CN111347845A - Electrochromic glass adjusting method and device and electronic equipment - Google Patents
Electrochromic glass adjusting method and device and electronic equipment Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60J—WINDOWS, WINDSCREENS, NON-FIXED ROOFS, DOORS, OR SIMILAR DEVICES FOR VEHICLES; REMOVABLE EXTERNAL PROTECTIVE COVERINGS SPECIALLY ADAPTED FOR VEHICLES
- B60J3/00—Antiglare equipment associated with windows or windscreens; Sun visors for vehicles
- B60J3/04—Antiglare equipment associated with windows or windscreens; Sun visors for vehicles adjustable in transparency
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- G—PHYSICS
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- G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
- G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
- G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour
- G02F1/15—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour based on an electrochromic effect
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- G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
- G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour
- G02F1/15—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour based on an electrochromic effect
- G02F1/163—Operation of electrochromic cells, e.g. electrodeposition cells; Circuit arrangements therefor
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Abstract
The application discloses an electrochromic glass adjusting method, an electrochromic glass adjusting device and electronic equipment, and relates to the technical field of artificial intelligence. The specific implementation scheme is as follows: obtaining an eye image of a target object; according to the eye image, obtaining an eye stimulation degree value of the target object; obtaining a current output value by using the eye stimulation degree value; and adjusting the light transmittance of the electrochromic glass according to the current output value. Because the adjustment of the light transmission of the electrochromic glass is determined based on the eye image of the target object, the basis for adjusting the light transmission of the electrochromic glass is more accurate and meets the requirements of users.
Description
Technical Field
The application relates to an artificial intelligence technology in the technical field of computers, in particular to an electrochromic glass adjusting method, an electrochromic glass adjusting device and electronic equipment.
Background
The color of the color-changing glass can be changed along with the intensity of light, for example, when the light is stronger, the color of the glass is darker; when light is weaker, the color of the glass is lightened, and the color-changing glass is applied to a vehicle, so that driving interference of a driver caused by the change of the light intensity can be avoided, and safety accidents are caused.
However, in the above-mentioned photosensitive adjustment method, if strong lateral light is irradiated on the front windshield, the strong lateral light does not greatly affect the sight of the driver, but the color-changing glass still adjusts the color, and the color adjustment in this case may affect the sight of the driver.
That is to say, the current photochromic adjusting method based on photosensitive triggering has poor adjusting effect.
Disclosure of Invention
The embodiment of the application provides an electrochromic glass adjusting method, an electrochromic glass adjusting device and electronic equipment, and aims to solve the problem that an existing glass color changing adjusting method is poor in adjusting effect.
In order to solve the above technical problem, the present application is implemented as follows:
in a first aspect, the present application provides an electrochromic glass conditioning method comprising:
acquiring an eye image of a target object;
according to the eye image, obtaining an eye stimulation degree value of the target object;
obtaining a current output value by using the eye stimulation degree value;
and adjusting the light transmittance of the electrochromic glass according to the current output value.
Further, the obtaining an eye stimulation level value of the target object according to the eye image includes:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object;
wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
Further, the obtaining a current output value by using the eye stimulation degree value includes:
obtaining a middle current output value by using the eye stimulation degree value;
obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
inputting the deviation value into a second network model to obtain a plurality of parameter values, wherein the second network model is obtained by training a neural network model through an input sample and an expected output value;
and inputting the parameter values into a Proportional Integral Derivative (PID) controller to obtain the current output value.
Further, the determining an eye irritation level value of the target object according to the eye image includes:
if the eye images comprise N eye images which are sequentially acquired within a preset time period, acquiring eye stimulation degree values according to the eye images in the N eye images respectively to determine N eye stimulation degree values, wherein N is an integer greater than 1;
updating the Nth eye stimulation level value by using the eye stimulation level value which is acquired before the Nth eye stimulation level value in the N eye stimulation level values;
taking the updated Nth eye stimulation degree value as the eye stimulation degree value of the target object.
In a second aspect, the present application provides an electrochromic glass conditioning device comprising:
the first acquisition module is used for acquiring an eye image of a target object;
the second acquisition module is used for acquiring the eye stimulation degree value of the target object according to the eye image;
the third acquisition module is used for acquiring a current output value by utilizing the eye stimulation degree value;
and the adjusting module is used for adjusting the light transmittance of the electrochromic glass according to the current output value.
Further, the second obtaining module is configured to:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object;
wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
Further, the third obtaining module includes:
the first acquisition submodule is used for acquiring a middle current output value by utilizing the eye stimulation degree value;
the second obtaining submodule is used for obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
the third obtaining submodule is used for inputting the deviation value into a second network model to obtain a plurality of parameter values, and the second network model is obtained by training a neural network model through an input sample and an expected output value;
and the fourth acquisition submodule is used for inputting the parameter values into a proportional-integral-derivative (PID) controller to acquire the current output value.
Further, the second obtaining module includes:
the first determining submodule is used for obtaining eye stimulation degree values according to eye images in the N eye images respectively to determine the N eye stimulation degree values if the eye images comprise the N eye images which are obtained in sequence within a preset time period, wherein N is an integer greater than 1;
an updating submodule, configured to update an nth eye stimulation level value with an eye stimulation level value that is obtained before the nth eye stimulation level value among the N eye stimulation level values;
a second determining submodule, configured to use the updated nth eye stimulation degree value as the eye stimulation degree value of the target object.
A third aspect of the present application provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
A fourth aspect of the present application provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of the first aspect.
One embodiment in the above application has the following advantages or benefits:
obtaining an eye image of a target object; according to the eye image, obtaining an eye stimulation degree value of the target object; obtaining a current output value by using the eye stimulation degree value; and adjusting the light transmittance of the electrochromic glass according to the current output value. Because the adjustment of the light transmission of the electrochromic glass is determined based on the eye image of the target object, the adjustment method is more accurate in the basis of the adjustment of the light transmission of the electrochromic glass and better meets the requirements of users.
Obtaining an eye irritation degree value of the target object by inputting the eye image into a first network model; wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network. The first network model is utilized to obtain the eye stimulation degree value according to the eye image, so that the accuracy of the eye stimulation degree value can be improved, the subsequent light transmittance of the electrochromic glass can be adjusted more accurately, and the requirements of users can be met better.
The current output value is predicted by the second network model, so that the accuracy of the current output value can be improved, the adjustment accuracy is higher when the light transmittance of the electrochromic glass is adjusted based on the current output value, and the optimal adjustment effect of the sight line of the target object can be obtained.
When a plurality of eye images are acquired, the acquired eye stimulation degree value is smoothed, so that shaking is avoided, the electrochromic glass can be prevented from flickering, and the adjusting effect of the electrochromic glass is improved.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a flow chart of an electrochromic glass conditioning method provided in an embodiment of the present application;
FIG. 2 is a block diagram of an eye detection system provided in an embodiment of the present application;
FIG. 3 is a block diagram of an embodiment of the present application for adjusting an electrochromic glazing according to the degree of eye irritation;
FIG. 4 is another flow chart of an electrochromic glass conditioning method provided in an embodiment of the present application;
FIG. 5 is a block diagram of an electrochromic glass conditioning device as provided in an embodiment of the present application;
FIG. 6 is a block diagram of an electronic device for implementing the electrochromic glass conditioning method of an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Referring to fig. 1, fig. 1 is a flowchart of an electrochromic glass conditioning method provided in an embodiment of the present application, and as shown in fig. 1, the embodiment provides an electrochromic glass conditioning method applied to an electronic device, including the following steps:
The target object may be a user, such as a driver, using electrochromic glazing. The method comprises the steps of collecting a facial image of a user through a camera device, then regressing the facial image through face key points, obtaining coordinates of eye key points, and intercepting an eye image from the facial image. As shown in fig. 2, the camera sends the acquired face image to the image acquisition device, the image acquisition device sends the face image to the computing device, the computing device obtains the coordinates of the eye key points, and the eye image is intercepted from the face image. The eye detection algorithm is deployed on a computing device, and can detect the eye stimulation state according to the acquired real-time eye image.
And step 102, obtaining an eye stimulation degree value of the target object according to the eye image.
The eye image is analyzed, for example, the size of the pupil in the eye image can be analyzed, so as to determine the eye irritation degree value of the target object, and the eye irritation degree value can be used for representing the irritation degree of the eye.
And 103, obtaining a current output value by using the eye stimulation degree value.
The eye stimulation degree value and the current output value can be in a linear relation or a nonlinear relation. For example, a network model may be pre-trained to obtain current output values based on eye stimulus level values.
And 104, adjusting the light transmittance of the electrochromic glass according to the current output value.
The electrochromic glass can realize the purpose of regulating the illumination by utilizing the adjustability of the light transmission (or absorption) performance of the electrochromic material under the action of an electric field. The electrochromic system can keep the interior of the vehicle cool in summer and warm in winter by selectively absorbing or reflecting external heat radiation and preventing internal heat from being diffused.
The light transmittance of the electrochromic glass is adjusted based on the current output value, and the current output value is obtained based on the eye image of the target object, so that the aim of automatically and intelligently adjusting the light transmittance of the electrochromic glass based on the eye image of the target object can be fulfilled.
In the embodiment, an eye image of a target object is obtained; according to the eye image, obtaining an eye stimulation degree value of the target object; obtaining a current output value by using the eye stimulation degree value; and adjusting the light transmittance of the electrochromic glass according to the current output value. Because the adjustment of the light transmission of the electrochromic glass is determined based on the eye image of the target object, the adjustment method is more accurate in the basis of the adjustment of the light transmission of the electrochromic glass and better meets the requirements of users.
In an embodiment of the present application, the obtaining 102 an eye irritation level value of the target object according to the eye image includes:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object; wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
In this embodiment, the first network model is an algorithm model based on a convolutional neural network, and since the pupil is a light entrance door in the eye, it is narrowed at the bright light and largely diverged at the dark light, and therefore, based on the eye image, the degree of the eye stimulation by the light can be determined.
In this embodiment, in the stage of training the convolutional neural network model, a large number of images of eyes stimulated by light rays are collected first, and the images are labeled, and then the images and labeled data are input into the convolutional neural network to perform model parameter training, so as to obtain the first network model. The first network model can extract specific eye pattern features and is used for outputting eye stimulation degree values of eyes stimulated by light.
The first network model inputs an eye image with a preset size in a detection stage, obtains weight parameters through training, performs layer-by-layer convolution calculation to extract eye features from the eye image, and finally outputs an eye stimulation degree value.
In this embodiment, the eye stimulation level value of the target object is obtained by inputting the eye image into a first network model; wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network. The first network model is utilized to obtain the eye stimulation degree value according to the eye image, so that the accuracy of the eye stimulation degree value can be improved, the subsequent light transmittance of the electrochromic glass can be adjusted more accurately, and the requirements of users can be met better.
In an embodiment of the present application, obtaining the current output value by using the eye stimulation level value includes:
obtaining a middle current output value by using the eye stimulation degree value;
obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
inputting the deviation value into a second network model to obtain a plurality of parameter values, wherein the second network model is obtained by training a neural network model through an input sample and an expected output value;
and inputting the parameter values into a Proportional Integral Derivative (PID) controller to obtain the current output value.
In this embodiment, the eye stimulation level value and the intermediate current output value may have a linear relationship, and the linear relationship may be preset. The second network model is obtained by training the neural network model, the training phase of the neural network model is performed off-line, and a step signal is used as an input during training, namely, the input sample is the step signal. Loss function of neural network model training:
model training was performed by gradient descent. Wherein e (t) represents an error between the current output value obtained by using the predicted plurality of parameter values and the expected output value, that is, after obtaining the plurality of parameter values based on the input sample by the neural network model, obtaining the current output value based on the plurality of parameter values by a Proportional-Integral-Derivative (PID) controller, and then obtaining the current output value based on the current output value and the periodAnd (5) observing the error between the output values, and performing model training through gradient descent. And after the training is finished, obtaining the weight parameters of the neural network model, namely obtaining a second network model, wherein the second network model can be used for online prediction.
The second network model obtains a plurality of parameter values based on the deviation values of the intermediate current output value and the feedback current value, the feedback current value is the current output value obtained at the last moment, and the intermediate current output value is the current output value obtained at the current moment based on the eye stimulation degree value. Initially, the current output value obtained at the previous time is 0.
Fig. 3 is a schematic structural diagram of adjusting electrochromic glass according to an eye stimulation degree value, a PID parameter prediction module in fig. 3 is a second network model, and in fig. 3, a deviation value between an output current (i.e., a feedback current value) and a middle current output value at a previous time is input to the PID parameter prediction module to obtain a plurality of parameter values. Fig. 4 is a flowchart of an electrochromic glass adjustment method provided in an embodiment of the present application, in which the eye irritation detection device is configured to output a plurality of parameter values, and the control system may be understood as a PID controller, and the current output by the PID controller adjusts the color of the electrochromic glass.
The plurality of parameter values can comprise three parameter values which are respectively a proportional parameter value, an integral parameter value and a differential parameter value, and the three parameter values are input into the PID controller to obtain a current output value so as to adjust the electrochromic glass. The PID controller adopts a plurality of parameter values as input, outputs a current output value as a control quantity to control the color of the electrochromic glass, and can realize intelligent adjustment of the electrochromic glass along with stimulation of human eyes.
In this embodiment, when obtaining the current output value by using the eye stimulation degree value, obtaining an intermediate current output value by using the eye stimulation degree value; obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment; inputting the deviation value into a second network model to obtain a plurality of parameter values, wherein the second network model is obtained by training a neural network model through an input sample and an expected output value; and inputting the parameter values into a Proportional Integral Derivative (PID) controller to obtain the current output value. The current output value is predicted by the second network model, so that the accuracy of the current output value can be improved, the adjustment accuracy is higher when the light transmittance of the electrochromic glass is adjusted based on the current output value, and the optimal adjustment effect of the sight line of the target object can be obtained.
In one embodiment of the present application, the determining an eye irritation level value of the target object according to the eye image includes:
if the eye images comprise N eye images which are sequentially acquired within a preset time period, acquiring eye stimulation degree values according to the eye images in the N eye images respectively to determine N eye stimulation degree values, wherein N is an integer greater than 1;
updating the Nth eye stimulation level value by using the eye stimulation level value which is acquired before the Nth eye stimulation level value in the N eye stimulation level values;
taking the updated Nth eye stimulation degree value as the eye stimulation degree value of the target object.
In this embodiment, when the eye image of the target object is obtained, one eye image may be obtained at intervals of a preset time to determine the eye stimulation level value of the target object, or a plurality of eye images may be obtained within a preset time period, for example, 25 eye images may be obtained in one minute. In the case of acquiring a plurality of eye images, in order to avoid the flicker effect of the electrochromic glass caused by the jitter of the obtained eye stimulation degree values, the smoothing can be performed by adopting a low-pass filtering mode. Namely, for a first eye image and a second eye image acquired at adjacent time, a first eye stimulation degree value and a second eye stimulation degree value are acquired, and then the second eye stimulation degree value is updated by using the first eye stimulation degree value.
The eye stimulation level values of the eye images acquired at adjacent times are updated using low pass filtering p _ cur' ═ e _ cur + (1-e) × p _ prev. Wherein e is a filter coefficient, the value range is 0 to 1, and p _ cur is the eye stimulation degree value at the current moment; p _ prev is the eye stimulation level value at the last moment; p _ cur' is the updated eye stimulation level value at the current time. And weighting the eye stimulation degree value at the current moment and the eye stimulation degree value at the previous moment to obtain the updated eye stimulation degree value at the current moment.
According to the N eye images which are acquired in sequence, N eye stimulation degree values can be acquired, and the sequence of each eye stimulation degree value in the N eye stimulation degree values corresponds to the N eye images, for example, the first eye image and the second eye image are sequentially acquired at adjacent time, so that the first eye stimulation degree value determined according to the first eye image is adjacent to the second eye stimulation degree value determined according to the second eye image and are sequentially sequenced.
And sequentially updating the eye stimulation level values except the first eye stimulation level value in the N eye stimulation level values. And updating the (i + 1) th eye stimulation degree value by using the ith eye stimulation degree value, wherein i is a positive integer less than N. For example, the second eye stimulation level value is updated with the first eye stimulation level value, and then the third eye stimulation level value is updated with the second eye stimulation level value (at this time, the second eye stimulation level value is updated), which are sequentially performed until the nth eye stimulation level value is updated. That is, the update of the nth eye stimulation level value depends on the N-1 eye stimulation level values that are arranged in front thereof. And then, taking the updated Nth eye stimulation degree value as the eye stimulation degree value of the target object, so as to smooth the obtained eye stimulation degree value when a plurality of eye images are obtained, avoid shaking, prevent the electrochromic glass from flickering and improve the adjusting effect of the electrochromic glass.
In this embodiment, when determining the eye irritation level value of the target object according to the eye image, if the eye image includes N eye images sequentially acquired within a preset time period, the eye irritation level value is acquired according to each of the N eye images, so as to determine N eye irritation level values, where N is an integer greater than 1; updating the Nth eye stimulation level value by using the eye stimulation level value which is acquired before the Nth eye stimulation level value in the N eye stimulation level values; taking the updated Nth eye stimulation degree value as the eye stimulation degree value of the target object. When a plurality of eye images are acquired, the acquired eye stimulation degree value is smoothed, so that shaking is avoided, the electrochromic glass can be prevented from flickering, and the adjusting effect of the electrochromic glass is improved.
Referring to fig. 5, fig. 5 is a structural diagram of an electrochromic glass conditioning device provided in the present embodiment, and as shown in fig. 5, the present embodiment provides an electrochromic glass conditioning device 500, including:
a first obtaining module 501, configured to obtain an eye image of a target object;
a second obtaining module 502, configured to obtain an eye stimulation level value of the target object according to the eye image;
a third obtaining module 503, configured to obtain a current output value by using the eye stimulation level value;
and the adjusting module 504 is used for adjusting the light transmittance of the electrochromic glass according to the current output value.
In an embodiment of the present application, the second obtaining module 502 is configured to:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object;
wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
In an embodiment of the present application, the third obtaining module 503 includes:
the first acquisition submodule is used for acquiring a middle current output value by utilizing the eye stimulation degree value;
the second obtaining submodule is used for obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
the third obtaining submodule is used for inputting the deviation value into a second network model to obtain a plurality of parameter values, and the second network model is obtained by training a neural network model through an input sample and an expected output value;
and the fourth acquisition submodule is used for inputting the parameter values into a proportional-integral-derivative (PID) controller to acquire the current output value.
In an embodiment of the present application, the second obtaining module 502 includes:
the first determining submodule is used for obtaining eye stimulation degree values according to eye images in the N eye images respectively to determine the N eye stimulation degree values if the eye images comprise the N eye images which are obtained in sequence within a preset time period, wherein N is an integer greater than 1;
an updating submodule, configured to update an nth eye stimulation level value with an eye stimulation level value that is obtained before the nth eye stimulation level value among the N eye stimulation level values;
a second determining submodule, configured to use the updated nth eye stimulation degree value as the eye stimulation degree value of the target object.
The electrochromic glass adjusting apparatus 500 can implement the processes implemented by the electronic device in the method embodiment shown in fig. 1, and in order to avoid repetition, the details are not described here.
The electrochromic glass adjusting device 500 of the embodiment of the application obtains an eye image of a target object; according to the eye image, obtaining an eye stimulation degree value of the target object; obtaining a current output value by using the eye stimulation degree value; and adjusting the light transmittance of the electrochromic glass according to the current output value. Because the adjustment of the light transmission of the electrochromic glass is determined based on the eye image of the target object, the adjustment method is more accurate in the basis of the adjustment of the light transmission of the electrochromic glass and better meets the requirements of users.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 6, is a block diagram of an electronic device of an electrochromic glass conditioning method according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the electrochromic glass conditioning method provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the electrochromic glass conditioning method provided herein.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the first obtaining module 501, the second obtaining module 502, the third obtaining module 503, and the adjusting module 504 shown in fig. 5) corresponding to the electrochromic glass adjusting method in the embodiments of the present application. The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 602, namely, implements the electrochromic glass conditioning method in the above method embodiment.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device implementing the electrochromic glass adjusting method, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory located remotely from the processor 601, and these remote memories may be connected over a network to an electronic device implementing the electrochromic glass conditioning method. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device implementing the electrochromic glass conditioning method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of an electronic device implementing the electrochromic glass conditioning method, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or like input device. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the eye image of the target object is obtained; according to the eye image, obtaining an eye stimulation degree value of the target object; obtaining a current output value by using the eye stimulation degree value; and adjusting the light transmittance of the electrochromic glass according to the current output value. Because the adjustment of the light transmission of the electrochromic glass is determined based on the eye image of the target object, the adjustment method is more accurate in the basis of the adjustment of the light transmission of the electrochromic glass and better meets the requirements of users.
Obtaining an eye irritation degree value of the target object by inputting the eye image into a first network model; wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network. The first network model is utilized to obtain the eye stimulation degree value according to the eye image, so that the accuracy of the eye stimulation degree value can be improved, the subsequent light transmittance of the electrochromic glass can be adjusted more accurately, and the requirements of users can be met better.
The current output value is predicted by the second network model, so that the accuracy of the current output value can be improved, the adjustment accuracy is higher when the light transmittance of the electrochromic glass is adjusted based on the current output value, and the optimal adjustment effect of the sight line of the target object can be obtained.
When a plurality of eye images are acquired, the acquired eye stimulation degree value is smoothed, so that shaking is avoided, the electrochromic glass can be prevented from flickering, and the adjusting effect of the electrochromic glass is improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An electrochromic glass conditioning method, comprising:
acquiring an eye image of a target object;
according to the eye image, obtaining an eye stimulation degree value of the target object;
obtaining a current output value by using the eye stimulation degree value;
and adjusting the light transmittance of the electrochromic glass according to the current output value.
2. The method of claim 1, wherein obtaining the eye irritation level value of the target subject from the eye image comprises:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object;
wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
3. The method of claim 1, wherein obtaining a current output value using the eye-stimulus-level value comprises:
obtaining a middle current output value by using the eye stimulation degree value;
obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
inputting the deviation value into a second network model to obtain a plurality of parameter values, wherein the second network model is obtained by training a neural network model through an input sample and an expected output value;
and inputting the parameter values into a Proportional Integral Derivative (PID) controller to obtain the current output value.
4. The method of claim 1, wherein determining the eye irritation level value of the target object from the eye image comprises:
if the eye images comprise N eye images which are sequentially acquired within a preset time period, acquiring eye stimulation degree values according to the eye images in the N eye images respectively to determine N eye stimulation degree values, wherein N is an integer greater than 1;
updating the Nth eye stimulation level value by using the eye stimulation level value which is acquired before the Nth eye stimulation level value in the N eye stimulation level values;
taking the updated Nth eye stimulation degree value as the eye stimulation degree value of the target object.
5. An electrochromic glass conditioning device, comprising:
the first acquisition module is used for acquiring an eye image of a target object;
the second acquisition module is used for acquiring the eye stimulation degree value of the target object according to the eye image;
the third acquisition module is used for acquiring a current output value by utilizing the eye stimulation degree value;
and the adjusting module is used for adjusting the light transmittance of the electrochromic glass according to the current output value.
6. The apparatus of claim 5, wherein the second obtaining module is configured to:
inputting the eye image into a first network model to obtain an eye stimulation degree value of the target object;
wherein the first network model is obtained by training the labeled eye sample image by using a convolutional neural network.
7. The apparatus of claim 5, wherein the third obtaining module comprises:
the first acquisition submodule is used for acquiring a middle current output value by utilizing the eye stimulation degree value;
the second obtaining submodule is used for obtaining a deviation value of the intermediate current output value and a feedback current value, wherein the feedback current value is a current output value obtained at the last moment;
the third obtaining submodule is used for inputting the deviation value into a second network model to obtain a plurality of parameter values, and the second network model is obtained by training a neural network model through an input sample and an expected output value;
and the fourth acquisition submodule is used for inputting the parameter values into a proportional-integral-derivative (PID) controller to acquire the current output value.
8. The apparatus of claim 5, wherein the second obtaining module comprises:
the first determining submodule is used for obtaining eye stimulation degree values according to eye images in the N eye images respectively to determine the N eye stimulation degree values if the eye images comprise the N eye images which are obtained in sequence within a preset time period, wherein N is an integer greater than 1;
an updating submodule, configured to update an nth eye stimulation level value with an eye stimulation level value that is obtained before the nth eye stimulation level value among the N eye stimulation level values;
a second determining submodule, configured to use the updated nth eye stimulation degree value as the eye stimulation degree value of the target object.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-4.
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| CN202111130223.6A CN113771601B (en) | 2020-03-17 | 2020-03-17 | Electrochromic glass adjusting method and device and electronic equipment |
| CN202010185011.7A CN111347845B (en) | 2020-03-17 | 2020-03-17 | Electrochromic glass adjusting method and device and electronic equipment |
| CN202110895417.9A CN113580893B (en) | 2020-03-17 | 2020-03-17 | Electrochromic glass adjusting method and device and electronic equipment |
| CN202111131649.3A CN113771602B (en) | 2020-03-17 | 2020-03-17 | Electrochromic glass adjusting method and device and electronic equipment |
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| CN202111131649.3A Division CN113771602B (en) | 2020-03-17 | 2020-03-17 | Electrochromic glass adjusting method and device and electronic equipment |
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| CN113771601B (en) | 2023-01-10 |
| CN111347845B (en) | 2021-09-21 |
| CN113771602A (en) | 2021-12-10 |
| CN113771602B (en) | 2023-01-10 |
| CN113771601A (en) | 2021-12-10 |
| CN113580893A (en) | 2021-11-02 |
| CN113580893B (en) | 2022-12-09 |
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