CN108281197A - A method of relationship between analysis environmental factor and juvenile shortsightedness - Google Patents
A method of relationship between analysis environmental factor and juvenile shortsightedness Download PDFInfo
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Abstract
The invention discloses the methods of relationship between analysis environmental factor and juvenile shortsightedness, including：Step 1, objective detecting and acquisition are carried out at the same time to the near work of user and outdoor exposure data；Step 2, the eye parameter of user is obtained；Step 3, the eye parameter of the near work of user and outdoor exposure data and user cloud platform is respectively transmitted to store；Step 4, the near work and outdoor exposure data and the eye parameter of user stored to cloud platform database using big data analysis system carries out noise reduction and conversion according to the requirement of big data analysis, then user is extracted with eye behavioural characteristic, and to being associated with the eye parameter of eye behavioural characteristic and user.This method in use can in real time, dynamic, objectively monitoring user really uses eye behavior and visual environment, overcome the problem of can not accurately analyzing relationship between environmental factor and juvenile shortsightedness in the prior art.
Description
Technical Field
The invention relates to the field of analysis of myopia and environmental relationships, in particular to a method for analyzing a relationship between environmental factors and teenager myopia.
Background
The prevalence rate of the myopia is increased very rapidly, and no effective prevention and control means exist at present. It is generally believed that myopia is the result of the combined action of genes and environmental factors, and the phenomenon that the prevalence rate of myopia rapidly increases in the past decades suggests that the environmental factors are the leading cause. The environmental factors mainly include two major aspects of close-range work and outdoor exposure. To date, the specific role of environmental factors in the development of myopia remains unclear. The reason for this is that the previous research on the relationship between environmental factors and myopia is mainly performed by questionnaires.
At present, some researches adopt wearable equipment capable of objectively monitoring close-range work or outdoor exposure to evaluate a certain aspect of environmental factors, and no intelligent wearable equipment capable of objectively monitoring close-range work and outdoor exposure simultaneously in research and application exists. For example, Leung et al applied a head mounted close range work analyzer to objectively monitor the close range work of a user-these researchers utilized portable photoreceptors, Actiwatch, and FitSight fixesstracker, respectively, to monitor the user's outdoor exposure.
Questionnaires this approach suffers from two major drawbacks. Firstly, it requires the writer to answer the questions with very wide area and very specific content according to his memory, which can not avoid the memory bias and results in the poor accuracy of the statistical data. Secondly, it can only provide the total data of environmental factors, but cannot record the high-density ethological data of the interviewee dynamically in real time, so that the behavior pattern (such as continuous work or intermittent work in close-distance work) cannot be drawn, and animal experiments have proved that the behavior pattern has important influence on diopter development.
With the development of information technology, some of the above-described methods capable of objectively monitoring environmental factors have appeared, but they also have some major disadvantages. First, these methods can only monitor some aspect of the environmental factors, for example, the head-mounted short-distance analyzer can only monitor the short-distance work, and Actiwatch can only monitor the outdoor exposure time, which is obviously incomplete, cannot fully evaluate the environmental factors, and cannot fully reveal the effect of the environmental factors on the development of the myopic eye. Second, the ambient environment illumination monitored by these methods is not that which is acceptable to the eye. Thirdly, the methods do not have a perfect data storage platform, cannot acquire the relevant behavioural data of the myopia of the teenagers in a large scale, cannot generate a corresponding behavioural database, and cannot utilize a big data means to mine the association between the behavioural data and diopter.
Therefore, the real eye using behavior and the visual environment of the user can be monitored dynamically and objectively in real time in the using process, and the defects that the prior method has memory bias and can only provide environment total data are overcome; two dimensions, namely the real working distance and the ambient environment illumination, where the eyes are close to can be monitored simultaneously, and the defects that the monitoring dimension of the existing method is single and the measured data is not the real environment data where the eyes are located are overcome; the cloud platform for storing data overcomes the defect that the prior method cannot store data in a large scale; the invention discloses a method for analyzing the relationship between environmental factors and teenagers' myopia, which is a method for mining and analyzing data by using a big data method and is expected to really determine the quantitative relationship between the environmental factors and the myopia.
Disclosure of Invention
Aiming at the technical problems, the invention aims to provide a method which can monitor the real eye using behavior and the visual environment of a user dynamically and objectively in real time in the using process and overcomes the defects that the prior method has memory bias and can only provide environment total data; two dimensions, namely the real working distance and the ambient environment illumination, where the eyes are close to can be monitored simultaneously, and the defects that the monitoring dimension of the existing method is single and the measured data is not the real environment data where the eyes are located are overcome; the cloud platform for storing data overcomes the defect that the prior method cannot store data in a large scale; and the data is mined and analyzed by using a big data method, so that the method for analyzing the relationship between the environmental factors and the teenager myopia is expected to really determine the quantitative relationship between the environmental factors and the myopia.
In order to achieve the above objects, the present invention provides a method of analyzing a relationship between environmental factors and teenagers' myopic eyes, the method comprising:
step 1, objectively monitoring and collecting close-range work and outdoor exposure data of a user at the same time;
step 2, obtaining eye parameters of a user;
step 3, respectively transmitting the close-range work and outdoor exposure data of the user and the eye parameters of the user to a cloud platform for storage;
and 4, denoising and converting the close-range working and outdoor exposure data stored in the cloud platform database and the eye parameters of the user according to the requirement of big data analysis by using a big data analysis system, extracting the eye using behavior characteristics of the user, correlating the eye using behavior characteristics and the eye parameters of the user, and further clarifying the influence of environmental factors on the myopic eye.
Preferably, the ocular parameters of the user include: objective refractive power and ocular axis data.
Preferably, the big data analysis system includes: the system comprises a CPU (central processing unit), a data management module, a behavior feature extraction module, an association model module, an optimal model decision module and an environmental impact index generation module; the data management module is used for denoising and converting the close-range working and outdoor exposure data stored in the cloud platform and the eye parameters of the user according to the requirement of big data analysis; the behavior feature extraction module is used for extracting eye use behavior features of the users by utilizing the close-range working data and the outdoor exposure data stored by the cloud platform, and depicting the behavior features of different users; the association model establishing module is used for establishing the association between the behavior characteristics and the eye parameters by using the association model after the extraction of the eye using behavior characteristics of the user is finished, namely establishing the association between the behavior characteristics and the diopter progress; the optimal model decision module is used for selecting and deciding an optimal model; the environment influence index generating module is used for generating an environment influence index capable of reflecting the eye use behavior habit of the user; the CPU processor is used for coordinating the work of each module and analyzing data.
Preferably, the data management module filters out high frequency data, i.e., noise, using fast fourier transform.
Preferably, the eye use behavior characteristics of the user include: and mapping the VD data and EI data after noise reduction to a 2-dimensional space (VD-EI space) by using a working distance (VD) and the illuminance (EI) of the surrounding environment, wherein the vertical axis is VD, and the horizontal axis is log10(EI), so that a behavior distribution curve graph of the user is obtained, behavior distribution thermodynamic diagrams of the user group are obtained by respectively superposing the behavior curves of all the users, and the eye using behavior characteristics of the user group are drawn immediately.
Preferably, said indicator of diopter progression is at least a 2-year variation Δ SER of the equivalent spherical power (SER) and a variation Δ AL of the ocular Axis (AL); wherein, the SER is S + 1/2C; s and C are the sphere power and the cylinder power obtained by a computer optometry instrument after paralyzing ciliary muscles respectively; the Δ SER ═ SER last-SER baseline, Δ AL ═ AL last-AL baseline; the SER last time and AL last time represent the refractive power and eye axis data, respectively, that the user last provided, and the SER baseline and AL baseline represent the refractive power and eye axis data, respectively, that the user first provided.
Preferably, before the correlation model building module works, the key assumption for building the model needs to be explained, and then the influence of the continuous pixels around the correlation model building module needs to be considered to obey the assumption: calculating the proportion (PoT) of the time of each user behavior contained in each pixel to the total time of the user group behaviors by taking the pixel as a unit, weighting the pixels at different distances by adopting a radial basis function (RBF kernel function), and analyzing the relation between PoT and SER by using Weighted Linear Regression (WLR), wherein the closer the pixel is, the larger the influence is, and the farther the pixel is, the smaller the influence is; for 2 pixels x and x 'of the RBF kernel, given the weight of x and x' relative to x is defined as:
||xind-x′ind||2the whole VD-EI space is divided into 40-40 pixels by the square of the Euclidean distance between two pixels, each pixel can be endowed with a pair of index values, so that the Euclidean distance between any two pixels is calculated, and the RBF kernel function can endow a far pixel with a small weight and endow a near pixel with a large weight.
Preferably, when analyzing a certain pixel, it is necessary to define a certain size area around the pixel as the center to determine the influence range, where the size of the area is defined by a parameter δ (0 ≦ δ ≦ 20), and 2 times δ is the radius of a circle around the certain pixel, so that the pixels in all circles have certain influence on the certain pixel.
Preferably, each pixel can establish PoT a weighted linear regression model between diopter progress, the slope (K) of the model represents the correlation property and strength between a certain behavior feature and diopter progress, if the value is positive, the usage is good, if the value is more positive, the behavior is better if the more positive, the usage is protective for the myopic eye as a whole; the value is a negative value, which indicates that the eye-using habit is poor, and the behavior habit is poor when the negative value is smaller, which represents that the eye-using behavior of the user has dangerous effect on the myopia on the whole.
Preferably, wear to carry out objective monitoring and collection simultaneously to user's work closely and outdoor exposure data with intelligent wearable equipment, its frequency of gathering data is 4-6 s/time, the data of intelligent wearable equipment collection can be sent to cell-phone APP through the bluetooth, APP passes through the network again with data transmission to the server on.
According to the technical scheme, the method for analyzing the relationship between the environmental factors and the teenager myopia can monitor the real eye using behaviors and the visual environment of the user dynamically and objectively in real time in the using process, and overcomes the defects that the existing method is biased in memory and only can provide environment total data; two dimensions, namely the real working distance and the ambient environment illumination, where the eyes are close to can be monitored simultaneously, and the defects that the monitoring dimension of the existing method is single and the measured data is not the real environment data where the eyes are located are overcome; the cloud platform for storing data overcomes the defect that the prior method cannot store data in a large scale; and the data is mined and analyzed by using a big data method, so that the quantitative relation between the environmental factors and the myopic eyes is expected to be really determined.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block flow diagram of a method for analyzing the relationship between environmental factors and teenager myopia in accordance with a preferred embodiment of the present invention;
fig. 2 is a schematic structural diagram of a wearable device in a method for analyzing a relationship between an environmental factor and a near-sightedness of a teenager according to a preferred embodiment of the present invention;
fig. 3 is an assembly view of a smart wearable device worn on glasses in a method for analyzing a relationship between environmental factors and teenagers' near sightedness according to a preferred embodiment of the present invention.
Description of the reference numerals
1 ultraviolet sensor 2 Bluetooth
3 three-axis angular velocity sensor 4 distance sensor
5 light intensity sensor
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
As shown in fig. 1 to 3, the present invention provides a method for analyzing a relationship between environmental factors and a myopic eye of a teenager, the method comprising: step 1, objectively monitoring and collecting close-range work and outdoor exposure data of a user at the same time; step 2, obtaining eye parameters of a user; step 3, respectively transmitting the close-range work and outdoor exposure data of the user and the eye parameters of the user to a cloud platform for storage; and 4, denoising and converting the close-range working and outdoor exposure data stored in the cloud platform database and the eye parameters of the user according to the requirement of big data analysis by using a big data analysis system, extracting the eye using behavior characteristics of the user, correlating the eye using behavior characteristics and the eye parameters of the user, and further clarifying the influence of environmental factors on the myopic eye.
According to the technical scheme, the method for analyzing the relationship between the environmental factors and the teenager myopia can monitor the real eye using behaviors and the visual environment of the user dynamically and objectively in real time in the using process, and overcomes the defects that the existing method is biased in memory and only can provide environment total data; two dimensions, namely the real working distance and the ambient environment illumination, where the eyes are close to can be monitored simultaneously, and the defects that the monitoring dimension of the existing method is single and the measured data is not the real environment data where the eyes are located are overcome; the cloud platform for storing data overcomes the defect that the prior method cannot store data in a large scale; and the data is mined and analyzed by using a big data method, so that the quantitative relation between the environmental factors and the myopic eyes is expected to be really determined.
In a preferred embodiment of the present invention, the user's eye parameters include: objective refractive power and ocular axis data, which are the main ocular parameters of the user, although the ocular parameters of the user of the present invention are not limited to these two ocular parameters.
In a preferred embodiment of the present invention, a big data analyzing system includes: the system comprises a CPU (central processing unit), a data management module, a behavior feature extraction module, an association model module, an optimal model decision module and an environmental impact index generation module; the data management module is used for denoising and converting the close-range working and outdoor exposure data stored in the cloud platform and the eye parameters of the user according to the requirement of big data analysis; the behavior feature extraction module is used for extracting eye use behavior features of the users by utilizing the close-range working data and the outdoor exposure data stored by the cloud platform, and depicting the behavior features of different users; the association model establishing module is used for establishing the association between the behavior characteristics and the eye parameters by using the association model after the extraction of the eye using behavior characteristics of the user is finished, namely establishing the association between the behavior characteristics and the diopter progress; the optimal model decision module is used for selecting and deciding an optimal model; the environment influence index generating module is used for generating an environment influence index capable of reflecting the eye use behavior habit of the user; the CPU processor is used for coordinating the work of each module and analyzing data.
In a preferred embodiment of the present invention, the data management module filters out high frequency data, i.e., noise, using fast fourier transform. The presentation mode of the ethological data acquired from the cloud platform is that the order of the acquisition time is taken, one piece of ethological data is displayed at intervals of 4-6s, each piece of data comprises the time point of the acquisition data, the working distance corresponding to the time point and the ambient illumination, and the distribution characteristics of the data cannot be known and certain processing is needed due to the original data in the form. Taking a certain user as an example, the behavioral data of the user is subjected to Arabic numeral numbering according to time sequence, the number is taken as an abscissa, and the Environmental Illumination (EI) or working distance (VD) corresponding to the number is taken as an ordinate, so that a working distance distribution map or a surrounding environmental illumination distribution map of the user is obtained. It can be seen from the data distribution diagram that the noise of the data is too large, so that the analysis of the large data cannot be performed, and the noise reduction processing needs to be performed on the data. Fast Fourier Transform (FFT) is an effective noise reduction method that filters out high frequency data (noise) and after noise reduction the data has a more suitable distribution for analysis.
In a preferred embodiment of the present invention, the eye use behavior characteristics of the user include: and mapping the VD data and EI data after noise reduction to a 2-dimensional space (VD-EI space) by using a working distance (VD) and the illuminance (EI) of the surrounding environment, wherein the vertical axis is VD, and the horizontal axis is log10(EI), so that a behavior distribution curve graph of the user is obtained, behavior distribution thermodynamic diagrams of the user group are obtained by respectively superposing the behavior curves of all the users, and the eye using behavior characteristics of the user group are drawn immediately. The behavioural data consists of 3 features: a continuous time series, a working distance or illumination corresponding to each acquisition time point. The present invention focuses on the eye-using behavior characteristics of the user, so the time dimension can be ignored, and the remaining 2 characteristics are analyzed: working distance (VD) and ambient illuminance (EI). Taking a certain user as an example, mapping the VD and EI data after noise reduction to a 2-dimensional space (VD-EI space), wherein the vertical axis is VD, and the horizontal axis is log10(EI), so as to obtain a behavior distribution curve graph of the certain user, and immediately draw the eye use behavior characteristics of the user. And then, the behavior curves of all the users are respectively superposed to obtain the behavior distribution thermodynamic diagram of the user group, and the eye use behavior characteristics of the user group are drawn immediately.
In a preferred embodiment of the invention, said index of diopter progression is at least a 2-year value of variation Δ SER of the spherical equivalent power (SER) and a value of variation Δ AL of the ocular Axis (AL); wherein, the SER is S + 1/2C; s and C are the sphere power and the cylinder power obtained by a computer optometry instrument after paralyzing ciliary muscles respectively; the Δ SER ═ SER last-SER baseline, Δ AL ═ AL last-AL baseline; the SER last time and AL last time represent the refractive power and eye axis data, respectively, that the user last provided, and the SER baseline and AL baseline represent the refractive power and eye axis data, respectively, that the user first provided. After the behavior feature extraction module obtains the general behavior distribution thermodynamic diagram of the user population, the overall eye use behavior feature of the user population can be obtained. Clearly, global eye use behavior characteristics cannot be correlated with diopter progression, and therefore, the total behavior distribution thermodynamic diagram is equally divided into 40 × 40 grids (pixels), each representing a local behavior characteristic. Next, a correlation between the local behavior characteristics and the diopter progression is established.
In a preferred embodiment of the present invention, before the association model is built, a key assumption for building the model is described, and the key assumption is that the human eye-using behavior is spatially continuous, meaning that the human eye-using behavior covers a continuous piece of pixels. Therefore, for each pixel, the effect of its surrounding consecutive pixels is taken into account to obey the assumptions.
The proportion of the time of each subject's behavior contained in each pixel to the total time of the subject's behaviors is calculated (PoT) in pixel units, and for a subject, PoT is the proportion of the time it takes in a pixel to the time it takes in all pixels through which its behavior curve passes, reflecting the proportion of the time the subject spends in this pixel (behavior feature). To this end, there are arguments PoT that are hooked to the behavioral characteristics of the subject, and there are also variables Δ that reflect the progression of the subject's dioptersSERAnd ΔALA correlation between PoT for the subject per unit pixel and their diopter progression can be established. As described above, when analyzing a certain pixel, the influence of the surrounding continuous pixels needs to be considered. Obviously, the closer the pixel is, the greater the influence is, the further the pixel is, the less the influence is, so the radial basis function (RBF kernel) is used to weight the pixels at different distances, and then the Weighted Linear Regression (WLR) is used to analyze PoT the relation to SER;
for an RBF kernel function of 2 pixels x and x ', the weight given to x and x' relative to each other is defined as:
||xind-x′ind||2the whole VD-EI space is divided into 40-40 pixels by the square of the Euclidean distance between two pixels, each pixel can be endowed with a pair of index values, so that the Euclidean distance between any two pixels is calculated, and the RBF kernel function can endow a far pixel with a small weight and endow a near pixel with a large weight.
In a preferred embodiment of the present invention, when analyzing a certain pixel, since there is a certain influence on the peripheral pixels, it is necessary to define a region with a certain size around the pixel as the center to determine the influence range, where the size of the region is defined by a parameter δ (δ ≦ 0 ≦ 20), and 2 times δ is the radius of a circle around the certain pixel, so that the pixels in all circles have a certain influence on the pixel;
wherein, the optimal model decision module: the module is mainly used for deciding which correlation model is the optimal model. Different values of delta, the obtained PoT relation model is different from the relation model between diopter progress, so the relation model established when the value of delta is the most accurate needs to be verified. If there is no behavior distribution in a pixel and pixels contained in a circle whose radius is 2 δ from the center, i.e., PoT is 0, the pixel cannot establish a weighted linear regression model between PoT and diopter progression, and the slope (K) of the model can be considered to be 0. In the extreme case, if there is no distribution of behavior in all pixels and pixels contained within a circle with its center at 2 δ as the radius, a model of the relationship between PoT and diopter progression cannot be established. The fewer the number of pixels for which K is 0, the more effectively PoT reflects the relationship between diopter progression. Obviously, the number of pixels with K equal to 0 has a large relationship with the value of δ, and the larger the value of δ, the larger the area of influence around a certain pixel, the more pixels are included in the area, the smaller the probability of no behavior distribution at all, and the more likely the relationship model between PoT and diopter progress is established (i.e., the smaller the probability of K equal to 0). Therefore, theoretically speaking, the larger the value of δ, the more accurate the relationship model between PoT and diopter progression is established.
In a preferred embodiment of the invention, each pixel can establish PoT weighted linear regression model between diopter progress, the slope (K) of the model represents the correlation property and intensity between certain behavior characteristics and diopter progress, if the value is positive, the behavior habit is good, if the positive value is larger, the behavior habit is better, and the user's eye behavior is generally protective to the myopic eye; the value is a negative value, which indicates that the eye-using habit is poor, and the behavior habit is poor when the negative value is smaller, which represents that the eye-using behavior of the user has a dangerous effect on the myopia on the whole; the environmental impact index generation module included in the big data analysis system is used for generating an environmental impact index which can reflect the good and bad habit of the eye behavior of the user: the parameter is named as 'environmental impact index' which is self-defined, such as anemia, and can be judged according to the content of hemoglobin; hypertension, which can be determined according to the values of systolic pressure and diastolic pressure; the invention hopes to establish a parameter capable of reflecting the habit of using the eye to act: "environmental impact index".
In a preferred embodiment of the invention, the intelligent wearable device is used for wearing to perform objective monitoring and acquisition on the close-range work and outdoor exposure data of a user at the same time, the frequency of the acquired data is 4-6 s/time, the data acquired by the intelligent wearable device is sent to a mobile phone APP through Bluetooth, and the APP transmits the data to a server through a network.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (10)
1. A method of analyzing a relationship between environmental factors and a juvenile myopic eye, the method comprising:
step 1, objectively monitoring and collecting close-range work and outdoor exposure data of a user at the same time;
step 2, obtaining eye parameters of a user;
step 3, respectively transmitting the close-range work and outdoor exposure data of the user and the eye parameters of the user to a cloud platform for storage;
and 4, denoising and converting the close-range working and outdoor exposure data stored in the cloud platform database and the eye parameters of the user according to the requirement of big data analysis by using a big data analysis system, extracting the eye using behavior characteristics of the user, correlating the eye using behavior characteristics and the eye parameters of the user, and further clarifying the influence of environmental factors on the myopic eye.
2. The method of analyzing the relationship between environmental factors and teenager's near vision according to claim 1, wherein the eye parameters of the user comprise: objective refractive power and ocular axis data.
3. The method of analyzing the relationship between environmental factors and juvenile myopic eyes according to claim 1, wherein the big data analysis system comprises: the system comprises a CPU (central processing unit), a data management module, a behavior feature extraction module, an association model module, an optimal model decision module and an environmental impact index generation module; wherein,
the data management module is used for denoising and converting the close-range working and outdoor exposure data stored by the cloud platform and the eye parameters of the user according to the requirement of big data analysis;
the behavior feature extraction module is used for extracting eye use behavior features of the users by utilizing the close-range working data and the outdoor exposure data stored by the cloud platform, and depicting the behavior features of different users;
the association model establishing module is used for establishing the association between the behavior characteristics and the eye parameters by using the association model after the extraction of the eye using behavior characteristics of the user is finished, namely establishing the association between the behavior characteristics and the diopter progress;
the optimal model decision module is used for selecting and deciding an optimal model;
the environment influence index generating module is used for generating an environment influence index capable of reflecting the eye use behavior habit of the user;
the CPU processor is used for coordinating the work of each module and analyzing data.
4. The method of claim 3, wherein the data management module filters high frequency data, i.e., noise, using fast Fourier transform.
5. The method of analyzing the relationship between environmental factors and teenager's near vision according to claim 3, wherein the eye use behavior characteristics of the user comprise: and mapping the VD data and EI data after noise reduction to a 2-dimensional space (VD-EI space) by using a working distance (VD) and the illuminance (EI) of the surrounding environment, wherein the vertical axis is VD, and the horizontal axis is log10(EI), so that a behavior distribution curve graph of the user is obtained, behavior distribution thermodynamic diagrams of the user group are obtained by respectively superposing the behavior curves of all the users, and the eye using behavior characteristics of the user group are drawn immediately.
6. A method of analysing a relationship between environmental factors and a teenager's near vision according to claim 3, wherein the indicator of diopter progression is at least 2 years of change value Δ SER of equivalent spherical power (SER) and change value Δ AL of the eye Axis (AL); wherein,
(ii) the SER is S + 1/2C; s and C are the sphere power and the cylinder power obtained by a computer optometry instrument after paralyzing ciliary muscles respectively;
the Δ SER ═ SER last-SER baseline, Δ AL ═ AL last-AL baseline; the SER last time and AL last time represent the refractive power and eye axis data, respectively, that the user last provided, and the SER baseline and AL baseline represent the refractive power and eye axis data, respectively, that the user first provided.
7. The method of claim 6, wherein before said correlation model building module works, a key assumption for building the model needs to be explained, and then the influence of its surrounding continuous pixels needs to be considered to obey the assumption: calculating the proportion (PoT) of the time of each user behavior contained in each pixel to the total time of the user group behaviors by taking the pixel as a unit, weighting the pixels at different distances by adopting a radial basis function (RBF kernel function), and analyzing the relation between PoT and SER by using Weighted Linear Regression (WLR), wherein the closer the pixel is, the larger the influence is, and the farther the pixel is, the smaller the influence is;
for 2 pixels x and x 'of the RBF kernel, given the weight of x and x' relative to x is defined as:
||xind-x′ind||2the whole VD-EI space is divided into 40-40 pixels by the square of the Euclidean distance between two pixels, each pixel can be endowed with a pair of index values, so that the Euclidean distance between any two pixels is calculated, and the RBF kernel function can endow a far pixel with a small weight and endow a near pixel with a large weight.
8. The method of claim 7, wherein the area of influence is determined by defining a region of a certain size around the pixel, wherein the size of the region is defined by a parameter δ (δ ≦ 0 ≦ 20), and wherein 2 times δ is a radius of a circle around the pixel, such that all pixels in the circle have a certain influence on the pixel.
9. The method according to claim 3, wherein each pixel is capable of establishing PoT weighted linear regression model between diopter progression and slope (K) representing the correlation property and intensity between certain behavior feature and diopter progression, if the value is positive, it indicates that the eye usage is good, the larger the positive value, the better the behavior is, it indicates that the user's eye usage has protection effect on the myopic eye as a whole; the value is a negative value, which indicates that the eye-using habit is poor, and the behavior habit is poor when the negative value is smaller, which represents that the eye-using behavior of the user has dangerous effect on the myopia on the whole.
10. The method of claim 1, wherein the smart wearable device is used for simultaneously and objectively monitoring and collecting the close-range work and outdoor exposure data of the user, the frequency of data collection is 4-6 s/time, the data collected by the smart wearable device is transmitted to a mobile phone APP through Bluetooth, and the APP transmits the data to the server through a network.
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