CN113923839A - Light adjusting method and system - Google Patents
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Abstract
The invention provides a light adjusting method and a light adjusting system, wherein the method comprises the following steps: obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located; obtaining vision information of a first user; training the neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which a first user is located; inputting the first environment light intensity and the vision information into a light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme. The technical problems that in the prior art, the application range of light adjustment is small, the street lamp adjusting device is mainly applied to street lamp adjustment, the applicability to places such as office places, learning places and the like is poor, the user adaptability of an adjusting scheme is poor, and the accuracy is low are solved.
Description
Technical Field
The invention relates to the technical field of light adjustment, in particular to a light adjustment method and a light adjustment system.
Background
The lighting lamp can be generally divided into a fluorescent lamp and an incandescent lamp, the spectrum of the fluorescent lamp is close to that of daytime, the light is soft, the eyesight is not damaged, dense shadows are not generated, the service life is long, and the luminous efficiency is 2-4 times higher than that of a common bulb. However, the desk lamp is not suitable for using a fluorescent lamp as a light source, and the obvious black-white contrast formed under the fluorescent lamp easily causes visual fatigue and is not beneficial to long-time work. For a long time, it is preferable to use incandescent lamps, which are the most commonly used electric lamps. The incandescent lamp has uniform light, the surrounding objects are clearly visible, the contrast of the brightness of the light is small, the vision is easy, and the incandescent lamp is beneficial to study and work under the lamp for a long time. How to use the light scientifically and reasonably to make it more beneficial to our needs and health has attracted extensive attention.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the street lamp adjusting method has the advantages that the application range of light adjustment is small, the street lamp adjusting method is mainly applied to street lamp adjustment, the applicability to places such as offices and studies is poor, and the user adaptability of an adjusting scheme is poor and the accuracy is not high.
Disclosure of Invention
The embodiment of the application provides a light adjusting method and a light adjusting system, and solves the technical problems that in the prior art, the light adjusting application range is small, the light adjusting method is mainly applied to street lamp adjustment, the applicability to places such as office and study is poor, the user adaptability of an adjusting scheme is poor, and the accuracy is low. The technical effects of enhancing the user adaptability of the light adjusting method, expanding the light adjusting application scene, improving the applicability of the light adjusting scheme and enhancing the accuracy of the adjusting scheme by deeply analyzing the ambient light information and the user attributes are achieved.
In view of the foregoing problems, embodiments of the present application provide a light adjusting method and system.
In a first aspect, an embodiment of the present application provides a light adjusting method, where the method includes: obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located; obtaining vision information of a first user; training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which the first user is located; inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
In another aspect, an embodiment of the present application provides a light adjusting system, where the system includes: the first obtaining unit is used for obtaining a historical ambient light intensity data set in a preset period of time of the environment where a first user is located; a second obtaining unit configured to obtain visual information of the first user; the first construction unit is used for training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light regulation model; a third obtaining unit, configured to obtain a first ambient light intensity of an environment in which the first user is located; a fourth obtaining unit, configured to input the first ambient light intensity and the vision information into the light adjustment model to obtain a first light adjustment scheme; a fifth obtaining unit, configured to obtain an eye usage habit parameter of the first user; and the sixth obtaining unit is used for adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
In a third aspect, an embodiment of the present application provides a light adjusting system, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
obtaining a historical ambient light intensity data set within a preset time period of the environment where the first user is located; obtaining vision information of a first user; training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which the first user is located; inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; according to the technical scheme, the first light adjusting scheme is adjusted according to the eye use habit parameters of the first user, and the second light adjusting scheme is obtained.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of a light adjusting method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a light adjustment method according to an embodiment of the present application for obtaining a plurality of sub-light adjustment models;
fig. 3 is a schematic flowchart of a first decision tree model constructed by a lighting adjustment method according to an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of a light adjustment method for obtaining a first light adjustment scheme according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a light adjustment method according to an embodiment of the present application for obtaining an eye usage habit parameter of the first user;
fig. 6 is a schematic flow chart of a light adjustment method according to an embodiment of the present application for obtaining a third light adjustment scheme;
fig. 7 is a schematic flowchart of a light adjustment method according to an embodiment of the present application for obtaining a fourth light adjustment scheme;
fig. 8 is a schematic structural diagram of a light adjusting system according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a sixth obtaining unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.
Detailed Description
The embodiment of the application provides a light adjusting method and a light adjusting system, and solves the technical problems that in the prior art, the light adjusting application range is small, the light adjusting method is mainly applied to street lamp adjustment, the applicability to places such as office and study is poor, the user adaptability of an adjusting scheme is poor, and the accuracy is low. The technical effects of enhancing the user adaptability of the light adjusting method, expanding the light adjusting application scene, improving the applicability of the light adjusting scheme and enhancing the accuracy of the adjusting scheme by deeply analyzing the ambient light information and the user attributes are achieved.
Summary of the application
The lighting lamp can be generally divided into a fluorescent lamp and an incandescent lamp, the spectrum of the fluorescent lamp is close to that of daytime, the light is soft, the eyesight is not damaged, dense shadows are not generated, the service life is long, and the luminous efficiency is 2-4 times higher than that of a common bulb. However, the desk lamp is not suitable for using a fluorescent lamp as a light source, and the obvious black-white contrast formed under the fluorescent lamp easily causes visual fatigue and is not beneficial to long-time work. For a long time, it is preferable to use incandescent lamps, which are the most commonly used electric lamps. The incandescent lamp has uniform light, the surrounding objects are clearly visible, the contrast of the brightness of the light is small, the vision is easy, and the incandescent lamp is beneficial to study and work under the lamp for a long time. How to use the light scientifically and reasonably to make it more beneficial to our needs and health has attracted extensive attention. The technical problems that the application range of light adjustment is small, the street lamp adjusting device is mainly applied to street lamp adjustment, the applicability to places such as offices and studies is poor, the user adaptability of an adjusting scheme is poor, and the accuracy is low exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a light adjusting method, wherein the method comprises the following steps: obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located; obtaining vision information of a first user; training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which the first user is located; inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a light adjusting method, where the method includes:
s100: obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located;
s200: obtaining vision information of a first user;
specifically, the first user is any user, and the historical light intensity data set in the preset time period of the environment where the first user is located is obtained, wherein the environment refers to a high-frequency light-on place of the first user, such as an office, a classroom, a home and the like. In the environment, a preset time period can be an average time period of using light, and historical light intensity information can be obtained through information such as electricity consumption, lamp models and the like, so that a historical environment light intensity data set is formed. Furthermore, the vision information of the first user is obtained through methods such as vision test, the basis for deep analysis of the light intensity information received by the first user can be laid through collecting the historical environment light intensity data set and the vision information of the first user, meanwhile, the vision condition of the user can be mastered, and the basis for light adjustment and tamping is provided.
S300: training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model;
specifically, the ambient light intensity is provided by the lighting facility, but not all light intensities are beneficial to the eyesight of the first user, the eyesight of the first user is damaged by over-bright or over-dark light, and the eyesight is irreversibly damaged by using the light intensity under unsuitable light for a long time, so that the light is required to be adjusted, the neural network model is trained by using the historical ambient light intensity data set and the eyesight information of the first user, since the eyesight of the first user may change in the historical time of collecting the historical ambient light intensity data, a certain logical relationship exists between a large amount of collected light intensity data and the eyesight information of the first user, the historical ambient light intensity data set and the eyesight information of the first user are input into the neural network model by constructing the training set, the model is trained to a convergence state, and a light adjustment model with accuracy and reliability can be obtained, thereby laying a foundation for subsequent light adjustment.
S400: obtaining a first ambient light intensity of an environment in which the first user is located;
s500: inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme
In particular, a first ambient light intensity of an environment where the first user needs to light up frequently, such as an office, a classroom, a residence, etc., is obtained. And inputting the vision information of the user and the first environment light intensity into the light adjusting model, wherein the light adjusting model can obtain the light intensity information which is most suitable for the vision of the user according to the vision information of the first user and the first environment light intensity, and provides a light intensity adjusting scheme, namely the light intensity adjusting scheme is the first light adjusting scheme. The suitability of the environment light intensity information to the first user can be judged according to the vision information of the user, so that the first user is attracted attention, and the vision of the user is prevented from being reduced due to the fact that the user is under the unsuitable light intensity condition for a long time.
S600: obtaining a first user's eye usage habit parameter;
s700: and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
Specifically, the eye use habit parameters comprise average eye use time, eye rest time, eye use posture, eye fatigue relieving mode and the like of the user. Because the eye use habit and the illumination intensity are important reasons influencing the vision of the first user and causing the eyes of the first user to be sour, astringent and tired, the first light adjusting scheme is adjusted according to the eye use habit parameters of the first user to obtain a second light adjusting scheme. For example: the first user is not good in eye habit, the first light adjusting scheme is adjusted according to the eye angle and the eye distance, the flexibility and pertinence of the first light adjusting scheme are improved through the obtained second light adjusting scheme, the adjustment can be carried out along with the change of the eye habit of the user, and therefore the positive effects of improving the visual fatigue of the user and maintaining the eyesight of the user are achieved through efficient adjustment of light.
Further, as shown in fig. 2, the training of the neural network model according to the historical environment light intensity dataset and the vision information of the first user is performed to construct a light adjustment model, which further includes:
s310: performing characteristic classification on the historical environment light intensity data sets to obtain a plurality of historical environment light intensity sub-data sets;
s320: respectively inputting the plurality of historical environment light intensity sub-data sets and the vision information of the first user into the neural network model for training to obtain a plurality of sub-lighting regulation models;
s330: obtaining a plurality of groups of sub model parameters according to the plurality of sub light adjusting models;
s340: and updating the neural network model according to the plurality of groups of sub-model parameters to obtain the light adjusting model.
In particular, the historical ambient light intensity data set has different characteristics according to light intensity and time, for example, the light attribute at night is different from the light attribute at day. And capturing the difference characteristics to perform characteristic classification to obtain a plurality of subdata sets, namely the plurality of historical environment light intensity subdata sets. Because the subdata sets have obvious characteristics, the plurality of historical environment light intensity subdata sets and the vision information of the first user are respectively input into the neural network model for training, and a plurality of subdued light adjusting models can be obtained. Each group of sub-light regulation model corresponds to one group of sub-model parameters, the neural network model is updated by using the plurality of groups of sub-model parameters, and the light regulation model is obtained, so that the effectiveness and the accuracy of the light regulation model can be improved.
Further, as shown in fig. 3, the performing feature classification on the historical ambient light intensity data set to obtain a plurality of historical ambient light intensity data sets, and step S310 further includes:
s311: obtaining time characteristics, color temperature characteristics and light intensity characteristics of the historical environment light intensity data set;
s312: performing information coding theory operation on the time characteristics to obtain time characteristic information entropy;
s313: performing information coding theory operation on the color temperature characteristics to obtain color temperature characteristic information entropy;
s314: carrying out information encoding theory operation on the light intensity characteristics to obtain light intensity characteristic information entropy;
s315: comparing the time characteristic information entropy, the color temperature characteristic information entropy and the light intensity characteristic information entropy to obtain first root node characteristic information;
s316: constructing a first decision tree model according to the first root node characteristic information and the historical environment light intensity data set;
s317: classifying the historical ambient light intensity dataset based on the first decision tree model to obtain a plurality of historical ambient light intensity sub-datasets.
Specifically, the historical light intensity data set is classified according to the time characteristic, the color temperature characteristic and the light intensity characteristic, and information coding theory operation is respectively performed on the time characteristic, the color temperature characteristic and the light intensity characteristic to obtain the time characteristic information entropy, the light intensity characteristic information entropy and the light intensity characteristic information entropy. The entropy of information, i.e., the expression of a quantitative measure of information, is used to describe the uncertainty of the source, and the greater the entropy of a data set, the higher the "purity" of the data classification. Further, the time characteristic information entropy, the color temperature characteristic information entropy and the light intensity characteristic information entropy are compared in size and value, then the characteristic with the minimum entropy value, namely the first root node characteristic information is obtained, the characteristic with the minimum entropy value is preferentially classified, then classification of the recursion algorithms is sequentially carried out on the characteristics according to the sequence from the minimum entropy value to the maximum entropy value, and finally a first decision tree model is constructed. The plurality of historical ambient light intensity sub-data sets are obtained according to the first decision tree model and are the result of reasonably dividing the historical ambient light intensity data sets based on time characteristics, color temperature characteristics and light intensity characteristics. The light adjusting method can provide a light adjusting scheme for a user according to different time, color temperature and light intensity information, further refine the adjusting scheme and improve the light adjusting efficiency.
Further, as shown in fig. 4, the step S500 of inputting the first ambient light intensity and the vision information into the light adjustment model to obtain a first light adjustment scheme includes:
s510: inputting the first environment light intensity and the vision information into the light adjusting model as input data;
s520: the light adjusting model is obtained by training a plurality of groups of training data to convergence, wherein each group of training data in the plurality of groups of training data comprises the first environment light intensity, the vision information and identification information for identifying a light adjusting scheme;
s530: and obtaining the output information of the light regulation model, wherein the output information comprises the first light regulation scheme.
Specifically, the light adjustment model is obtained by training to converge based on a neural network model, wherein the training data includes the first ambient light intensity and the vision information, and identification information identifying the light adjustment scheme. Neural Networks (NN) are complex Neural network systems formed by a large number of simple processing units (called neurons) widely interconnected, reflect many basic features of human brain functions, and are highly complex nonlinear dynamical learning systems. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. And through training of a large amount of training data, inputting the first environment light intensity and the vision information into the light regulation model as input data, and obtaining output information as the first light regulation scheme. The training process is essentially a supervised learning process, the light regulation model carries out continuous self-correction and adjustment until the obtained first light regulation scheme is consistent with the identification information, the group of data supervised learning is finished, and the next group of data supervised learning is carried out; and when the output information of the light adjusting model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervision and learning of the light adjusting model, the output first light adjusting scheme is more reasonable and accurate.
Further, as shown in fig. 5, the obtaining of the eye usage habit parameter of the first user, step S600 further includes:
s610: obtaining a value threshold of the eye use habit parameter of the first user;
s620: randomly obtaining M eye usage habit parameters from the value threshold of the eye usage habit parameters of the first user;
s630: calculating the M eye usage habit parameters according to a genetic algorithm to obtain M predicted eye usage state curves, wherein the M predicted eye usage state curves are in one-to-one correspondence with the M eye usage habit parameters;
s640: obtaining an actual eye use state curve of the first user;
s650: and comparing the M predicted eye use state curves with the actual eye use state curve to obtain the eye use habit parameters of the first user, wherein the predicted eye use state curve corresponding to the eye use habit parameters of the first user has the maximum similarity with the actual eye use state curve.
Specifically, each person has unique eye usage habits, the value threshold of the eye usage habit parameter refers to the threshold range from normal eye usage to eye fatigue starting rest, the value threshold of the eye usage habit parameter of the first user is obtained, M eye usage habit parameters are randomly selected from the value threshold, the M treatment habit coefficients are calculated according to a genetic algorithm, and M predicted treatment state curves are obtained, wherein the M predicted eye usage state curves are in one-to-one correspondence with the M eye usage habit parameters. The essence of the genetic algorithm is that random search is continuously carried out in a solution space, new solutions are continuously generated in the search process, and a more optimal solution algorithm is reserved, so that the realization difficulty is low, and a satisfactory result can be obtained in a short time. The genetic algorithm directly operates the structural object when in use, has no limitation of derivation and function continuity, has inherent implicit parallelism and better global optimization capability, adopts a probabilistic optimization method, can automatically acquire and guide an optimized search space without determining rules, and adaptively adjusts the search direction, so the genetic algorithm is widely applied to various fields. And obtaining an actual eye use state curve of the first user, wherein the actual eye use state curve of the first user is recorded data of an eye state of the first user after actual eye use, obtaining a predicted value with the closest similarity by comparing the M predicted eye use state curves with the actual eye use state curve, and the corresponding eye use habit parameter is the eye use habit parameter of the first user.
Further, as shown in fig. 6, the embodiment of the present application further includes:
s710: obtaining an eye-using posture of the first user at the first ambient light intensity;
s720: obtaining first visual object angle information according to the eye posture;
s730: obtaining standard object-viewing angle information under the first ambient light intensity;
s740: obtaining a first angle difference value according to the first visual object angle information and the standard visual object angle information;
s750: obtaining a first incidence relation between the first angle difference and the visual definition;
s760: obtaining a first visual object definition based on the first angle difference and the first incidence relation;
s770: and adjusting the second light adjusting scheme according to the first visual object definition to obtain a third light adjusting scheme.
Specifically, the eye-using posture of the first user, which includes the viewing angle (upward, downward, oblique, forward, etc.), the viewing distance, etc., plays an important role in the eye health under the same ambient light intensity. And acquiring the standard viewing object angle information, and acquiring the standard viewing object angle information of different objects (books, televisions, computers, mobile phones and the like) based on big data. And obtaining the first view object angle information according to the actual view object angle of a first user, and subtracting the first view object angle information from the standard view object angle information to obtain the first angle difference value. Because the observation angles are different and the definition of objects is different, the first angle difference and the quality inspection of the definition of the object to be observed have a first correlation, and if the angle difference is larger, the definition is poorer. And adjusting the second light adjusting scheme according to the first object vision definition, if the definition is poor, adjusting parameters such as light intensity and color temperature of light, supplementing the unsharpness caused by the irregular object vision angle, and thus obtaining a third light adjusting scheme. The adjusted light adjusting scheme can take bad eye using habits of people in daily life into consideration, and the damage to the vision of people is reduced through light adjustment to the maximum extent.
Further, as shown in fig. 7, the embodiment of the present application further includes:
s771: obtaining a historical eye-use time of the first user at the first ambient light intensity;
s772: according to the first environment light intensity and the historical eye use time, obtaining eye use fatigue of the first user under the first environment light intensity;
s773: correcting the first incidence relation between the first angle difference and the visual definition according to the eye fatigue degree to obtain a second incidence relation;
s774: obtaining a second visual definition based on the first angle difference and the second incidence relation;
s775: and adjusting the second light adjusting scheme according to the second visual object definition to obtain a fourth light adjusting scheme.
Specifically, the historical eye-using time refers to the total eye-using time of the first user in the first ambient light intensity, the eye-using fatigue of the first user can be obtained according to the first ambient light intensity and the historical eye-using time, namely the ambient light intensity, the eye-using time and the eye-using fatigue have strong correlation, and the eye-using fatigue of the first user can be accurately estimated through the ambient light intensity and the eye-using time. Due to different eye fatigue degrees, the first angle difference and the object vision definition of the first user can be changed to a certain degree, for example, the eye fatigue degree is heavy, the definition is reduced, and the user looks up. Further, according to the eye fatigue degree is right the first incidence relation of first angle difference and look thing definition is revised, obtains the second incidence relation, and the further combination user of second incidence relation uses the eye state in real time, and the accuracy is higher, based on first angle difference with the second incidence relation, obtains the second and looks the thing definition, according to the second looks the thing definition is right the second light adjustment scheme is adjusted, can make the acquisition the fourth light adjustment scheme flexibility is stronger, laminates user's eye state more, improves the scheme and adjusts the effect and brings better user experience.
To sum up, the light adjusting method and the light adjusting system provided by the embodiment of the application have the following technical effects:
1. obtaining a historical ambient light intensity data set within a preset time period of the environment where the first user is located; obtaining vision information of a first user; training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which the first user is located; inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; according to the technical scheme, the first light adjusting scheme is adjusted according to the eye use habit parameters of the first user, and the second light adjusting scheme is obtained.
2. Due to the adoption of the method for collecting and analyzing the eye using posture of the first user, the eye using habit of the user is fitted, the adjusting effect of the light adjusting scheme can be better, and the customized adjusting scheme can be brought to the user, so that the technical effects of improving the user experience and reducing the visual damage are achieved.
Example two
Based on the same inventive concept as one of the light adjusting methods in the foregoing embodiments, as shown in fig. 8, an embodiment of the present application provides a light adjusting system, wherein the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is configured to obtain a historical ambient light intensity data set within a predetermined time period of an environment where a first user is located;
a second obtaining unit 12, wherein the second obtaining unit 12 is used for obtaining the vision information of the first user;
the first construction unit 13 is configured to train a neural network model according to the historical environment light intensity data set and the vision information of the first user, and construct a light adjustment model;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain a first ambient light intensity of an environment where the first user is located;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to input the first ambient light intensity and the vision information into the light adjustment model to obtain a first light adjustment scheme;
a fifth obtaining unit 16, wherein the fifth obtaining unit 16 is configured to obtain the eye usage habit parameters of the first user;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to adjust the first light adjustment scheme according to the eye usage habit parameter of the first user, so as to obtain a second light adjustment scheme.
Further, the system comprises:
a seventh obtaining unit, configured to input the multiple historical ambient light intensity sub-data sets and the vision information of the first user into the neural network model for training, respectively, to obtain multiple sub-lighting adjustment models;
an eighth obtaining unit, configured to obtain multiple sets of sub-model parameters according to the plurality of sub-lighting adjustment models;
a ninth obtaining unit, configured to obtain, according to the multiple groups of sub-model parameters, update the neural network model, and obtain the light adjustment model.
Further, the system comprises:
a tenth obtaining unit, configured to obtain a time characteristic, a color temperature characteristic, and a light intensity characteristic of the historical ambient light intensity data set;
an eleventh obtaining unit, configured to perform information coding theory operation on the time feature to obtain a time feature information entropy;
a twelfth obtaining unit, configured to perform information coding theory operation on the color temperature characteristics to obtain color temperature characteristic information entropy;
a thirteenth obtaining unit, configured to perform information coding theory operation on the light intensity characteristics to obtain light intensity characteristic information entropy;
a fourteenth obtaining unit, configured to obtain and compare magnitudes of the time characteristic information entropy, the color temperature characteristic information entropy, and the light intensity characteristic information entropy, and obtain first root node characteristic information;
a second construction unit, configured to construct a first decision tree model according to the first root node feature information and the historical environment light intensity data set;
a fifteenth obtaining unit, configured to classify the historical ambient light intensity data set based on the first decision tree model to obtain a plurality of historical ambient light intensity sub-data sets.
Further, the system comprises:
the first execution unit is used for inputting the first environment light intensity and the vision information into the light adjustment model as input data;
a sixteenth obtaining unit, configured to train the light adjustment model to convergence through multiple sets of training data, where each set of training data in the multiple sets of training data includes the first ambient light intensity, the eyesight information, and identification information for identifying a light adjustment scheme;
a seventeenth obtaining unit, configured to obtain output information of the light adjustment model, where the output information includes the first light adjustment scheme.
Further, the system comprises:
an eighteenth obtaining unit, configured to obtain a value threshold of the eye usage habit parameter of the first user;
a nineteenth obtaining unit, configured to randomly obtain M eye usage habit parameters from a value threshold of the eye usage habit parameter of the first user;
a twentieth obtaining unit, configured to calculate the M eye usage habit parameters according to a genetic algorithm, and obtain M predicted eye usage state curves, where the M predicted eye usage state curves are in one-to-one correspondence with the M eye usage habit parameters;
a twenty-first obtaining unit, configured to obtain an actual eye use state curve of the first user;
a twenty-second obtaining unit, configured to compare the M predicted eye usage state curves with the actual eye usage state curve, and obtain an eye usage habit parameter of the first user, where a similarity between a predicted eye usage state curve corresponding to the eye usage habit parameter of the first user and the actual eye usage state curve is the largest.
Further, the system comprises:
a twenty-third obtaining unit for obtaining an eye-using posture of the first user at the first ambient light intensity;
a twenty-fourth obtaining unit configured to obtain first viewing object angle information according to the eye posture;
a twenty-fifth obtaining unit, configured to obtain standard viewing object angle information under the first ambient light intensity;
a twenty-sixth obtaining unit, configured to obtain a first angle difference according to the first viewing object angle information and the standard viewing object angle information;
a twenty-seventh obtaining unit, configured to obtain a first association relationship between the first angle difference and a visual definition;
a twenty-eighth obtaining unit configured to obtain a first visual object sharpness based on the first angle difference and the first association relation;
a twenty-ninth obtaining unit, configured to adjust the second light adjustment scheme according to the first object resolution to obtain a third light adjustment scheme.
Further, the system comprises:
a thirtieth obtaining unit for obtaining a historical eye-use time of the first user at the first ambient light intensity;
a thirty-first obtaining unit, configured to obtain eye fatigue of the first user under the first ambient light intensity according to the first ambient light intensity and the historical eye use time;
a thirty-second obtaining unit, configured to correct the first association relationship between the first angle difference and the object vision definition according to the eye fatigue, and obtain a second association relationship;
a thirty-third obtaining unit, configured to obtain a second visual object sharpness based on the first angle difference and the second association relation;
a thirty-fourth obtaining unit, configured to adjust the second light adjustment scheme according to the second visual clarity, and obtain a fourth light adjustment scheme.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 9,
based on the same inventive concept as the light adjusting method in the foregoing embodiment, an embodiment of the present application further provides a light adjusting system, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact-read-only-memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement a light adjustment method provided by the above-mentioned embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a light adjusting method, wherein the method comprises the following steps: obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located; obtaining vision information of a first user; training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model; obtaining a first ambient light intensity of an environment in which the first user is located; inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme; obtaining a first user's eye usage habit parameter; and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The methods or steps of the methods described in the embodiments of the present application may be embodied directly in hardware, in a software unit executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.
Claims (9)
1. A light conditioning method, wherein the method comprises:
obtaining a historical ambient light intensity data set within a predetermined time period of the environment where the first user is located;
obtaining vision information of a first user;
training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light adjusting model;
obtaining a first ambient light intensity of an environment in which the first user is located;
inputting the first environment light intensity and the vision information into the light adjusting model to obtain a first light adjusting scheme;
obtaining a first user's eye usage habit parameter;
and adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
2. The method of claim 1, wherein training a neural network model based on the historical ambient light intensity dataset and the first user's vision information to construct a light adjustment model comprises:
performing characteristic classification on the historical environment light intensity data sets to obtain a plurality of historical environment light intensity sub-data sets;
respectively inputting the plurality of historical environment light intensity sub-data sets and the vision information of the first user into the neural network model for training to obtain a plurality of sub-lighting regulation models;
obtaining a plurality of groups of sub model parameters according to the plurality of sub light adjusting models;
and updating the neural network model according to the plurality of groups of sub-model parameters to obtain the light adjusting model.
3. The method of claim 2, wherein said feature classifying said historical ambient light intensity dataset to obtain a plurality of historical ambient light intensity datasets comprises:
obtaining time characteristics, color temperature characteristics and light intensity characteristics of the historical environment light intensity data set;
performing information coding theory operation on the time characteristics to obtain time characteristic information entropy;
performing information coding theory operation on the color temperature characteristics to obtain color temperature characteristic information entropy;
carrying out information encoding theory operation on the light intensity characteristics to obtain light intensity characteristic information entropy;
comparing the time characteristic information entropy, the color temperature characteristic information entropy and the light intensity characteristic information entropy to obtain first root node characteristic information;
constructing a first decision tree model according to the first root node characteristic information and the historical environment light intensity data set;
classifying the historical ambient light intensity dataset based on the first decision tree model to obtain a plurality of historical ambient light intensity sub-datasets.
4. The method of claim 1, wherein said inputting said first ambient light intensity and said vision information into said light adjustment model to obtain a first light adjustment scheme comprises:
inputting the first environment light intensity and the vision information into the light adjusting model as input data;
the light adjusting model is obtained by training a plurality of groups of training data to convergence, wherein each group of training data in the plurality of groups of training data comprises the first environment light intensity, the vision information and identification information for identifying a light adjusting scheme;
and obtaining the output information of the light regulation model, wherein the output information comprises the first light regulation scheme.
5. The method of claim 1, wherein the obtaining the eye usage habit parameters of the first user comprises:
obtaining a value threshold of the eye use habit parameter of the first user;
randomly obtaining M eye usage habit parameters from the value threshold of the eye usage habit parameters of the first user;
calculating the M eye usage habit parameters according to a genetic algorithm to obtain M predicted eye usage state curves, wherein the M predicted eye usage state curves are in one-to-one correspondence with the M eye usage habit parameters;
obtaining an actual eye use state curve of the first user;
and comparing the M predicted eye use state curves with the actual eye use state curve to obtain the eye use habit parameters of the first user, wherein the predicted eye use state curve corresponding to the eye use habit parameters of the first user has the maximum similarity with the actual eye use state curve.
6. The method of claim 1, wherein the method further comprises:
obtaining an eye-using posture of the first user at the first ambient light intensity;
obtaining first visual object angle information according to the eye posture;
obtaining standard object-viewing angle information under the first ambient light intensity;
obtaining a first angle difference value according to the first visual object angle information and the standard visual object angle information;
obtaining a first incidence relation between the first angle difference and the visual definition;
obtaining a first visual object definition based on the first angle difference and the first incidence relation;
and adjusting the second light adjusting scheme according to the first visual object definition to obtain a third light adjusting scheme.
7. The method of claim 6, wherein the method further comprises:
obtaining a historical eye-use time of the first user at the first ambient light intensity;
according to the first environment light intensity and the historical eye use time, obtaining eye use fatigue of the first user under the first environment light intensity;
correcting the first incidence relation between the first angle difference and the visual definition according to the eye fatigue degree to obtain a second incidence relation;
obtaining a second visual definition based on the first angle difference and the second incidence relation;
and adjusting the second light adjusting scheme according to the second visual object definition to obtain a fourth light adjusting scheme.
8. A light conditioning system, wherein the system comprises:
the first obtaining unit is used for obtaining a historical ambient light intensity data set in a preset period of time of the environment where a first user is located;
a second obtaining unit configured to obtain visual information of the first user;
the first construction unit is used for training a neural network model according to the historical environment light intensity data set and the vision information of the first user to construct a light regulation model;
a third obtaining unit, configured to obtain a first ambient light intensity of an environment in which the first user is located;
a fourth obtaining unit, configured to input the first ambient light intensity and the vision information into the light adjustment model to obtain a first light adjustment scheme;
a fifth obtaining unit, configured to obtain an eye usage habit parameter of the first user;
and the sixth obtaining unit is used for adjusting the first light adjusting scheme according to the eye using habit parameters of the first user to obtain a second light adjusting scheme.
9. A light conditioning system, comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1-7.
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Application publication date: 20220111 |