CN117717309A

CN117717309A - Method, device and storage medium for detecting human eye higher-order aberration

Info

Publication number: CN117717309A
Application number: CN202311808175.0A
Authority: CN
Inventors: 夏明亮; 解洪升; 袁邵隆; 高响; 张峰
Original assignee: Shanghai Supore Instruments Co ltd
Current assignee: Shanghai Supore Instruments Co ltd
Priority date: 2023-12-25
Filing date: 2023-12-25
Publication date: 2024-03-19

Abstract

A detection method, device and storage medium for human eye higher order aberration relates to the field of optical measurement, and the method comprises the following steps: acquiring eye feature data of a target object to be detected; evaluating and analyzing the eye feature data to obtain an eye fatigue value; determining whether the eye fatigue value is lower than a preset normal detection threshold value; and if the eye fatigue value is lower than a preset normal detection threshold value, performing high-order phase difference detection. By implementing the method, the fatigue state of human eyes is evaluated before the step of measuring the higher-order aberration is implemented, and the higher-order aberration detection is performed only when the fatigue state is lower than a preset threshold value. Measurement errors caused by the influence of eye fatigue state are avoided, so that more accurate aberration detection is realized.

Description

Method, device and storage medium for detecting human eye higher-order aberration

Technical Field

The present disclosure relates to the field of optical measurement, and in particular, to a method and apparatus for detecting high-order aberration of a human eye, and a storage medium.

Background

In aberration detection of the human eye, higher order aberrations refer to all aberrations except spherical aberration and astigmatic aberration, and their presence may result in light not being completely concentrated at one point, thereby affecting image quality. In the human eye vision system, the higher order aberration may cause the decrease of visual clarity, and symptoms such as glare and starburst appear. Therefore, detection and correction of higher order aberrations is of great importance for improving human visual quality.

The related art detects higher order aberrations with a wavefront sensor that can measure the difference between light passing through the eye and compared with an ideal planar wavefront, thereby obtaining information about the aberrations. Such a device is capable of measuring all aberrations including higher order aberrations, providing an important reference for ophthalmic surgery such as laser refractive surgery, while also providing data support for the manufacture of personalized lenses and contact lenses.

However, the tired state of the eye may affect the measurement of higher order aberrations, e.g. tired eyes may lead to mydriasis, increasing the effect of certain aberrations. When eyes are in a fatigue state, errors exist in measurement results of higher-order aberrations obtained by the related technology, accuracy of optometry is affected, and errors are caused to prediction of eye surgery and visual research.

Disclosure of Invention

The application provides a detection method, a detection device and a storage medium for high-order aberration of human eyes, which are used for optimizing a measurement process of the high-order aberration, namely, the fatigue state of the human eyes is estimated before the step of measuring the high-order aberration is implemented, and the high-order aberration detection is performed only when the fatigue state is lower than a preset threshold value. Measurement errors caused by the influence of eye fatigue state are avoided, so that more accurate aberration detection is realized.

In a first aspect, the present application provides a method for detecting higher-order aberration of a human eye, applied to a detection device, the method comprising: acquiring eye feature data of a target object to be detected; evaluating and analyzing the eye feature data to obtain an eye fatigue value; determining whether the eye fatigue value is lower than a preset normal detection threshold value; and if the eye fatigue value is lower than a preset normal detection threshold value, performing high-order phase difference detection.

In the above embodiment, the detection device first acquires the eye feature data of the object to be detected, then performs evaluation analysis on the eye feature data to obtain the eye fatigue value, and then determines whether the eye fatigue value is lower than the preset normal detection threshold, and only when the eye fatigue value is lower than the preset threshold, performs higher-order aberration detection. The method for evaluating the eye fatigue state and then performing aberration detection avoids the influence of the eye fatigue state on the accuracy of a higher-order aberration detection result, thereby improving the accuracy of aberration detection. Compared with the prior art, the method has the advantages that the problem that the high-order aberration detection is possibly influenced by eye fatigue and generates errors is solved, and the aberration detection accuracy can be effectively improved.

With reference to some embodiments of the first aspect, in some embodiments, the ocular feature data includes an ocular appearance feature, a blink frequency, an ocular movement parameter, a pupil response rate; the step of acquiring the eye feature data of the target object to be detected specifically comprises the following steps: shooting an eye image of a target object to be detected, and extracting to obtain eyeball appearance characteristics; playing the test video to the target object; and collecting blink frequency, eyeball motion parameters and pupil response speed of the target object in the playing process of the test video.

In the above embodiment, the detection device acquires the appearance characteristics of the eyeball by capturing the eye image, and simultaneously collects the blink frequency, the eyeball motion parameter and the pupil response speed of the target object in the playing process of the test video. The comprehensive eye feature data can be obtained by combining two modes of image analysis and video test, so that the finally estimated eye fatigue value is more accurate and reliable, and the follow-up aberration detection accuracy is improved. The comprehensive acquisition mode can obtain richer and more accurate characteristic data, and further accurately evaluate the eye fatigue state.

With reference to some embodiments of the first aspect, in some embodiments, performing evaluation analysis on the eye feature data to obtain an eye fatigue value specifically includes: inputting the eye feature data into a fatigue evaluation large model to obtain an eye fatigue predicted value; outputting a corresponding eye use query sentence to a target object based on the eye fatigue predicted value; and correcting the eye fatigue predicted value based on the answer sentence of the target object using the inquiry sentence for eyes to obtain the eye fatigue value.

In the above embodiment, the detection device obtains the eye fatigue predicted value by using the eye characteristic data and the pre-constructed fatigue evaluation model, then generates the query sentence based on the predicted value to confirm, and finally corrects the predicted value according to the confirmation result of the target object to obtain the final eye fatigue value. The model prediction and automatic query and confirmation evaluation mode is combined, so that the singleness and the limitation of the model prediction are avoided, the real fatigue state of eyes can be evaluated more accurately, and the accuracy of fatigue state judgment is improved.

With reference to some embodiments of the first aspect, in some embodiments, after the step of outputting the corresponding eye use query sentence to the target object based on the eye fatigue prediction value, the method further includes: determining eye data of the target object based on the answer sentence of the target object for the eye using the query sentence; binding the eye data with the identity information of the target object, and uploading the eye data to a cloud database.

In the above embodiment, the detection device determines the eye-using data of the target object through the answer of the target object to the query statement, and uploads the eye-using data and the identity information of the target object to the cloud database after binding. The eye features of the target object can be continuously accumulated, and a personalized eye database is built. The eye use data can reflect daily eye use conditions of the target object, such as eye use time, frequency and the like, and the binding of the identity information is used for distinguishing different objects. The eye data can provide basis for subsequent eye fatigue evaluation, aberration detection parameter selection and the like, and personalized and accurate ophthalmic detection is realized.

With reference to some embodiments of the first aspect, in some embodiments, after the step of determining whether the eyestrain value is below a preset normal detection threshold, the method further includes: if the eye fatigue value is not lower than the preset normal detection threshold, displaying prompt information to the target object for prompting the target object to pay attention to relieving eye fatigue; based on the eye fatigue values, displaying the corresponding fatigue relief schemes to the target object.

In the above embodiment, when the eye fatigue value is not lower than the threshold value, the detection device prompts the target object to pay attention to relieving eye fatigue, and displays a corresponding relieving scheme. The method can intervene in time on the target object with the too high fatigue value, and avoid the influence on the health caused by further aggravation of fatigue. Fatigue status can be evaluated and corresponding intervention can be made to protect the eye health of the user.

With reference to some embodiments of the first aspect, in some embodiments, performing high-order phase difference detection specifically includes: acquiring identity information data of a target object; the identity information data comprises name, age, eye habit data and history detection data; determining corresponding aberration detection parameters based on the age and the history detection data; and adjusting equipment parameters to aberration detection parameters, and performing high-order aberration detection on the target object to obtain an aberration detection result.

In the above embodiment, the detection device acquires the identity information and the history data of the target object, selects appropriate detection parameters based on the age and the history data, and then uses the personalized parameters to perform high-order aberration detection. The way of combining age and historical data to implement personalized parameter selection can enable aberration detection results to be more accurate and reliable. Compared with the unified detection parameters, the personalized parameter selection mode can eliminate the influence of age difference and individual difference on the detection result, and improves the detection accuracy.

With reference to some embodiments of the first aspect, in some embodiments, after the step of adjusting the device parameter to the aberration detection parameter, performing higher order aberration detection on the target object to obtain an aberration detection result, the method further includes: after comparing and analyzing the aberration detection result with the historical detection data, determining a detection report representing the eye change trend based on the analysis result; determining a corresponding eye improvement scheme based on the detection report and the eye habit data of the target object; the detection report and the eye improvement plan are displayed to the target subject.

In the above embodiment, the detection device determines the change trend of the eyes by comparing and analyzing the current aberration detection result with the historical detection data of the target object, and judges the change situation of the eye health condition with the lapse of time. The detection means may also combine the personal ocular habits of the target object, such as frequent use of electronic products or long reading time to determine personalized ocular improvement programs. The final detection device displays the analysis report and the improvement scheme of the detection result to the target object. The time series-based tracking analysis and comparison can intuitively reflect the evolution of the eye health condition and perform targeted eye conditioning. Compared with the single detection, the technical scheme realizes the dynamic management of the eye health condition on the time axis, can better predict the change trend of the eye health condition and timely take improvement measures, and realizes the continuous tracking and maintenance of individual eye health.

In a second aspect, embodiments of the present application provide a detection apparatus, including: the characteristic acquisition module is used for acquiring the eye characteristic data of the target object to be detected; the fatigue evaluation module is used for evaluating and analyzing the eye characteristic data to obtain an eye fatigue value; the threshold judging module is used for determining whether the eyestrain value is lower than a preset normal detection threshold value; and the aberration detection module is used for carrying out high-order phase difference detection when the eye fatigue value is lower than a preset normal detection threshold value.

In a third aspect, embodiments of the present application provide a detection apparatus, including: one or more processors and memory; the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors call for causing the detection apparatus to perform the method as described in the first aspect and any one of the possible implementations of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a detection apparatus, cause the detection apparatus to perform a method as described in the first aspect and any possible implementation manner of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer-readable storage medium comprising instructions that, when executed on a detection apparatus, cause the detection apparatus to perform a method as described in the first aspect and any possible implementation manner of the first aspect.

It will be appreciated that the detection apparatus provided in the second aspect, the third aspect, the computer program product provided in the fourth aspect and the computer storage medium provided in the fifth aspect are each configured to perform the method provided in the embodiments of the present application. Therefore, the advantages achieved by the method can be referred to as the advantages of the corresponding method, and will not be described herein.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. because the technical scheme that the eye fatigue state is estimated before the higher-order aberration detection is performed and the detection is performed only when the fatigue state is lower than the threshold value is adopted, the problem that the eye fatigue state affects the accuracy of the higher-order aberration detection result and errors are possibly caused by the influence of the eye fatigue state when the higher-order aberration detection is directly performed in the related technology is effectively solved. Pupil is enlarged during eye fatigue, the influence of partial aberration is increased, and errors exist when direct detection is performed. The scheme realizes the fatigue evaluation at first, and only the detection is performed in the non-fatigue state, so that the influence of the eye fatigue state on the detection result can be greatly reduced, the detection accuracy is ensured, and the effect of more accurate and practical high-order aberration detection is achieved.

2. Due to the adoption of the technical scheme of combining the big data model prediction and confirming the user eye fatigue feedback, the possible limitation caused by the fact that the model prediction is only relied on can be avoided, and the problem that the judgment is wrong due to the fact that the individual difference, the sample limitation and the like exist in the eye fatigue state of the model prediction is effectively solved. The scheme increases the flow of automatic query and confirmation on the basis of model prediction, fully utilizes the calculation capability of the model, and simultaneously gives consideration to individuation difference, thus remarkably improving the accuracy of judging the eye fatigue state and achieving the effect of more accurately judging the eye fatigue state.

3. Due to the adoption of the technical scheme of eye health condition tracking and dynamic management based on time sequences, the change trend of the eye health condition can be predicted in time and improvement measures can be taken, so that the problem that continuous tracking cannot be realized by single detection in the related technology is effectively solved. The single detection can not judge the time change of the eye health condition, and the scheme can analyze the change trend of the eye health condition by comparing the detection results of multiple times on the time axis, thereby providing an improvement scheme in a targeted way. Continuous tracking and maintenance of individual eye health are realized, and preventive measures are timely taken, so that the effect of dynamically managing eye health is achieved.

Drawings

FIG. 1 is a flow chart of a method for detecting higher order aberrations of the human eye according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of a method for detecting higher order aberrations of the human eye according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a functional module of a detecting device according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a physical device of the detection device in the embodiment of the present application.

Detailed Description

The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application is intended to encompass any or all possible combinations of one or more of the listed items.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.

For easy understanding, the application scenario of the embodiments of the present application is described below.

Mr's king often uses electronics, recently feels vision degraded, and wants to go to the clinic for personalized ophthalmic examination and prescription. The wavefront sensor used in the clinic can detect various aberrations of human eyes so as to make a personalized refraction scheme. However, when the eye drop is detected, the office boss finds that Wang Xian is serious in eye fatigue, more in eyeball blood filaments and enlarged in pupils. This can lead to the testing result to appear great error, can't accurate aassessment king's vision state, influences the precision of optometry. Thus, in ophthalmic detection, the eye fatigue state may interfere with the detection of higher order aberrations, and needs to be effectively solved.

In the related art, the eye aberration information may be acquired by a method of detecting the eye higher order aberration directly using the wavefront sensor. But this method does not take into account the effects of eye fatigue.

A scene using a detection method of high-order aberration of the human eye in the related art is described below.

Early clinics were directed to using wavefront sensors to detect the eyes of an ophthalmic attendant without regard to fatigue factors. The ms was examined as a consultant in the state of eye fatigue, and the result showed that she had mild astigmatism. But in reality her eye has no apparent astigmatism, pupil dilation due to fatigue, and false detection of aberration parameters. Based on this erroneous detection, there is an error in the prescription of glasses prescribed by office boss, resulting in high cost of fitting for women at plum but insignificant improvement of vision in the actual life later. This reflects that direct detection cannot solve the problem of eye fatigue affecting the result.

By adopting the method for evaluating eye fatigue and then performing aberration detection in the embodiment of the application, the step of evaluating eye fatigue is added before higher-order aberration detection, so that the influence of eye fatigue on a detection result is eliminated, eye aberration information can be obtained, and the detection accuracy can be improved.

A scene using the detection method of the higher order aberration of the human eye in the present application is described below.

After the detection method is adopted, the detection device is used for testing the ocular characteristics of Mr. in the detection of the boss of the pre-diagnosis office, the ocular characteristics are input into the model to evaluate the ocular fatigue degree, and Mr. is allowed to confirm. After confirming that the eyes of the Mr. are in a non-fatigue state, the detection device detects the eyes. The final result shows that Mr. has only slight astigmatism and is consistent with the vision condition of his daily life. The scheme adds the pre-process of fatigue detection and confirmation, avoids the fatigue influence, ensures the accuracy of the detection result of the Mr, and also obtains the accurate prescription glasses conforming to the vision condition of the Mr.

Therefore, by adopting the method for evaluating eye fatigue and then detecting the eye fatigue in the embodiment of the application, the problem of interference of eye fatigue on a detection result can be effectively solved while the eye aberration information is acquired, and further more accurate higher-order aberration detection is realized.

For ease of understanding, the method provided in this embodiment is described in the following in conjunction with the above scenario. Fig. 1 is a flowchart of a method for detecting higher order aberrations of a human eye according to an embodiment of the present disclosure.

S101, acquiring eye feature data of a target object to be detected.

The detection device can shoot an eye image of a target object, and the appearance characteristics of eyeballs are extracted and recorded through an image processing algorithm, such as white eye color, blood streak condition and the like; the eyeball reaction speed of the target object is also detected and is used as the basis for judging the subsequent fatigue degree. For example, mr. Wang is taken as a detection object, and the detection device judges that mr. Wang's eyes are white and red by shooting images, and tests that the pupil response speed is slightly slower than that of normal people.

S102, evaluating and analyzing the eye characteristic data to obtain an eye fatigue value.

The detection device is internally provided with an eye fatigue evaluation model for integrating various eye characteristics, and the model can be various algorithm models, such as a support vector machine, a neural network and the like. The detection device inputs the collected eye feature data of the target object into the model for analysis and calculation, and the model gives a score result representing the eye fatigue degree according to a certain algorithm based on different combination conditions of the features, and a specific algorithm model is not limited. For example, mr. Wang's various eye feature data may be analyzed by the model to obtain a fatigue score of 73, which indicates mr. Wang's current eye fatigue is heavy.

S103, determining whether the eyestrain value is lower than a preset normal detection threshold value.

After the eye fatigue score is obtained, the detection device needs to determine whether the fatigue degree corresponding to the score reaches a normal level at which accurate detection can be performed, so that a fatigue score threshold representing the upper limit of the normal level needs to be preset, for example, 50 points are set.

The detection device compares mr. Wang 73 with the threshold 50 and determines whether mr. Wang 73 falls below the threshold to determine whether the eyes are in a normal non-tired state that can be detected. The threshold may be determined according to a device performance parameter or accuracy, and is not particularly limited. In addition, the eye fatigue value and the normal detection threshold value may be represented by no value, and may be directly divided into the excellent, good, medium, and poor steps.

S104, performing high-order phase difference detection.

If the eye fatigue value is lower than a preset normal detection threshold, the detection device can perform high-order phase difference detection. After determining that the current eyes of the Mr. king have fatigue, the detection device prompts the Mr. king to be unsuitable for detection and gives a suggestion for relieving the fatigue. When the mr of the king detects again in the future, if the newly detected eye fatigue evaluation value is reduced to be within the normal level, if the eye fatigue evaluation value is reduced to 40 minutes and is lower than the threshold value by 50 minutes, the detection device starts the wave front sensor at the moment, and the high-order aberration project measurement is carried out according to the personal parameters of the mr of the king.

Therefore, the detection device evaluates eye fatigue before detection, avoids fatigue influence results, and ensures the detection accuracy. The process can be expanded and applied to more algorithm models, and different fatigue normal thresholds can be set for different crowds, so that flexible ophthalmology personalized detection is realized.

In the above examples, the method was described using mr. King as the detection subject, in which the ocular characteristics of mr. King showed a higher degree of fatigue. This is to better illustrate the technical solution of the present method by means of specific examples. In practical ophthalmic clinic applications, the target subject may be any suitable age of the ophthalmic visit group, who may have different levels of eye fatigue. The method can judge the current eye fatigue state of any target object by evaluating the eye fatigue value. In addition, the present method also needs to consider individual differences that may exist in the target object.

The following supplements the scenario of the present embodiment.

The wang women act as a long-term user of the method, with her ocular data stored in a cloud database for long periods. In the third detection, the detection device analyzes according to the historical data to find that the vision of the king women is somewhat reduced, and the system automatically recommends the king women to carry out the recent review. The system automatically calls the personal parameters of the wang lady during detection, and the personal parameters do not need to be determined again. The resulting test report clearly indicates the cause of the vision change of the king women and gives a customized improvement scheme.

The scheme realizes personalized modeling, historical data comparison and dynamic tracking of vision change of the user. Big data analysis enables detection to be more accurate, and the system can actively pay attention to vision change of a user to perform timely intervention without spontaneous inspection of the user, so that user experience is remarkably improved.

In combination with the above scenario, a further more specific flow of the method provided in this embodiment will be described below. Fig. 2 is a schematic flow chart of a method for detecting higher-order aberrations of a human eye according to an embodiment of the present disclosure.

S201, acquiring eye feature data of a target object to be detected.

Referring to step S101, the detection device captures an eye image of the target object, extracts an eyeball appearance feature through an image processing algorithm, and records the eyeball appearance feature.

In some embodiments, the detection device may capture an eye image of a target object to be detected, and extract an eyeball appearance feature; the method can also play the test video to the target object, and then collect the blink frequency, eyeball motion parameters and pupil response speed of the target object in the playing process of the test video.

Specifically, the detection device comprises an image camera, and can shoot a still image of the eyes of the target object. Then, through an image processing algorithm, the appearance characteristics of the extracted eyeballs, such as white color change, blood streak condition and the like, can be analyzed. In addition, the detection device also comprises a display screen capable of playing video and a camera with a target tracking function. When a test video with a certain time length is played for a target object to watch, the camera can capture and analyze the pupil movement condition of the target object in real time, and record dynamic information such as blink frequency, eyeball movement track change parameters and response speed of the pupil of the target object when the video is watched, and the like when light stimulus is added into the video. The method combines static eye image analysis and video test dynamic collection to obtain richer and comprehensive eye feature data, and is used for evaluating the eye fatigue state of a target object, for example, mr. as a detection object, the eye image of the Mr. as a detection object displays the conditions of white and red eyes, the pupil response speed is slow in the video test, and the like, and the fatigue of the eyes of Mr. is comprehensively judged.

S202, inputting the eye feature data into a fatigue evaluation large model to obtain an eye fatigue predicted value.

The detection device is internally provided with an eye fatigue evaluation model for synthesizing various eye characteristics, and the model can be various algorithm models, such as a support vector machine, a neural network and the like, and is not limited to a specific type. The detection device inputs the eye feature data of the target object obtained in the previous step into the model for analysis and calculation. The model gives an eye fatigue estimated value between 0 and 100 according to a certain algorithm based on different combination conditions of the features. For example, mr. Wang's various eye features may be analyzed by the model to estimate a 73 eye fatigue score, which indicates mr. Wang's current eye fatigue is heavy. The predicted value can be used as an important basis for subsequent determination of the final fatigue value.

S203, outputting a corresponding eye use query statement to the target object based on the eye fatigue predicted value.

To confirm whether the predicted value of eye fatigue given by the model is accurate, the detection device needs to confirm to the target object in a query manner. For example, a query to mr. You feel that the current eye fatigue is heavy. The content and form of the query statement may be adjusted according to the information of the target object, or may query the target object about a specific eye environment, for example, whether there are other eye diseases, how long to look at the electronic product a day, whether to perform related tasks consuming eye power, etc., without limiting the specific query manner.

In some embodiments, the detection means may determine eye data of the target object based on an answer sentence of the target object to the eye using the query sentence; and binding the eye data with the identity information of the target object, and uploading the eye data to a cloud database.

Specifically, when performing eye fatigue evaluation, the detection device needs to confirm the specific eye condition of the target object, so that an eye use related query is presented to the target object in the form of voice or text. For example, inquiring whether the target object is always using the eyes overnight or not for a long time of day to watch the mobile phone or the television. The answer content of the target object may determine its daily eye feature data, e.g. looking for a long time at the electronic screen as part of its eye data. And then the detection device binds the obtained eye data with the identity information of the target object, such as a name, an identity card and the like, establishes a personalized eye statistics model, and can upload and store the model into a cloud database to realize the persistent storage of the data. The eye data stored in the cloud may provide a historical reference for future ophthalmic health assessment of the target subject.

S204, correcting the eye fatigue predicted value based on the answer sentence of the target object using the inquiry sentence for the eyes to obtain the eye fatigue value.

If the target object answers confirm that the eye fatigue predicted value is accurate, the predicted value is directly taken as a final eye fatigue value. If the target object answer is different from the predicted value, the predicted value needs to be corrected, and a new corrected eye fatigue value is obtained. For example, mr. King repudiation that the current eye fatigue is heavy and eye force consuming work is not performed, the detection device may adjust the estimated value 73 to a value more suitable for mr. King state, for example, 40, as the final fatigue value, or directly select an appropriate fatigue value by the target subject himself.

S205, determining whether the eyestrain value is lower than a preset normal detection threshold.

Referring to step S103, after obtaining the eye fatigue score, the detecting device needs to determine whether the fatigue degree corresponding to the score reaches a normal level at which accurate detection can be performed.

S206, acquiring identity information data of the target object.

After determining that the eye fatigue state of the target object is normal, the detection device needs to acquire the identity information of the target object so as to select a suitable aberration detection parameter. The identity information data comprises information such as the name, age, history of ophthalmic detection records, daily eye habits and the like of the target object. The identity of the object can be directly confirmed by iris recognition or face recognition, and the related identity information data bound with the identity can be filled in by the target object or confirmed and uploaded by a doctor in a clinic, and the identity information data can be stored in a database in the cloud.

For example, the detection device can identify the identity of mr. King through the camera, and then acquire that mr. King's age is 40 years old in the database, make many ophthalmic examinations in the last two years, and eyes mainly read books and computers.

S207, determining corresponding aberration detection parameters based on the age and the history detection data.

There are differences in the eye conditions of individuals and different age groups, and the detection parameters need to be set individually. The detection device can find out reasonable parameter combinations corresponding to the object according to the age and history detection record of the acquired target object, and prepares for aberration detection.

For example, according to the detection standard of the middle aged 40, the combination of the historical data of recent vision change of the mr. King is used for determining to detect by using the parameter combination of the middle aged people.

S208, adjusting the equipment parameters to the aberration detection parameters, and performing high-order aberration detection on the target object to obtain an aberration detection result.

The detection device can automatically adjust the parameters of the internal hardware equipment to the personalized parameter combination determined in the previous step so as to adapt to the eye conditions of different target objects. For example, the light source intensity, image analysis algorithm, etc. are adjusted to the group of parameters of the middle aged 40. And then starting a detection program to perform overall higher-order aberration detection on the Mr. king eyes and obtain a specific aberration detection result.

Therefore, the detection device can adjust parameters individually for different target objects, eliminate the influence of age difference and individual difference on detection results, and improve the detection accuracy. The flow can be expanded and applicable to adjustment of more detection parameters, so that the detection equipment has stronger adaptability and universality.

In some embodiments, the detection device compares the aberration detection result with the historical detection data, and then determines a detection report representing the eye change trend based on the analysis result; further, based on the detection report and the eye habit data of the target object, a corresponding eye improvement scheme is determined; and finally, displaying the detection report and the eye improvement scheme to the target object.

Specifically, after the current aberration detection result data is obtained, the detection device accesses the cloud database, extracts the aberration detection result of the target object for the past time, and performs comparison analysis. For example, the change trend of the eye health condition of the target object along with time is determined by curve fitting and other modes, whether the eye is gradually aggravated or not is judged, and then an inspection report representing the eye time change is generated. The detection device can also combine the eye habit of the stored target object, such as a mobile phone or a computer, and a scheme for improving the eye health of the target object is set based on the information of the two aspects. Finally, the display of the detection device outputs a report of the detection result and suggested eye treatment or health care measures at the same time, and the report and the suggested eye treatment or health care measures are provided for reference of the target object. The whole process realizes eye change trend analysis and customized eye care scheme generation based on individual historical data.

S209, displaying prompt information to the target object for prompting the target object to pay attention to relieve eye fatigue.

In the early evaluation step, if the fatigue of the eyes of the target object is detected, and the threshold value of normal detection is exceeded, the detection device displays corresponding prompt information to prompt that the current eye state of the target object is not suitable for detection and needs to be relieved first.

The prompt information can be displayed through a display screen of the detection device and can also be converted into a voice form. The prompt information comprises contents for notifying the target that the eye fatigue of the target object is too heavy to detect, and the like. For example, the detection device may display "you are currently tired with eyes, and recommends detection after rest" to prompt mr. Wang.

S210, displaying a corresponding fatigue relieving scheme to the target object based on the eye fatigue value.

Not only prompting the eye fatigue of the target object, but also providing corresponding relieving measures for the target object to refer to by the detection device. The detection device can display an eye relaxation mode suitable for the fatigue degree according to the specific eye fatigue value obtained by the early detection. Such as eye massage, hot compress, etc. Rest advice for different lengths of time may also be displayed. For example, mr. Wang fatigue has a value of 73 minutes and the detection device will prompt him to take a closed eye rest for 10-15 minutes.

The detection device can detect the eye state, evaluate the eye health condition of the target object and recommend corresponding intervention measures. The maintenance and the attention to the eye health of the user are realized, and the detection experience of the user is improved.

In the embodiment of the application, the technical scheme that the eye fatigue state is evaluated before the higher-order aberration detection is performed and the detection is performed only when the fatigue state is lower than the threshold value is realized, so that the influence of the eye fatigue on the detection result is eliminated, the detection accuracy is ensured, and the effect of more accurately detecting the ophthalmic aberration is achieved. The unique eye feature evaluation method has the advantages that the unique eye feature evaluation method is adopted to obtain the fatigue value, then the target object is inquired to confirm the actual condition of eye fatigue, and then the aberration detection process is carried out, so that the problem of interference of the eye fatigue state on the detection result in the prior art is effectively solved.

In addition, the method and the device can eliminate the difference between different age periods and individuals and improve the detection accuracy by acquiring the identity information of the target object and selecting the personalized detection parameters. In addition, the method and the device can analyze and judge the change trend of the eye health condition based on the detection result of the time sequence, realize continuous tracking of the individual eye health and can take preventive measures in time. In a word, the detection method of the high-order aberration of the human eye, provided by the application, further optimizes the detection flow while acquiring accurate ophthalmic aberration parameters, realizes the evaluation and maintenance of the health condition of the eye, and provides continuous and personalized eye health care service for users.

The detection device in the embodiment of the present application is described below from the viewpoint of a module. Fig. 3 is a schematic structural diagram of a functional module of the detection device according to the embodiment of the present application.

The detection device comprises:

the feature acquisition module 301 is configured to acquire eye feature data of a target object to be detected;

the fatigue evaluation module 302 is configured to perform evaluation analysis on the eye feature data to obtain an eye fatigue value;

a threshold value judging module 303, configured to determine whether the eyestrain value is lower than a preset normal detection threshold value;

the aberration detection module 304 is configured to perform high-order phase difference detection when the eye fatigue value is lower than a preset normal detection threshold.

In some embodiments, the feature acquisition module 301 specifically includes:

the static feature unit 3011 is used for shooting an eye image of a target object to be detected and extracting to obtain eyeball appearance features;

a video test unit 3012, configured to play a test video to a target object;

and the dynamic characteristic unit 3013 is used for collecting blink frequency, eyeball motion parameters and pupil response speed of the target object in the playing process of the test video.

In some embodiments, the fatigue evaluation module 302 specifically includes:

A fatigue estimating unit 3021 for inputting the eye feature data into the fatigue estimating large model to obtain an eye fatigue estimated value;

an object query unit 3022 for outputting a corresponding eye use query sentence to the target object based on the eye fatigue prediction value;

the fatigue correction unit 3023 corrects the predicted value of the eye fatigue based on the answer sentence of the query sentence for the eye of the target object, and obtains the eye fatigue value.

In some embodiments, the fatigue evaluation module 302 further includes:

an eye use confirmation unit 3024 for determining eye use data of the target object based on the answer sentence of the target object for the eye use query sentence;

and the data uploading unit 3025 is used for binding the eye-using data with the identity information of the target object and uploading the eye-using data to the cloud database.

In some embodiments, the detection apparatus further comprises:

the information prompt module 305 is configured to display prompt information to the target object when the eye fatigue value is not lower than a preset normal detection threshold, and prompt the target object to pay attention to alleviating eye fatigue;

the fatigue relieving module 306 is configured to display a corresponding fatigue relieving scheme to the target object based on the eye fatigue value.

In some embodiments, the aberration detection module 304 specifically includes:

an identity confirmation unit 3041, configured to obtain identity information data of a target object; the identity information data comprises name, age, eye habit data and history detection data;

a parameter confirmation unit 3042 for determining a corresponding aberration detection parameter based on the age and history detection data;

the phase difference detection unit 3043 is configured to adjust the device parameter to the aberration detection parameter, and perform higher-order aberration detection on the target object to obtain an aberration detection result.

In some embodiments, the aberration detection module 304 further includes:

a result analysis unit 3044 for determining a detection report characterizing the eye change trend based on the analysis result after comparing the aberration detection result with the history detection data;

a regimen determining unit 3045 for determining a corresponding eye improvement regimen based on the detection report and the eye habit data of the target subject;

a plan display unit 3046 for displaying a detection report and an eye improvement plan to the target object.

The detection device in the embodiment of the present application is described above from the point of view of the modularized functional entity, and the detection device in the embodiment of the present application is described below from the point of view of hardware processing, please refer to fig. 4, which is a schematic structural diagram of an entity device of the detection device in the embodiment of the present application.

It should be noted that the structure of the detection device shown in fig. 4 is only an example, and should not limit the functions and the application scope of the embodiment of the present invention.

As shown in fig. 4, the detection apparatus includes a central processing unit (Central Processing Unit, CPU) 401 which can perform various appropriate actions and processes, such as performing the method described in the above embodiment, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage section 408 into a random access Memory (Random Access Memory, RAM) 403. In the RAM 403, various programs and data required for the system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An Input/Output (I/O) interface 405 is also connected to bus 404.

The following components are connected to the I/O interface 405: an input section 406 including a camera, a switch button, a voice acquisition device, and the like; an output portion 407 including a liquid crystal display (Liquid Crystal Display, LCD), an indicator light, a voice output device, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. When executed by a Central Processing Unit (CPU) 401, the computer program performs various functions defined in the present invention.

It should be noted that, the computer readable medium shown in the embodiments of the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Specifically, the detection device of the present embodiment includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the detection method for the higher-order aberration of the human eye provided in the foregoing embodiment is implemented.

As another aspect, the present invention also provides a computer-readable storage medium that may be contained in the detection apparatus described in the above embodiment; or may be present alone without being fitted into the detection device. The storage medium carries one or more computer programs which, when executed by a processor of the detection device, cause the detection device to implement the method of detecting higher order aberrations of the human eye provided in the above-described embodiment.

The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

As used in the above embodiments, the term "when …" may be interpreted to mean "if …" or "after …" or "in response to determination …" or "in response to detection …" depending on the context. Similarly, the phrase "at the time of determination …" or "if detected (a stated condition or event)" may be interpreted to mean "if determined …" or "in response to determination …" or "at the time of detection (a stated condition or event)" or "in response to detection (a stated condition or event)" depending on the context.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from a website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), etc.

Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.

Claims

1. A method for detecting high-order aberration of human eyes, which is applied to a detection device, and is characterized in that the method comprises the following steps:

acquiring eye feature data of a target object to be detected;

evaluating and analyzing the eye feature data to obtain an eye fatigue value;

determining whether the eyestrain value is lower than a preset normal detection threshold;

and if the eyestrain value is lower than a preset normal detection threshold value, performing high-order phase difference detection.

2. The method of claim 1, wherein the ocular characteristic data comprises ocular appearance characteristics, blink frequency, ocular movement parameters, pupil response rate; the step of acquiring the eye feature data of the target object to be detected specifically includes:

Shooting an eye image of a target object to be detected, and extracting to obtain the eyeball appearance characteristics;

playing the test video to the target object;

and collecting the blink frequency, the eyeball motion parameter and the pupil response speed of the target object in the playing process of the test video.

3. The method according to claim 1 or 2, wherein the evaluation analysis of the eye feature data is performed to obtain an eye fatigue value, specifically comprising:

inputting the eye feature data into a fatigue evaluation large model to obtain an eye fatigue predicted value;

outputting a corresponding eye use query sentence to the target object based on the eye fatigue predicted value;

and correcting the eye fatigue predicted value based on the answer sentence of the target object using the inquiry sentence for the eyes to obtain an eye fatigue value.

4. The method according to claim 3, wherein after the step of outputting a corresponding eye use query sentence to the target object based on the eye fatigue prediction value, the method further comprises:

determining eye-using data of the target object based on an answer sentence of the target object using an inquiry sentence for the eye;

Binding the eye-using data with the identity information of the target object, and uploading the eye-using data to a cloud database.

5. The method of claim 1, wherein after the step of determining whether the eye fatigue value is below a preset normal detection threshold, the method further comprises:

if the eyestrain value is not lower than a preset normal detection threshold, displaying prompt information to the target object for prompting the target object to pay attention to relieving eyestrain;

and displaying a corresponding fatigue relieving scheme to the target object based on the eyestrain value.

6. The method according to claim 1, wherein the performing higher-order phase difference detection specifically comprises:

acquiring identity information data of the target object; the identity information data comprises name, age, eye habit data and history detection data;

determining a corresponding aberration detection parameter based on the age and the history detection data;

and adjusting equipment parameters to the aberration detection parameters, and performing high-order aberration detection on the target object to obtain an aberration detection result.

7. The method of claim 6, wherein after the step of adjusting the device parameters to the aberration detection parameters, performing higher order aberration detection on the target object to obtain an aberration detection result, the method further comprises:

After comparing the aberration detection result with the historical detection data, determining a detection report representing the eye change trend based on the analysis result;

determining a corresponding eye improvement scheme based on the detection report and the eye habit data of the target object;

displaying the detection report and the eye improvement plan to the target object.

8. A detection apparatus, characterized by comprising:

the characteristic acquisition module is used for acquiring the eye characteristic data of the target object to be detected;

the fatigue evaluation module is used for evaluating and analyzing the eye feature data to obtain an eye fatigue value;

the threshold judging module is used for determining whether the eyestrain value is lower than a preset normal detection threshold value;

and the aberration detection module is used for carrying out high-order phase difference detection when the eye fatigue value is lower than a preset normal detection threshold value.

9. A detection apparatus, characterized by comprising: one or more processors and memory;

the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke to cause the detection apparatus to perform the method of any of claims 1-7.

10. A computer readable storage medium comprising instructions which, when run on a detection apparatus, cause the detection apparatus to perform the method of any of claims 1-7.