Multi-sensor fused portable intelligent digital photo frame and control method thereof

Abstract

The invention discloses a multi-sensor fusion portable intelligent digital photo frame and a control method thereof, and belongs to the technical field of intelligent display and adaptive control. The method comprises the following steps: firstly, constructing an environment quality model through environment light color temperature, uniformity and infrared intensity, and obtaining an environment quality coefficient; constructing an equipment constraint model in combination with the screen temperature and the battery capacity, and obtaining an equipment constraint coefficient; and constructing a content optimization model by using the image highlight proportion, the backlight current and the screen opening duration, and obtaining a content optimization coefficient. And then, based on the environment quality coefficient and the equipment constraint coefficient, carrying out multi-dimensional balance adaptation calculation on the environment illuminance, the image average brightness and the viewing angle to obtain a comprehensive adaptation degree. And finally, fusing the adaptation degree, the content optimization coefficient and the basic brightness, and calculating the final target display brightness through a brightness optimization model. According to the invention, the self-adaptive intelligent adjustment of the display brightness is realized, and the visual comfort, the content presentation effect and the equipment energy efficiency are effectively considered.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent display and adaptive control, and particularly relates to a portable smart digital photo frame with multi-sensor fusion and a control method thereof. BACKGROUND

[0002] With the popularization of digital image technology and the development of smart homes, as an important carrier for family photo display and emotional interaction, the display effect and user experience of portable smart digital photo frames are increasingly concerned. Users expect the digital photo frame to automatically present clear, comfortable and visually comfortable photo brightness under complex and variable environmental light conditions, which puts higher requirements on the intelligent perception and adaptive adjustment capability of the photo frame.

[0003] At present, the brightness adjustment of common digital photo frames mostly relies on a single ambient light sensor, and performs simple linear or segmented brightness mapping according to the ambient illuminance. Some advanced schemes introduce image content analysis and attempt to compensate according to the average brightness of the photo.

[0004] In combination with the current situation in the field, the existing technology mainly has the following defects: first, the deep influence of environmental light quality (such as color temperature, uniformity, and infrared interference) on visual comfort is not comprehensively considered; second, the constraints of the device's own state (such as temperature and power) are not considered, which may cause overheating or sudden decrease in battery life; third, the key interactive factor of user viewing angle is not included in the adjustment closed loop, resulting in a decrease in experience when viewing at an angle; fourth, the adjustment models in each dimension are isolated from each other, and there is a lack of a unified quantitative framework to balance the multi-dimensional demand conflicts between the environment, content, device and user, resulting in that the final brightness output may be excellent in one aspect, but the overall adaptability is poor. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a portable smart digital photo frame with multi-sensor fusion and a control method thereof, which solves the above problems.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a portable smart digital photo frame with multi-sensor fusion and a control method thereof, comprising the following steps: Based on the ambient light color temperature, ambient light uniformity (illumination variance) and infrared light intensity, an environmental quality coefficient is obtained through an environmental quality model; Based on the screen panel temperature and the battery remaining power percentage, a device constraint coefficient is obtained through a constraint model; Based on the image highlight pixel proportion (the percentage of pixels with a brightness value greater than 200 in the total pixels), the current screen backlight current and the screen on duration, a content optimization coefficient is obtained through a content optimization model; The multi-dimensional balance adaptation degree is obtained through a multi-dimensional balance adaptation model based on the ambient light intensity, the image average pixel brightness, and the relative angle between the device and the viewer under the ambient quality coefficient and the device constraint coefficient. The target photo display brightness is obtained through a brightness optimization model based on the multi-dimensional balance adaptation degree, the content optimization coefficient, and the basic photo display brightness.

[0007] Based on the above technical solutions, the application further provides the following optional technical solutions. A further technical solution is that the brightness optimization model determines a basic display brightness between the minimum brightness and the maximum brightness of the screen based on the multi-dimensional balance adaptation degree; and The basic display brightness is adjusted based on the difference between the content optimization coefficient and a preset target optimization coefficient to obtain the target display brightness.

[0008] A further technical solution is that the step of obtaining the multi-dimensional balance adaptation degree through a multi-dimensional balance adaptation model based on the ambient light intensity, the image average pixel brightness, and the relative angle between the device and the viewer under the ambient quality coefficient and the device constraint coefficient comprises: obtaining the ambient quality coefficient, the device constraint coefficient, the ambient light intensity, the image average pixel brightness, and the relative angle between the device and the viewer; performing ratio processing on the ambient light intensity and the sum of the ambient light intensity and the reference light intensity to obtain an ambient light intensity index; performing ratio processing on the image average pixel brightness and the maximum pixel brightness to obtain a pixel brightness index; performing ratio processing on the relative angle between the device and the viewer and the maximum angle (90 degrees) to obtain an angle index; inputting the ambient quality coefficient and the ambient light intensity index into a preset ambient light intensity adaptation degree model to obtain an ambient light intensity adaptation degree, wherein the ambient light intensity adaptation degree is positively correlated with the ambient quality coefficient and the ambient light intensity index in the ambient light intensity adaptation degree model; inputting the device constraint coefficient and the pixel brightness index into a preset pixel brightness adaptation degree model to obtain a pixel brightness adaptation degree, wherein the image brightness adaptation degree is positively correlated with the device constraint coefficient and the pixel brightness index in the pixel brightness adaptation degree model; inputting the ambient quality coefficient and the angle index into a preset angle adaptation degree model to obtain an angle adaptation degree, wherein the angle adaptation degree is positively correlated with the ambient quality coefficient and negatively correlated with the angle index in the angle adaptation degree model; inputting the ambient light intensity adaptation degree, the pixel brightness adaptation degree, and the angle adaptation degree into a multi-dimensional balance adaptation degree model to obtain the multi-dimensional balance adaptation degree, wherein the multi-dimensional balance adaptation degree is obtained by weighting and fusing the ambient light intensity adaptation degree, the image brightness adaptation degree, and the angle adaptation degree in the multi-dimensional balance adaptation degree model.

[0009] Further technical solutions: based on the image highlight pixel proportion (pixels with brightness value > 200 account for the percentage of total pixels), the current screen backlight current and the screen on duration, the steps for obtaining the content optimization coefficient through the content optimization model are: Obtain the image highlight pixel proportion, the current screen backlight current and the screen on duration; The image highlight pixel proportion is introduced into the formula , to obtain the highlight pixel proportion factor, , which represents the image highlight pixel proportion; The current screen backlight current and the screen on duration are subjected to maximum-minimum normalization processing to obtain the backlight current factor and the screen on duration factor; The highlight pixel proportion factor, the backlight current factor and the screen on duration factor are subjected to complement processing to obtain the highlight pixel proportion index, the backlight current index and the screen on duration index; The highlight pixel proportion index, the backlight current index and the screen on duration index are introduced into the content optimization model to obtain the content optimization coefficient.

[0010] Further technical solutions: based on the screen panel temperature and the battery remaining power percentage, the steps for obtaining the device constraint coefficient through the device constraint model are: Obtain the screen panel temperature and the battery remaining power percentage; The screen panel temperature is subjected to ratio processing with the maximum allowed temperature to obtain the temperature factor, and the temperature factor is subjected to difference processing with the temperature factor threshold to obtain the temperature deviation index; The battery remaining power percentage and the temperature deviation index are introduced into the device constraint model to obtain the device constraint coefficient.

[0011] Further technical solutions: based on the ambient light color temperature, the ambient light uniformity (illumination variance) and the infrared light intensity, the steps for obtaining the environment quality coefficient through the environment quality model are: Obtain the ambient light color temperature, the ambient light uniformity (illumination variance) and the infrared light intensity; The absolute difference between the ambient light color temperature and the reference color temperature is subjected to ratio processing with the reference color temperature to obtain the color temperature deviation index; The ambient light uniformity and the infrared light intensity are subjected to ratio processing with the corresponding reference values to obtain the ambient light uniformity index and the infrared light intensity index; The color temperature deviation index, the ambient light uniformity index and the infrared light intensity index are introduced into the environment quality model to obtain the environment quality coefficient.

[0012] Further technical solutions: in the content optimization model, the content optimization coefficient is positively correlated with the highlight pixel proportion index, the backlight current index and the screen on duration index.

[0013] Further technical solutions: the device constraint model in the device constraint coefficient is positively related to the battery remaining percentage, and is negatively related to the temperature deviation index.

[0014] Further technical solutions: the environmental quality model in the environmental quality coefficient is negatively related to the color temperature deviation index, the environmental light uniformity index, and the infrared light intensity index.

[0015] A multi-sensor fusion portable intelligent digital photo frame adopts the multi-sensor fusion portable intelligent digital photo frame control method.

[0016] The present application provides a multi-sensor fusion portable intelligent digital photo frame and its control method, which has the following advantages compared with the prior art: 1. The present application constructs an environmental quality model by fusing multi-dimensional parameters such as environmental light color temperature, uniformity, and infrared intensity, so that the system can more finely perceive the quality of environmental light, surpassing the traditional method of relying only on illuminance, and significantly improving the visual comfort in different spectral and distributed light environments. 2. The present application introduces screen temperature and battery power as device constraint coefficients, so that brightness adjustment has the ability to perceive device status, can effectively prevent device overheating and optimize energy consumption while ensuring display effect, prolongs the battery life, and improves the reliability and practicality of the system. 3. The present application incorporates user viewing angle into the multi-dimensional balanced adaptation model, so that brightness adjustment can compensate for screen surface reflection and brightness perception attenuation caused by viewing angle changes, achieving adaptive optimization in the "human-computer interaction" level. 4. The collaborative computing framework proposed by the present application, which takes multi-dimensional balanced adaptation degree as the core, organically unifies the needs and constraints of the four dimensions of environment, content, device, and user, quantifies the potential conflicts through mathematical models, and finally outputs the target brightness which is the global optimal solution of the system, realizing intelligent and personalized adaptive display. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The present application is a flowchart.

[0018] Figure 2 The present application is a three-dimensional structure diagram.

[0019] Legend: 1, photo frame body. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0021] The specific implementation of the present application is described in detail below in combination with specific embodiments.

[0022] Please refer to Figure 1 For an embodiment of the present application, a multi-sensor fusion portable smart digital photo frame control method is provided, including the following steps: Based on the color temperature of the ambient light, the uniformity of the ambient light (illumination variance) and the intensity of the infrared light, the environmental quality coefficient is obtained through the environmental quality model; Based on the screen panel temperature and the percentage of the remaining battery power, the device constraint coefficient is obtained through the device constraint model; Based on the image highlight pixel ratio (the percentage of pixels with a brightness value greater than 200 in the total pixels), the current screen backlight current and the screen on duration, the content optimization coefficient is obtained through the content optimization model; Based on the ambient light intensity, the average pixel brightness of the image and the relative angle between the device and the viewer under the environmental quality coefficient and the device constraint coefficient, the multi-dimensional balance adaptation degree is obtained through the multi-dimensional balance adaptation model; Based on the multi-dimensional balance adaptation degree, the content optimization coefficient and the basic photo display brightness, the target photo display brightness is obtained through the brightness optimization model.

[0023] The present application introduces multi-sensor fusion technology, constructs an environmental quality model, a device constraint model, a content optimization model, a multi-dimensional balance adaptation model and a brightness optimization model, and forms a multi-dimensional and adaptive brightness control closed loop. Compared with the prior art, the advantages of the present application are: Firstly, the environmental quality model comprehensively considers the color temperature of the ambient light, the uniformity of the ambient light and the intensity of the infrared light, which can more comprehensively evaluate the quality of the ambient light. This makes the brightness adjustment not only a simple increase or decrease of the illumination, but also a fine adjustment according to the "quality" of the ambient light, thereby improving the visual comfort.

[0024] Secondly, the introduction of the device constraint model enables the brightness adjustment to fully consider the device's own state such as the screen panel temperature and the percentage of the remaining battery power. This effectively avoids the problems of device overheating or sudden power drop caused by pursuing extreme brightness, prolongs the service life and endurance time of the device.

[0025] Furthermore, the content optimization model analyzes the proportion of highlight pixels in the image, the current screen backlight current, and the screen-on duration, so that the brightness adjustment can better adapt to the image content characteristics and the screen display state. This helps to achieve a more natural brightness transition between different types of photos (such as high-contrast and low-contrast) and takes into account the effects of long-term screen operation.

[0026] More importantly, the multi-dimensional balance adaptation model unifies and balances multiple dimensions such as environmental quality coefficient, device constraint coefficient, ambient light intensity, image average pixel brightness, and the relative angle between the device and the viewer. This solves the problem of isolated dimension adjustment models in the prior art and provides a unified quantification framework to balance the multi-dimensional demand conflicts between the environment, content, device, and user.

[0027] Therefore, the control method of the present application can output an optimal target photo display brightness under multi-dimensional demand conflicts, significantly improving the display effect and user experience of the portable smart digital photo frame, and overcoming the limitations of brightness adjustment in complex and variable scenarios in the prior art.

[0028] Preferably, the step of obtaining the environmental quality coefficient based on the ambient light color temperature, ambient light uniformity (light intensity variance), and infrared light intensity through the environmental quality model is as follows: Obtain the ambient light color temperature, ambient light uniformity (light intensity variance), and infrared light intensity; Obtain the ambient light color temperature, ambient light uniformity (light intensity variance), and infrared light intensity; Obtain the ambient light uniformity index and the infrared light intensity index by ratio processing the ambient light uniformity and the infrared light intensity with the corresponding reference values; Obtain the ambient light uniformity index and the infrared light intensity index by ratio processing the ambient light uniformity and the infrared light intensity with the corresponding reference values; The environmental quality coefficient in the environmental quality model is negatively correlated with the color temperature deviation index, the ambient light uniformity index, and the infrared light intensity index; The environmental light quality model is represented as: ; wherein, represents the environmental quality coefficient, represents the color temperature deviation index, represents the ambient light uniformity index, represents the infrared light intensity index, and The greater the value, the better the quality of the ambient light.

[0029] Among them, the ambient light color temperature refers to the color characteristics of ambient light, usually expressed in Kelvin (K), which reflects the coolness or warmth of the light source. The ambient light color temperature can be obtained by integrating a color temperature sensor in the portable smart digital photo frame. For example, an RGBW sensor or a dedicated color temperature sensor can be used to measure the color temperature value of ambient light in real time. Ambient light uniformity (illumination variance) refers to the uniformity of ambient light distribution in the display area. It is quantified by calculating the variance of illumination at different positions. The smaller the illumination variance, the more uniform the ambient light. Ambient light uniformity can be obtained by integrating multiple illumination sensors on the digital photo frame panel or by measuring the illumination at different points using an illumination sensor array with spatial perception capability, and then calculating the variance. Infrared light intensity refers to the intensity of infrared radiation in the environment. Although infrared light is invisible, excessive infrared radiation may affect sensor readings or indicate special environmental conditions. Infrared light intensity can be obtained by integrating an infrared sensor in the digital photo frame. This sensor can detect and quantify the level of infrared radiation in the environment.

[0030] The ambient light quality model is a nonlinear model characterized by the ability to integrate multiple negative factors (color temperature deviation, uneven illumination, infrared interference) in an exponentially decaying manner. When any one of the exponents increases, the negative exponential term in the exponential function becomes smaller, resulting in a rapid decrease in the ambient quality coefficient . This design enables the model to sensitively reflect the decline in environmental quality and ensures that the value of is always between 0 and 1, where 1 represents the best environmental quality and close to 0 represents poor environmental quality. This model structure effectively captures the comprehensive impact of environmental factors on visual comfort and provides accurate quantitative basis for subsequent brightness adaptation.

[0031] By the technical solution, the present application can overcome the deficiency in the prior art that the influence of the quality of ambient light on visual comfort is not comprehensively considered. By obtaining the color temperature of ambient light, the uniformity of ambient light and the intensity of infrared light, and converting them into a standardized color temperature deviation index, an ambient light uniformity index and an infrared light intensity index, the present application can comprehensively capture multiple key quality dimensions of ambient light. Further, by introducing these indexes into an environmental quality model in the form of an index, the quality of ambient light can be accurately quantified in a nonlinear manner to generate an environmental quality coefficient between 0 and 1. The coefficient not only reflects the basic situation of ambient light intensity, but also incorporates factors such as color temperature, uniformity and infrared interference that have an important influence on the comfort of human eye perception. In view of this, when the environmental quality coefficient is introduced into a multi-dimensional balance adaptation model, it can work together with other factors such as ambient light intensity, image average pixel brightness and the relative angle between the device and the viewer to determine the multi-dimensional balance adaptation degree. This comprehensive evaluation mechanism enables the portable smart digital photo frame to no longer rely solely on ambient light intensity when adjusting brightness, but can more accurately reflect the comprehensive influence of the current environment on visual comfort. Therefore, the target photo display brightness output can better adapt to complex environmental conditions, significantly improve the visual comfort of users in different lighting environments, and optimize the overall brightness display effect, avoiding visual fatigue or display distortion caused by poor ambient light quality.

[0032] Preferably, based on the screen panel temperature and the battery remaining percentage, the step of obtaining the device constraint coefficient through the constraint model is: obtaining the screen panel temperature and the battery remaining percentage; performing ratio processing on the screen panel temperature and the maximum allowed temperature to obtain a temperature factor, and performing difference processing on the temperature factor and a temperature deviation index threshold to obtain the temperature deviation index; introducing the battery remaining percentage and the temperature deviation index into the device constraint model to obtain the device constraint coefficient; the device constraint coefficient in the device constraint model is positively correlated with the battery remaining percentage and negatively correlated with the temperature deviation index; the device constraint model is represented as: ; wherein, the device constraint coefficient is represented as C, the battery remaining percentage is represented as P, the temperature deviation index is represented as T, and the greater the value, the better the device state.

[0033] Specifically, first, the screen panel temperature and the battery remaining percentage are obtained. The screen panel temperature refers to the actual temperature of the digital photo frame display panel when it is running, which reflects the current thermal load state of the device. A temperature that is too high can cause the device to perform poorly, shorten its lifespan, or even be damaged. The screen panel temperature can be obtained by real-time monitoring through a temperature sensor built into the screen panel or its vicinity, such as a thermistor, a thermocouple, or an integrated temperature sensor. The battery remaining percentage refers to the proportion of the current remaining battery capacity of the device, usually represented by 0% to 100%. It reflects the device's endurance and energy reserves. If the battery level is too low, it may limit the device's operating mode or cause it to shut down. The battery remaining percentage can be obtained by reading the battery's state information through the battery management system (BMS) or by estimating it through voltage, current, and other parameters.

[0034] Subsequently, the screen panel temperature is divided by the maximum allowed temperature to obtain the temperature factor. This step aims to standardize the real-time monitored screen panel temperature, making it a dimensionless relative value. The maximum allowed temperature is the highest temperature threshold that the screen panel can withstand as specified by the device design or safety specifications. Exceeding this temperature can cause damage to the device. By dividing, the current temperature relative to the safety upper limit can be intuitively evaluated. The temperature factor is the ratio of the screen panel temperature to the maximum allowed temperature, reflecting the relative position of the current temperature within the safe operating range of the device.

[0035] On this basis, the temperature factor is subtracted from the temperature factor threshold to obtain the temperature deviation index. The temperature factor threshold is a preset reference value used to determine whether the screen panel temperature is within the ideal or safe range. It may be lower than the maximum allowed temperature to provide a certain safety margin. The difference between the temperature factor and the temperature factor threshold is calculated by the difference processing, quantifying the degree to which the current temperature deviates from the ideal state. The temperature deviation index is the result of the difference processing, and its value directly reflects the severity of the temperature deviation from the threshold, providing a key temperature state input for the device constraint model.

[0036] Finally, the battery remaining percentage and the temperature deviation index are introduced into the device constraint model to obtain the device constraint coefficient, which comprehensively reflects the available resources and operating potential of the device under the current temperature and power conditions, providing an important constraint basis for subsequent brightness adjustment.

[0037] By the technical solution, the application can effectively solve the problem of inaccurate device state quantization in the prior art. By comprehensively considering the screen panel temperature and the battery remaining power percentage, and using a nonlinear model to accurately calculate the device constraint coefficient, the coefficient can sensitively reflect the health status and available resources of the device under different operating conditions. When the device temperature is too high or the power is insufficient, the device constraint coefficient will decrease accordingly, so that the brightness output can be properly limited or adjusted in the subsequent brightness adjustment process. This not only avoids the risk of device overheating due to excessive pursuit of brightness, prolongs the service life of the device, but also effectively prevents the sudden shutdown due to power depletion, ensuring the continuity and stability of user experience. In addition, the accurate quantization of the device constraint coefficient is integrated into the multi-dimensional balanced adaptation model, so that the final target photo display brightness can not only adapt to the environmental light and image content, but also dynamically respond to the hardware status of the device. This multi-dimensional balancing and adjusting mechanism ensures that the portable smart digital photo frame always maintains a safe and efficient operating state while providing the best visual effect, significantly improving the intelligence level and user satisfaction of the device.

[0038] Preferably, based on the image highlight pixel proportion (the percentage of pixels with a brightness value > 200 in the total pixels), the current screen backlight current, and the screen on duration, the step of obtaining the content optimization coefficient through the content optimization model is: obtaining the image highlight pixel proportion, the current screen backlight current, and the screen on duration; introducing the image highlight pixel proportion into the formula to obtain the highlight pixel proportion factor, wherein the image highlight pixel proportion is represented by performing maximum-minimum normalization on the current screen backlight current and the screen on duration to obtain the backlight current factor and the on duration factor; performing complement number processing on the highlight pixel proportion factor, the backlight current factor, and the on duration factor to obtain the highlight pixel proportion index, the backlight current index, and the on duration index; introducing the highlight pixel proportion index, the backlight current index, and the on duration index into the content optimization model to obtain the content optimization coefficient; the content optimization coefficient in the content optimization model is positively correlated with the highlight pixel proportion index, the backlight current index, and the on duration index; the content optimization model is represented as: ; wherein the content optimization coefficient is represented by the highlight pixel proportion index is represented by the backlight current index is represented by represents the opening screen duration index, the opening screen duration index The greater the value, the lower the demand for brightness optimization according to the content characteristics and the display state.

[0039] wherein the image highlight pixel ratio refers to the percentage of the number of pixels in the image whose luminance value exceeds a certain threshold (for example, 200) in the total number of pixels, which reflects the overall brightness distribution of the image content and whether there is an over-bright area. The higher the highlight pixel ratio, the brighter the image content, or the more high-brightness areas there are, which can affect the viewing comfort or cause visual fatigue. The image highlight pixel ratio can be obtained by real-time analysis and statistics of the pixel data of the current display frame by the image processing module. The current screen backlight current refers to the current value of the current consumed by the screen backlight module. The backlight current directly determines the overall brightness output of the screen. This parameter can be monitored and provided by the display driving circuit or the power management unit in real time. The screen opening duration refers to the length of time the screen has been working since the last lighting. The screen opening duration can reflect the workload of the device and the potential panel temperature accumulation. This parameter can be recorded and provided by the system timer or the operating system. The image highlight pixel ratio is introduced into the formula to obtain a highlight pixel ratio factor. The hyperbolic tangent function can map any real number input to the range of (-1, 1), which is used here to transform the highlight pixel ratio The mapping to a more smooth and bounded factor, typically within the range of [0, 1] or [0, 100%], can avoid the drastic impact of original scale values that are too large or too small on subsequent calculations, thereby enhancing the robustness of the model. The current screen backlight current and screen-on duration are subjected to max-min normalization to obtain the backlight current factor and screen-on duration factor. Max-min normalization is a common data preprocessing method, and its purpose is to convert data of different dimensions or ranges into a unified interval (e.g., [0, 1]). For backlight current and screen-on duration, since their original value ranges may differ greatly, normalization can eliminate the dimensional influence, making them comparable in subsequent models and ensuring that their contribution to the content optimization coefficient is based on their relative size rather than their absolute size. The high-light pixel proportion factor, backlight current factor, and screen-on duration factor are subjected to complement processing to obtain the high-light pixel proportion index, backlight current index, and screen-on duration index. The purpose of complement processing (e.g., subtracting the original factor value by 1) is to convert the physical meaning of the factor into the physical meaning of "index". If a larger original factor value indicates a "better" or "higher" state, then its complement indicates the "deficiency" or "low" of that state. Here, if the high-light pixel proportion factor, backlight current factor, and screen-on duration factor value is larger, it indicates a lower brightness optimization demand, and the smaller the index value obtained after complement processing, the higher the brightness optimization demand. This conversion allows the subsequent multiplication model to correctly reflect the comprehensive impact of each factor on the optimization demand. The high-light pixel proportion index, backlight current index, and screen-on duration index are imported into the content optimization model to obtain the content optimization coefficient, which is represented as: This step combines the three independent indices (high-light pixel proportion index, backlight current index, and screen-on duration index) through a multiplication model to calculate the final content optimization coefficient The multiplication model can reflect the mutual influence and constraint relationship between factors, i.e., any lower index (indicating higher brightness optimization demand) will result in a lower final , thereby reflecting the overall brightness optimization demand. The value of this coefficient ranges between [0, 1], and a larger value indicates a lower brightness optimization demand based on content characteristics and display state.

[0040] By the technical solution, the application can accurately quantify the demand of image content features and device display state for brightness optimization. By introducing the image highlight pixel proportion, the system can identify the over-bright area in the image that may cause visual discomfort, so as to make targeted consideration when adjusting the brightness. The monitoring and processing of the current screen backlight current and the screen on-screen time make the brightness optimization process fully consider the real-time running load of the device and the potential power consumption and heat dissipation demand. In particular, by using the hyperbolic tangent function to smooth the highlight pixel proportion, and normalizing the backlight current and on-screen time, the influence of different parameter dimensions and extreme values is effectively eliminated, ensuring the stability and effectiveness of the model input. Further, by taking the complement processing to convert the factor into an index, and using a multiplication model for comprehensive consideration, the content optimization coefficient can dynamically and accurately reflect the actual demand degree of brightness optimization under specific content and device state. This enables the portable smart digital photo frame to achieve more refined and intelligent brightness adjustment, avoiding the problem of inaccurate brightness adjustment or decreased user experience caused by insufficient consideration of content or device state in traditional methods, thereby improving the user's viewing comfort and the energy efficiency of the device.

[0041] Preferably, the step of obtaining the multi-dimensional balance adaptation degree based on the environmental quality coefficient and the device constraint coefficient, the ambient light intensity, the image average pixel brightness, and the relative angle between the device and the viewer, through a multi-dimensional balance adaptation model, is as follows: obtaining the environmental quality coefficient, the device constraint coefficient, the ambient light intensity, the image average pixel brightness, and the relative angle between the device and the viewer; performing ratio processing on the ambient light intensity and the sum of the ambient light intensity and the reference light intensity to obtain an ambient light intensity index; performing ratio processing on the image average pixel brightness and the maximum pixel brightness to obtain a pixel brightness index; performing ratio processing on the relative angle between the device and the viewer and the maximum angle (90 degrees) to obtain an angle index; introducing the environmental quality coefficient and the ambient light intensity index into a preset ambient light intensity adaptation degree model to obtain an ambient light intensity adaptation degree, wherein the ambient light intensity adaptation degree in the ambient light intensity adaptation degree model is positively correlated with the environmental quality coefficient and the ambient light intensity index, and the ambient light intensity adaptation degree model is represented as: ; wherein, represents the ambient light intensity adaptation degree, represents the environmental quality coefficient, represents the ambient light intensity index; The device constraint coefficient and the pixel brightness index are introduced into a preset pixel brightness adaptation model to obtain a pixel brightness adaptation degree, the pixel brightness adaptation degree in the pixel brightness adaptation model is positively correlated with the device constraint coefficient and the pixel brightness index, and the pixel brightness adaptation model is represented as: ; wherein, represents the pixel brightness adaptation degree, represents the device constraint coefficient, represents the pixel brightness index; The environment quality coefficient and the angle index are introduced into a preset angle adaptation model to obtain an angle adaptation degree, the angle adaptation degree in the angle adaptation model is positively correlated with the environment quality coefficient and negatively correlated with the angle index, and the angle adaptation model is represented as: ; wherein, represents the angle adaptation degree, represents the environment quality coefficient, represents the angle index; The environment light adaptation degree, the pixel brightness adaptation degree and the angle adaptation degree are introduced into a multi-dimensional balance adaptation model to obtain a multi-dimensional balance adaptation degree, and the multi-dimensional balance adaptation model is represented as: ; wherein, represents the multi-dimensional balance adaptation degree, represents the environment light adaptation degree, represents the pixel brightness adaptation degree, represents the angle adaptation degree, represents a weight coefficient and , the and the greater the value is, the better the coordination of the current environment light condition, the device hardware state and the user viewing posture is.

[0042] In the above scheme, the environment quality coefficient is an index for measuring the advantages and disadvantages of the current environment light condition, and the greater the value is, the better the environment light quality is, the coefficient is used to evaluate the influence of the environment light on the visual comfort, thereby guiding the subsequent brightness adaptation. The device constraint coefficient is an index reflecting the current hardware state of the portable smart digital photo frame, and the larger the value, the better the device state and the larger the supported brightness adjustment range. The coefficient is used to limit the performance and endurance of the device during brightness optimization to avoid overheating or rapid power consumption due to excessive brightness. Ambient light intensity refers to the light flux received per unit area in the environment of the portable smart digital photo frame, which is the basic input parameter for brightness adjustment and directly reflects the light and dark degree of the environment. This parameter can be collected in real time by an ambient light sensor (such as a photoresistor, a photodiode, or a CMOS light sensor) integrated in the digital photo frame, or it can also be estimated by sampling the environment through an image sensor and combining image processing algorithms. Image average pixel brightness refers to the average of the brightness values of all pixels in the current display photo, reflecting the overall light and dark degree of the image. This parameter is used to evaluate the brightness characteristics of the image content to ensure that image details are clearly presented under different ambient light. This parameter can be obtained by analyzing the pixel data of the current display image, calculating the sum of all pixel brightness values and dividing by the total number of pixels, or it can also be obtained by histogram analysis of the image and then calculating the weighted average value of the histogram. The relative angle between the device and the viewer refers to the angle between the display screen plane of the portable smart digital photo frame and the viewing direction of the viewer. This angle reflects the viewing posture of the user and is used to compensate for the decline in visual experience due to changes in viewing angle. This angle can be estimated by integrating a posture sensor (such as a gyroscope or accelerometer) into the digital photo frame, combining face recognition or eye tracking technology to estimate the viewing direction of the viewer, and then calculating the relative angle, or it can also be detected by a simple tilt sensor to detect the tilt angle of the device and assume that the viewer's line of sight is perpendicular to the device plane.

[0043] The ambient light intensity adaptation degree model is used to calculate the ambient light intensity adaptation degree , which reflects the matching degree between the current ambient light condition and the ideal viewing condition. This model combines the environment quality coefficient and the ambient light intensity index, and uses an exponential decay function to dynamically evaluate the impact of ambient light on visual comfort. The pixel brightness adaptation degree model is used to calculate the pixel brightness adaptation degree , which reflects the matching degree between the image content brightness and the device hardware state. This model combines the device constraint coefficient and the pixel brightness index, and uses a product form to evaluate the display suitability of image content under the current device state. The angle adaptation degree model is used to calculate the angle adaptation degree , which reflects the matching degree between the user viewing angle and the ideal viewing angle. This model combines the environment quality coefficient and the angle index, and uses an exponential decay function to evaluate the impact of viewing angle on visual comfort. The multi-dimensional balance adaptation degree model is used to calculate the multi-dimensional balance adaptation degree The value thereof comprehensively reflects the overall coordination among the ambient light condition, the device hardware state and the user viewing posture. The model is obtained by weighted summation of the ambient light adaptation degree, the pixel brightness adaptation degree and the angle adaptation degree , which provides a unified quantitative index for guiding the final photo display brightness optimization.

[0044] Through the above technical solutions, the present application can effectively solve the problem of lack of multi-dimensional coordination quantification in brightness optimization in the prior art. By comprehensively obtaining the environmental quality coefficient, the device constraint coefficient, the ambient illuminance, the image average pixel brightness and the relative angle between the device and the viewer, and performing standardization processing thereon, the present application provides an accurate and unified data basis for multi-dimensional brightness adaptation. By introducing the ambient light adaptation degree model, the pixel brightness adaptation degree model and the angle adaptation degree model, the system can quantitatively evaluate the suitability of brightness adaptation from three key dimensions of environment, device-content and user viewing posture. These models not only consider the independent influence of each factor, but also realize deep consideration of the ambient light quality and the device state through the introduction of the environmental quality coefficient and the device constraint coefficient, so that the adaptation degree evaluation is more comprehensive and accurate. Further, by weighted integration of the adaptation degrees of the three dimensions, the present application successfully constructs a multi-dimensional balanced adaptation degree, which can comprehensively reflect the overall coordination among the ambient light condition, the device hardware state and the user viewing posture. This integration avoids the limitations of single factor adjustment, ensuring the comprehensiveness and balance of the brightness optimization decision. When the multi-dimensional balanced adaptation degree is used as the key input of the above brightness optimization model, the portable smart digital photo frame can intelligently adjust the photo display brightness according to the actual viewing scene, device condition and user habit, thereby significantly improving the accuracy and overall adaptability of brightness optimization. This not only ensures the clear presentation of image content, but also greatly improves the visual comfort and use experience of users in various complex environments, effectively avoiding visual fatigue or decreased viewing experience caused by improper brightness adjustment.

[0045] Preferably, the brightness optimization model determines a basic display brightness between the minimum brightness and the maximum brightness of the screen based on the multi-dimensional balanced adaptation degree; and adjusts the basic display brightness based on the difference between the content optimization coefficient and a preset target optimization coefficient to obtain the target display brightness, and the brightness optimization model is represented as: ; wherein, represents the target photo display brightness, represents the basic photo display brightness, represents the multi-dimensional balanced adaptation degree, represents the screen minimum brightness, represents the maximum screen brightness, represents the content optimization coefficient, represents the target content optimization coefficient.

[0046] wherein, The target photo display brightness refers to the final brightness value that the digital photo frame screen should present after the brightness optimization model is calculated. It is the brightness of the photo that the user actually perceives, directly affects the visual experience, and is the final output of the brightness optimization process. It guides the display driving circuit to adjust the backlight or pixel brightness to achieve the best display effect. The basic photo display brightness is an intermediate calculation value in the brightness optimization process. It mainly reflects the basic demand for brightness of external factors such as environment, equipment, and user viewing posture, providing a dynamic reference brightness for subsequent content optimization, ensuring that the brightness has been preliminarily adapted to external conditions before considering content characteristics. The multi-dimensional balance adaptation degree is a comprehensive quantitative index, usually between 0 and 1, used to evaluate the coordination or adaptation degree between the current environmental lighting conditions, device hardware status, and user viewing posture. The larger the value, the better the coordination. It is an important input of the brightness optimization model, integrating complex external factors into a single parameter that can be used for brightness calculation, allowing brightness adjustment to fully consider these factors. The screen minimum brightness is the lowest brightness value that the digital photo frame display screen can reach in normal working state. It sets the lower limit of brightness adjustment, ensuring that the screen can maintain a certain visibility even in the darkest environment, avoiding too low brightness causing content unrecognizable. The screen maximum brightness is the highest brightness value that the digital photo frame display screen can reach in normal working state. It sets the upper limit of brightness adjustment, preventing too high brightness from causing visual discomfort or excessive power consumption, and is also a physical limitation of device hardware performance. The content optimization coefficient is a quantitative index that reflects the characteristics of the current display content (photo) and the demand of the screen display state for brightness optimization. It is another important input of the brightness optimization model, allowing brightness adjustment to be finely adjusted according to the characteristics of the image itself to improve the visual expressiveness of the image. The target content optimization coefficient is a preset, ideal or reference content optimization coefficient value. It serves as a reference point for the content optimization coefficient , used to calculate the adjustment amount of the content to the basic brightness in the brightness optimization model. When deviates , it will trigger an exponential adjustment of the brightness.

[0047] Specifically, by comparing with the screen minimum brightness and screen maximum brightness Linear interpolation calculations were performed to ensure the basic brightness. It can dynamically adjust within the screen's physical brightness range, thus initially adapting to various external conditions. For example, when ambient lighting conditions are good, the device is in good condition, and the user's viewing posture is ideal, The value is relatively high, indicating a high base brightness. Will accordingly Move closer to provide a brighter and more comfortable viewing experience; conversely, when external conditions are poor, The value is low, indicating low base brightness. Then it will be directed to Proximity is used to conserve power or protect equipment. Building on this, the model further incorporates content optimization coefficients. To adjust the base brightness Make fine adjustments to achieve the final target image display brightness. Content optimization coefficient This reflects the characteristics of the currently displayed content and the screen's brightness optimization requirements. (Using an exponential function...) The model can optimize coefficients based on content. Optimization coefficient with preset target content The difference between them affects the base brightness. It makes non-linear adjustments. This exponential adjustment mechanism allows brightness optimization to respond more sensitively and accurately to changes in content characteristics. For example, when A higher value (indicating that the content has a lower need for brightness optimization, such as when the image itself is bright or the screen is already bright) The term may be less than 1, making exist On the basis of reducing; conversely, when When the value is low (indicating a higher need for brightness optimization in the content, such as a darker image or a brighter screen), this value may be close to or greater than 1, thus... Based on this, appropriate reductions or increases are made to achieve the best visual effect. Through the above two-stage calculations, the brightness optimization model of this application will achieve multi-dimensional balance adaptation. The external comprehensive factors and content optimization coefficients represented The internal content characteristics they represent are organically combined to form a unified and precise brightness calculation framework. This layered and interconnected calculation method enables the final target image to display brightness... It can not only adapt to complex and ever-changing environments and equipment conditions, but also take into account the display needs of different image content, thereby achieving the best display effect in a multi-dimensional balance.

[0048] Through the above technical solution, this application provides a specific brightness optimization model. This model can accurately calculate the display brightness of the target photo, thereby effectively solving the problems of inaccurate brightness calculation and inability to effectively adapt to different environments, devices, and content conditions in traditional methods. This model achieves multi-dimensional balanced adaptability. With the minimum brightness of the screen and maximum brightness Combined, the display brightness of the base photo is dynamically determined. This ensures that brightness adjustment fully considers external factors such as environment, device, and user viewing posture, resulting in good adaptability of brightness output at a macroscopic level. Building on this, the model further introduces a content optimization coefficient. It also uses an exponential function to finely adjust the base brightness, resulting in a final target image with optimal brightness. It can precisely compensate based on the characteristics of the image content itself and the screen display status, thereby improving the visual expressiveness of the image at the microscopic level. This layered and interactive brightness calculation mechanism enables the digital photo frame to output a brightness value that is balanced in multiple dimensions, significantly improving the user's visual comfort and viewing experience in complex and changing scenes, avoiding problems such as excessive brightness or darkness, color distortion, etc., while also helping to optimize device power consumption and extend hardware life.

[0049] Please see Figure 2 As an embodiment of the present invention, a portable smart digital photo frame with multi-sensor fusion is provided. The portable smart digital photo frame control method with multi-sensor fusion described above includes a photo frame body and a battery, a lidar, a face recognition camera and a voice array module embedded in the photo frame body 1.

[0050] In this embodiment of the invention, the screen is turned on when a person arrives and turned off when the person leaves, the face recognition camera identifies the current user's face, automatically retrieves the user's photo and displays it, the voice array module identifies different people, retrieves the photo of the person speaking, and the functions of the photo frame are controlled through voice interaction. At the same time, the built-in voice model can answer questions raised by the user, and the device can also interact with other smart home appliances.

[0051] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0052] While the embodiments of the application have been shown and described herein, it will be understood by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made to the embodiments without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. A portable smart digital photo frame control method using multi-sensor fusion, characterized in that, Includes the following steps: Based on ambient light color temperature, ambient light uniformity, and infrared light intensity, the environmental quality coefficient is obtained through an environmental quality model. Based on the screen panel temperature and the remaining battery percentage, the device constraint coefficients are obtained through a constraint model. Based on the image highlight pixel ratio, the current screen backlight current, and the screen on-time, the content optimization coefficient is obtained through the content optimization model. Based on the ambient light intensity, average pixel brightness of the image, and the relative angle between the device and the viewer under the environmental quality coefficient and device constraint coefficient, a multidimensional balance adaptation model is used to obtain the multidimensional balance adaptation degree. Based on multidimensional balance adaptation, content optimization coefficient, and basic photo display brightness, the target photo display brightness is obtained through a brightness optimization model.

2. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 1, characterized in that, The brightness optimization model determines a basic display brightness between the minimum and maximum brightness of the screen based on the multidimensional balance adaptation degree. as well as Based on the difference between the content optimization coefficient and a preset target optimization coefficient, the basic display brightness is adjusted to obtain the target display brightness.

3. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 2, characterized in that, Based on ambient illuminance, average image pixel brightness, and the relative angle between the device and the viewer under environmental quality coefficient and device constraint coefficient, the steps to obtain the multidimensional balance fit degree through the multidimensional balance fit model are as follows: Acquire environmental quality coefficient, device constraint coefficient, ambient illuminance, average pixel brightness of the image, and the relative angle between the device and the viewer; The ambient light intensity index is obtained by taking the ratio of the ambient light intensity to the sum of the ambient light intensity and the reference light intensity. The pixel brightness index is obtained by processing the ratio of the average pixel brightness to the maximum pixel brightness of the image. The angle index is obtained by comparing the relative angle between the device and the viewer with the maximum angle. The environmental quality coefficient and the ambient light intensity index are imported into a preset ambient light illuminance adaptation model to obtain the ambient light illuminance adaptation degree. In the ambient light illuminance adaptation model, the ambient light illuminance adaptation degree is positively correlated with the environmental quality coefficient and the ambient light intensity index. The device constraint coefficient and pixel brightness index are imported into a preset pixel brightness adaptation model to obtain the pixel brightness adaptation. In the pixel brightness adaptation model, the image brightness adaptation is positively correlated with the device constraint coefficient and the pixel brightness index. The environmental quality coefficient and the angle index are imported into a preset angle fit model to obtain the angle fit. In the angle fit model, the angle fit is positively correlated with the environmental quality coefficient and negatively correlated with the angle index. Ambient lighting adaptation, pixel brightness adaptation, and angle adaptation are imported into a multidimensional balanced adaptation model to obtain multidimensional balanced adaptation. The multidimensional adaptation model obtains the multidimensional balanced adaptation by weighted fusion of ambient lighting adaptation, image brightness adaptation, and angle adaptation.

4. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 2, characterized in that, Based on the image highlight pixel ratio, current screen backlight current, and screen on-time, the steps to obtain content optimization coefficients using a content optimization model are as follows: Obtain the image highlight pixel ratio, current screen backlight current, and screen on-time. Import the image highlight pixel ratio into the formula In the process, obtain the highlight pixel scaling factor. Indicates the proportion of highlight pixels in an image; The current screen backlight current and screen on-time are processed by maximum-minimum normalization to obtain the backlight current factor and screen on-time factor. The highlight pixel ratio factor, backlight current factor, and screen-on duration factor are complemented to obtain the highlight pixel ratio index, backlight current index, and screen-on duration index. Import the highlight pixel ratio index, backlight current index, and screen-on time index into the content optimization model to obtain the content optimization coefficient.

5. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 3, characterized in that, Based on the screen panel temperature and the remaining battery percentage, the steps to obtain the device constraint coefficients using the constraint model are as follows: Get the screen panel temperature and the remaining battery percentage; The temperature factor is obtained by comparing the screen panel temperature with the maximum allowable temperature, and the temperature deviation index is obtained by comparing the temperature factor with the temperature factor threshold. Import the remaining battery charge percentage and temperature deviation index into the device constraint model to obtain the device constraint coefficients.

6. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 3, characterized in that, The steps to obtain the environmental quality coefficient through an environmental quality model based on ambient light color temperature, ambient light uniformity, and infrared light intensity are as follows: Acquire ambient light color temperature, ambient light uniformity, and infrared light intensity; The color temperature deviation index is obtained by comparing the absolute difference between the ambient light color temperature and the reference color temperature with the reference color temperature. The ambient light uniformity index and infrared light intensity index are obtained by comparing the ambient light uniformity and infrared light intensity with the corresponding reference values. The color temperature deviation index, ambient light uniformity index, and infrared light intensity index are imported into the environmental quality model to obtain the environmental quality coefficient.

7. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 4, characterized in that, In the content optimization model, the content optimization coefficient is positively correlated with the highlight pixel ratio index, the backlight current index, and the screen-on time index.

8. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 5, characterized in that, In the device constraint model, the device constraint coefficient is positively correlated with the remaining battery charge percentage and negatively correlated with the temperature deviation index.

9. The portable smart digital photo frame control method based on multi-sensor fusion according to claim 6, characterized in that, In the environmental quality model, the environmental quality coefficient is negatively correlated with the color temperature deviation index, the ambient light uniformity index, and the infrared light intensity index.

10. A portable smart digital photo frame with multi-sensor fusion, characterized in that, The portable smart digital photo frame control method using multi-sensor fusion as described in any one of claims 1-9 is adopted.

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