CN118155803A - Eye habit correction analysis method, system, equipment and medium for xerophthalmia - Google Patents
Eye habit correction analysis method, system, equipment and medium for xerophthalmia Download PDFInfo
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Abstract
The invention relates to the technical field of medical data processing, and discloses an eye habit correction analysis method, system, equipment and medium for xerophthalmia, which comprise the following steps: acquiring eye behavior monitoring data, life habit data and treatment execution condition data; according to the eye use behavior monitoring data, obtaining an eye use behavior score through a first monitoring model, and sending out eye use behavior correction reminding information when the eye use behavior score is lower than a first preset threshold value; according to the life habit data, obtaining a life habit score through a second monitoring model; obtaining a treatment execution condition score through a third monitoring model according to the treatment execution condition data; and obtaining an eye habit correction scheme of the tested person according to the eye habit score, the life habit score and the treatment execution condition score of the preset time period. The invention monitors the eye use behaviors of the testee in real time, sends out the eye use behavior correction reminding information in time, and can correct the bad eye use behaviors of the testee at any time.
Description
Technical Field
The invention relates to the technical field of medical data processing, in particular to an eye habit correction analysis method, system, equipment and medium for xerophthalmia.
Background
Dry eye has become one of the global epidemic diseases, and improving ocular habit is one of the important contents of prevention and treatment of dry eye. For dry eye patients and high risk groups, improvement of eye habit is a difficult and hard-to-adhere matter, and the main reasons include that no one monitors the daily eye behaviors of the patients, no one can timely remind the patients to correct the occurring bad eye behaviors, and improvement suggestions are difficult to be specifically proposed. Current techniques and products focus on dry eye assessment, prediction, etc., and ignore correction of daily adverse eye behavior.
Therefore, there is a need for an ocular habit correction and analysis method for dry eye, which helps to recognize and improve ocular habits by assisting in monitoring and recording of daily eye behaviors and giving correction reminders and suggestions for bad eye behaviors.
Disclosure of Invention
The invention provides an eye habit correction analysis method, system, equipment and medium for xerophthalmia, which are used for solving the defect that the correction of daily bad eye behaviors is neglected when the existing products and equipment focus on the aspects of dry eye evaluation, prediction and the like.
The invention provides an eye habit correction analysis method aiming at xerophthalmia, which comprises the following steps:
Acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
According to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
And obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
In one embodiment, the eye usage monitoring data includes blink frequency data, blink amplitude data, near eye data, local air humidity data, and the obtaining eye usage monitoring data, lifestyle data, and treatment performance data of the subject includes:
shooting the superposition condition of eyelashes of the to-be-detected person from the side surface of eyes of the to-be-detected person through a monitoring spectacle frame to obtain blink frequency data and blink amplitude data of the to-be-detected person;
The distance between eyes of a person to be detected and an object in front of the eyes is monitored through a monitoring spectacle frame, and near-eye distance data and near-eye time data of the person to be detected are obtained;
and obtaining local air humidity data of the position of the person to be measured through a humidity sensor.
In one embodiment, the obtaining, according to the eye behavior monitoring data of the to-be-tested person, an eye behavior score of the to-be-tested person through a first monitoring model, and when the eye behavior score of the to-be-tested person is lower than a first preset threshold, sending out eye behavior correction reminding information, includes:
According to the blink frequency data and blink amplitude data of the testee, obtaining the complete blink rate of the testee through a first monitoring model, comparing the complete blink rate of the testee with a preset blink qualification rate, and sending blink correction reminding information when the complete blink rate of the testee is lower than the preset blink qualification rate;
According to near-to-eye distance data and near-to-eye time data of a person to be measured, obtaining near-to-eye duration data of the person to be measured through a first monitoring model, comparing the near-to-eye duration data of the person to be measured with a preset near-to-eye duration threshold, and sending near-to-eye correction reminding information when the near-to-eye duration of the person to be measured exceeds the preset near-to-eye duration threshold;
Obtaining humidity maintenance time data of the position of the to-be-detected person through a first monitoring model according to the local air humidity data of the position of the to-be-detected person, comparing the humidity maintenance time data of the position of the to-be-detected person with preset air humidity time-keeping reminding conditions, and sending humidity adjustment reminding information when the humidity maintenance time data of the position of the to-be-detected person accords with the preset air humidity time-keeping reminding conditions;
Counting the times of sending eye-using behavior correction reminding information (including blink correction reminding information, near eye correction reminding information and humidity adjustment reminding information) within a preset time period, and obtaining the eye-using behavior score of the person to be tested.
In one embodiment, the predetermined blink eligibility rate is greater than 6 complete blinks per minute.
In one embodiment, the preset near-eye duration threshold is a single duration of less than 1 hour.
In one embodiment, the preset air humidity hold reminding conditions are: the air humidity is lower than 30%, and the maintenance time is more than 1 hour.
In one embodiment, the obtaining the near-eye duration data of the person to be tested according to the near-eye distance data and the near-eye time data of the person to be tested through the first monitoring model includes:
When the near-eye distance data of the to-be-measured person indicates that the distance between the eyes of the to-be-measured person and the object in front of the eyes is smaller than the preset eye recommended distance, the to-be-measured person is judged to be the near-eye, and the near-eye duration data of the to-be-measured person are obtained.
In one embodiment, the predetermined eye relief distance is any value from 50 cm to 70 cm.
In one embodiment, lifestyle data includes: outdoor activity time data, sweet/greasy food intake, sleep quality data, make-up time data, contact lens/pupil wear time data.
In one embodiment, the obtaining, according to the life habit data of the person to be tested, the life habit score of the person to be tested through the second monitoring model includes:
According to life habit data of the person to be tested, obtaining life habit scores of the person to be tested by using a life habit score expression through a second monitoring model;
wherein, the lifestyle score expression is:
In the life habit score expression, F life habit represents a life habit score, F i represents a score of the ith life habit data, β represents a weight of the ith life habit data, I represents the number of the life habit data, H i represents an actual numerical value of the ith life habit data, and H i represents a preset threshold of the ith life habit data.
In one embodiment, the treatment performance data comprises treatment medication performance data and treatment performance data, the treatment performance data comprising performance data of any one or any combination of the following: hot compress, fumigation, atomization, meibomian gland massage and eyelid margin cleaning.
In one embodiment, the obtaining, according to the treatment performance data of the person to be tested, the treatment performance score of the person to be tested through the third monitoring model includes:
According to the treatment execution condition data of the person to be tested, a treatment execution condition score expression is utilized by a third monitoring model to obtain a treatment execution condition score of the person to be tested;
Wherein, the treatment performance score expression is:
In the treatment execution condition score expression, F treatment execution represents a treatment execution condition score, K m represents an actual numerical value of treatment medication condition data, K represents a preset threshold value of treatment medication condition data, and n represents the execution times of treatment behaviors.
In one embodiment, the obtaining the eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period includes:
according to eye behavior score, life habit score and treatment execution condition score of the measured person in a preset time period, the fluctuation condition of the measured person in the aspects of eye behavior, life habit and treatment execution condition in the preset time period is represented by a line graph, the type of the measured person is judged, and eye use advice is provided for the measured person.
In one embodiment, the types of testers include: eye habit is good, eye habit is to be improved, and eye advice includes any one or any combination of the following: the eye blink learning course is performed, the eyes are prevented from being used nearby for a long time, the humidity of the surrounding environment is adjusted, the outdoor activity time is prolonged, the intake of sweet and greasy food is reduced, the sleep quality is improved, the makeup holding time is shortened, the contact lens wearing time and the pupil beautifying time are shortened, the treatment medication rate is improved, and the treatment behavior execution rate is improved.
The invention also provides an eye habit correction analysis system for xerophthalmia, comprising:
The data acquisition module is used for: acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
A first monitoring module for: according to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
a second monitoring module for: according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
A third monitoring module for: obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
the comprehensive analysis module is used for: and obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
The invention also provides electronic equipment, which comprises a processor and a memory storing a computer program, wherein the processor realizes the eye habit correction analysis method for xerophthalmia when executing the computer program.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the above-described eye habit correction analysis methods for dry eye.
The present invention also provides a computer program product comprising a computer program storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing any one of the above-described eye habit correction analysis methods for dry eye.
According to the eye habit correction analysis method, system, equipment and medium for the xerophthalmia, the eye habit of the testee is monitored in real time through the machine learning model according to the eye behavior monitoring data, the life habit data and the treatment execution condition data, the eye behavior correction reminding information of the testee is timely sent out, the bad eye behavior of the testee can be corrected at any time, the testee is gradually cultured in daily life to have good eye behavior habits, the difficulty of improving the eye habit of the testee is reduced, the eye behavior score, the life habit score and the treatment execution condition score of the testee are comprehensively analyzed, the eye habit correction scheme of the testee in a preset time period is obtained, and effective data reference can be provided for preventing or treating the xerophthalmia of the testee.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following brief description will be given of the drawings used in the embodiments or the description of the prior art, it being obvious that the drawings in the following description are some embodiments of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an analysis method for correcting eye habit for dry eye.
Fig. 2 is a schematic structural diagram of an eye habit correction analysis system for dry eye according to the present invention.
Fig. 3 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions thereof will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, which should not be construed as limiting the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In the description of the present invention, it is to be understood that the terminology used is for the purpose of description only and is not to be interpreted as indicating or implying relative importance.
The eye habit correction analysis method, system, device and medium for dry eye syndrome provided by the invention are described below with reference to fig. 1 to 3.
Fig. 1 is a flow chart of the eye habit correction analysis method for dry eye. Referring to fig. 1, the method for analyzing ocular habit correction for dry eye according to the present invention may include:
step S110, eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested are obtained;
step S120, according to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
Step S130, according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Step S140, according to the treatment execution condition data of the person to be tested, obtaining a treatment execution condition score of the person to be tested through a third monitoring model;
And step S150, obtaining an eye habit correction scheme of the testee in a preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the testee in the preset time period.
In one embodiment, the eye behavior monitoring data includes blink frequency data, blink amplitude data, near eye data, local air humidity data, and the subject may be a dry eye patient, an individual in a high risk group of dry eye, a myopic patient, an individual in a high risk group of myopia, a patient having an eye disease associated with eye habit, a normal person, or the like.
In one embodiment, step S110 may include:
shooting the superposition condition of eyelashes of the to-be-detected person from the side surface of eyes of the to-be-detected person through a monitoring spectacle frame to obtain blink frequency data and blink amplitude data of the to-be-detected person;
The distance between eyes of a person to be detected and an object in front of the eyes is monitored through a monitoring spectacle frame, and near-eye distance data and near-eye time data of the person to be detected are obtained;
and obtaining local air humidity data of the position of the person to be measured through a humidity sensor.
Specifically, before the eye behavior monitoring data of the to-be-measured person is obtained, the to-be-measured person can wear the monitoring spectacle frame, the monitoring spectacle frame is provided with a camera device, a distance sensor, a timer and a humidity sensor, the miniature camera device is used for shooting the superposition condition of the eyelashes of the to-be-measured person from the side surface of the eyes of the to-be-measured person, images or videos can be shot, blink frequency data and blink amplitude data of the to-be-measured person are obtained by judging the superposition times of the eyelashes of the to-be-measured person and the repetition coverage rate of upper eyelashes and lower eyelashes when the to-be-measured person blinks, the distance sensor is used for measuring the distance between the eyes of the to-be-measured person and an object in front of the eyes, the timer is used for measuring the maintenance time of the distance between the eyes of the to-be-measured person and the object in front of the eyes of the to obtain near-eye distance data and near-eye time data of the to-be-measured person, and the humidity sensor is used for measuring local air humidity of the position of the to be-measured person to obtain local air humidity data of the position of the to be-measured person.
In one embodiment, the eye-using behavior score of the subject may be composed of three parts, a part of the eye-using score is a complete blink score, a part of the eye-using score is a near eye score, and a part of the environment humidity score, and step S120 may include:
According to the blink frequency data and blink amplitude data of the testee, obtaining the complete blink rate of the testee through a first monitoring model, comparing the complete blink rate of the testee with a preset blink qualification rate, and sending blink correction reminding information when the complete blink rate of the testee is lower than the preset blink qualification rate;
According to near-to-eye distance data and near-to-eye time data of a person to be measured, obtaining near-to-eye duration data of the person to be measured through a first monitoring model, comparing the near-to-eye duration data of the person to be measured with a preset near-to-eye duration threshold, and sending near-to-eye correction reminding information when the near-to-eye duration of the person to be measured exceeds the preset near-to-eye duration threshold;
Obtaining humidity maintenance time data of the position of the to-be-detected person through a first monitoring model according to the local air humidity data of the position of the to-be-detected person, comparing the humidity maintenance time data of the position of the to-be-detected person with preset air humidity time-keeping reminding conditions, and sending humidity adjustment reminding information when the humidity maintenance time data of the position of the to-be-detected person accords with the preset air humidity time-keeping reminding conditions;
Counting the times of sending eye-using behavior correction reminding information (including blink correction reminding information, near eye correction reminding information and humidity adjustment reminding information) within a preset time period, and obtaining the eye-using behavior score of the person to be tested.
Specifically, in step S120, the first monitoring model may be trained in advance according to the sample data of the blink frequency and the sample data of the blink amplitude and the tag data of the corresponding complete blink rate, so that the first monitoring model may obtain the complete blink rate of the testee according to the blink frequency data and the blink amplitude data of the testee, and the definition of "complete blink" may be understood that the repeated coverage rate of the upper eyelashes and the lower eyelashes of the eyes of the testee reaches more than a preset coverage rate (e.g. 85%), and the complete blink rate needs to be determined according to the total blink number and the blink number of the repeated coverage rate reaching the preset coverage rate in a certain period, for example, the repeated coverage rate may be the blink number reaching the preset coverage rate in a certain period/the total blink number in a certain period, etc. After the first monitoring model obtains the complete blink rate of the testee, comparing the complete blink rate with the preset blink qualification rate, and when the complete blink rate of the testee is lower than the preset blink qualification rate (the preset blink qualification rate in the embodiment is that the complete blink is more than 6 times per minute), sending out blink correction reminding information, and reminding by means of voice broadcasting, vibration of a monitoring spectacle frame and the like.
Specifically, in step S120, whether the person to be measured is a near-eye or not may be determined according to the near-eye distance data of the person to be measured by the first monitoring model, where the specific determination conditions are: when the near-eye distance data of the to-be-measured person indicates that the distance between the eyes of the to-be-measured person and the object in front of the eyes is smaller than the preset eye recommended distance (the preset eye recommended distance in the embodiment is any value in 50-70 cm), the to-be-measured person is judged to be the near-eye, the corresponding near-eye duration data is obtained, then the near-eye duration data of the to-be-measured person is compared with the preset near-eye duration threshold (the preset near-eye duration threshold in the embodiment is a single duration time of less than 1 hour), and when the near-eye duration of the to-be-measured person exceeds the preset near-eye duration threshold, near-eye correction reminding information is sent out, and reminding can be carried out through voice broadcasting, vibration monitoring and the like.
Specifically, in step S120, the humidity maintenance time data of the position of the person to be tested (for example, according to the recording time of the local air humidity data, when the humidity changes, that is, the end of the previous humidity, the start of the next humidity to obtain the maintenance time of each humidity) may be obtained according to the local air humidity data of the position of the person to be tested by the first monitoring model, and the humidity maintenance time data of the position of the person to be tested and the preset air humidity maintenance reminding condition (in this embodiment, the preset air humidity maintenance reminding condition is that the air humidity is lower than 30% and the maintenance time is more than 1 hour) are compared, when the humidity maintenance time data of the position of the person to be tested accords with the preset air humidity maintenance reminding condition, the humidity adjustment reminding information is sent, and the person to be tested may be reminded by means of voice broadcasting and/or making the monitoring glasses frame vibrate. The device is an important risk factor for generating dry eyes in a refrigerating or heating closed environment (such as a heating or air-conditioning room) for a long time, so that a user to be tested can be timely reminded of optimizing the humidity of the surrounding environment.
If the mode of vibration of the monitoring spectacle frame is adopted for reminding, different vibration modes can be adopted for distinguishing different reminding, for example, if the reminding information is blink correction reminding information, a long vibration low-frequency mode can be adopted, if the reminding information is near-eye correction reminding information, a short vibration high-frequency mode can be adopted, if the reminding information is humidity adjustment reminding information, a short vibration low-frequency mode can be adopted, and the like, so that the aim of reminding a testee to change eye use behaviors or environmental humidity is achieved. If the first monitoring model judges that the measured person does not belong to the near-eye according to the near-eye distance data of the measured person, comparison is not needed.
Step S120 may count the number of times of sending blink correction reminding information, near-eye correction reminding information and humidity adjustment reminding information in a preset time period, obtain-0.1 score (including blink correction reminding information, near-eye correction reminding information and humidity adjustment reminding information, once, if only once), and obtain 1 score if no reminding information has been sent in the preset time period.
In one embodiment, lifestyle data includes: outdoor activity time data, sweet/greasy food intake, sleep quality data, make-up time data, contact lens/pupil wear time data. Lifestyle data can be recorded and input by the testee himself.
In one embodiment, step S130 may obtain the lifestyle score of the person to be tested according to the lifestyle data of the person to be tested by using the lifestyle score expression through the second monitoring model;
wherein, the lifestyle score expression is:
In the life habit score expression, F life habit represents a life habit score, F i represents a score of the ith life habit data, β represents a weight of the ith life habit data, I represents the number of the life habit data, H i represents an actual numerical value of the ith life habit data, and H i represents a preset threshold of the ith life habit data.
Step S130 may obtain the lifestyle score of the person to be tested according to the lifestyle data of the person to be tested corresponding to the preset time period by using the lifestyle score expression through the second monitoring model.
In one embodiment, the treatment performance data comprises treatment medication performance data and treatment performance data, the treatment performance data comprising performance data of any one or any combination of the following: hot compress, fumigation, atomization, meibomian gland massage and eyelid margin cleaning. Treatment performance data may be recorded and entered by the tester himself.
In one embodiment, step S140 may include:
According to the treatment execution condition data of the person to be tested, a treatment execution condition score expression is utilized by a third monitoring model to obtain a treatment execution condition score of the person to be tested;
Wherein, the treatment performance score expression is:
in the treatment execution condition score expression, F treatment execution indicates a treatment execution condition score, K m indicates an actual value of treatment medication condition data, that is, the number of times of actually taking treatment medication, K indicates a preset threshold of treatment medication condition data, that is, the number of times of taking treatment medication should be taken, and n indicates the number of times of execution of treatment behavior.
Step S140 may obtain the score of the treatment execution condition of the person to be tested according to the treatment execution condition data of the person to be tested corresponding to the preset time period by using the score expression of the treatment execution condition through the third monitoring model. If the subject does not need to be treated, the treatment performance score of the subject is marked as 1.
In one embodiment, step S150 may include:
According to eye use behavior score, life habit score and treatment execution condition score of the to-be-measured person in a preset time period, the fluctuation condition of the to-be-measured person in the aspect of eye use behavior, life habit and treatment execution condition in the preset time period is represented by a line graph, the type of the to-be-measured person is judged, eye use advice is provided for the to-be-measured person, and the mode of providing the eye use advice can be voice broadcasting or image, audio and video broadcasting and the like.
Specifically, the types of the testees may include: the eye habit is good, the eye habit is to be improved, specifically, step S150 may perform a summation function operation on the eye habit score, the life habit score, and the treatment execution score of the testee in a preset time period to obtain an eye habit total score of the testee, and judge the type of the testee according to the eye habit total score of the testee, for example, when the eye habit total score of the testee is smaller than the preset total score and the classification score, judge the type of the testee is the eye habit to be improved, and when the eye habit total score of the testee is equal to or greater than the preset total score and the classification score, judge the type of the testee is the eye habit to be good. For the person to be tested whose eye habit is to be improved, the targeted eye use advice can be sent out according to the specific situations of the person to be tested in the aspects of eye use behavior, living habit and treatment execution situation. The ocular advice may include any one or any combination of the following: the method comprises the steps of performing blink learning course, avoiding long-time near eyes, adjusting ambient humidity, improving outdoor activity time, reducing sweet and greasy food intake, improving sleep quality, reducing makeup holding time, reducing wearing time of contact lenses/pupils, improving therapeutic application rate and improving therapeutic action execution rate. The eye habit correction scheme of the testee can record all specific situations and all corresponding eye suggestions of the testee in the aspects of eye behaviors, living habits and treatment execution conditions, and is beneficial to carrying out data backtracking in the future.
In one embodiment, step S150 may further obtain a line pattern of the testee in a plurality of preset time periods, and obtain the application effect of the eye habit correction analysis method for dry eye according to the present invention by comparing the fluctuation conditions of the testee in the aspects of eye behaviors, life habits and treatment execution conditions in different preset time periods, so as to adjust the optimization scheme of the eye behaviors of the testee.
According to the eye habit correction analysis method for the xerophthalmia, the eye behaviors of the testee are monitored in real time through the machine learning model according to the eye behavior monitoring data, the life habit data and the treatment execution condition data, eye behavior correction reminding information is timely sent out, bad eye behaviors of the testee can be corrected at any time, the testee is gradually cultured in daily life to have good eye behavior habits, the difficulty of improving the eye habits of the testee is reduced, the eye behavior score, the life habit score and the treatment execution condition score of the testee are comprehensively analyzed, an eye habit correction scheme of the testee in a preset time period is obtained, and effective data reference can be provided for preventing or treating xerophthalmia of the testee.
The eye habit correction analysis system for dry eye, which is provided by the invention, is described below, and the eye habit correction analysis system for dry eye, which is described below, and the eye habit correction analysis method for dry eye, which is described above, can be referred to correspondingly with each other.
Referring to fig. 2, an eye habit correction analysis system for dry eye according to the present invention may include:
The data acquisition module is used for: acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
A first monitoring module for: according to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
a second monitoring module for: according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
A third monitoring module for: obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
the comprehensive analysis module is used for: and obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
In one embodiment, the data acquisition module may include:
A first data acquisition sub-module configured to: shooting the superposition condition of eyelashes of the to-be-detected person from the side surface of eyes of the to-be-detected person through a monitoring spectacle frame to obtain blink frequency data and blink amplitude data of the to-be-detected person;
a second data acquisition sub-module for: the distance between eyes of a person to be detected and an object in front of the eyes is monitored through a monitoring spectacle frame, and near-eye distance data and near-eye time data of the person to be detected are obtained;
A third data acquisition sub-module configured to: and obtaining local air humidity data of the position of the person to be measured through a humidity sensor.
In one embodiment, the first monitoring module may include:
The first reminding sub-module is used for: according to the blink frequency data and blink amplitude data of the testee, obtaining the complete blink rate of the testee through a first monitoring model, comparing the complete blink rate of the testee with a preset blink qualification rate, and sending blink correction reminding information when the complete blink rate of the testee is lower than the preset blink qualification rate;
a second reminding sub-module for: according to near-to-eye distance data and near-to-eye time data of a person to be measured, obtaining near-to-eye duration data of the person to be measured through a first monitoring model, comparing the near-to-eye duration data of the person to be measured with a preset near-to-eye duration threshold, and sending near-to-eye correction reminding information when the near-to-eye duration of the person to be measured exceeds the preset near-to-eye duration threshold;
a third reminding sub-module for: obtaining humidity maintenance time data of the position of the to-be-detected person through a first monitoring model according to the local air humidity data of the position of the to-be-detected person, comparing the humidity maintenance time data of the position of the to-be-detected person with preset air humidity time-keeping reminding conditions, and sending humidity adjustment reminding information when the humidity maintenance time data of the position of the to-be-detected person accords with the preset air humidity time-keeping reminding conditions;
A statistics sub-module for: counting the times of sending eye-using behavior correction reminding information (including blink correction reminding information, near eye correction reminding information and humidity adjustment reminding information) within a preset time period, and obtaining the eye-using behavior score of the person to be tested.
In one embodiment, the first monitoring module may include:
a fourth data acquisition sub-module configured to: when the near-eye distance data of the to-be-measured person indicates that the distance between the eyes of the to-be-measured person and the object in front of the eyes is smaller than the preset eye recommended distance, the to-be-measured person is judged to be the near-eye, and the near-eye duration data of the to-be-measured person are obtained.
In one embodiment, the second monitoring module may include:
A second monitoring sub-module for: according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested by using a life habit score expression through a second monitoring model;
wherein, the lifestyle score expression is:
In the life habit score expression, F life habit represents a life habit score, F i represents a score of the ith life habit data, β represents a weight of the ith life habit data, I represents the number of the life habit data, H i represents an actual numerical value of the ith life habit data, and H i represents a preset threshold of the ith life habit data.
In one embodiment, the third monitoring module may include:
A third monitoring sub-module for: according to the treatment execution condition data of the person to be tested, a treatment execution condition score expression is utilized by a third monitoring model to obtain a treatment execution condition score of the person to be tested;
Wherein, the treatment performance score expression is:
In the treatment execution condition score expression, F treatment execution represents a treatment execution condition score, K m represents an actual numerical value of treatment medication condition data, K represents a preset threshold value of treatment medication condition data, and n represents the execution times of treatment behaviors.
In one embodiment, the integrated analysis module may include:
A suggestion sub-module for: according to eye behavior score, life habit score and treatment execution condition score of the measured person in a preset time period, the fluctuation condition of the measured person in the aspects of eye behavior, life habit and treatment execution condition in the preset time period is represented by a line graph, the type of the measured person is judged, and eye use advice is provided for the measured person.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. Processor 810 may invoke logic instructions in memory 830 to perform an eye habit correction analysis method for dry eye, the method comprising:
Acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
According to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
And obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the eye habit correction analysis method for dry eye symptoms provided by the methods described above, the method comprising:
Acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
According to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
And obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the eye habit correction analysis method for dry eye provided by the above methods, the method comprising:
Acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
According to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
And obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will 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 and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for eye habit correction analysis for dry eye, comprising:
Acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
According to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
Obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
And obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
2. The eye habit correction and analysis method for dry eye according to claim 1, wherein the eye behavior monitoring data includes blink frequency data, blink amplitude data, near eye data, and local air humidity data, and the acquiring the eye behavior monitoring data, life habit data, and treatment execution data of the subject includes:
shooting the superposition condition of eyelashes of the to-be-detected person from the side surface of eyes of the to-be-detected person through a monitoring spectacle frame to obtain blink frequency data and blink amplitude data of the to-be-detected person;
The distance between eyes of a person to be detected and an object in front of the eyes is monitored through a monitoring spectacle frame, and near-eye distance data and near-eye time data of the person to be detected are obtained;
and obtaining local air humidity data of the position of the person to be measured through a humidity sensor.
3. The eye habit correction analysis method for dry eye according to claim 2, wherein the obtaining, according to the eye behavior monitoring data of the person to be tested, the eye behavior score of the person to be tested through the first monitoring model, and when the eye behavior score of the person to be tested is lower than a first preset threshold, sending out eye behavior correction reminding information includes:
According to the blink frequency data and blink amplitude data of the testee, obtaining the complete blink rate of the testee through a first monitoring model, comparing the complete blink rate of the testee with a preset blink qualification rate, and sending blink correction reminding information when the complete blink rate of the testee is lower than the preset blink qualification rate;
According to near-to-eye distance data and near-to-eye time data of a person to be measured, obtaining near-to-eye duration data of the person to be measured through a first monitoring model, comparing the near-to-eye duration data of the person to be measured with a preset near-to-eye duration threshold, and sending near-to-eye correction reminding information when the near-to-eye duration of the person to be measured exceeds the preset near-to-eye duration threshold;
Obtaining humidity maintenance time data of the position of the to-be-detected person through a first monitoring model according to the local air humidity data of the position of the to-be-detected person, comparing the humidity maintenance time data of the position of the to-be-detected person with preset air humidity time-keeping reminding conditions, and sending humidity adjustment reminding information when the humidity maintenance time data of the position of the to-be-detected person accords with the preset air humidity time-keeping reminding conditions;
Counting the times of sending out eye-using behavior correction reminding information in a preset time period to obtain eye-using behavior scores of the testee, wherein the eye-using behavior correction reminding information comprises blink correction reminding information, near eye correction reminding information and humidity adjustment reminding information.
4. The method for correcting and analyzing eye habit for dry eye according to claim 3, wherein the obtaining the duration data of the near-eye of the person to be tested according to the near-eye distance data and the near-eye time data of the person to be tested through the first monitoring model comprises:
When the near-eye distance data of the to-be-measured person indicates that the distance between the eyes of the to-be-measured person and the object in front of the eyes is smaller than the preset eye recommended distance, the to-be-measured person is judged to be the near-eye, and the near-eye duration data of the to-be-measured person are obtained.
5. The eye habit correction and analysis method for dry eye according to claim 4, wherein the lifestyle data includes: outdoor activity time data, sweet/greasy food intake, sleep quality data, make-up time data, contact lens/pupil wear time data;
according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model, wherein the life habit scores comprise the following steps:
According to life habit data of the person to be tested, obtaining life habit scores of the person to be tested by using a life habit score expression through a second monitoring model;
wherein, the lifestyle score expression is:
In the life habit score expression, F life habit represents a life habit score, F i represents a score of the ith life habit data, β represents a weight of the ith life habit data, I represents the number of the life habit data, H i represents an actual numerical value of the ith life habit data, and H i represents a preset threshold of the ith life habit data.
6. The eye habit correction and analysis method for dry eye according to claim 5, wherein the treatment performance data includes treatment medication performance data and treatment behavior performance data, and the treatment behavior performance data includes performance data of any one or any combination of the following: hot compress, fumigation, atomization, meibomian gland massage and eyelid margin cleaning;
the step of obtaining the score of the treatment execution condition of the person to be tested according to the treatment execution condition data of the person to be tested through a third monitoring model comprises the following steps:
According to the treatment execution condition data of the person to be tested, a treatment execution condition score expression is utilized by a third monitoring model to obtain a treatment execution condition score of the person to be tested;
Wherein, the treatment performance score expression is:
In the treatment execution condition score expression, F treatment execution represents a treatment execution condition score, K m represents an actual numerical value of treatment medication condition data, K represents a preset threshold value of treatment medication condition data, and n represents the execution times of treatment behaviors.
7. The eye habit correction analysis method for dry eye according to any one of claims 1 to 6, wherein the obtaining an eye habit correction scheme of the testee in a preset time period according to the eye behavior score, the life habit score, and the treatment execution score of the testee in the preset time period includes:
According to eye use behavior score, life habit score and treatment execution condition score of the to-be-measured person in a preset time period, the fluctuation condition of the to-be-measured person in the aspects of eye use behavior, life habit and treatment execution condition in the preset time period is represented by a line graph, the type of the to-be-measured person is judged, and eye use advice is provided for the to-be-measured person;
Wherein, the types of the testee include: eye habit is good, eye habit is to be improved, and eye advice includes any one or any combination of the following: the method comprises the steps of performing blink learning course, avoiding long-time near eyes, adjusting ambient humidity, improving outdoor activity time, reducing sweet and greasy food intake, improving sleep quality, reducing makeup holding time, reducing wearing time of contact lenses/pupils, improving therapeutic application rate and improving therapeutic action execution rate.
8. An eye habit correction analysis system for dry eye, comprising:
The data acquisition module is used for: acquiring eye behavior monitoring data, life habit data and treatment execution condition data of a person to be tested;
A first monitoring module for: according to eye behavior monitoring data of a to-be-measured person, obtaining an eye behavior score of the to-be-measured person through a first monitoring model, and sending out eye behavior correction reminding information when the eye behavior score of the to-be-measured person is lower than a first preset threshold value;
a second monitoring module for: according to life habit data of the person to be tested, obtaining life habit scores of the person to be tested through a second monitoring model;
A third monitoring module for: obtaining a treatment execution condition score of the person to be tested through a third monitoring model according to the treatment execution condition data of the person to be tested;
the comprehensive analysis module is used for: and obtaining an eye habit correction scheme of the tested person in the preset time period according to the eye habit score, the life habit score and the treatment execution condition score of the tested person in the preset time period.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the eye habit correction analysis method for dry eye according to any one of claims 1 to 7 when the program is executed.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the eye habit correction analysis method for dry eye according to any one of claims 1 to 7.
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