CN118155803A - A method, system, device and medium for correcting and analyzing eye habits for dry eyes - Google Patents

Abstract

The present invention relates to the field of medical data processing technology, and discloses an eye habit correction analysis method, system, device and medium for dry eye syndrome, including: obtaining eye behavior monitoring data, life habit data, and treatment execution data; obtaining an eye behavior score through a first monitoring model according to the eye behavior monitoring data, and when the eye behavior score is lower than a first preset threshold, issuing an eye behavior correction reminder message; obtaining a life habit score through a second monitoring model according to the life habit data; obtaining a treatment execution score through a third monitoring model according to the treatment execution data; obtaining an eye habit correction plan for a subject according to the eye behavior score, life habit score, and treatment execution score in a preset time period. The present invention monitors the eye behavior of a subject in real time, issues an eye behavior correction reminder message in a timely manner, and can correct the subject's bad eye behavior at any time.

Description

A method, system, device and medium for correcting and analyzing eye habits for dry eyes

Technical Field

The present invention relates to the field of medical data processing technology, and in particular to a method, system, device and medium for correcting eye habits for dry eye syndrome.

Background technique

Dry eye has become one of the global epidemic diseases, and improving eye habits is one of the important aspects of dry eye prevention and treatment. For dry eye patients and high-risk groups, improving eye habits is a relatively difficult and difficult thing to stick to. The main reasons include that no one monitors their daily eye behavior, no one can remind them to correct the bad eye behavior in time, and it is difficult to make targeted improvement suggestions. Current technologies and products focus on the evaluation and prediction of dry eyes, and ignore the correction of daily bad eye behavior.

Therefore, there is an urgent need for an eye habit correction and analysis method for dry eye syndrome, which can help people understand and improve eye habits by assisting in the monitoring and recording of daily eye behaviors and providing correction reminders and suggestions for bad eye behaviors.

Summary of the invention

The present invention provides an eye habit correction analysis method, system, device and medium for dry eye syndrome, so as to solve the defect that current products and devices focus on the evaluation and prediction of dry eyes, but neglect the correction of daily bad eye behavior.

The present invention provides an eye habit correction and analysis method for dry eye syndrome, comprising:

Obtain the subject's eye behavior monitoring data, living habits data, and treatment execution data;

According to the eye behavior monitoring data of the subject, an eye behavior score of the subject is obtained through a first monitoring model, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued;

According to the living habit data of the subject, a living habit score of the subject is obtained through a second monitoring model;

According to the treatment execution data of the subject, a treatment execution score of the subject is obtained through a third monitoring model;

According to the eye use behavior score, life habit score and treatment execution score of the subject within the preset time period, a correction plan for the eye use habits of the subject within the preset time period is obtained.

In one embodiment, the eye behavior monitoring data includes blink frequency data, blink amplitude data, near eye data, and local air humidity data. The method of obtaining the eye behavior monitoring data, life habit data, and treatment execution status data of the subject to be tested includes:

By monitoring the spectacle frame, the overlap of the eyelashes of the subject is photographed from the side of the subject's eyes to obtain the blinking frequency data and blinking amplitude data of the subject;

The distance between the eyes of the subject and the object in front of the eyes is monitored by monitoring the glasses frame, so as to obtain the near eye distance data and near eye time data of the subject;

The local air humidity data of the location of the person to be measured is obtained through the humidity sensor.

In one embodiment, the eye behavior score of the subject is obtained through a first monitoring model based on the eye behavior monitoring data of the subject, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued, including:

According to the blink frequency data and the blink amplitude data of the subject, the complete blink rate of the subject is obtained through the first monitoring model, and the complete blink rate of the subject is compared with the preset blink pass rate, and when the complete blink rate of the subject is lower than the preset blink pass rate, a blink correction reminder message is issued;

According to the near eye distance data and the near eye time data of the subject, the near eye duration data of the subject is obtained through the first monitoring model, and the near eye duration data of the subject is compared with the preset near eye duration threshold, and when the near eye duration of the subject exceeds the preset near eye duration threshold, a near eye correction reminder message is issued;

According to the local air humidity data at the location of the person to be tested, the humidity maintenance time data at the location of the person to be tested is obtained through the first monitoring model, and the humidity maintenance time data at the location of the person to be tested is compared with the preset air humidity maintenance time reminder condition. When the humidity maintenance time data at the location of the person to be tested meets the preset air humidity maintenance time reminder condition, a humidity adjustment reminder message is issued;

The number of times that eye behavior correction reminder messages (including blink correction reminder messages, nearsightedness correction reminder messages, and humidity adjustment reminder messages) are sent out within a preset time period is counted to obtain the eye behavior score of the subject.

In one embodiment, the preset blink qualification rate is greater than 6 complete blinks per minute.

In one embodiment, the preset near eye use time threshold is a single duration of less than 1 hour.

In one embodiment, the preset air humidity constant reminder condition is: the air humidity is lower than 30% and is maintained for more than 1 hour.

In one embodiment, the method of obtaining the near eye duration data of the subject through the first monitoring model according to the near eye distance data and the near eye duration data of the subject includes:

When the near eye distance data of the subject indicates that the distance between the subject's eyes and the object directly in front of the eyes is less than the preset recommended distance for near eye use, the subject is determined to be near eye use, and the duration data of the near eye use of the subject is obtained.

In one embodiment, the preset recommended eye distance is any value between 50-70 centimeters.

In one embodiment, the lifestyle data includes: outdoor activity time data, sweet/greasy food intake, sleep quality data, makeup lasting time data, and contact lens/cosmetic lens wearing time data.

In one embodiment, obtaining the subject's living habit score through the second monitoring model based on the subject's living habit data includes:

According to the living habit data of the subject, the living habit score of the subject is obtained by using the living habit score expression through the second monitoring model;

Wherein, the living habit score expression is:

In the lifestyle score expression, Flifestyle represents the lifestyle score, _Fi represents the score of the i-th lifestyle habit data, β represents the weight of the i-th lifestyle habit data, I represents the number of lifestyle habit data, _hi represents the actual value of the i-th lifestyle habit data, and _Hi represents the preset threshold value of the i-th lifestyle habit data.

In one embodiment, the treatment execution status data includes treatment medication status data and treatment behavior execution status data, and the treatment behavior execution status data includes execution status data of any one of the following or any combination thereof: hot compress, fumigation, atomization, meibomian gland massage, and eyelid margin cleaning.

In one embodiment, obtaining the treatment execution score of the subject through the third monitoring model according to the treatment execution data of the subject includes:

According to the treatment execution data of the subject, the treatment execution score of the subject is obtained by using the treatment execution score expression through the third monitoring model;

The treatment execution score expression is:

In the treatment execution score expression, Ftreatmentexecution represents the treatment execution score, _km represents the actual value of the treatment medication data, K represents the preset threshold of the treatment medication data, and n represents the number of times the treatment behavior is executed.

In one embodiment, the method of obtaining a correction plan for the eye habits of the subject within the preset time period according to the eye behavior score, life habit score, and treatment execution score of the subject within the preset time period includes:

Based on the eye behavior score, life habit score, and treatment execution score of the subject within the preset time period, a line graph is used to represent the fluctuations in the subject's eye behavior, life habit, and treatment execution within the preset time period, to determine the type of the subject and provide eye advice for the subject.

In one embodiment, the types of subjects include: those with good eye habits and those with eye habits that need to be improved, and the eye use suggestions include any one of the following or any combination thereof: taking a blink learning course, avoiding prolonged close eye use, adjusting the humidity of the surrounding environment, increasing the time spent on outdoor activities, reducing the intake of sweets/greasy foods, improving sleep quality, reducing the time spent wearing makeup, reducing the time spent wearing contact lenses/cosmetic lenses, increasing the rate of medication use for treatment, and increasing the rate of execution of treatment behaviors.

The present invention also provides an eye habit correction and analysis system for dry eye syndrome, comprising:

The data acquisition module is used to obtain the eye behavior monitoring data, life habit data, and treatment execution data of the subject;

The first monitoring module is used to obtain the eye behavior score of the subject through the first monitoring model according to the eye behavior monitoring data of the subject, and issue an eye behavior correction reminder message when the eye behavior score of the subject is lower than a first preset threshold;

The second monitoring module is used to obtain the living habit score of the subject through the second monitoring model according to the living habit data of the subject;

The third monitoring module is used to obtain the treatment execution score of the subject through the third monitoring model according to the treatment execution data of the subject;

The comprehensive analysis module is used to obtain a correction plan for the eye habits of the subject within a preset time period based on the subject's eye behavior score, life habit score, and treatment execution score within a preset time period.

The present invention also provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the computer program, any of the above-mentioned methods for correcting and analyzing eye habits for dry eye syndrome is implemented.

The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for correcting eye habits for dry eye syndrome.

The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned methods for correcting eye habits for dry eye syndrome.

The present invention provides an eye habit correction and analysis method, system, device and medium for dry eye syndrome. According to the eye behavior monitoring data, life habit data and treatment execution status data, the eye behavior of the subject is monitored in real time through a machine learning model, and eye behavior correction reminder information is issued in time. The bad eye behavior of the subject can be corrected at any time, and good eye behavior habits of the subject can be gradually cultivated in daily life, thereby reducing the difficulty of the subject to improve his eye habits. The present invention also comprehensively analyzes the eye behavior score, life habit score and treatment execution status score of the subject to obtain the eye habit correction plan of the subject within a preset time period, which can provide effective data reference for the prevention or treatment of dry eyes for the subject.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a method for correcting and analyzing eye habits for dry eye syndrome provided by the present invention.

FIG. 2 is a schematic diagram of the structure of an eye habit correction and analysis system for dry eye provided by the present invention.

FIG. 3 is a schematic diagram of the structure of an electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

The following describes the eye habit correction and analysis method, system, device and medium for dry eye syndrome provided by the present invention in conjunction with Figures 1 to 3.

FIG1 is a flow chart of a method for correcting eye habits for dry eye syndrome provided by the present invention. Referring to FIG1 , a method for correcting eye habits for dry eye syndrome provided by the present invention may include:

Step S110, obtaining the eye behavior monitoring data, life habit data, and treatment execution status data of the subject;

Step S120: obtaining the eye behavior score of the subject through the first monitoring model according to the eye behavior monitoring data of the subject, and issuing an eye behavior correction reminder message when the eye behavior score of the subject is lower than a first preset threshold;

Step S130, obtaining the subject's living habit score through the second monitoring model according to the subject's living habit data;

Step S140, obtaining a treatment execution score of the subject through a third monitoring model according to the treatment execution data of the subject;

Step S150, obtaining a correction plan for the eye habits of the subject within the preset time period according to the eye behavior score, life habit score, and treatment execution score of the subject within the preset time period.

In one embodiment, the eye behavior monitoring data includes blinking frequency data, blinking amplitude data, near eye use data, and local air humidity data. The subjects to be measured can be dry eye patients, individuals in a high-risk group for dry eye, myopic patients, individuals in a high-risk group for myopia, patients with eye diseases related to eye use habits, or normal people, etc.

In one embodiment, step S110 may include:

By monitoring the spectacle frame, the overlap of the eyelashes of the subject is photographed from the side of the subject's eyes to obtain the blinking frequency data and blinking amplitude data of the subject;

The distance between the eyes of the subject and the object in front of the eyes is monitored by monitoring the glasses frame, so as to obtain the near eye distance data and near eye time data of the subject;

The local air humidity data of the location of the person to be measured is obtained through the humidity sensor.

Specifically, before obtaining the eye behavior monitoring data of the subject, the subject can be asked to wear a monitoring glasses frame, which is provided with a camera, a distance sensor, a timer, and a humidity sensor. The micro camera is used to photograph the overlap of the subject's eyelashes from the side of the subject's eye, and can take images or videos. The blinking frequency data and the blinking amplitude data of the subject are obtained by judging the number of overlaps of the subject's eyelashes and the repeated coverage rate of the upper eyelashes and the lower eyelashes when the subject blinks. The distance sensor is used to measure the distance between the subject's eye and the object directly in front of the eye. The timer is used to measure the maintenance time of the distance between the subject's eye and the object directly in front of the eye, so as to obtain the subject's near eye distance data and near eye time data. The humidity sensor is used to measure the local air humidity at the subject's location, so as to obtain the local air humidity data at the subject's location.

In one embodiment, the eye behavior score of the subject may be composed of three parts, one part is a complete blink score, one part is a near eye score, and one part is an ambient humidity score. Step S120 may include:

According to the blink frequency data and the blink amplitude data of the subject, the complete blink rate of the subject is obtained through the first monitoring model, and the complete blink rate of the subject is compared with the preset blink pass rate, and when the complete blink rate of the subject is lower than the preset blink pass rate, a blink correction reminder message is issued;

According to the near eye distance data and the near eye time data of the subject, the near eye duration data of the subject is obtained through the first monitoring model, and the near eye duration data of the subject is compared with the preset near eye duration threshold, and when the near eye duration of the subject exceeds the preset near eye duration threshold, a near eye correction reminder message is issued;

According to the local air humidity data at the location of the person to be tested, the humidity maintenance time data at the location of the person to be tested is obtained through the first monitoring model, and the humidity maintenance time data at the location of the person to be tested is compared with the preset air humidity maintenance time reminder condition. When the humidity maintenance time data at the location of the person to be tested meets the preset air humidity maintenance time reminder condition, a humidity adjustment reminder message is issued;

The number of times that eye behavior correction reminder messages (including blink correction reminder messages, nearsightedness correction reminder messages, and humidity adjustment reminder messages) are sent out within a preset time period is counted to obtain the eye behavior score of the subject.

Specifically, step S120 can pre-train the first monitoring model according to the sample data of blink frequency and the sample data of blink amplitude and the corresponding label data of complete blink rate, so that the first monitoring model can obtain the complete blink rate of the subject according to the blink frequency data and the blink amplitude data of the subject. The definition of "complete blink" can be understood as the repeated coverage rate of the upper eyelashes and the lower eyelashes of the subject's eyes reaches a preset coverage rate (for example, 85%) or more. The complete blink rate needs to be determined according to the total number of blinks in a certain time period and the number of blinks with a repeated coverage rate reaching a preset coverage rate, for example, it can be the number of blinks with a repeated coverage rate reaching a preset coverage rate in a certain time period/the total number of blinks in a certain time period, etc. After the first monitoring model obtains the complete blink rate of the subject, it compares it with the preset blink pass rate. When the complete blink rate of the subject is lower than the preset blink pass rate (the preset blink pass rate in this embodiment is that the complete blink is greater than 6 times/minute), a blink correction reminder message is issued, which can be reminded by voice broadcast and/or by vibrating the monitoring glasses frame.

Specifically, step S120 can first determine whether the person under test is near use according to the near eye distance data of the person under test through the first monitoring model. The specific judgment condition is: when the near eye distance data of the person under test indicates that the distance between the person under test's eyes and the object directly in front of the eyes is less than the preset recommended eye distance (the preset recommended eye distance in this embodiment is any value between 50-70 cm), the person under test is determined to be near use, and then the corresponding near eye duration data is obtained, and then the near eye duration data of the person under test is compared with the preset near eye duration threshold (the preset near eye duration threshold in this embodiment is a single duration of less than 1 hour). When the near eye duration of the person under test exceeds the preset near eye duration threshold, a near eye correction reminder message is issued, and the reminder can be made through voice broadcast and/or vibration of the monitoring glasses frame.

Specifically, step S120 can first obtain the humidity maintenance time data of the location of the person to be tested according to the local air humidity data of the location of the person to be tested through the first monitoring model (for example, according to the recording time of the local air humidity data, when the humidity changes, it is the end of the previous humidity and the beginning of the next humidity, so as to obtain the maintenance time of each humidity), and compare the humidity maintenance time data of the location of the person to be tested with the preset air humidity maintenance reminder condition (in this embodiment, the preset air humidity maintenance reminder condition is that the air humidity is lower than 30%, and the maintenance time is more than 1 hour). When the humidity maintenance time data of the location of the person to be tested meets the preset air humidity maintenance reminder condition, a humidity adjustment reminder message is issued, and the reminder can be made by voice broadcast and/or vibration of the monitoring glasses frame. Being in a closed environment of cooling or heating for a long time (such as a heated or air-conditioned room) is an important risk factor for dry eyes, so the person to be tested can be reminded in time to optimize the humidity of the surrounding environment.

Among them, if the reminder is made by vibrating the monitoring glasses frame, different vibration modes can be used to distinguish different reminders. For example, if it is a blink correction reminder message, a long vibration low-frequency mode can be used, if it is a near eye correction reminder message, a short vibration high-frequency mode can be used, if it is a humidity adjustment reminder message, a short vibration low-frequency mode can be used, and so on. The purpose is to remind the subject to be tested to change the eye behavior or environmental humidity. If the first monitoring model determines that the subject to be tested does not belong to near eye use according to the near eye distance data of the subject to be tested, no comparison is required.

Step S120 can count the number of blink correction reminder messages, nearsightedness correction reminder messages, and humidity adjustment reminder messages issued within a preset time period. Each time a reminder message is issued, -0.1 points are awarded (including blink correction reminder messages, nearsightedness correction reminder messages, and humidity adjustment reminder messages, as long as they are issued once, it counts as one time). If no reminder message is issued during the preset time period, 1 point is awarded.

In one embodiment, the life habit data includes: outdoor activity time data, sweets/greasy food intake, sleep quality data, makeup lasting time data, contact lens/cosmetic lens wearing time data. The life habit data can be recorded and input by the subject himself.

In one embodiment, step S130 can obtain the life habit score of the subject according to the life habit data of the subject by using the life habit score expression through the second monitoring model;

Wherein, the living habit score expression is:

In the lifestyle score expression, Flifestyle represents the lifestyle score, _Fi represents the score of the i-th lifestyle habit data, β represents the weight of the i-th lifestyle habit data, I represents the number of lifestyle habit data, _hi represents the actual value of the i-th lifestyle habit data, and _Hi represents the preset threshold value of the i-th lifestyle habit data.

Step S130 can obtain the life habit score of the subject according to the life habit data of the subject corresponding to the preset time period by using the life habit score expression through the second monitoring model.

In one embodiment, the treatment execution data includes treatment medication data and treatment behavior execution data, and the treatment behavior execution data includes any one of the following or any combination thereof: hot compress, fumigation, atomization, meibomian gland massage, eyelid margin cleaning. The treatment execution data can be recorded and input by the subject himself.

In one embodiment, step S140 may include:

According to the treatment execution data of the subject, the treatment execution score of the subject is obtained by using the treatment execution score expression through the third monitoring model;

The treatment execution score expression is:

In the treatment execution score expression, Ftreatmentexecution represents the treatment execution score, _km represents the actual value of the treatment medication data, that is, the actual number of times the treatment medication is taken, K represents the preset threshold of the treatment medication data, that is, the number of times the treatment medication should be taken, and n represents the number of times the treatment behavior is executed.

Step S140 can obtain the treatment execution score of the subject according to the treatment execution data of the subject corresponding to the preset time period through the third monitoring model using the treatment execution score expression. If the subject does not need treatment, the treatment execution score of the subject is recorded as 1.

In one embodiment, step S150 may include:

Based on the eye behavior score, life habit score, and treatment execution score of the subject within the preset time period, a line graph is used to represent the fluctuations in the subject's eye behavior, life habit, and treatment execution within the preset time period, so as to determine the type of the subject and provide eye advice to the subject. The eye advice may be provided in the form of voice broadcast, image, audio or video playback, etc.

Specifically, the types of the test subject may include: good eye habits, eye habits to be improved. Specifically, step S150 may perform a summation function operation on the test subject's eye behavior score, life habit score, and treatment execution score within a preset time period to obtain the test subject's total eye habit score, and determine the test subject's type based on the test subject's total eye habit score. For example, when the test subject's total eye habit score is less than the preset total passing score, the test subject's type is determined to have eye habits to be improved. When the test subject's total eye habit score is equal to or greater than the preset total passing score, the test subject's type is determined to have good eye habits. For test subjects whose eye habits need to be improved, targeted eye suggestions may be issued based on their specific conditions in terms of eye behavior, life habits, and treatment execution. Eye care suggestions may include any of the following or any combination thereof: taking blinking lessons, avoiding prolonged close eye use, adjusting the humidity of the surrounding environment, increasing outdoor activity time, reducing the intake of sweets/greasy foods, improving sleep quality, reducing makeup holding time, reducing the wearing time of contact lenses/cosmetic lenses, increasing the rate of treatment medication, and increasing the rate of treatment behavior execution. The eye care habit correction plan for the subject can record all the specific circumstances of the subject's eye care behavior, living habits, treatment execution, and all corresponding eye care suggestions, which is conducive to data tracing in the future.

In one embodiment, step S150 can also obtain a line graph of the subject in multiple preset time periods. By comparing the fluctuations in the subject's eye behavior, living habits, and treatment execution status in different preset time periods, the application effect of the eye habit correction and analysis method for dry eye syndrome provided by the present invention on the subject is obtained, and then the optimization plan for the subject's eye behavior is adjusted.

The eye habit correction and analysis method for dry eye syndrome provided by the present invention monitors the eye behavior of the subject in real time through a machine learning model based on eye behavior monitoring data, life habit data, and treatment execution data, and promptly issues eye behavior correction reminder information, which can correct the subject's bad eye behavior at any time, gradually cultivate the subject's good eye behavior habits in daily life, and reduce the difficulty of the subject to improve their eye habits. The present invention also comprehensively analyzes the subject's eye behavior score, life habit score, and treatment execution score to obtain the subject's eye habit correction plan within a preset time period, which can provide effective data reference for the subject to prevent or treat dry eyes.

The eye habit correction and analysis system for dry eye syndrome provided by the present invention is described below. The eye habit correction and analysis system for dry eye syndrome described below and the eye habit correction and analysis method for dry eye syndrome described above can be referenced to each other.

2 , the present invention provides an eye habit correction and analysis system for dry eye syndrome, which may include:

The data acquisition module is used to obtain the eye behavior monitoring data, life habit data, and treatment execution data of the subject;

The first monitoring module is used to obtain the eye behavior score of the subject through the first monitoring model according to the eye behavior monitoring data of the subject, and issue an eye behavior correction reminder message when the eye behavior score of the subject is lower than a first preset threshold;

The second monitoring module is used to obtain the living habit score of the subject through the second monitoring model according to the living habit data of the subject;

The third monitoring module is used to obtain the treatment execution score of the subject through the third monitoring model according to the treatment execution data of the subject;

The comprehensive analysis module is used to obtain a correction plan for the eye habits of the subject within a preset time period based on the subject's eye behavior score, life habit score, and treatment execution score within a preset time period.

In one embodiment, the data acquisition module may include:

The first data acquisition submodule is used to obtain the blinking frequency data and blinking amplitude data of the subject by monitoring the eyeglass frame to photograph the overlap of the subject's eyelashes from the side of the subject's eyes;

The second data acquisition submodule is used to: monitor the distance between the test subject's eyes and the object in front of the eyes by monitoring the glasses frame, and obtain the test subject's near eye distance data and near eye time data;

The third data acquisition submodule is used to obtain local air humidity data of the location of the person to be measured through a humidity sensor.

In one embodiment, the first monitoring module may include:

The first reminder submodule is used to obtain the complete blink rate of the subject through the first monitoring model according to the blink frequency data and the blink amplitude data of the subject, and compare the complete blink rate of the subject with a preset blink pass rate, and issue a blink correction reminder message when the complete blink rate of the subject is lower than the preset blink pass rate;

The second reminder submodule is used to obtain the near eye duration data of the subject through the first monitoring model according to the near eye distance data and the near eye time data of the subject, and compare the near eye duration data of the subject with a preset near eye duration threshold, and issue a near eye correction reminder message when the near eye duration of the subject exceeds the preset near eye duration threshold;

The third reminder submodule is used to: obtain the humidity maintenance time data of the position of the person to be measured through the first monitoring model according to the local air humidity data of the position of the person to be measured, compare the humidity maintenance time data of the position of the person to be measured with the preset air humidity maintenance time reminder condition, and issue a humidity adjustment reminder message when the humidity maintenance time data of the position of the person to be measured meets the preset air humidity maintenance time reminder condition;

The statistical submodule is used to: count the number of times eye behavior correction reminder messages (including blink correction reminder messages, near eye correction reminder messages and humidity adjustment reminder messages) are issued within a preset time period to obtain the eye behavior score of the subject.

In one embodiment, the first monitoring module may include:

The fourth data acquisition submodule is used to: when the near eye distance data of the subject indicates that the distance between the subject's eyes and the object directly in front of the eyes is less than the preset recommended eye distance, determine that the subject is using near eyes, and obtain the duration data of the near eye use of the subject.

In one embodiment, the second monitoring module may include:

The second monitoring submodule is used to obtain the living habit score of the subject by using the living habit score expression through the second monitoring model according to the living habit data of the subject;

Wherein, the living habit score expression is:

In the lifestyle score expression, Flifestyle represents the lifestyle score, _Fi represents the score of the i-th lifestyle habit data, β represents the weight of the i-th lifestyle habit data, I represents the number of lifestyle habit data, _hi represents the actual value of the i-th lifestyle habit data, and _Hi represents the preset threshold value of the i-th lifestyle habit data.

In one embodiment, the third monitoring module may include:

The third monitoring submodule is used to obtain the treatment execution score of the subject under test by using the treatment execution score expression through the third monitoring model according to the treatment execution data of the subject under test;

The treatment execution score expression is:

In the treatment execution score expression, Ftreatmentexecution represents the treatment execution score, _km represents the actual value of the treatment medication data, K represents the preset threshold of the treatment medication data, and n represents the number of times the treatment behavior is executed.

In one embodiment, the comprehensive analysis module may include:

The suggestion submodule is used to: determine the type of the subject based on the subject's eye behavior score, life habit score, and treatment execution score within a preset time period, by using a line graph to represent the fluctuations in the subject's eye behavior, life habit, and treatment execution within a preset time period, and provide eye suggestions for the subject.

FIG3 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG3 , the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the eye habit correction analysis method for dry eye syndrome, the method comprising:

Obtain the subject's eye behavior monitoring data, living habits data, and treatment execution data;

According to the eye behavior monitoring data of the subject, an eye behavior score of the subject is obtained through a first monitoring model, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued;

According to the living habit data of the subject, a living habit score of the subject is obtained through a second monitoring model;

According to the treatment execution data of the subject, a treatment execution score of the subject is obtained through a third monitoring model;

According to the eye use behavior score, life habit score and treatment execution score of the subject within the preset time period, a correction plan for the eye use habits of the subject within the preset time period is obtained.

In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the eye habit correction analysis method for dry eye provided by the above methods, the method comprising:

Obtain the subject's eye behavior monitoring data, living habits data, and treatment execution data;

According to the eye behavior monitoring data of the subject, an eye behavior score of the subject is obtained through a first monitoring model, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued;

According to the living habit data of the subject, a living habit score of the subject is obtained through a second monitoring model;

According to the treatment execution data of the subject, a treatment execution score of the subject is obtained through a third monitoring model;

According to the eye use behavior score, life habit score and treatment execution score of the subject within the preset time period, a correction plan for the eye use habits of the subject within the preset time period is obtained.

In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the eye habit correction analysis method for dry eye provided by the above methods, the method comprising:

Obtain the subject's eye behavior monitoring data, living habits data, and treatment execution data;

According to the eye behavior monitoring data of the subject, an eye behavior score of the subject is obtained through a first monitoring model, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued;

According to the living habit data of the subject, a living habit score of the subject is obtained through a second monitoring model;

According to the treatment execution data of the subject, a treatment execution score of the subject is obtained through a third monitoring model;

According to the eye use behavior score, life habit score and treatment execution score of the subject within the preset time period, a correction plan for the eye use habits of the subject within the preset time period is obtained.

The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for correcting eye habits for dry eye syndrome, comprising: Obtain the subject's eye behavior monitoring data, living habits data, and treatment execution data; According to the eye behavior monitoring data of the subject, an eye behavior score of the subject is obtained through a first monitoring model, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued; According to the living habit data of the subject, a living habit score of the subject is obtained through a second monitoring model; According to the treatment execution data of the subject, a treatment execution score of the subject is obtained through a third monitoring model; According to the eye use behavior score, life habit score and treatment execution score of the subject within the preset time period, a correction plan for the eye use habits of the subject within the preset time period is obtained. 2. The eye behavior correction analysis method for dry eye syndrome according to claim 1, characterized in that the eye behavior monitoring data includes blink frequency data, blink amplitude data, near eye data, and local air humidity data, and the eye behavior monitoring data, life habit data, and treatment execution status data of the subject to be tested are obtained, including: By monitoring the spectacle frame, the overlap of the eyelashes of the subject is photographed from the side of the subject's eyes to obtain the blinking frequency data and blinking amplitude data of the subject; The distance between the eyes of the subject and the object in front of the eyes is monitored by monitoring the glasses frame, so as to obtain the near eye distance data and near eye time data of the subject; The local air humidity data of the location of the person to be measured is obtained through the humidity sensor. 3. The eye habit correction analysis method for dry eye syndrome according to claim 2 is characterized in that the eye behavior score of the subject is obtained through a first monitoring model based on the eye behavior monitoring data of the subject, and when the eye behavior score of the subject is lower than a first preset threshold, an eye behavior correction reminder message is issued, including: According to the blink frequency data and the blink amplitude data of the subject, the complete blink rate of the subject is obtained through the first monitoring model, and the complete blink rate of the subject is compared with the preset blink pass rate, and when the complete blink rate of the subject is lower than the preset blink pass rate, a blink correction reminder message is issued; According to the near eye distance data and the near eye time data of the subject, the near eye duration data of the subject is obtained through the first monitoring model, and the near eye duration data of the subject is compared with the preset near eye duration threshold, and when the near eye duration of the subject exceeds the preset near eye duration threshold, a near eye correction reminder message is issued; According to the local air humidity data at the location of the person to be tested, the humidity maintenance time data at the location of the person to be tested is obtained through the first monitoring model, and the humidity maintenance time data at the location of the person to be tested is compared with the preset air humidity maintenance time reminder condition. When the humidity maintenance time data at the location of the person to be tested meets the preset air humidity maintenance time reminder condition, a humidity adjustment reminder message is issued; The number of times the eye behavior correction reminder information is sent out within a preset time period is counted to obtain the eye behavior score of the subject, wherein the eye behavior correction reminder information includes blink correction reminder information, near eye correction reminder information, and humidity adjustment reminder information. 4. The eye habit correction and analysis method for dry eye syndrome according to claim 3, characterized in that the method of obtaining the near eye duration data of the subject through the first monitoring model based on the near eye distance data and the near eye time data of the subject comprises: When the near eye distance data of the subject indicates that the distance between the subject's eyes and the object directly in front of the eyes is less than the preset recommended distance for near eye use, the subject is determined to be near eye use, and the duration data of the near eye use of the subject is obtained. 5. The eye habit correction analysis method for dry eye syndrome according to claim 4, characterized in that the life habit data includes: outdoor activity time data, sweets/greasy food intake, sleep quality data, makeup holding time data, contact lens/cosmetic lens wearing time data; The step of obtaining the life habit score of the subject through the second monitoring model according to the life habit data of the subject includes: According to the living habit data of the subject, the living habit score of the subject is obtained by using the living habit score expression through the second monitoring model; Wherein, the living habit score expression is: In the lifestyle score expression, Flifestyle represents the lifestyle score, _Fi represents the score of the i-th lifestyle habit data, β represents the weight of the i-th lifestyle habit data, I represents the number of lifestyle habit data, _hi represents the actual value of the i-th lifestyle habit data, and _Hi represents the preset threshold value of the i-th lifestyle habit data. 6. The eye habit correction analysis method for dry eye according to claim 5, characterized in that the treatment execution data includes treatment medication data and treatment behavior execution data, and the treatment behavior execution data includes any one of the following or any combination thereof: hot compress, fumigation, atomization, meibomian gland massage, eyelid margin cleaning; The step of obtaining the treatment execution score of the subject through the third monitoring model according to the treatment execution data of the subject includes: According to the treatment execution data of the subject, the treatment execution score of the subject is obtained by using the treatment execution score expression through the third monitoring model; The treatment execution score expression is: In the treatment execution score expression, Ftreatmentexecution represents the treatment execution score, _km represents the actual value of the treatment medication data, K represents the preset threshold of the treatment medication data, and n represents the number of times the treatment behavior is executed. 7. The eye habit correction analysis method for dry eye according to any one of claims 1 to 6, characterized in that the eye habit correction plan for the subject in the preset time period is obtained according to the eye behavior score, life habit score, and treatment execution score of the subject in the preset time period, comprising: According to the eye behavior score, life habit score, and treatment execution score of the subject in the preset time period, a line graph is used to show the fluctuation of the subject's eye behavior, life habit, and treatment execution in the preset time period, to determine the type of the subject, and provide eye suggestions for the subject; Among them, the types of subjects include: good eye habits, eye habits that need to be improved, and eye use suggestions include any of the following or any combination of them: taking blinking learning courses, avoiding prolonged close eye use, adjusting the humidity of the surrounding environment, increasing outdoor activity time, reducing the intake of sweets/greasy foods, improving sleep quality, reducing makeup holding time, reducing the wearing time of contact lenses/cosmetic lenses, increasing the rate of treatment medication, and increasing the execution rate of treatment behavior. 8. A system for correcting eye habits and analyzing dry eye syndrome, comprising: The data acquisition module is used to obtain the eye behavior monitoring data, life habit data, and treatment execution data of the subject; The first monitoring module is used to obtain the eye behavior score of the subject through the first monitoring model according to the eye behavior monitoring data of the subject, and issue an eye behavior correction reminder message when the eye behavior score of the subject is lower than a first preset threshold; The second monitoring module is used to obtain the living habit score of the subject through the second monitoring model according to the living habit data of the subject; The third monitoring module is used to obtain the treatment execution score of the subject through the third monitoring model according to the treatment execution data of the subject; The comprehensive analysis module is used to obtain a correction plan for the eye habits of the subject within a preset time period based on the subject's eye behavior score, life habit score, and treatment execution score within a preset time period. 9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for correcting and analyzing eye habits for dry eye syndrome as described in any one of claims 1 to 7 is implemented. 10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for correcting and analyzing eye habits for dry eye syndrome as described in any one of claims 1 to 7 is implemented.