CN111965847B - Lens fitting method, device and medium - Google Patents
Lens fitting method, device and medium Download PDFInfo
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- CN111965847B CN111965847B CN202010900343.9A CN202010900343A CN111965847B CN 111965847 B CN111965847 B CN 111965847B CN 202010900343 A CN202010900343 A CN 202010900343A CN 111965847 B CN111965847 B CN 111965847B
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Abstract
The embodiment of the application discloses a lens adapting method, a device and a medium, wherein the method comprises the following steps: acquiring M physiological parameters, wherein M is a positive integer; acquiring missing physiological parameters in the M physiological parameters; acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer; inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N; and acquiring a first lens corresponding to the L lens parameters. By the method and the device, the accuracy of obtaining the lens parameters is improved, and the accuracy of lens adaptation can be improved.
Description
Technical Field
The application relates to the technical field of computers, and mainly relates to a lens adapting method, device and medium.
Background
Currently, more and more people suffer from eye diseases, such as myopia, hyperopia, astigmatism, glaucoma, dry eye syndrome, and the like. Vision is improved by wearing spectacles or contact lenses, which however have high requirements on the accuracy of the lens parameters. If the physiological parameters of the user are missing, it is difficult to obtain accurate lens parameters and thus a fitted lens.
Disclosure of Invention
The embodiment of the application provides a lens adaptation method, a lens adaptation device and a lens adaptation medium, which can improve the accuracy of obtaining lens parameters and improve the accuracy of lens adaptation.
In a first aspect, embodiments of the present application provide a lens fitting method, wherein:
acquiring M physiological parameters, wherein M is a positive integer;
acquiring missing physiological parameters in the M physiological parameters;
acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer;
inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N;
and acquiring a first lens corresponding to the L lens parameters.
In a second aspect, embodiments of the present application provide a lens adapting device, wherein:
the storage unit is used for storing a pre-established decision tree;
the processing unit is used for acquiring M physiological parameters, wherein M is a positive integer; acquiring missing physiological parameters in the M physiological parameters; acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer; inputting the N physiological characteristic data into the decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N; and acquiring a first lens corresponding to the L lens parameters.
In a third aspect, an embodiment of the present application provides another lens fitting apparatus, including a processor, a memory, a communication interface, and one or at least one program, where the one or at least one program is stored in the memory and configured to be executed by the processor, and the program includes instructions for some or all of the steps described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, where the computer program makes a computer execute to implement part or all of the steps as described in the first aspect.
The embodiment of the application has the following beneficial effects:
after the lens adapting method, the device and the medium are adopted, the M physiological parameters are firstly obtained, and then the missing physiological parameters in the M physiological parameters are obtained. Obtaining N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, and obtaining a first lens corresponding to the L lens parameters. Therefore, the lens parameters can be obtained without depending on the staff of the hospital or the glasses merchant, and the user experience is improved. And the accuracy of obtaining the lens parameters is improved according to the lens parameters obtained by the pre-established decision tree, and the lens adaptation precision can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a schematic diagram of a network architecture to which embodiments of the present application are applied;
FIG. 2 is a schematic flow chart of a lens fitting method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a sub-decision tree according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram illustrating a logical structure of a lens adapting device according to an embodiment of the present application;
fig. 5 is a schematic physical structure diagram of a lens adapting device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work according to the embodiments of the present application are within the scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a network architecture to which the present application is applied. The network architecture diagram includes a server 101, an electronic device 102, and a user 103. It should be noted that the number and the form of each device in the network architecture diagram shown in fig. 1 are used for example, and do not constitute a limitation to the embodiment of the present application.
The electronic device 102 may be a Personal Computer (PC), a notebook computer, or a smart phone shown in fig. 1, or may also be an all-in-one machine, a palm computer, a tablet computer (pad), a smart television playing terminal, a vehicle-mounted terminal, or a portable device. The PC end user terminal, such as a kiosk, etc., may have an operating system including, but not limited to, linux system, unix system, windows series system (e.g., windows xp, windows 7, etc.), mac OS X system (operating system of apple computer), etc. The operating system of the mobile end user terminal, such as a smart phone, may include, but is not limited to, an operating system such as an android system, an IOS (operating system of an apple mobile phone), a Window system, and the like.
The server 101 is similar to a general computer architecture and includes a processor, a hard disk, a memory, a system bus, etc. for providing services to the electronic device 102. The server 101 may operate on a single device, or may operate on a server cluster formed by a plurality of servers, which is not limited herein.
The user 103 may be a user who actually operates the electronic device 102 or may be a developer. The user 103 may input an operation instruction to the electronic device 102 to control the electronic device 102 to perform a corresponding operation.
The electronic device 102 in this embodiment may install and run an application program, and the server 101 may be a server corresponding to the application program installed in the electronic device 102, and provide an application service for the application program. The application program may be a shopping application or a health application, or may also be an application including a lens fitting function, such as a page browsing application, which is not limited herein.
For example: in the scenario of the shopping application, when the electronic device 102 runs the shopping application, the user 103 inputs a physiological parameter on the shopping application and sends a lens fitting instruction based on the physiological parameter, and the electronic device 102 receives the lens fitting instruction and sends a fitting request for the lens parameter to the server 101. The server 101 responds to the adaptation request, obtains a lens parameter corresponding to the physiological parameter, provides feedback of a lens corresponding to the lens parameter to the electronic device 102, and the electronic device 102 displays the lens.
The application provides a lens adapting method, which can be executed by a lens adapting device, wherein the device can be realized by software and/or hardware, and can be generally integrated in a server corresponding to electronic equipment and lens adapting application, thereby improving the accuracy of obtaining lens parameters and improving the precision of lens adaptation.
Referring to fig. 2, fig. 2 is a schematic flow chart of a lens fitting method according to an embodiment of the present application, including:
s201: m physiological parameters are acquired.
In the embodiment of the application, M is a positive integer. The physiological parameter may be physiological characteristic data such as age, corneal hypoxia, astigmatism, tear secretion, left-eye vision, right-eye vision, etc., or may be a specific eye parameter such as anterior chamber depth, axial length of eye, weight of lens, thickness of lens, etc., which is not limited herein.
It will be appreciated that the physiological parameters may vary from person to person and that professional or regular measurement equipment may be relied upon to obtain the physiological parameters in order to improve the accuracy of the lens configuration. The method for acquiring the physiological parameter is not limited in the present application, and the data acquired by using a professional or regular measuring device may be input to the electronic device by a user (or uploaded to a server by the electronic device), or may be remotely measured based on the professional or regular measuring device connected to the electronic device, or may be measured based on an application program or a function installed in the current electronic device, or may be corrected based on the application program, or the like, the data acquired by the previous measuring device.
In one possible example, step S201 includes: emitting electromagnetic waves to a target position; receiving reflected electromagnetic waves corresponding to the electromagnetic waves; and acquiring M physiological parameters according to the reflected electromagnetic waves and a physiological parameter curve of the frequency corresponding to the prestored electromagnetic waves.
The target position is a preset acquisition position of the electronic equipment, and the target user can be reminded to move the face through a square frame mode so that the face image is located in the square frame, and therefore the eye image is acquired. The transmission frequency range of the electromagnetic waves is 100GHz to 1000GHz.
It is understood that after the transmission of the electromagnetic wave, the transmission electromagnetic wave corresponding to the electromagnetic wave may be received. In this example, the frequency of the electromagnetic wave and the physiological parameter curve corresponding to the transmitted electromagnetic wave are determined in advance, so that the physiological parameter can be acquired based on the electromagnetic wave.
S202: and acquiring the missing physiological parameters in the M physiological parameters.
There may be instances of loss of physiological parameters, e.g., corneal hypoxia, inability to acquire tear data. The method for obtaining the missing physiological parameter is not limited in the present application, and in a possible example, the step S204 includes the following steps A1 to A3, wherein:
a1: determining a physiological parameter type of a missing physiological parameter of the M physiological parameters;
a2: determining the missing grade of the missing physiological parameter according to the physiological parameter type of the missing physiological parameter;
a3: and acquiring the missing physiological parameters according to the missing grade.
The physiological parameter type is the dimension of the missing physiological parameter, such as age, corneal hypoxia, astigmatism, tear secretion, left eye vision, right eye vision and other physiological characteristic dimensions, or the dimension of the measurement parameter such as anterior chamber depth, axial length of the eye, weight of the crystalline lens, thickness of the crystalline lens and the like.
The deletion grade can be determined according to the age, corneal hypoxia, astigmatism, tear secretion, the correlation degree between vision and physiological characteristic data such as vision power, refractive index, diopter, astigmatism power and the like. Vision directly determines the power of vision, astigmatism directly determines the power of astigmatism, corneal hypoxia, tear secretion, age may affect the lens parameters, so in this example the power of vision, the power of astigmatism, is higher than the power of refraction, the power of diopters. The higher the deletion level, the larger the data amount of the reference data corresponding to the physiological characteristic data is determined to be. For example, the data amount of the reference data corresponding to the refractive index is 100, and the data amount of the reference data corresponding to the visual acuity is 200.
It can be understood that, in steps A1-A3, the missing level of the missing physiological parameter is determined according to the type of the physiological parameter of the missing physiological parameter, and then the missing physiological parameter is obtained according to the missing level, so as to improve the accuracy of obtaining the missing physiological parameter.
The present application is not limited to the method for determining the type of physiological parameter missing from the physiological parameter, and in one possible example, step A1 includes: discretizing the M physiological parameters based on a preset rule corresponding to the physiological characteristic dimension to obtain a plurality of numerical values; and determining the physiological characteristic dimension with the value of 0 as the physiological parameter type of the missing physiological parameter.
Wherein discretization maps finite individuals in infinite space into finite space, thereby increasing the spatio-temporal efficiency of the algorithm. In a popular way, discretization is to reduce data accordingly without changing the relative size of the data. For example: the original data is 1,999,100000,15, and 1,3,4,2 is obtained after discretization; the original data is {100,200}, {20,50000}, {1,400}; after discretization, {3,4}, {2,6}, {1,5}, are obtained.
In this example, M physiological parameters are discretized based on a preset rule corresponding to the physiological feature dimension. If the value corresponding to one of the physiological feature dimensions is 0, the missing physiological feature dimension is identified, so that the physiological feature dimension corresponding to the value 0 is determined to be the physiological parameter type of the missing physiological parameter, and the accuracy of determining the physiological parameter type can be improved.
The method for determining the deletion level is not limited in the present application, and in one possible example, the step A2 includes: acquiring a correlation value between the physiological parameter type of the missing physiological parameter and a physiological characteristic dimension corresponding to each physiological characteristic data in the N physiological characteristic data to obtain N correlation values; obtaining the number of missing physiological parameters; and obtaining the missing grade corresponding to the missing physiological parameter according to the N correlation values and the quantity.
Wherein the correlation value is used to describe the effect of the missing physiological parameter on the acquisition of the physiological characteristic data.
It will be appreciated that the greater the number of missing physiological parameters, the greater the impact on the acquisition of physiological characteristic data. Therefore, in this example, the missing level corresponding to the missing physiological parameter is obtained according to the number of the missing physiological parameters and the correlation value between the missing physiological parameters and the physiological characteristic dimension of the physiological characteristic data, and the accuracy of obtaining the missing level can be improved.
The present application is not limited to the method for obtaining the missing physiological parameter according to the missing level, and in a possible example, the step A3 includes: selecting a first physiological parameter from the M physiological parameters according to the deletion grade; acquiring a mapping relation between a physiological parameter type corresponding to the first physiological parameter and a physiological parameter type corresponding to the missing physiological parameter; and acquiring the missing physiological parameter according to the mapping relation and the first physiological parameter.
Wherein the first physiological parameter may be any one of age, corneal hypoxia, astigmatism, tear secretion, non-loss of vision. The higher the level of absence, the greater the number of first physiological parameters.
The study found that physiological characteristic data is related to the age of the individual and the accommodation of the eyeball. For example: in mild myopia, if the degree of myopia increases, the axial length of the eye increases, and there is a close relationship between them. The anterior chamber depth increases from the juvenile stage to parallel with the development of the body, and increases to the maximum in the adult stage, and then gradually becomes shallower in accordance with the degeneration of the body. The weight of the lens increases with age. Referring to tables 1 and 2, table 1 is a value of the anterior chamber depth taken in terms of the correlation of age and lens power. Table 2 is the values of the axial length taken in terms of correlation of age and approximate lens power. Table 2 shows the values of the eye axis length used in the correlation between age and lens power.
TABLE 1
TABLE 2
In one possible example, if the type of physiological parameter missing the physiological parameter is age, the target user is prompted to enter the age.
As previously mentioned, age has a greater effect on physiological parameters, and age is a physiological parameter that can be obtained without measurement. Therefore, when the type of the physiological parameter of the missing physiological parameter is the age, the target user can be prompted to input the age, and the accuracy of obtaining the missing physiological parameter can be improved.
In the embodiment of the present application, a mapping relationship between a physiological parameter type corresponding to a first physiological parameter and a physiological parameter type corresponding to a missing physiological parameter may be stored in advance, for example, table 1 and table 2. And obtaining a plurality of reference data based on the missing level, and analyzing the plurality of reference data to obtain a mapping relation between the physiological parameter type corresponding to the first physiological parameter and the physiological parameter type corresponding to the missing physiological parameter. The larger the deletion grade is, the more the reference data is, the more the accuracy of the mapping relation is favorably improved, and the accuracy of obtaining the missing physiological parameters is favorably improved.
It can be understood that, first, a first physiological parameter of the M physiological parameters is obtained according to the missing level, and then the missing physiological parameter is obtained according to the mapping relationship between the physiological parameter type corresponding to the first physiological parameter and the physiological parameter type corresponding to the missing physiological parameter. That is, the missing physiological parameters are obtained according to the existing physiological parameters that are not missing and the mapping relationship obtained in advance, so that the accuracy of obtaining the missing physiological parameters can be improved.
S203: and acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters.
In the embodiment of the application, N is a positive integer. The physiological characteristic data may be age, corneal hypoxia, astigmatism, tear secretion, left-eye vision, right-eye vision, etc., and is not limited herein. The physiological characteristic data can be used for determining lens parameters such as vision power, astigmatism power, refractive index, diopter, lens hardness, lens thickness and the like of the glasses or the contact lenses.
S204: and inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters.
In the embodiment of the present application, L is an integer less than or equal to N and greater than or equal to 1. The L lens parameters may be the vision power, astigmatism power, refractive index, diopter, lens hardness and lens thickness corresponding to the above-mentioned glasses or contact lenses, and may also be the radiation resistance and wear resistance corresponding to the glasses, or the water content and service time corresponding to the contact lenses.
A decision tree is a tree built up by means of decisions. In machine learning, a decision tree is a predictive model representing a mapping between object attributes and object values, each node representing an object, each diverging path in the tree representing a possible attribute value, and each leaf node corresponding to the value of the object represented by the path traversed from the root node to the leaf node. The decision tree has only a single output, and if there are multiple outputs, independent decision trees can be established to process different outputs.
The information entropy is used to describe the probability of discrete random events occurring, the more ordered a system, the lower the information entropy,conversely, the more chaotic a system is, the higher its entropy becomes. The entropy of information can be considered a measure of the degree of ordering of the system. For the classification system, assume that a random variable x takes on the value { x } 1 ,x 2 ,……,x n Each probability of getting is { p (x) } 1 ),p(x 2 ),……,p(x n ) N is the number of classes, the entropy of x is defined as
The information gain is for a feature, which is the amount of information that the system has and does not have the feature, and the difference between the two is the amount of information that the feature brings to the system, i.e., the information gain. In the decision tree classification problem, the information gain is the difference between the information before and after attribute selection and division of the decision tree.
The calculation formula of the information gain is as follows:
wherein S is the set of all samples, value (T) is the set of all values of attribute T, v is one of the attribute values of T, S v Is a sample set of S with attribute T of value v, | S v Is | S v The number of samples contained in (1). Before each non-leaf node of the decision tree is divided, information gain brought by each attribute is calculated, and the attribute with the largest information gain is selected for division, because the larger the information gain is, the stronger the capacity of distinguishing samples is, and the more representative the samples are.
In the embodiment of the application, the decision tree is a lens parameter model obtained by clustering according to a large amount of physiological characteristic data by a developer. The present application is not limited to the method for pre-establishing the decision tree, and in a possible example, the decision tree includes L sub-decision trees, and the method further includes: acquiring at least two sample data; acquiring information gain between a physiological characteristic dimension corresponding to each sample physiological characteristic data in the N sample physiological characteristic data and a lens characteristic dimension corresponding to each sub decision tree in the L sub decision trees based on the at least two sample data; determining a root node of each sub-decision tree in the L sub-decision trees according to information gain between the physiological characteristic dimensions corresponding to the N sample physiological characteristic data and the lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees to obtain L root nodes; generating the L sub-resolution trees based on the at least two sample data and the L root nodes.
Each sample data includes N sample physiological characteristic data and L sample lens parameters, that is, the sample physiological characteristic data and the sample lens parameters of the sample data are complete. If missing, default data may be used to fill in. The default data may be obtained by methods similar to the missing physiological parameter.
The information gain can be obtained by referring to the above calculation formula. The root node of the sub-decision tree may be a physiological feature dimension corresponding to a maximum value of information gains between the physiological feature dimension and the lens feature dimension, and if one physiological feature dimension corresponds to a maximum value of a plurality of information gains, the physiological feature dimension may be used as the root node of the decision tree. Otherwise, the physiological characteristic dimension corresponding to the maximum value in the plurality of information gains can be selected as the root node of the decision tree.
It can be understood that, in this example, first, according to the sample data, an information gain between the sample physiological characteristic data and the lens characteristic dimension is obtained, and a root node of each sub-decision tree is determined by the information gain. After the root node is determined, the child nodes of the root node are selected according to the information gain relation between the root node and the correlation values between the root node and other characteristic dimensions. And then, continuously selecting downstream nodes according to the scheme until the division is completed. That is, the attribute with the largest information gain after splitting is selected for splitting, and then a top-down greedy search is adopted to traverse the possible decision space. The accuracy of determining the lens parameters can be improved by respectively establishing the sub-decision trees corresponding to the lens parameters.
The method for generating the sub-decision based on the root node and the at least two sample data is not limited, and the relevance between (N-1) sample physiological characteristic data and the root node of the sub-decision tree and the previously calculated information gain acquisition sub-node can be determined based on the at least two sample data, and then the next sub-node is acquired one by one.
The sub-decision tree corresponding to the lens type of the contact lens is taken as an example for explanation, and fig. 3 is referred to. Assuming that the physiological characteristic dimensions corresponding to the N sample physiological characteristic data include lacrimal secretion, astigmatism, age, eyesight, and corneal hypoxia, information gain between the N sample physiological characteristic data and the lens type is obtained based on at least two sample data. And if the information gain between the lacrimal secretion and the lens type is maximum, taking the lacrimal secretion as a root node of the sub-decision tree corresponding to the lens type. Based on at least two sample data, acquiring the relevance between (N-1) sample physiological characteristic data and lacrimal secretion, and analyzing to obtain child nodes based on the previously calculated information gain: astigmatic, thick lenses.
As shown in fig. 3, if the tear secretion is low, the lens type is directly determined to be a thick lens. If tear secretion is normal, the sub-node visited for astigmatism 101, i.e. without astigmatism, is age 103. If the age is young, the child node soft lens 106 of age 103 is accessed, i.e., the lens type is determined to be a soft lens. If the age is not young, the child vision 105 of age 103 is accessed. If it is myopic, the sub-node accessing vision 105 is not appropriate 110, i.e. contact lenses are not recommended. If hyperopia is present, the sub-node of the visit 105 lacks corneal oxygen 109. If the cornea is not anoxic, the child node soft lens 113 of the cornea anoxia 109 is accessed, and the lens type is determined to be a soft lens. Otherwise, the oxygen-permeable soft lens 114 at the sub-node of the corneal hypoxia 109 is accessed, and the type of the lens is determined to be the oxygen-permeable soft lens. If there is astigmatism, the child node visited is vision 104. Further, if the vision is near vision, the child hard lens 107 of the vision 104 is accessed, i.e. the lens type is determined to be hard lens. If it is far vision, the child node age 108 of vision 104 is accessed. If the age is not young, the child node accessing the age 108 is not suitable 111, i.e. contact lens use is not recommended. If the age is young, the sub-node of age 108 is visited for corneal hypoxia 112. If there is no corneal hypoxia, the child hard lens 115 of corneal hypoxia 112 is accessed to determine the lens type as a hard lens. Otherwise, the oxygen permeable hard lens 116 at the sub-node of the corneal hypoxia 109 is accessed, and the type of the lens is determined to be an oxygen permeable hard lens.
S205: and acquiring first lenses corresponding to the L lenses.
In the embodiment of the present application, the first lens is a lens satisfying L lens parameters simultaneously, and may be one or more. The present application is not limited to the method for recommending the first lens, and in a possible example, the step S205 includes the following steps B1-B3, wherein:
b1: acquiring at least two second lenses corresponding to the L lens parameters;
b2: acquiring user browsing records and/or user purchasing records of target users corresponding to the M physiological parameters;
b3: and selecting a first lens from the at least two second lenses according to the user browsing record and/or the user purchasing record.
The first lens is a lens which simultaneously satisfies L lens parameters. The target users are users corresponding to the M physiological parameters, namely users to be pushed with lenses. The user browsing record can be a record of the user browsing the glasses or the lenses, and can also be a browsing record of the user in a shopping application. The user purchase record may be a record of the user purchasing glasses or lenses, or a record of the user purchasing in the shopping application, which is not limited herein.
It will be appreciated that the user viewing records and user purchase records may embody the user's preferences, and in particular the preferences of such lenses for the user may be determined with respect to the review information in the user purchase records. Therefore, in steps B1-B3, the second lenses satisfying L lens parameters are acquired first, and then the first lens is selected from at least two second lenses according to the browsing record of the user and/or the purchasing record of the user, so as to improve the accuracy of acquiring the lenses.
In one possible example, the lens type of the first lens is a cosmetic lens type, step B3 comprises: acquiring a facial image of the target user; determining a first evaluation value of the second lens according to the face image; acquiring a second evaluation value of the second lens according to the user browsing record and/or the user purchasing record; and selecting a first lens from the at least two second lenses according to the first evaluation value and the second evaluation value.
The facial image may be an image pre-stored in the electronic device, or an image collected at that time, which is not limited herein. The first evaluation value is used to describe how beautiful the first lens is worn by the target user, and the second evaluation value is used to describe how good the first lens is likely to be by the target user. It can be understood that the first lens is obtained by combining the aesthetic degree and the good sensitivity of wearing, and the accuracy of obtaining the lens can be further improved.
In the method shown in fig. 2, input of M physiological parameters is received first, and then missing physiological parameters of the M physiological parameters are acquired. Obtaining N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, and obtaining lenses corresponding to the L lens parameters. Therefore, the lens parameters can be obtained without depending on the staff of the hospital or the glasses merchant, and the user experience is improved. And the accuracy of obtaining the lens parameters is improved according to the lens parameters obtained by the pre-established decision tree, and the lens adaptation precision can be improved.
The method of the embodiments of the present application is set forth above in detail and the apparatus of the embodiments of the present application is provided below.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a lens adapting device according to the present application, and as shown in fig. 4, the lens adapting device 400 includes:
a storage unit 402, configured to store a pre-established decision tree;
a processing unit 401, configured to obtain M physiological parameters, where M is a positive integer; acquiring missing physiological parameters in the M physiological parameters; acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer; inputting the N physiological characteristic data into the decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N; and acquiring a first lens corresponding to the L lens parameters.
In one possible example, the processing unit 401 is specifically configured to determine a physiological parameter type of a missing physiological parameter of the M physiological parameters; determining the missing grade of the missing physiological parameter according to the physiological parameter type of the missing physiological parameter; and acquiring the missing physiological parameters according to the missing grade.
In a possible example, the processing unit 401 is specifically configured to obtain a correlation value between the type of the missing physiological parameter and a physiological characteristic dimension corresponding to each physiological characteristic data in the N physiological characteristic data, so as to obtain N correlation values; obtaining the number of missing physiological parameters; and obtaining the missing grade corresponding to the missing physiological parameter according to the N correlation values and the quantity.
In one possible example, the processing unit 401 is specifically configured to select a first physiological parameter from the M physiological parameters according to the loss level; acquiring a mapping relation between the physiological parameter type corresponding to the first physiological parameter and the physiological parameter type corresponding to the missing physiological parameter; and acquiring the missing physiological parameter according to the mapping relation and the first physiological parameter.
In one possible example, the decision tree comprises L sub-decision trees, and the processing unit 401 is further configured to obtain at least two sample data, each sample data comprising N sample physiological characteristic data and L sample lens parameters; acquiring information gain between a physiological characteristic dimension corresponding to each sample physiological characteristic data in the N sample physiological characteristic data and a lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees based on the at least two sample data; determining a root node of each sub-decision tree in the L sub-decision trees according to information gain between the physiological characteristic dimensions corresponding to the N sample physiological characteristic data and the lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees to obtain L root nodes; generating the L sub-decision trees based on the at least two sample data and the L root nodes.
In one possible example, the processing unit 401 is further configured to obtain at least two second lenses corresponding to the L lens parameters; acquiring user browsing records and/or user purchasing records of target users corresponding to the M physiological parameters; and selecting a first lens from the at least two second lenses according to the user browsing record and/or the user purchasing record.
In one possible example, the lens type of the first lens is a cosmetic lens type, and the processing unit 401 is specifically configured to acquire a facial image of the target user; determining a first evaluation value of the second lens according to the face image; acquiring a second evaluation value of the second lens according to the user browsing record and/or the user purchasing record; and selecting the first lens from the at least two second lenses according to the first evaluation value and the second evaluation value.
The detailed processes performed by each unit in the lens adapting device 400 can refer to the steps performed in the foregoing method embodiments, and are not described herein.
Referring to fig. 5, fig. 5 is a schematic structural diagram of another lens adapting device according to an embodiment of the present application, where the lens adapting device is a server corresponding to an electronic device or a lens adapting application. As shown in fig. 5, the lens fitting apparatus 500 includes a processor 510, a memory 520, a communication interface 530, and one or at least one program 540. The related functions implemented by the storage unit 402 shown in fig. 4 may be implemented by the memory 520, and the related functions implemented by the processing unit 401 shown in fig. 4 may be implemented by the processor 510.
The one or more programs 540 are stored in the memory 520 and configured to be executed by the processor 510, the programs 540 including instructions for:
acquiring M physiological parameters, wherein M is a positive integer;
acquiring missing physiological parameters in the M physiological parameters;
acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer;
inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N;
and acquiring a first lens corresponding to the L lens parameters.
In one possible example, in terms of said obtaining a missing one of said M physiological parameters, said program 540 is specifically adapted to execute the instructions of:
determining a physiological parameter type of a missing physiological parameter of the M physiological parameters;
determining the missing grade of the missing physiological parameter according to the physiological parameter type of the missing physiological parameter;
and acquiring the missing physiological parameters according to the missing grade.
In one possible example, in the determining the missing level of the missing physiological parameter based on the physiological parameter type of the missing physiological parameter, the program 540 is specifically configured to execute the following steps:
obtaining correlation values between the physiological parameter types of the missing physiological parameters and the physiological characteristic dimensions corresponding to each physiological characteristic data in the N physiological characteristic data to obtain N correlation values;
obtaining the number of the missing physiological parameters;
and obtaining the missing grade corresponding to the missing physiological parameter according to the N correlation values and the quantity.
In one possible example, in said obtaining said missing physiological parameter according to said missing level, said program 540 is specifically adapted to execute the following steps:
selecting a first physiological parameter from the M physiological parameters according to the deletion grade;
acquiring a mapping relation between the physiological parameter type corresponding to the first physiological parameter and the physiological parameter type corresponding to the missing physiological parameter;
and acquiring the missing physiological parameter according to the mapping relation and the first physiological parameter.
In one possible example, where the decision tree includes L sub-decision trees, the program 540 is further operable to execute the instructions of:
acquiring at least two sample data, wherein each sample data comprises N sample physiological characteristic data and L sample lens parameters;
acquiring information gain between a physiological characteristic dimension corresponding to each sample physiological characteristic data in the N sample physiological characteristic data and a lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees based on the at least two sample data;
determining a root node of each sub-decision tree in the L sub-decision trees according to information gain between the physiological characteristic dimensions corresponding to the N sample physiological characteristic data and the lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees to obtain L root nodes;
generating the L sub-resolution trees based on the at least two sample data and the L root nodes.
In one possible example, in the aspect of acquiring the first lens corresponding to the L lens parameters, the program 540 is specifically configured to execute the following steps:
acquiring at least two second lenses corresponding to the L lens parameters;
acquiring user browsing records and/or user purchasing records of target users corresponding to the M physiological parameters;
and selecting a first lens from the at least two second lenses according to the user browsing record and/or the user purchasing record.
In one possible example, the lens type of the first lens is a cosmetic lens type, and in the selecting a first lens from the at least two second lenses according to the user viewing record and/or the user purchase record, the program 540 is specifically configured to execute the following steps:
acquiring a facial image of the target user;
determining a first evaluation value of the second lens according to the face image;
acquiring a second evaluation value of the second lens according to the user browsing record and/or the user purchasing record;
and selecting the first lens from the at least two second lenses according to the first evaluation value and the second evaluation value.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for causing a computer to execute to implement part or all of the steps of any one of the methods described in the method embodiments, and the computer includes an electronic device and a server.
Embodiments of the application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform to implement some or all of the steps of any of the methods recited in the method embodiments. The computer program product may be a software first installation package, the computer comprising an electronic device and a server.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will recognize that the embodiments described in this specification are preferred embodiments and that no particular act or mode of operation is required.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, at least one unit or component may be combined or integrated with another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on at least one network unit. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in a hardware form, or may be implemented in a software program mode.
The integrated unit, if implemented in the form of a software program module and sold or used as a stand-alone product, may be stored in a computer readable memory. With such an understanding, the technical solution of the present application may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory comprises: various media that can store program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (9)
1. A method of lens fitting, comprising:
acquiring M physiological parameters, wherein M is a positive integer;
acquiring missing physiological parameters in the M physiological parameters, wherein the physiological parameter types of the missing physiological parameters comprise physiological characteristic dimensions and measurement parameter dimensions;
acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer;
inputting the N physiological characteristic data into a pre-established decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N;
acquiring first lenses corresponding to the L lens parameters;
wherein the obtaining of the missing physiological parameter of the M physiological parameters includes:
determining a physiological parameter type of a missing physiological parameter of the M physiological parameters;
determining the missing grade of the missing physiological parameter according to the physiological parameter type of the missing physiological parameter;
and acquiring the missing physiological parameters according to the missing grade.
2. The method of claim 1, wherein said determining a level of absence of said missing physiological parameter based on a type of physiological parameter of said missing physiological parameter comprises:
obtaining correlation values between the physiological parameter types of the missing physiological parameters and the physiological characteristic dimensions corresponding to each physiological characteristic data in the N physiological characteristic data to obtain N correlation values;
obtaining the number of missing physiological parameters;
and obtaining the missing grade corresponding to the missing physiological parameter according to the N correlation values and the quantity.
3. The method of claim 1, wherein said obtaining the missing physiological parameter according to the missing level comprises:
selecting a first physiological parameter from the M physiological parameters according to the deletion grade;
acquiring a mapping relation between a physiological parameter type corresponding to the first physiological parameter and a physiological parameter type corresponding to the missing physiological parameter;
and acquiring the missing physiological parameters according to the mapping relation and the first physiological parameters.
4. The method according to any of claims 1-3, wherein said decision tree comprises L sub-decision trees, the method further comprising:
acquiring at least two sample data, wherein each sample data comprises N sample physiological characteristic data and L sample lens parameters;
acquiring information gain between a physiological characteristic dimension corresponding to each sample physiological characteristic data in the N sample physiological characteristic data and a lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees based on the at least two sample data;
determining a root node of each sub-decision tree in the L sub-decision trees according to information gain between the physiological characteristic dimensions corresponding to the N sample physiological characteristic data and the lens characteristic dimension corresponding to each sub-decision tree in the L sub-decision trees to obtain L root nodes;
generating the L sub-decision trees based on the at least two sample data and the L root nodes.
5. The method according to any one of claims 1-3, wherein said obtaining a first lens for the L lens parameters comprises:
acquiring at least two second lenses corresponding to the L lens parameters;
acquiring user browsing records and/or user purchasing records of target users corresponding to the M physiological parameters;
and selecting a first lens from the at least two second lenses according to the user browsing record and/or the user purchasing record.
6. The method of claim 5, wherein the lens type of the first lens is a cosmetic lens type, and wherein selecting the first lens from the at least two second lenses according to the user viewing record and/or the user purchase record comprises:
acquiring a facial image of the target user;
determining a first evaluation value of the second lens according to the face image;
acquiring a second evaluation value of the second lens according to the user browsing record and/or the user purchasing record;
and selecting the first lens from the at least two second lenses according to the first evaluation value and the second evaluation value.
7. An ophthalmic lens adapting device, comprising:
the storage unit is used for storing a pre-established decision tree;
the processing unit is used for acquiring M physiological parameters, wherein M is a positive integer; acquiring missing physiological parameters in the M physiological parameters, wherein the physiological parameter types of the missing physiological parameters comprise physiological characteristic dimensions and measurement parameter dimensions; acquiring N physiological characteristic data according to the M physiological parameters and the missing physiological parameters, wherein N is a positive integer; inputting the N physiological characteristic data into the decision tree to obtain L lens parameters, wherein L is a positive integer less than or equal to N; acquiring first lenses corresponding to the L lens parameters;
the processing unit is specifically configured to determine a physiological parameter type of a missing physiological parameter of the M physiological parameters; determining the missing grade of the missing physiological parameter according to the physiological parameter type of the missing physiological parameter; and acquiring the missing physiological parameters according to the missing grade.
8. A lens fitting apparatus comprising a processor, a memory, a communication interface, and one or at least one program, wherein the one or at least one program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the steps in the method of any one of claims 1-6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that causes a computer to execute to implement the method of any one of claims 1-6.
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