminary visual acuity index range of the subject is obtained;

[0029] The average response time is calculated based on the response time of each test corresponding to the target difficulty category.

[0030] The response time ratio is calculated based on the average response time and the preset ideal response time.

[0031] Based on a preset mapping function, the visual acuity index value is calculated according to the response time ratio and the preliminary visual acuity index range, thereby obtaining the visual acuity test result of the subject.

[0032] By employing the aforementioned technical solution, the visual acuity of the subjects is comprehensively assessed based on the difficulty of the test optotype images, response verification results, and response time of each test. First, the accuracy rate of each difficulty category is determined through statistics and classification. The category with the highest difficulty that meets the preset accuracy requirements is selected as the target difficulty category. Then, a preliminary visual acuity index range is obtained based on the target difficulty category. The response time ratio is calculated by combining the average response time and the ideal response time. Finally, a specific visual acuity index value is derived through a preset mapping function. This calculation method not only considers the subjects' accuracy rate but also incorporates the influence of response time, thus providing a more comprehensive and accurate assessment of the subjects' visual acuity.

[0033] Optionally, the calculation formula for the preset mapping function is:

[0034]

[0035] In the above formula, f(x) is the visual acuity index value, mid is the median 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.

[0036] Optionally, after obtaining the visual acuity test results of the subject, the method further includes:

[0037] Based on a preset index mapping table, the index interval is determined according to the visual acuity index in the visual acuity test results, and the corresponding visual acuity level is determined according to the index interval.

[0038] Determine whether the visual acuity level is lower than a preset level threshold;

[0039] If so, an eye care recommendation strategy is generated based on the vision level and sent to the subject's user terminal.

[0040] By employing the aforementioned technical solution, a pre-defined index mapping table is used to convert visual acuity index values ​​into easily understandable visual acuity levels, making visual acuity test results more intuitive. By setting visual acuity level thresholds, individuals whose visual conditions may require special attention are identified. The system automatically generates targeted eye care suggestions and delivers them to the subjects via their user terminals. This series of steps not only helps to accurately assess the subjects' visual health status but also provides timely and effective interventions, promoting the subjects' visual health and demonstrating the practical application value of this application in the prevention and management of vision problems.

[0041] Secondly, this application provides a vision testing system, which adopts the following technical solution:

[0042] A vision testing system, the vision testing system comprising:

[0043] The first acquisition module is used to acquire the subject's identity feature information;

[0044] The vision chart type determination module is used to determine the corresponding vision chart type based on the identity feature information.

[0045] The vision chart display module is used to generate and display a corresponding vision chart based on the vision chart type; wherein the vision chart includes multiple optotype images of different sizes and random orientations;

[0046] The vision chart display module is also used to select one optotype image as the test optotype image and highlight it according to preset rules.

[0047] The response signal acquisition module is used to acquire the response signals made by the subject to each selected test target image until a preset number of tests is reached; wherein, the response signals include gesture response signals or voice response signals;

[0048] The response verification module is used to verify the response signal made by the subject each time according to the correct orientation of the selected test target image, and obtain the response verification result of each test.

[0049] 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 target image in each test;

[0050] The vision test result generation module is used to calculate the vision index based on the difficulty of the test optotype image, the response verification result, and the corresponding response time for each test, and obtain the vision test result of the subject.

[0051] Optionally, the detection system further includes:

[0052] The vision level determination module is used to determine the index range based on the vision index in the vision test result according to a preset index mapping table, and to determine the corresponding vision level according to the index range.

[0053] The judgment module is used to determine whether the visual acuity level is lower than a preset level threshold; if so, it outputs an abnormal judgment result; the eye care suggestion module is used to respond to the abnormal judgment result, generate an eye care suggestion strategy based on the visual acuity level, and send it to the user terminal of the subject.

[0054] Thirdly, this application provides a computer device, which adopts the following technical solution:

[0055] A computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to perform the steps of the method as described in the first aspect.

[0056] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0057] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.

[0058] In summary, this application includes at least one of the following beneficial technical effects: based on the selection of personalized vision charts, multiple tests of optotype images of varying difficulty, real-time acquisition and verification of response signals, and consideration of response time, it can meet the special needs of different subjects. At the same time, through comprehensive evaluation of multi-dimensional test data, it achieves accurate assessment of the subject's vision, ensures the scientificity and reliability of the test results, enhances the comprehensiveness and accuracy of vision assessment, and provides strong support for daily vision care and professional vision testing. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the first process of a vision testing method according to one embodiment of this application.

[0060] Figure 2 This is a second flowchart of a vision testing method according to one embodiment of this application.

[0061] Figure 3 This is a schematic diagram of the third process of a vision testing method according to one embodiment of this application.

[0062] Figure 4 This is a schematic diagram of the fourth process of a vision testing method according to one embodiment of this application.

[0063] Figure 5 This is a schematic diagram of the fifth process of a vision testing method according to one embodiment of this application. Detailed Implementation

[0064] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-5 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0065] This application discloses a vision testing method.

[0066] Reference Figure 1 A vision testing method, comprising:

[0067] Step S101: Obtain the subject's identity information;

[0068] In some embodiments, by acquiring the subject's identity characteristics (such as age, gender, occupation, etc.), the system can customize suitable testing methods and vision charts for specific groups of people, making vision testing more personalized and helping to adapt to the needs of different groups of people, thereby avoiding errors caused by generalized testing.

[0069] Step S102: Determine the corresponding vision chart type based on the identity feature information;

[0070] Based on the subject's identity information, the system will select or generate a vision chart type suitable for the subject through preset rules or algorithms; in some embodiments, the vision chart type may include standard E-charts, character charts, etc., or it may be a chart customized according to the subject's characteristics.

[0071] For example, based on factors such as the subject's age and visual history, the system selects the most suitable type of visual acuity chart for that subject. The appropriate size and type of optotypes may differ for different age groups, making the selection of the appropriate chart type crucial. For instance, for children, the images on the visual acuity chart can be adjusted to cartoon characters to improve their attention; and for the elderly, a visual acuity chart with relatively large images can be selected.

[0072] Understandably, by choosing the type of chart, the test can better match the actual visual acuity and recognition ability of the test subjects, reduce errors or difficulties caused by inappropriate testing, and improve the accuracy of the test and the applicability of the diagnosis.

[0073] Step S103: Based on the type of vision chart, generate and display the corresponding vision chart;

[0074] The vision chart includes multiple optotype images of different sizes and random orientations;

[0075] Specifically, the system creates a corresponding vision chart based on the selected vision chart type and displays it on the screen for the subject to use during testing.

[0076] In some embodiments, this vision chart contains optotype images of different orientations and sizes. This design aims to test the subject's vision more comprehensively and ensures that the content presented to the subject during the vision test is of reasonable difficulty and clarity, thereby assessing vision levels at multiple levels.

[0077] Step S104: Select a visual target image as the test visual target image in sequence according to the preset rules and highlight it, and collect the response signal of the subject to each selected test visual target image until the preset number of tests is reached;

[0078] The response signals include gesture response signals or voice response signals;

[0079] Specifically, the system can select a visual target image according to predefined rules (such as random selection, preset order, gradually increasing difficulty, etc.) and highlight it in the chart as the test visual target image to start the specific vision test. The system collects the subject's response signals through sensors (such as cameras, microphones, etc.), ensuring the authenticity and statistical validity of the test results. At the same time, the highlighting format also ensures that the subject's attention is focused on the current test image.

[0080] Understandably, response signals can be gestures or voice, and the variety of response methods makes the test more natural and diverse, adaptable to different testing environments and the specific needs of the test subjects.

[0081] It should be noted that the preset number of tests can be pre-configured and adjusted according to the actual situation. In each test, a different test target image is selected in sequence. After the subject responds or after the preset time, the test is completed and the next test target image can be changed.

[0082] As some implementation methods of preset rules, the random selection rule is to randomly select an optotype image from the current vision chart for highlighting; the preset order rule is to select optotype images in sequence according to a preset order for highlighting, which can be fixed or dynamically adjusted based on some logic; the progressively increasing difficulty rule is to progressively increase the difficulty of the selected optotypes; the preset rules can be configured according to the actual situation, so as to ensure the randomness and fairness of the test, and to make personalized adjustments according to the actual situation of the subjects.

[0083] Step S105: Verify the response signal made by the subject each time according to the correct orientation of the selected test target image, and obtain the response verification result of each test.

[0084] The system compares the correct orientation of the selected test target image with the subject's response signal to determine its accuracy. This process requires processing each response signal and performing corresponding verifications to accurately assess whether the subject has identified the correct orientation of the target image, thus laying the foundation for calculating the visual acuity index.

[0085] Step S106: Obtain the response time corresponding to the response signal made by the subject in each test and the difficulty of the test target image in each test;

[0086] In this process, when the subject emits a response signal, the system records the time elapsed between the highlighting of the target image and the response signal, which is the response time. The difficulty of the test target images varies depending on their size; the smaller the target image, the greater the difficulty.

[0087] Understandably, response time provides information about the subject's reaction speed. Combined with the accuracy of response verification results and the difficulty of the test target images, response time data can more meticulously and comprehensively reflect the subject's visual acuity and neural reflex speed.

[0088] Step S107: Based on the difficulty of the test target image, the response verification result, and the corresponding response time for each test, calculate the visual acuity index to obtain the visual acuity test result of the subject.

[0089] The difficulty of the optotype image is a preset difficulty value corresponding to different optotype images. The response verification result includes a correct response or an incorrect response. The response time is the time period from when the test optotype image is highlighted to when the subject's response signal is received. Specifically, by combining the difficulty, accuracy and response time of each test, the system can calculate a quantitative visual acuity index. This index is like the visual acuity value in a general visual acuity chart, used to describe the subject's visual acuity level, making the test results intuitive and easy to understand, and facilitating doctors or users to judge and track their visual health status.

[0090] In the above implementation method, the selection of personalized vision charts, testing with multiple optotype images of varying difficulty, collection and verification of real-time response signals, and consideration of response time can meet the special needs of different subjects. At the same time, through comprehensive evaluation of multi-dimensional test data, accurate assessment of the subjects' vision is achieved, ensuring the scientificity and reliability of the test results, enhancing the comprehensiveness and accuracy of vision assessment, and providing strong support for daily vision care and professional vision testing.

[0091] Currently, most vision testing methods lack personalized adjustments for specific populations (such as children and the elderly). For example, vision charts for adults typically consist of text, numbers, or standard symbols, which may be too abstract for young children. Problems such as lack of concentration and poor comprehension exhibited by children during testing may affect the accuracy of the measurements. Similarly, current vision testing methods do not adequately consider the special needs of groups with gradually declining vision, such as the elderly.

[0092] Therefore, refer to Figure 2 As one implementation of step S102, the step of determining the corresponding vision chart type based on identity feature information includes:

[0093] Step S201: Determine the age group of the subject based on the identity characteristics information;

[0094] The age groups include children, adults, and the elderly;

[0095] Step S202: Based on the preset chart library, determine the corresponding vision chart type according to the age group.

[0096] 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 optotype images.

[0097] Specifically, children typically refer to individuals from infancy to pre-adolescence, such as from birth to around 12 years old. During this stage, their visual system is still developing and maturing, and children may prefer brightly colored and interesting patterns. Therefore, vision charts can use cartoon characters or other design elements that attract children's attention. Adults typically refer to individuals from late adolescence to old age, such as from around 13 to 60 years old. Vision charts for adults can use standard E-charts or letter charts, as this group is generally familiar with traditional vision testing methods, and their visual system is mature and stable. Older adults typically refer to people over 60 years old. As they age, older adults may experience vision decline, so vision charts should use larger optotypes for easier identification.

[0098] In the above implementation method, the vision chart is customized according to the specific characteristics of the subject, which reflects the meticulous consideration of the needs of different groups of people, ensures the accuracy and effectiveness of the vision test process, and also improves the user experience of the vision test system.

[0099] Reference Figure 3 As one implementation of step S105, the step of verifying the response signal made by the subject each time according to the correct direction of the selected test target image 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.

[0100] Specifically, for gesture response signals, the system can detect the subject's hand posture or movement through image recognition technology. This requires a series of preprocessing steps, such as background subtraction, edge detection, and hand position recognition. Then, the shape or movement of the gesture is analyzed through feature extraction algorithms (such as CNN convolutional neural networks) to determine the corresponding response meaning (e.g., directional indications, such as "up", "down", "left", "right").

[0101] For speech response signals, the system can use speech recognition technology to convert speech signals into text. Speech recognition involves sound acquisition, feature extraction (such as MFCC, PLP, etc.), and then matching the most likely sentences or keywords through acoustic models and language models. Natural language processing technology is then used to parse the meaning of the response, for example, to recognize directional instructions in speech.

[0102] In some embodiments, if the subject gives a gesture pointing to the right, the system will recognize it as the "right" direction using 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 comparing the sound.

[0103] Step S302: Determine whether the correct orientation of the selected test target image is consistent with the corresponding response meaning; if it is consistent, proceed to step S303; if it is inconsistent, proceed to step S304.

[0104] Specifically, the system needs to determine whether the correct orientation of the selected test target image is consistent with the meaning of the subject's response derived from the analysis, in order to assess the subject's visual acuity level.

[0105] Step S303: Output the response verification result as a correct response;

[0106] Step S304: Output the response verification result as an error response.

[0107] The output validation results not only provide immediate feedback on the subjects' behavior, but also provide a data foundation for subsequent analysis and recording, which can help to further optimize the subjects' training process and design feedback mechanisms.

[0108] In the above embodiments, the recognition and analysis of multimodal signals (such as gestures and voice) combined with the correct orientation information of the optotype image to verify the subject's response can be applied to various visual function testing scenarios. Through this scheme, the system can automatically complete tests on visual acuity, visual sensitivity, etc., greatly improving testing efficiency and accuracy, while also enhancing the subject's experience and testing comfort.

[0109] Reference Figure 4 As one implementation of step S107, the step of calculating the visual acuity index and obtaining the visual acuity test result of the subject based on the difficulty of the test target image, the response verification result and the corresponding response time of each test includes: step S401, obtaining the test result of each test based on the difficulty of the test target image, the response verification result and the corresponding response time of each test.

[0110] Specifically, each test generates a set of data, including the difficulty of the test target image, the subject's response verification result (correct or incorrect), and the subject's response time.

[0111] Step S402: Classify the test target images according to their difficulty, count the number of correct responses and the number of incorrect responses in the response verification results corresponding to each difficulty category, and determine the accuracy rate corresponding to each difficulty category;

[0112] Specifically, by statistical analysis and classification, the accuracy rates for different difficulty levels can be obtained, providing a basis for selecting an appropriate range of visual acuity indices.

[0113] Step S403: Compare the accuracy rate of each difficulty category with the preset accuracy rate requirement, and take the difficulty category that meets the preset accuracy rate requirement and has the highest accuracy rate as the target difficulty category.

[0114] 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 subject's actual ability.

[0115] Step S404: Obtain the preliminary visual acuity index range of the subject based on the visual acuity index range corresponding to the target difficulty category;

[0116] In this process, based on historical data or practical experience, a visual acuity index range corresponding to each difficulty category is pre-set. The selected target difficulty category is then assigned a specific visual acuity index range, which provides a preliminary range for determining the visual acuity index value in the next step.

[0117] Step S405: Calculate the average response time based on the response time of each test corresponding to the target difficulty category; wherein, extract the response time of all tests under the target difficulty category, calculate the average of these response times, and provide a response time indicator for subsequent adjustment of the vision index;

[0118] Step S406: Calculate the response time ratio based on the average response time and the preset ideal response time;

[0119] Among them, an ideal response time is preset based on historical experimental data or standards. The response time ratio is the ratio of the average response time to the ideal response time. This ratio can reflect the degree of the subject's response speed relative to the ideal state.

[0120] Step S407: Based on the preset mapping function, calculate the visual acuity index value according to the response time ratio and the preliminary visual acuity index range to obtain the visual acuity test result of the subject.

[0121] It should be noted that the goal of the preset mapping function is to map a specific visual acuity index value based on the ratio of response time within the initial visual acuity index range. This mapping function needs to ensure that the smaller the response time is than the ideal response time, the faster the subject's reaction, and the higher the visual acuity index; the further the response time is from the ideal response time, the slower the subject's reaction, and the lower the visual acuity index.

[0122] In the above implementation method, the visual acuity of the subjects is comprehensively assessed based on the difficulty of the test target images, the response verification results, and the response time of each test. First, the accuracy rate of each difficulty category is determined through statistics and classification, and the category with the highest difficulty that meets the preset accuracy requirements is selected as the target difficulty category. Then, a preliminary visual acuity index range is obtained based on the target difficulty category, and the response time ratio is calculated by combining the average response time and the ideal response time. Finally, a specific visual acuity index value is obtained through a preset mapping function. This calculation method not only considers the accuracy rate of the subjects but also incorporates the influence of response time, thereby providing a more comprehensive and accurate assessment of the subjects' visual acuity level.

[0123] In this embodiment of the application, as one implementation of the preset mapping function, the calculation formula of the preset mapping function is as follows:

[0124]

[0125] In the above formula, f(x) is the visual acuity index value, mid is the median 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.

[0126] 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 visual acuity index should be in the middle of the initial visual range; when x > 1, it means that the average response time is greater than the ideal response time, and the visual acuity index should gradually decrease; when x < 1, it means that the average response time is less than the ideal response time, and the visual acuity index should gradually increase.

[0127] Therefore, for the mapping function f(x), when x = 1, f(x) = mid, that is, the visual index is the maximum value of the initial visual range; when x > 1, the visual index gradually decreases as x increases; when x < 1, the visual index gradually increases as x decreases.

[0128] For example, assuming the initial visual range is [0.6, 0.8] and the ideal response time is 100ms, if the average response time is 123.3ms, then the ratio x is 1.233, and the corresponding visual index value is 0.6767 calculated according to the preset exponential function; if the ratio x is 1, then the visual index value is the median value of the initial visual range, 0.7; if the ratio x < 1, for example, x is 0.8, then the visual index value is 0.72, slightly higher than the median value.

[0129] Reference Figure 5 As a further implementation of the vision testing method, after obtaining the subject's vision test results, the method further includes:

[0130] Step S501: Based on the preset index mapping table, determine the index interval according to the visual acuity index in the visual acuity test results, and determine the corresponding visual acuity level according to the index interval.

[0131] The preset index mapping table includes multiple sets of index intervals and visual acuity levels, which convert continuous visual acuity index values ​​into discrete visual acuity levels, such as A, B, and C, making it easier to understand the results of the visual acuity test.

[0132] Step S502: Determine whether the visual acuity level is lower than the preset level threshold; if yes, proceed to step S503; if no, do not perform any operation.

[0133] Among these measures, subjects requiring further attention or intervention can be screened by pre-setting vision level thresholds;

[0134] Step S503: Generate eye care recommendation strategies based on vision level and send them to the subject's user terminal.

[0135] Specifically, personalized eye care recommendations are provided to subjects with poor visual acuity to help improve their vision. In some embodiments, eye care recommendations include, for example, resting the eyes for at least 20 minutes each day, maintaining a screen distance of more than 50 centimeters, and engaging in regular outdoor activities.

[0136] In the above embodiments, a preset index mapping table is used to convert visual acuity index values ​​into easily understandable visual acuity levels, making the visual acuity test results more intuitive. By setting visual acuity level thresholds, individuals whose visual conditions may require special attention are identified. The system automatically generates targeted eye care suggestions and delivers them to the subject through the user's terminal device. This series of steps not only helps to accurately assess the subject's visual health status but also provides timely and effective intervention measures to promote the subject's visual health, demonstrating the practical application value of this application in the prevention and management of vision problems.

[0137] In practical applications, the technical solution of this application not only simplifies the vision testing process and improves the user experience, but also reduces human error through automation, thereby enhancing the scientific nature and objectivity of vision testing. It is of great practical significance for the early detection of vision problems, tracking of vision change trends, and the development of personalized eye care plans.

[0138] This application also discloses a vision testing system.

[0139] A vision testing system, comprising:

[0140] The first acquisition module is used to acquire the subject's identity feature information;

[0141] The vision chart type determination module is used to determine the corresponding vision chart type based on identity feature information;

[0142] The vision chart display module is used to generate and display corresponding vision charts based on the vision chart type; wherein, the vision chart includes multiple optotype images of different sizes and random orientations;

[0143] The vision chart display module is also used to select one optotype image as the test optotype image and highlight it according to preset rules.

[0144] The response signal acquisition module is used to acquire the response signals made by the subject to each selected test target image until a preset number of tests is reached; the response signals include gesture response signals or voice response signals.

[0145] The response verification module is used to verify the response signal made by the subject each time according to the correct orientation of the selected test target image, and obtain the response verification result for each test.

[0146] 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 target image in each test;

[0147] The vision test result generation module is used to calculate the vision index based on the difficulty of the test optotype image, the response verification result, and the corresponding response time for each test, and obtain the vision test result of the subject.

[0148] The above embodiments provide an efficient, accurate, and user-friendly vision testing solution by integrating modules such as personalized vision chart generation, intelligent optotype image highlighting, and multimodal response signal acquisition and verification. The system can determine the most suitable vision chart type based on the subject's identity characteristics, such as age, which helps improve the relevance and effectiveness of the test. Then, the system generates a vision chart containing multiple optotypes of random orientation and different sizes, and highlights these optotypes one by one according to preset rules to test the subject's visual sensitivity under different conditions. Simultaneously, the response signal acquisition module can receive feedback from the subject through gestures or voice, making the vision testing process more convenient and diverse. The response verification module verifies each of the subject's responses based on the correct optotype orientation and records the results of each test. Furthermore, the system can record the response time and optotype image difficulty for each test to comprehensively assess the subject's vision level. Finally, all this information is integrated to calculate a comprehensive vision index, thereby obtaining accurate and reliable vision test results.

[0149] As a further implementation of the detection system, the detection system also includes:

[0150] The vision level determination module is used to determine the index range based on the vision index in the vision test results, and then determine the corresponding vision level based on the index range, according to a preset index mapping table.

[0151] The judgment module is used to determine whether the visual acuity level is lower than the preset level threshold. If so, it outputs an abnormal judgment result.

[0152] The eye care suggestion module is used to generate eye care suggestion strategies based on vision level in response to abnormal judgment results and send them to the subject's user terminal.

[0153] In the above embodiments, while accurately assessing the visual health status of the subjects, effective suggestions and strategies can be provided in a timely manner to promote the subjects' visual health.

[0154] The vision testing system of this application embodiment can implement any of the above-described vision testing methods, and the specific working process of each module in the vision testing system can be referred to the corresponding process in the above-described method embodiments.

[0155] 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 merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0156] This application also discloses a computer device.

[0157] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vision detection method described above.

[0158] This application also discloses a computer-readable storage medium.

[0159] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as any of the vision testing methods described above.

[0160] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0161] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0162] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A vision testing method, characterized in that, The vision testing method includes: Obtain the subject's identity information; The corresponding vision chart type is determined based on the aforementioned identity feature information; Based on the aforementioned visual acuity chart type, a corresponding visual acuity chart is generated and displayed; wherein, the visual acuity chart includes multiple optotype images of different sizes and random orientations; According to preset rules, a visual target image is selected sequentially as the test visual target image and highlighted. The response signal of the subject to the selected test visual target image is collected until the preset number of tests is reached. The response signal includes a gesture response signal or a voice response signal. The response signal made by the subject each time is verified according to the correct orientation of the selected test target image each time, so as to obtain the response verification result of each test; The response time corresponding to the response signal made by the subject in each test and the difficulty of the test target image in each test are obtained; Based on the difficulty of the test optotype image, the response verification result, and the corresponding response time for each test, the visual acuity index is calculated to obtain the visual acuity test result of the subject. The steps for verifying the subject's response signal each time based on the correct orientation of the selected test target image, and obtaining the response verification result for each test, include: The corresponding response meaning is obtained by identifying and analyzing the response signals made by the subject each time. Each time, determine whether the correct orientation of the selected test target image is consistent with the corresponding response meaning; If the result is consistent, the output response verification result is a correct response; If the result is deemed inconsistent, the output response verification result is an error response; the steps for calculating the visual acuity index based on the difficulty of the test target image, the response verification result, and the corresponding response time for each test, to obtain the visual acuity test result of the subject, include: The test results for each test are obtained based on the difficulty of the test target image, the response verification results, and the corresponding response time. The test target images are classified according to their difficulty. The number of correct responses and the number of incorrect responses in the response verification results corresponding to each difficulty category are counted to determine the accuracy rate for each difficulty category. The accuracy rate for each difficulty category is compared with the preset accuracy rate requirement, and the difficulty category that meets the preset accuracy rate requirement and has the highest accuracy rate is taken as the target difficulty category. Based on the visual acuity index range corresponding to the target difficulty category, the preliminary visual acuity index range of the subject is obtained; The average response time is calculated based on the response time of each test corresponding to the target difficulty category. The response time ratio is calculated based on the average response time and the preset ideal response time; wherein, the response time ratio is the ratio of the average response time to the ideal response time, and is used to reflect the degree of the subject's response speed relative to the ideal state. Based on a preset mapping function, the visual acuity index value is calculated according to the response time ratio and the preliminary visual acuity index range to obtain the visual acuity test result of the subject; the mapping function ensures that the smaller the response time is than the ideal response time, the higher the visual acuity index; the further the response time is from the ideal response time, the lower the visual acuity index. The calculation formula for the preset mapping function is as follows: ； In the above formula, f(x) is the visual acuity index value, mid is the median 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.

2. The vision testing method according to claim 1, characterized in that, The steps for determining the corresponding vision chart type based on the identity feature information include: The age group of the subject is determined based on the identity characteristics information; wherein, the age group includes children, adults, and the elderly; Based on a preset chart library, the corresponding vision chart type is determined according to the age group.

3. A vision testing method according to any one of claims 1 to 2, characterized in that, The process of obtaining the subject's vision test results also includes: Based on a preset index mapping table, the index interval is determined according to the visual acuity index in the visual acuity test results, and the corresponding visual acuity level is determined according to the index interval. Determine whether the visual acuity level is lower than a preset level threshold; If so, an eye care recommendation strategy is generated based on the vision level and sent to the subject's user terminal.

4. A vision testing system, characterized in that, For performing a vision detection method according to any one of claims 1 to 3, the vision detection system comprises: The first acquisition module is used to acquire the subject's identity feature information; The vision chart type determination module is used to determine the corresponding vision chart type based on the identity feature information. The vision chart display module is used to generate and display a corresponding vision chart based on the vision chart type; wherein the vision chart includes multiple optotype images of different sizes and random orientations; The vision chart display module is also used to select one optotype image as the test optotype image and highlight it according to preset rules. The response signal acquisition module is used to acquire the response signals made by the subject to each selected test target image until a preset number of tests is reached; wherein, the response signals include gesture response signals or voice response signals; The response verification module is used to verify the response signal made by the subject each time according to the correct orientation of the selected test target image, 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 target image in each test; The vision test result generation module is used to calculate the vision index based on the difficulty of the test optotype image, the response verification result, and the corresponding response time for each test, and obtain the vision test result of the subject.

5. A vision testing system according to claim 4, characterized in that, The detection system also includes: The vision level determination module is used to determine the index range based on the vision index in the vision test result according to a preset index mapping table, and to determine the corresponding vision level according to the index range. The judgment module is used to determine whether the vision level is lower than a preset level threshold; if so, it outputs an abnormal judgment result. An eye care suggestion module is used to generate an eye care suggestion strategy based on the visual acuity level in response to the abnormal judgment result and send it to the subject's user terminal.

6. A computer device, characterized in that: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 3.

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