A myopia postoperative dry eye risk grading early warning and intervention system

Abstract

The present application relates to the technical field of health data processing, in particular to a myopia postoperative dry eye risk grading early warning and intervention system, comprising: acquiring different dimension data related to myopia postoperative patient dry eye risk detection, determining postoperative patient tear gland function evaluation index and postoperative abnormal index in the current monitoring stage according to different dimension data; determining risk threshold adjustment coefficient by combining tear gland function evaluation index and postoperative abnormal index, determining postoperative patient dry eye risk index in the current monitoring stage by using risk threshold adjustment coefficient; determining postoperative patient dry eye risk grade according to dry eye risk index in the current monitoring stage, and formulating personalized intervention measures for postoperative patients through dry eye risk grade. The present application can dynamically monitor postoperative recovery process of patients, which is beneficial to real-time updating risk assessment, so that the formed intervention measures have personalization and comprehensiveness, and meet the needs of different patients.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health data processing, in particular to a myopia postoperative dry eye risk grading early warning and intervention system. BACKGROUND

[0002] With the increasing incidence of myopia, refractive surgery has become a popular choice for improving vision. However, after myopia surgery, patients often face the risk of dry eye. Dry eye not only affects the visual quality of patients, but also can cause discomfort during the postoperative recovery process, further affecting the quality of life of patients. Therefore, it is particularly important to establish an effective risk grading early warning and intervention system.

[0003] The existing myopia postoperative dry eye risk grading early warning system usually includes the following steps: data collection, risk assessment model, risk grading, intervention measures, etc. However, the existing myopia postoperative dry eye risk grading early warning and intervention system has some shortcomings to some extent: the output analysis and processing capacity is relatively limited, it cannot effectively integrate multi-dimensional data, resulting in insufficient accuracy of risk assessment; most systems rely on a unified assessment model, and are not adaptable to individual differences, which may result in some high-risk patients not being identified in time; risk assessment is usually one-time, lacking dynamic monitoring of the postoperative recovery process of patients, and risk assessment cannot be updated in real time, resulting in a lack of personalized and comprehensive intervention measures that cannot effectively meet the needs of different patients. SUMMARY

[0004] In order to solve the above-mentioned technical problem of low risk detection accuracy of the existing dry eye risk grading early warning and intervention system, the purpose of the present application is to provide a myopia postoperative dry eye risk grading early warning and intervention system, and the technical solution adopted is as follows:

[0005] One embodiment of the present application provides a myopia postoperative dry eye risk grading early warning and intervention system, comprising a memory and a processor, the processor being configured to process instructions stored in the memory to implement the following processes:

[0006] Obtaining the tear viscosity value of the postoperative myopia patient at the current monitoring stage, the tear secretion amount at several times of testing, the tear film break-up time and the eye drop usage frequency, and the self-monitoring score value of several kinds of daily activities at several times of questionnaire survey;

[0007] Determining the lacrimal gland function evaluation index of the postoperative patient at the current monitoring stage according to the tear viscosity value at the current monitoring stage, the tear secretion amount at each time of testing and the tear film break-up time;

[0008] determine the postoperative abnormality index of the postoperative patient in the current monitoring stage according to the frequency of eye drops used by the postoperative patient at each test time and the self-monitoring score of each daily activity at each questionnaire in the current monitoring stage of the postoperative patient;

[0009] determine the risk threshold adjustment coefficient by combining the lacrimal gland function evaluation index and the postoperative abnormality index, and determine the dry eye risk index of the postoperative patient in the current monitoring stage by using the risk threshold adjustment coefficient;

[0010] determine the dry eye risk level of the postoperative patient according to the dry eye risk index in the current monitoring stage, and develop personalized intervention measures for the postoperative patient through the dry eye risk level.

[0011] Further, the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage is determined according to the tear viscosity value, the tear secretion amount at each test time and the tear film break-up time in the current monitoring stage, comprising:

[0012] determine the tear secretion abnormality index of the postoperative patient in the current monitoring stage according to the tear secretion amount at each test time in the current monitoring stage;

[0013] determine the tear film stability degree of the postoperative patient in the current monitoring stage according to the tear film break-up time at each test time in the current monitoring stage in combination with the tear secretion abnormality index;

[0014] determine the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage according to the tear viscosity value in the current monitoring stage in combination with the tear film stability degree.

[0015] Further, the tear secretion abnormality index of the postoperative patient in the current monitoring stage is determined according to the tear secretion amount at each test time in the current monitoring stage, comprising:

[0016] fit the tear secretion amount at each test time in chronological order to obtain a tear test fitting curve, and then determine the slope value of each point in the tear test fitting curve;

[0017] analyze the trend characteristics of the tear secretion amount changing with time according to the slope value of each point in the tear test fitting curve to determine the first tear secretion abnormality factor of the postoperative patient;

[0018] obtain a tear secretion amount threshold, and determine the second tear secretion abnormality factor of the postoperative patient according to the difference between the tear secretion amount threshold and the current test tear secretion amount;

[0019] determine the tear secretion abnormality index of the postoperative patient in the current monitoring stage by combining the first tear secretion abnormality factor and the second tear secretion abnormality factor of the postoperative patient;

[0020] The first tear secretion abnormality factor and the second tear secretion abnormality factor are positively correlated with the tear secretion abnormality index.

[0021] Further, the tear film stability degree of the postoperative patient in the current monitoring stage is determined according to the tear film break-up time at each test in the current monitoring stage and the tear secretion abnormality index, and the determination comprises:

[0022] A tear film break-up time threshold value is obtained, and a tear film break-up abnormality index of each test is determined according to a difference between the tear film break-up time threshold value and the tear film break-up time at each test.

[0023] The tear film break-up time trend feature is analyzed according to a difference between the tear film break-up abnormality index of the last test and the tear film break-up abnormality index of the previous test, and a first tear film stability factor of the postoperative patient in the current monitoring stage is determined.

[0024] A negative correlation value is obtained by performing negative correlation processing on the tear secretion abnormality index, and the negative correlation value is taken as a second tear film stability factor of the postoperative patient in the current monitoring stage.

[0025] The tear film stability degree of the postoperative patient in the current monitoring stage is determined in combination with the first tear film stability factor and the second tear film stability factor of the postoperative patient in the current monitoring stage.

[0026] The first tear film stability factor and the second tear film stability factor are positively correlated with the tear film stability degree.

[0027] Further, the tear film break-up time trend feature is analyzed according to a difference between the tear film break-up abnormality index of the last test and the tear film break-up abnormality index of the previous test, and a first tear film stability factor of the postoperative patient in the current monitoring stage is determined, and the determination comprises:

[0028] A ratio of the tear film break-up abnormality index of the last test to the tear film break-up abnormality index of the previous test is calculated, and is denoted as an abnormality index ratio.

[0029] An average value of all abnormality index ratios is calculated, and a negative correlation value is obtained by performing negative correlation processing on the average value of all abnormality index ratios, and is taken as the first tear film stability factor of the postoperative patient in the current monitoring stage.

[0030] Further, the tear gland function evaluation index of the postoperative patient in the current monitoring stage is determined according to the tear fluid viscosity value in the current monitoring stage and the tear film stability degree, and the determination comprises:

[0031] A normal range of the tear fluid viscosity value of the preoperative patient is obtained, and a maximum tear fluid viscosity value and a minimum tear fluid viscosity value are determined in the normal range.

[0032] According to the tear viscosity value, the maximum tear viscosity value and the minimum tear viscosity value in the current monitoring stage, it is analyzed whether the tear viscosity value is within the normal range, and a tear viscosity normal index of the postoperative patient in the current monitoring stage is determined.

[0033] In combination with the tear viscosity normal index and the tear film stability degree of the postoperative patient in the current monitoring stage, a lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage is determined.

[0034] The tear viscosity normal index and the tear film stability degree are positively correlated with the lacrimal gland function evaluation index.

[0035] Further, the determination of the tear viscosity normal index of the postoperative patient in the current monitoring stage according to the tear viscosity value, the maximum tear viscosity value and the minimum tear viscosity value in the current monitoring stage includes:

[0036] A ratio of the tear viscosity value in the current monitoring stage to the minimum tear viscosity value is determined, and is recorded as a first ratio;

[0037] A ratio of the maximum tear viscosity value to the tear viscosity value in the current monitoring stage is determined, and is recorded as a second ratio;

[0038] The tear viscosity normal index of the postoperative patient in the current monitoring stage is determined in combination with the first ratio and the second ratio.

[0039] The first ratio and the second ratio are positively correlated with the tear viscosity normal index.

[0040] Further, the determination of the postoperative abnormal index of the postoperative patient in the current monitoring stage according to the frequency of eye drops use at each test and the self-monitoring score value of each daily activity at each questionnaire survey in the current monitoring stage includes:

[0041] The self-monitoring score value of each daily activity at one questionnaire survey of the preoperative patient is obtained; according to the difference between the self-monitoring score value of each daily activity of the preoperative patient and the self-monitoring score value of each daily activity of the postoperative patient at each questionnaire survey in the current monitoring stage, a first postoperative abnormal factor of the postoperative patient in the current monitoring stage is determined.

[0042] An eye drop use test fitting curve is obtained according to the frequency of eye drop use at each test of the postoperative patient in the current monitoring stage, and then the slopes in the eye drop use test fitting curve are determined, and the average of all the slopes is taken as a second postoperative abnormal factor of the postoperative patient in the current monitoring stage.

[0043] Determine the postoperative abnormality index of the postoperative patient in the current monitoring stage by combining the first postoperative abnormality factor and the second postoperative abnormality factor of the postoperative patient in the current monitoring stage; wherein the first postoperative abnormality factor and the second postoperative abnormality factor are positively correlated with the postoperative abnormality index.

[0044] Further, the first postoperative abnormality factor of the postoperative patient in the current monitoring stage is determined according to the difference between the self-monitoring score value of the preoperative patient in each daily activity and the self-monitoring score value of the postoperative patient in each daily activity at the time of each questionnaire in the current monitoring stage, and the first postoperative abnormality factor comprises:

[0045] Determine the postoperative abnormality weight according to the average value of the self-monitoring score value of the preoperative patient in all daily activities.

[0046] Determine the ratio of the self-monitoring score value of the preoperative patient and the postoperative patient in the same daily activity, denoted as the score ratio, and take the average value of all score ratios as the initial postoperative abnormality factor.

[0047] Weight the initial postoperative abnormality factor by using the postoperative abnormality weight to obtain the first postoperative abnormality factor of the postoperative patient in the current monitoring stage.

[0048] Further, the risk threshold adjustment coefficient is determined by combining the lacrimal gland function evaluation index and the postoperative abnormality index, and the method comprises the following steps:

[0049] Obtain the negative correlation value of the lacrimal gland function evaluation index, and determine the Euclidean norm between the negative correlation value of the lacrimal gland function evaluation index and the postoperative abnormality index.

[0050] Perform negative correlation normalization processing on the Euclidean norm to obtain the risk threshold adjustment coefficient.

[0051] The present application has the following beneficial effects:

[0052] The existing dry eye risk grading early warning and intervention system is relatively limited in output analysis and processing capacity, cannot effectively integrate multi-dimensional data, leads to insufficient accuracy of risk assessment, and most systems rely on a unified evaluation model, which is not adaptive to individual differences, which may lead to some high-risk patients not being identified in time. Therefore, the present application provides a postoperative dry eye risk grading early warning and intervention system, which first acquires data related to dry eye risk, and then determines the lacrimal gland function evaluation index and postoperative abnormal index of the postoperative patient in the current monitoring stage based on the data. It effectively integrates data of different dimensions, making the numerical accuracy of the subsequent dry eye risk index higher. The lacrimal gland function evaluation index and postoperative abnormal index determined based on data from different angles are fused to obtain a risk threshold adjustment coefficient. The dry eye risk index is obtained by adjusting the risk threshold using the risk threshold adjustment coefficient. The postoperative abnormal index representing individual differences is also used as one of the key influences when determining the dry eye risk index, which is beneficial to timely identify some high-risk patients. Then, the dry eye risk grade is determined based on the dry eye risk index, and personalized intervention measures are developed for postoperative patients. It shows that the postoperative dry eye risk grading early warning and intervention system of the present application can dynamically monitor the postoperative recovery process of the patient, which is beneficial to real-time updating of risk assessment, so that the formed intervention measures have personalization and comprehensiveness, which is beneficial to meet the needs of different patients. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0054] Figure 1 An execution flowchart of a postoperative dry eye risk grading early warning and intervention system provided for an embodiment of the present application;

[0055] Figure 2 An implementation flowchart for step S2 in the embodiment of the present application;

[0056] Figure 3 A fitting curve diagram for tear test in the embodiment of the present application;

[0057] Figure 4 A fitting curve diagram for tear film test in the embodiment of the present application;

[0058] Figure 5 An implementation flowchart for step S3 in the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific implementation, structure, features and effects of the technical solutions according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0061] The application scenario to which the present application is directed can be:

[0062] In the clinical hospital ophthalmology center, the myopia postoperative dry eye risk grading early warning and intervention system can be used as an important tool for postoperative management. After receiving myopia surgery, the patient can perform real-time risk assessment and monitoring through the system, such as: the patient fills out the relevant questionnaire through the system, records personal eye health history, living habits and other information, and the system performs preliminary dry eye risk assessment; according to the dry eye risk assessment result, the system can provide personalized intervention suggestions for the patient.

[0063] One embodiment of the present application provides a myopia postoperative dry eye risk grading early warning and intervention system, comprising a memory and a processor, the processor being configured to process instructions stored in the memory to implement the following processes:

[0064] Obtaining the tear viscosity value of the myopia postoperative patient at the current monitoring stage, the tear secretion amount at each test, the tear film break-up time and the eye drop use frequency, and the self-monitoring score value of each daily activity at each questionnaire survey;

[0065] Determining a lacrimal gland function evaluation index of the postoperative patient at the current monitoring stage according to the tear viscosity value at the current monitoring stage, the tear secretion amount and the tear film break-up time at each test;

[0066] Determining a postoperative abnormal index of the postoperative patient at the current monitoring stage according to the eye drop use frequency at each test and the self-monitoring score value of each daily activity at each questionnaire survey at the current monitoring stage;

[0067] Determining a risk threshold adjustment coefficient in combination with the lacrimal gland function evaluation index and the postoperative abnormal index, and determining a dry eye risk index of the postoperative patient at the current monitoring stage by using the risk threshold adjustment coefficient;

[0068] Determining the dry eye risk grade of the postoperative patient according to the dry eye risk index at the current monitoring stage, and formulating personalized intervention measures for the postoperative patient through the dry eye risk grade.

[0069] Reference Figure 1 The above steps are described in detail as follows:

[0070] S1, obtaining the tear viscosity value of the post-myopia patient in the current monitoring stage, the tear secretion amount in several tests, the tear film break-up time, and the eye drop usage frequency, and the self-monitoring score value of several daily activities in several questionnaire surveys.

[0071] For example, the dry eye risk of a post-myopia patient is evaluated. In the evaluation of the dry eye risk of the post-myopia patient, the data required by the risk grading early warning system in different dimensions are collected, including the tear viscosity value, the tear secretion amount, the tear film break-up time, and the eye drop usage frequency, and the self-monitoring score value is obtained.

[0072] Here, the current monitoring stage can be set to 10 days, the number of tests in the current monitoring stage can be set to 10 times, the number of questionnaire surveys in the current monitoring stage is set to 5 times, and the types of daily activities can be reading, watching electronic devices, and exercising. Of course, the current monitoring stage, the number of tests, the number of questionnaire surveys, and the types of daily activities in the embodiment can be set by the implementer according to the specific actual situation, which is not limited here.

[0073] For the tear viscosity value, the concentrations and proportions of different components (such as water, mucin, and lipids) in the tear are determined by biochemical analysis technology. Specifically, the tear sample is first collected, and then the viscosity meter is used to measure the tear viscosity value of the post-myopia patient in the current monitoring stage. The viscosity of normal tears should be within a certain range, and too high or too low viscosity may affect the distribution and stability of tears.

[0074] For the tear secretion amount, the changes in the tear secretion amount can be monitored regularly to identify the occurrence of dry eye symptoms early. Specifically, the post-operative patient uses Schirmer test paper to test the tear secretion amount several times in the examination room. The standardized Schirmer test paper is usually placed under the eyelid, usually for 5 minutes; after the test, the tear secretion amount is evaluated according to the length of the wet paper, and the tear secretion amount in each test is obtained in millimeters.

[0075] For the tear film break-up time, the occurrence of dry eye is not only related to the tear secretion amount, but also related to the stability of the tear film. Specifically, the post-myopia patient is tested for TBUT (Tear Film Break-Up Time) in the current monitoring stage, and the time from the last blink to the appearance of the tear film break-up is recorded as the tear film break-up time in seconds.

[0076] For the frequency of eye drop use, the higher the frequency of eye drop use, the more severe the dry eye symptoms. Specifically, record the number of times the postoperative myopic patient uses eye drops per day in the current monitoring stage. Among them, the corresponding relationship between the test times and the monitoring time is that the test is carried out once a day in the current monitoring stage, and the eye drop use frequency of the day is obtained.

[0077] For the self-monitoring score value, in order to analyze the subjective experience and discomfort symptoms of the postoperative patient, the postoperative patient needs to perform self-monitoring. Specifically, use a self-monitoring tool, such as a mobile application or a web-based platform, the patient can record and submit self-monitoring data in real time, and the self-monitoring includes scores of different daily activities, a scoring system is set, for example, the score range is 1-5, 1 represents no discomfort, and 5 represents severe discomfort, and the self-monitoring score value of each daily activity of the postoperative patient at each questionnaire survey in the current monitoring stage is obtained.

[0078] Up to now, the data collection process of the postoperative patient is realized, and the dry eye risk analysis can be carried out according to the data collection results of different dimensions in the subsequent.

[0079] S2, according to the tear viscosity value in the current monitoring stage, the tear secretion amount and the tear film break-up time at each test, determine the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage.

[0080] Here, the lacrimal gland function evaluation index represents the normal condition of the lacrimal gland function, and the normal condition of the lacrimal gland function is affected by the ocular moisture retention ability and the tear film quality. The ocular moisture retention ability can be manifested by analyzing the tear viscosity value, and the tear film quality represents the stability of the tear film, which is affected by the change characteristics of the tear secretion amount and the tear film break-up time over time.

[0081] As an exemplary embodiment, the above step S2 can be implemented by steps S21 to S23 shown in the following table: Figure 2

[0082] S21, according to the tear secretion amount at each test in the current monitoring stage, determine the tear secretion abnormality index of the postoperative patient in the current monitoring stage.

[0083] Here, the tear secretion abnormality index can be used to represent the change of the tear secretion amount of the patient after the operation. By regularly monitoring the change of the tear secretion amount, the occurrence of dry eye symptoms can be identified early, the change of the tear secretion amount can be used as an index to evaluate the effect of the operation, and the monitoring data of the tear secretion amount can help medical staff to formulate individualized intervention strategies.

[0084] Exemplarily, the above step S21 can be implemented by steps S211 to S213 (not shown in the figure):

[0085] ​S211, fitting the tear secretion amount of each test in chronological order to obtain a tear test fitting curve, and then determining the slope values in the tear test fitting curve.

[0086] In this embodiment, the tear secretion amount obtained in each test is denoted as u, and the last test point before the current test is denoted as the last test point. The tear secretion amount of all tests is fitted in chronological order by using the least square method to obtain a fitting curve in the tear test process, which is the tear test fitting curve. In the tear test fitting curve, a group of slope values is formed between each two adjacent data points, denoted as K, and each slope value in the tear test fitting curve can be determined.

[0087] The schematic diagram of the tear test fitting curve is shown in Figure 3 , the abscissa is the test number, and the ordinate is the wet length of the test paper (the tear secretion amount), which is in millimeters.

[0088] S212, analyzing the trend characteristics of the tear secretion amount with time according to the slope values in the tear test fitting curve to determine the first tear secretion abnormality factor of the postoperative patient.

[0089] As an example, the calculation formula of the first tear secretion abnormality factor of the postoperative patient can be:

[0090] In the formula, the first tear secretion abnormality factor of the postoperative patient is denoted as , r denotes the rth postoperative patient, m denotes the number of slopes in the tear test fitting curve, denotes the cth slope value in the tear test fitting curve, denotes the (c+1)th slope value in the tear test fitting curve.

[0091] It should be noted that the larger the first tear secretion abnormality factor of the postoperative patient , the more obvious the characteristics of the tear secretion amount of the postoperative patient showing a decay trend, which means that the tear secretion of the postoperative patient is more likely to have potential problems, that is, the potential problem of the tear secretion amount of the postoperative patient in the current monitoring stage is greater.

[0092] S213, obtaining a tear secretion amount threshold, and determining the second tear secretion abnormality factor of the postoperative patient according to the difference between the tear secretion amount threshold and the tear secretion amount of the current test.

[0093] In this embodiment, the wet length of the test paper greater than 10 mm is generally considered normal, so the tear secretion amount threshold can be set to 10, denoted as .

[0094] As an example, In the formula, the second tear secretion abnormality factor of the postoperative patient is denoted as a second tear secretion abnormality factor representing a postoperative patient, a tear secretion threshold value, a current test tear secretion value, the current test being the test closest to the current time.

[0095] It should be noted that, The greater the value, the lower the current test tear secretion value is below the tear secretion threshold value, which can still indicate that the greater the potential problem of the postoperative patient's tear secretion in the current monitoring stage.

[0096] S214, in combination with the first tear secretion abnormality factor and the second tear secretion abnormality factor of the postoperative patient, determine the tear secretion abnormality index of the postoperative patient in the current monitoring stage.

[0097] Here, the first tear secretion abnormality factor and the second tear secretion abnormality factor are positively correlated with the tear secretion abnormality index, that is, the greater the first tear secretion abnormality factor and the second tear secretion abnormality factor, the greater the tear secretion abnormality index in the current monitoring stage.

[0098] In this embodiment, the first tear secretion abnormality factor representing the downward trend feature of the change of the tear secretion value with time and the second tear secretion abnormality factor representing the degree of the test tear secretion value below the normal value can obtain the tear secretion abnormality index of the postoperative patient in the current monitoring stage.

[0099] Exemplarily, the product of the first tear secretion abnormality factor and the second tear secretion abnormality factor is taken as the tear secretion abnormality index of the postoperative patient in the current monitoring stage.

[0100] There are individual differences in tear secretion, and physiological conditions, age, gender, etc. of different individuals can affect tear secretion. Some patients may still have no dry eye symptoms even if they show less than 10 millimeters in the Schirmer test, so this embodiment cannot determine that there is a potential problem in tear secretion only by the data of one dimension of tear secretion. Based on this, the embodiment gives the following steps S22 to S23.

[0101] S22, according to the tear film break-up time at each test in the current monitoring stage, in combination with the tear secretion abnormality index, determine the tear film stability of the postoperative patient in the current monitoring stage.

[0102] Here, the tear film stability degree refers to the tear film stability of the postoperative patient in the current monitoring stage. The occurrence of dry eye is not only related to the tear secretion amount, but also related to the stability of the tear film, the composition and quality of the tear, and multiple factors. For example, some patients may have a normal tear secretion amount, but the quality of the tear film is poor, and dry eye symptoms still occur. By analyzing the falling trend of the tear film break-up time over time, the quality of the tear film is quantified. The more obvious the falling trend is, the shorter the tear film break-up time is over time, and the lower the quality of the tear film is. A schematic diagram of a fitting curve of the tear film test is shown in FIG. 8. Figure 4 The abscissa in the figure is the test number, and the ordinate is the tear film break-up time, in seconds.

[0103] Exemplarily, the above step S22 can be implemented through steps S221 to S224 (not shown in the figure):

[0104] S221, a tear film break-up time threshold is obtained, and a tear film break-up abnormality index of each test is determined according to the difference between the tear film break-up time threshold and the tear film break-up time at each test.

[0105] Here, the tear film break-up abnormality index refers to the degree of the tear film break-up time of the test being less than the normal time. The smaller the tear film break-up time of the test is, the greater the tear film break-up abnormality index of the corresponding test is.

[0106] In this embodiment, the tear film break-up time greater than 10 seconds is considered to be normal, so the tear film break-up time threshold can be recorded as 10 seconds.

[0107] As an example, the calculation formula of the tear film break-up abnormality index of each test can be: In the formula, y represents the tear film break-up abnormality index of each test, represents the tear film break-up time threshold, and t represents the tear film break-up time at each test.

[0108] S222, the tear film break-up time change trend feature is analyzed according to the difference between the tear film break-up abnormality indexes of the last test and the previous test, and a first tear film stability factor of the postoperative patient in the current monitoring stage is determined.

[0109] Here, the first tear film stability factor refers to the falling degree of the tear film break-up abnormality index over time. The greater the difference between the tear film break-up abnormality indexes of the last test and the previous test is, the more it can be indicated that the tear film break-up time presents a falling trend, that is, the tear film break-up time is shortened continuously, which may indicate that the tear secretion is insufficient or the tear film is unstable.

[0110] Exemplarily, a ratio of the tear film breakage abnormality index of the last test to the tear film breakage abnormality index of the previous test is calculated, denoted as an abnormality index ratio; an average of all abnormality index ratios is calculated, and a negative correlation of the average of all abnormality index ratios is processed to obtain a negative correlation value, as a first tear film stability factor of the postoperative patient in the current monitoring stage.

[0111] As an example, a calculation formula of the first tear film stability factor of the postoperative patient in the current monitoring stage can be: ; in the formula, the first tear film stability factor of the postoperative patient in the current monitoring stage is denoted as n, the number of tests is denoted as the tear film breakage abnormality index of the postoperative patient in the j+1th test is denoted as the tear film breakage abnormality index of the postoperative patient in the jth test is denoted as

[0112] As another example, a calculation formula of the first tear film stability factor of the postoperative patient in the current monitoring stage can also be: ;

[0113] S223, a negative correlation of the tear secretion abnormality index is processed to obtain a negative correlation value, and the negative correlation value is taken as a second tear film stability factor of the postoperative patient in the current monitoring stage.

[0114] Here, the second tear film stability factor refers to the possibility that there is no potential problem in the tear secretion condition, so the tear secretion abnormality index needs to be processed in a negative correlation.

[0115] Exemplarily, the reciprocal of the tear secretion abnormality index can be taken as the second tear film stability factor of the postoperative patient in the current monitoring stage.

[0116] S224, the first tear film stability factor and the second tear film stability factor of the postoperative patient in the current monitoring stage are combined to determine the tear film stability degree of the postoperative patient in the current monitoring stage.

[0117] Here, the first tear film stability factor and the second tear film stability factor are positively correlated with the tear film stability degree, that is, the greater the first tear film stability factor and the second tear film stability factor, the greater the tear film stability degree.

[0118] In this embodiment, the fusion of the first tear film stability factor representing the rising or stable trend of the tear film break-up time and the second tear film stability factor representing the normal tear secretion condition can obtain the tear film stability degree of the postoperative patient in the current monitoring stage.

[0119] Exemplarily, the product of the first tear film stability factor and the second tear film stability factor is taken as the tear film stability degree of the postoperative patient in the current monitoring stage.

[0120] It should be noted that, in order to avoid the dimensional difference between different dimensions of dry eye related data, when fusing and analyzing different dimensions of dry eye related data, only the numerical size is considered, the unit influence is not considered, or all dimensions of dry eye related data are standardized.

[0121] S23, according to the tear viscosity value under the current monitoring stage, combined with the tear film stability degree, determine the lacrimal gland function evaluation index of the patient after the operation in the current monitoring stage.

[0122] Here, the tear film stability degree has comprehensively considered the normal characteristics of tear secretion and tear film breakage under the current monitoring stage, in order to improve the numerical accuracy and completeness of the lacrimal gland function evaluation index, because the tear viscosity value is also related to the lacrimal gland function, the tear viscosity value is also taken as one of the key factors. Therefore, by comprehensively analyzing the tear secretion, tear film breakage and tear viscosity, the lacrimal gland function evaluation index of the patient after the operation in the current monitoring stage can be obtained.

[0123] Exemplarily, the above step S23 can be realized by steps S231 to S233 (not shown in the figure):

[0124] S231, obtaining the normal range of the tear viscosity value corresponding to the patient before the operation, determining the maximum tear viscosity value and the minimum tear viscosity value in the normal range.

[0125] In this embodiment, the normal range of the tear viscosity value corresponding to the patient before the operation measured in the laboratory is obtained, and the boundary values of the normal range are respectively denoted as and , represents the minimum tear viscosity value, represents the maximum tear viscosity value. Wherein, the maximum tear viscosity value and the minimum tear viscosity value are used to judge whether the tear viscosity value of the patient after the operation is within the normal range.

[0126] S232, according to the tear viscosity value under the current monitoring stage, the maximum tear viscosity value and the minimum tear viscosity value, analyze whether the tear viscosity value is within the normal range, and determine the tear viscosity normal index of the patient after the operation in the current monitoring stage.

[0127] Exemplarily, the ratio of the tear viscosity value under the current monitoring stage to the minimum tear viscosity value is determined, denoted as the first ratio; the ratio of the maximum tear viscosity value to the tear viscosity value under the current monitoring stage is determined, denoted as the second ratio; the tear viscosity normal index of the patient after the operation in the current monitoring stage is determined in combination with the first ratio and the second ratio; wherein, the first ratio and the second ratio are positively correlated with the tear viscosity normal index.

[0128] As an example, the calculation formula of the tear viscosity normal index of the patient after the operation in the current monitoring stage can be:

[0129] wherein, represents the tear fluid viscosity normal indicator of the postoperative patient in the current monitoring stage, represents the maximum value of the tear fluid viscosity, represents the tear fluid viscosity value in the current monitoring stage, represents the minimum value of the tear fluid viscosity, represents the second ratio, represents the first ratio.

[0130] In the calculation formula of the tear fluid viscosity normal indicator, the second ratio is greater, the smaller the tear fluid viscosity value in the current monitoring stage is than the maximum value of the tear fluid viscosity, and the first ratio is greater, the greater the tear fluid viscosity value in the current monitoring stage is than the minimum value of the tear fluid viscosity; the tear fluid viscosity normal indicator represents the degree to which the tear fluid viscosity value in the current monitoring stage is within the normal tear fluid viscosity value range, and the tear fluid viscosity normal indicator is greater, the more the tear fluid viscosity value in the current monitoring stage conforms to the tear fluid viscosity characteristics of the preoperative patient, and the more normal the lacrimal gland function of the patient is.

[0131] It should be noted that the tear fluid viscosity normal indicator mainly reflects the persistence and uniformity of tear fluid on the ocular surface, which directly affects ocular comfort and vision quality. In the case of normal lacrimal gland function, timely secretion and renewal of tear fluid can effectively maintain the moisture of the ocular surface. However, sufficient tear fluid secretion alone is not enough to ensure the persistent retention of ocular moisture, and the stability of the tear film also plays an important role here.

[0132] S233, in combination with the tear fluid viscosity normal indicator and the tear film stability of the postoperative patient in the current monitoring stage, determines the lacrimal gland function evaluation indicator of the postoperative patient in the current monitoring stage.

[0133] Here, the tear fluid viscosity normal indicator and the tear film stability are positively correlated with the lacrimal gland function evaluation indicator, that is, the greater the tear fluid viscosity normal indicator and the tear film stability of the postoperative patient, the greater the lacrimal gland function evaluation indicator.

[0134] Exemplarily, the product of the tear fluid viscosity normal indicator and the tear film stability of the postoperative patient in the current monitoring stage is taken as the lacrimal gland function evaluation indicator of the postoperative patient in the current monitoring stage.

[0135] It should be noted that the change of the lacrimal gland function indicates the development of dry eye syndrome of the postoperative patient, and through the evaluation of the lacrimal gland function, the doctor can formulate a personalized postoperative management plan. Insufficient lacrimal gland function may increase the risk of dry eye syndrome, especially after surgery, the moisture retention ability of the ocular surface may be affected by the surgical operation and postoperative recovery.

[0136] So far, the embodiment determines the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage.

[0137] S3, according to the frequency of eye drops use of the postoperative patient at each test time in the current monitoring stage and the self-monitoring score value of each daily activity at each questionnaire time, determines the postoperative abnormal index of the postoperative patient in the current monitoring stage.

[0138] Here, the postoperative abnormal index refers to the significant degree of discomfort of the postoperative patient in the current monitoring stage, that is, the analysis of the postoperative discomfort symptoms. The self-monitoring score is analyzed because the subjective experience and discomfort symptoms of the patient can reflect the true feeling of the eye health condition and provide an important basis for further intervention measures.

[0139] As an exemplary embodiment, the above step S3 can be implemented by the steps S31 to S33 shown in the following table: Figure 5

[0140] S31, obtaining the self-monitoring score value of each daily activity of the preoperative patient at one questionnaire time; according to the difference between the self-monitoring score value of each daily activity of the preoperative patient and the self-monitoring score value of each daily activity of the postoperative patient at each questionnaire time in the current monitoring stage, determining the first postoperative abnormal factor of the postoperative patient in the current monitoring stage.

[0141] Here, the first postoperative abnormal factor represents the difference between the self-monitoring score values of the same daily activity of the patient before and after the operation. If the self-monitoring score values of the same daily activity of the patient before and after the operation are significantly different, it indicates that the suspected discomfort of the postoperative patient is higher.

[0142] Exemplarily, according to the average value of the self-monitoring score values of all kinds of daily activities of the preoperative patient, the postoperative abnormal weight is determined; the ratio of the self-monitoring score values of the same daily activity of the preoperative patient and the postoperative patient is determined, which is recorded as the score ratio value, and the average value of all score ratio values is taken as the initial postoperative abnormal factor; the initial postoperative abnormal factor is weighted by the postoperative abnormal weight to obtain the first postoperative abnormal factor of the postoperative patient in the current monitoring stage.

[0143] As an example, the calculation formula of the first postoperative abnormal factor of the postoperative patient in the current monitoring stage can be:

[0144] ; in the formula, wherein, B represents the number of types of daily activities, represents the self-monitoring score value of the postoperative patient in the xth daily activity, ​represents the self-monitoring score value of the preoperative patient in the xth daily activity, the postoperative patient and the preoperative patient are the same patient, represents the score ratio, represents the average value of the self-monitoring score value of the preoperative patient in all kinds of daily activities, h represents the hth questionnaire survey of the postoperative patient in the current monitoring stage, h ranges from 1 to H, H represents the number of questionnaire surveys of the postoperative patient in the current monitoring stage, and the experience value is taken as 5.

[0145] In the calculation formula of the first postoperative abnormality factor, represents the postoperative abnormality weight, and the greater the comprehensive value of the self-monitoring score value of the preoperative patient in all kinds of daily activities, the more accurate the self-monitoring score result of the patient before the operation, and the higher the confidence of the initial postoperative abnormality factor determined based on the self-monitoring score before the operation; represents the average value of all score ratios corresponding to the hth questionnaire survey of the postoperative patient in the current monitoring stage, represents the average value of the average values of all score ratios corresponding to all questionnaire surveys of the postoperative patient in the current monitoring stage, The greater, the higher the suspected discomfort of the postoperative patient in the current postoperative monitoring process, and since the score of the questionnaire survey has high subjectivity, the postoperative discomfort of the patient needs to be further evaluated.

[0146] S32, according to the eye drop use frequency of the postoperative patient in each test in the current monitoring stage, an eye drop use test fitting curve is obtained, and then each slope in the eye drop use test fitting curve is determined, and the average of all slopes is taken as the second postoperative abnormality factor of the postoperative patient in the current monitoring stage.

[0147] In this embodiment, the greater the second postoperative abnormality factor, the greater the average value of all slopes in the eye drop use test fitting curve, which indicates that the eye drop use frequency of the postoperative patient in the current monitoring stage is in a continuous rising state, and frequent use of tear substitutes may indicate aggravation of dry eye symptoms, so the discomfort symptoms of the postoperative patient in the current monitoring stage are more obvious, that is, the second postoperative abnormality factor of the patient in the current monitoring stage is greater.

[0148] S33, the first postoperative abnormality factor and the second postoperative abnormality factor of the postoperative patient in the current monitoring stage are combined to determine the postoperative abnormality index of the postoperative patient in the current monitoring stage.

[0149] Here, the first postoperative abnormality factor and the second postoperative abnormality factor are positively correlated with the postoperative abnormality index, that is, the greater the first postoperative abnormality factor and the second postoperative abnormality factor, the greater the postoperative abnormality index in the current monitoring stage.

[0150] In this embodiment, the first postoperative abnormal factor representing the difference between the self-monitoring scores of the same daily activity of the patient before and after surgery and the second postoperative abnormal factor representing the eye drop usage frequency status of the patient in the current monitoring stage can obtain a postoperative abnormal index with higher numerical accuracy.

[0151] It should be noted that the existing risk grading early warning and intervention system cannot effectively integrate multi-dimensional data, resulting in insufficient accuracy of risk assessment, and most systems rely on a unified assessment model, which is not adaptive to individual differences. However, the first postoperative abnormal factor determined in this step can reflect the individualization of the patient, thereby helping to improve the accuracy of the risk grading early warning and intervention system.

[0152] At this point, the postoperative abnormal index of the postoperative patient in the current monitoring stage is obtained in this embodiment.

[0153] S4, determine a risk threshold adjustment coefficient based on the lacrimal gland function evaluation index and the postoperative abnormal index, and determine the dry eye risk index of the postoperative patient in the current monitoring stage using the risk threshold adjustment coefficient.

[0154] Here, the risk threshold adjustment coefficient refers to a coefficient used to adjust the risk threshold set in the risk grading early warning and intervention system, which is helpful for determining the dry eye risk index, and the dry eye risk index determined thereby is more in line with the actual situation in the dynamic monitoring process.

[0155] In this embodiment, the risk threshold adjustment coefficient determined based on the lacrimal gland function evaluation index and the postoperative abnormal index helps the clinician to automatically identify high-risk patients, thereby promoting the implementation of personalized intervention measures; by integrating the adaptive dry eye risk index into the intelligent early warning system, not only the identification efficiency of dry eye symptoms can be improved, but also more timely and accurate treatment strategies can be provided for the patient, thereby improving the postoperative recovery quality and life satisfaction.

[0156] In order to balance the postoperative patient between the lacrimal gland function and the self-monitoring, so as to provide more comprehensive data support for the risk grading early warning and intervention system, the lacrimal gland function evaluation index and the postoperative abnormal index are calculated in this embodiment.

[0157] Exemplarily, a negative correlation value of the lacrimal gland function evaluation index is obtained, the Euclidean norm between the negative correlation value of the lacrimal gland function evaluation index and the postoperative abnormal index is determined, the Euclidean norm is normalized in a negative correlation manner, and the risk threshold adjustment coefficient is obtained; the product of the risk threshold adjustment coefficient and the set risk threshold is taken as the dry eye risk index.

[0158] As an example, the calculation formula of the dry eye risk index of the postoperative patient in the current monitoring stage can be: ; in the formula, an index of postoperative abnormality indicating the postoperative abnormality of the postoperative patient in the current monitoring stage, an index of postoperative abnormality indicating the postoperative abnormality of the postoperative patient in the current monitoring stage, an index of lacrimal gland function assessment indicating the lacrimal gland function assessment of the postoperative patient in the current monitoring stage, a negative correlation value of the index of lacrimal gland function assessment.

[0159] In the calculation formula of the dry eye risk index, the index of lacrimal gland function assessment indicates the normal condition of lacrimal gland function, and since it is used to analyze the dry eye risk condition, the index of lacrimal gland function assessment needs to be negatively correlated, that is, the larger the index of lacrimal gland function assessment, the smaller the negative correlation value of the index of lacrimal gland function assessment; the index of postoperative abnormality that can reflect the real-time characteristics of self-monitoring data enables the risk warning system to dynamically adjust the risk assessment according to the current symptom changes of the patient, so as to adapt to the needs of the patient at different stages; the larger, the higher the dry eye risk of the myopia postoperative patient in the current monitoring stage, that is, the warning threshold needs to be appropriately lowered for the risk classification warning and intervention system, which is helpful to timely identify abnormal conditions in the postoperative monitoring process and make timely intervention, so the negative correlation normalization processing needs to be performed on .

[0160] At this point, the dry eye risk index of the postoperative patient in the current monitoring stage is obtained in the embodiment.

[0161] S5, determining the dry eye risk level of the postoperative patient according to the dry eye risk index of the current monitoring stage, and formulating personalized intervention measures for the postoperative patient through the dry eye risk level.

[0162] In the risk classification warning system, a feedback mechanism is established to continuously monitor the self-monitoring data and clinical manifestations of the patient, and a new adjustment coefficient is calculated in real time to dynamically update the risk threshold. For example, a data warehouse can be established to automatically receive and analyze the dry eye related data of the patient, regularly adjust the risk threshold, and determine the dry eye risk index in different monitoring stages.

[0163] In the embodiment, after the dry eye risk index of the current monitoring stage is obtained, it is compared and analyzed with the threshold of different warning levels to determine the dry eye risk level, and the subsequent system will automatically send the risk warning notice to the patient and the medical team.

[0164] ​Exemplarily, the first risk threshold and the second risk threshold are set, the first risk threshold is less than the second risk threshold, and the two risk thresholds can be set by a person based on historical experience data, and the specific determination manner is not limited; if the dry eye risk index of the current monitoring stage is less than the first risk threshold, it is determined that the dry eye risk level of the postoperative patient in the current monitoring stage is a low risk level; if the dry eye risk index of the current monitoring stage is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is determined that the dry eye risk level of the postoperative patient in the current monitoring stage is a medium risk level; if the dry eye risk index of the current monitoring stage is greater than the second risk threshold, it is determined that the dry eye risk level of the postoperative patient in the current monitoring stage is a high risk level.

[0165] The low risk level means that all or most of the indexes are within the normal range, and the postoperative patient has no obvious discomfort; the medium risk level means that there is slight discomfort or slight decrease in lacrimal gland function; and the high risk level means that there is obvious discomfort and significant decrease in lacrimal gland function, and the related clinical indexes exceed the normal range.

[0166] Subsequently, personalized intervention measures can be developed according to the dry eye risk level of the patient, including increasing the use of artificial tears, recommending regular review, and providing eye care guidance. At the same time, the effect of the risk warning system can be regularly evaluated by tracking the improvement of the dry eye symptoms of the patient to improve the accuracy and effectiveness of the evaluation system.

[0167] Exemplarily, for the personalized intervention measures according to the dry eye risk level, specifically, the low-risk patients are recommended to use artificial tears combined with environmental regulation; the medium-risk patients can be superimposed with anti-inflammatory treatment and physical intervention; and the high-risk patients can start closed-loop management, real-time monitoring of tear secretion amount through intelligent wearable devices, and introduction of autologous serum eye drops or punctal plug embolization and other reinforcement measures.

[0168] It is worth noting that the dry eye risk index determined by the dry eye risk index only provides a reference for the doctor, and the doctor needs to set personalized intervention measures for the postoperative patient according to the specific actual situation.

[0169] Thus, the embodiment completes the early warning and intervention of the dry eye risk grading of the postoperative myopia patient.

[0170] The application provides a myopia postoperative dry eye risk grading early warning and intervention system, which acquires data related to dry eye risk, determines tear gland function evaluation indexes and postoperative abnormal indexes of a postoperative patient in a current monitoring stage based on the data, effectively integrates data in different dimensions, and makes the subsequent dry eye risk index more accurate in numerical accuracy; the tear gland function evaluation indexes and postoperative abnormal indexes determined based on data in different angles are fused to obtain a risk threshold adjustment coefficient, the dry eye risk index is obtained by adjusting the risk threshold by using the risk threshold adjustment coefficient, the postoperative abnormal index representing individual differences is also taken as one of the key influences when the dry eye risk index is determined, which is beneficial to timely identifying some high-risk patients; then, the dry eye risk grade is determined based on the dry eye risk index, and personalized intervention measures are formulated for the postoperative patient, which shows that the myopia postoperative dry eye risk grading early warning and intervention system can dynamically monitor the postoperative recovery process of the patient, is beneficial to real-time updating of risk assessment, makes the formed intervention measures personalized and comprehensive, and is beneficial to meeting the needs of different patients.

[0171] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A post-refractive surgery dry eye risk stratification, early warning and intervention system, characterized in that, The device comprises a memory and a processor, and the processor is used to process instructions stored in the memory to implement the following process: Obtain the tear viscosity value of the post-myopia patient in the current monitoring stage, the tear secretion amount at each test, the tear film break-up time, and the eye drop use frequency, and the self-monitoring score value of each daily activity at each questionnaire survey; Determine the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage according to the tear viscosity value in the current monitoring stage, the tear secretion amount and the tear film break-up time at each test; Determine the postoperative abnormal index of the postoperative patient in the current monitoring stage according to the eye drop use frequency at each test and the self-monitoring score value of each daily activity at each questionnaire survey; Determine the risk threshold adjustment coefficient by combining the lacrimal gland function evaluation index and the postoperative abnormal index, and determine the dry eye risk index of the postoperative patient in the current monitoring stage by using the risk threshold adjustment coefficient; Determine the dry eye risk level of the postoperative patient according to the dry eye risk index in the current monitoring stage, and develop personalized intervention measures for the postoperative patient through the dry eye risk level; The determination of the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage according to the tear viscosity value in the current monitoring stage, the tear secretion amount and the tear film break-up time at each test comprises: Determine the tear secretion abnormal index of the postoperative patient in the current monitoring stage according to the tear secretion amount at each test in the current monitoring stage; Determine the tear film stability degree of the postoperative patient in the current monitoring stage according to the tear film break-up time at each test in the current monitoring stage, in combination with the tear secretion abnormal index; Determine the lacrimal gland function evaluation index of the postoperative patient in the current monitoring stage according to the tear viscosity value in the current monitoring stage, in combination with the tear film stability degree; The determination of the tear secretion abnormal index of the postoperative patient in the current monitoring stage according to the tear secretion amount at each test in the current monitoring stage comprises: Fit the tear secretion amount at each test in chronological order to obtain a tear test fitting curve, and then determine each slope value in the tear test fitting curve; Analyze the trend characteristics of the tear secretion amount changing with time according to each slope value in the tear test fitting curve to determine the first tear secretion abnormal factor of the postoperative patient; Obtain a tear secretion amount threshold, and determine the second tear secretion abnormal factor of the postoperative patient according to the difference between the tear secretion amount threshold and the current test tear secretion amount; Determine the tear secretion abnormal index of the postoperative patient in the current monitoring stage by combining the first tear secretion abnormal factor and the second tear secretion abnormal factor of the postoperative patient; The first tear secretion abnormal factor and the second tear secretion abnormal factor are positively correlated with the tear secretion abnormal index; The determination of the risk threshold adjustment coefficient by combining the lacrimal gland function evaluation index and the postoperative abnormal index comprises: Obtain the negative correlation value of the lacrimal gland function evaluation index, and determine the Euclidean norm between the negative correlation value of the lacrimal gland function evaluation index and the postoperative abnormal index; Perform negative correlation normalization processing on the Euclidean norm to obtain the risk threshold adjustment coefficient.

2. The post-refractive dry eye risk grading, early warning and intervention system of claim 1, wherein, The tear film stability degree of the postoperative patient in the current monitoring stage is determined according to the tear film break-up time at each test time in the current monitoring stage and the tear secretion abnormality index, and includes: A tear film break-up time threshold value is obtained, and a tear film break-up abnormality index of each test is determined according to the difference between the tear film break-up time threshold value and the tear film break-up time at each test time; The tear film break-up time trend feature is analyzed according to the difference between the tear film break-up abnormality index of the last test and the tear film break-up abnormality index of the previous test, and a first tear film stability factor of the postoperative patient in the current monitoring stage is determined; The tear secretion abnormality index is negatively correlated to obtain a negative correlation value, and the negative correlation value is taken as a second tear film stability factor of the postoperative patient in the current monitoring stage; The tear film stability degree of the postoperative patient in the current monitoring stage is determined by combining the first tear film stability factor and the second tear film stability factor of the postoperative patient in the current monitoring stage; The first tear film stability factor and the second tear film stability factor are positively correlated with the tear film stability degree.

3. The post-refractive dry eye risk grading, early warning and intervention system of claim 2, wherein, The tear film break-up time trend feature is analyzed according to the difference between the tear film break-up abnormality index of the last test and the tear film break-up abnormality index of the previous test, and a first tear film stability factor of the postoperative patient in the current monitoring stage is determined, and includes: The ratio of the tear film break-up abnormality index of the last test to the tear film break-up abnormality index of the previous test is calculated, and is denoted as an abnormality index ratio; The average value of all abnormality index ratios is calculated, and the average value of all abnormality index ratios is negatively correlated to obtain a negative correlation value, which is taken as the first tear film stability factor of the postoperative patient in the current monitoring stage.

4. The post-refractive dry eye risk grading, early warning and intervention system of claim 1, wherein, The tear gland function evaluation index of the postoperative patient in the current monitoring stage is determined according to the tear fluid viscosity value in the current monitoring stage and the tear film stability degree, and includes: The normal range of the tear fluid viscosity value of the preoperative patient is obtained, and the maximum tear fluid viscosity value and the minimum tear fluid viscosity value are determined in the normal range; Whether the tear fluid viscosity value is located in the normal range is analyzed according to the tear fluid viscosity value in the current monitoring stage, the maximum tear fluid viscosity value and the minimum tear fluid viscosity value, and a tear fluid viscosity normal index of the postoperative patient in the current monitoring stage is determined; The tear gland function evaluation index of the postoperative patient in the current monitoring stage is determined by combining the tear fluid viscosity normal index of the postoperative patient in the current monitoring stage and the tear film stability degree; The tear fluid viscosity normal index and the tear film stability degree are positively correlated with the tear gland function evaluation index.

5. The post-refractive dry eye risk grading, early warning and intervention system of claim 4, wherein, Whether the tear fluid viscosity value is located in the normal range is analyzed according to the tear fluid viscosity value in the current monitoring stage, the maximum tear fluid viscosity value and the minimum tear fluid viscosity value, and a tear fluid viscosity normal index of the postoperative patient in the current monitoring stage is determined, and includes: The ratio of the tear fluid viscosity value in the current monitoring stage to the minimum tear fluid viscosity value is determined, and is denoted as a first ratio; The ratio of the maximum tear fluid viscosity value to the tear fluid viscosity value in the current monitoring stage is determined, and is denoted as a second ratio; The tear fluid viscosity normal index of the postoperative patient in the current monitoring stage is determined by combining the first ratio and the second ratio; The first ratio and the second ratio are positively correlated with the tear fluid viscosity normal index.

6. The post-refractive dry eye risk grading, early warning and intervention system of claim 1, wherein, The postoperative abnormality index of the postoperative patient in the current monitoring stage is determined according to the frequency of eye drops used by the postoperative patient at each test in the current monitoring stage and the self-monitoring score of each daily activity at each questionnaire in the current monitoring stage, and the postoperative abnormality index comprises: The self-monitoring score of each daily activity of the preoperative patient at one questionnaire is obtained, and the first postoperative abnormality factor of the postoperative patient in the current monitoring stage is determined according to the difference between the self-monitoring score of each daily activity of the preoperative patient and the self-monitoring score of each daily activity of the postoperative patient at each questionnaire in the current monitoring stage. The test fitting curve of eye drops is obtained according to the frequency of eye drops used by the postoperative patient at each test in the current monitoring stage, and then the slopes in the test fitting curve of eye drops are determined, and the average of all the slopes is taken as the second postoperative abnormality factor of the postoperative patient in the current monitoring stage. The postoperative abnormality index of the postoperative patient in the current monitoring stage is determined by combining the first postoperative abnormality factor and the second postoperative abnormality factor of the postoperative patient in the current monitoring stage, and the first postoperative abnormality factor and the second postoperative abnormality factor are positively correlated with the postoperative abnormality index.

7. The post-refractive dry eye risk grading, early warning and intervention system of claim 6, wherein, The first postoperative abnormality factor of the postoperative patient in the current monitoring stage is determined according to the difference between the self-monitoring score of each daily activity of the preoperative patient and the self-monitoring score of each daily activity of the postoperative patient at each questionnaire in the current monitoring stage, and the first postoperative abnormality factor comprises: The postoperative abnormality weight is determined according to the average of the self-monitoring scores of all daily activities of the preoperative patient. The ratio of the self-monitoring scores of the same daily activity of the preoperative patient and the postoperative patient is determined, which is recorded as a score ratio, and the average of all the score ratios is taken as an initial postoperative abnormality factor. The initial postoperative abnormality factor is weighted by the postoperative abnormality weight to obtain the first postoperative abnormality factor of the postoperative patient in the current monitoring stage.

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