Low-voltage direct-current UPS (Uninterrupted Power Supply) energy-saving control method and system with LCD screen display

Abstract

The invention relates to the field of display screens, and discloses a low-voltage direct-current UPS energy-saving control method and system with an LCD screen display, and the method comprises the steps: collecting eye image data of a user, and obtaining a sight focus coordinate and a watching distance value based on the eye image data; dividing the display screen into a concerned area and a non-concerned area based on the sight line focus coordinate and the watching distance value; determining a distribution range of active pixels based on the boundary range of the region of interest; acquiring battery electric quantity data and calculating a maximum threshold value of pixel activation through an energy distribution model based on the battery electric quantity data; and obtaining a pixel activation number based on a distribution range of active pixels, and adjusting the pixel activation number and the contour density of the non-attention area based on the battery electric quantity data and the contour line parameters so as to obtain an energy-saving optimization layout of full-screen display. The method has the following effects: the energy-saving effect is maximized while the watching experience is ensured.

Description

Technical Field

[0001] The present invention relates to the field of display screens, and particularly to a UPS energy-saving control method and system with a low-voltage direct current and an LCD screen display. Background Art

[0002] With the wide application of portable devices and intelligent terminals, users' demand for efficient and long-lasting power supply systems is becoming increasingly urgent. How to optimize energy consumption and improve the user experience with limited battery power has become a research hotspot in this field. Traditional UPS energy-saving methods mostly rely on static power consumption management or simple screen brightness adjustment. Although the energy consumption is reduced to a certain extent, these solutions often ignore the dynamic relationship between the actual user interaction behavior and the display content, resulting in limited energy-saving effects and difficulty in adapting to diverse usage scenarios.

[0003] In an existing technology, when dealing with the energy consumption of the display screen, a global power reduction strategy is usually adopted, such as uniformly reducing the resolution or refresh rate. This rough adjustment not only sacrifices the display quality but also may reduce the usage comfort because it cannot accurately match the user's needs.

[0004] How to solve the above technical problems is a technical challenge that those skilled in the art need to overcome. Summary of the Invention

[0005] The present invention provides a UPS energy-saving control method and system with a low-voltage direct current and an LCD screen display to at least partially solve the above technical problems.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a UPS energy-saving control method with a low-voltage direct current and an LCD screen display, including: Collecting the eye image data of the user and obtaining the line-of-sight focus coordinates and the viewing distance value based on the eye image data; Dividing the display screen into a concerned area and an unconcerned area based on the line-of-sight focus coordinates and the viewing distance value; determining the distribution range of active pixels based on the boundary range of the concerned area; Obtaining the battery power data and calculating the maximum threshold of pixel activation through an energy distribution model based on the battery power data; obtaining the number of pixel activations based on the distribution range of active pixels; If the number of pixel activations is greater than the maximum threshold, then adopting a dynamic pixel compression algorithm to adjust the pixel point density of the concerned area to obtain an adjusted pixel activation scheme; Based on the adjusted pixel activation scheme, extracting the thickness and density data of the contour lines of the display content in the concerned area to determine the contour line parameters; and, Adjust the number of pixel activations and contour density of the non - attention area based on the battery power data and contour line parameters to obtain an energy - saving optimized layout for full - screen display.

[0007] In an alternative embodiment, collect the user's eye image data and obtain the line - of - sight focus coordinates and viewing distance value based on the eye image data, including: Collect the user's eye image data through an infrared camera at a preset frequency; Use an ellipse detection algorithm based on the Hough transform to extract the pupil contour from the eye image data; When the user is looking at the screen, establish a three - dimensional coordinate system through the corneal reflection points formed by dual infrared light sources and calculate the line - of - sight vector; Based on the established three - dimensional coordinate system and the line - of - sight vector, determine the straight - line equation of the line of sight in space. Combine it with the equation of the plane where the screen is located, and solve the system of equations to obtain the intersection point of the line of sight and the screen plane. The coordinates of the intersection point are the coordinates of the line - of - sight focus in three - dimensional space; Convert the intersection point to the screen coordinate system through a space mapping matrix to obtain the line - of - sight focus coordinates in the screen coordinate system; Monitor the change rate of the pupil diameter; combine the preset focal - length adjustment parameters of the system, and input the time - series data including the pupil - diameter change rate and the focal - length adjustment parameters into a deep - learning model for analysis, so as to calculate the viewing distance between the user and the screen.

[0008] In an alternative embodiment, divide the display screen into an attention area and a non - attention area based on the line - of - sight focus coordinates and the viewing distance value, including: Based on the line - of - sight focus coordinates and the viewing distance value, use the region - growing algorithm to expand outward with the line - of - sight focus as the center and combine the pixel - density threshold to process the display screen to obtain the preliminary boundary range of the attention area and the non - attention area; Based on the pixel distribution within the preliminary boundary range, calculate the gray - value distribution of the pixel area within a preset range around the line - of - sight focus; use the K - means clustering algorithm to divide the display screen into a high - activity area and a low - activity area, and calculate the number of active pixels to determine the first distribution data; If the number of active pixels in the first distribution data is lower than the preset threshold, adjust the region - segmentation parameters to obtain the second distribution data again; According to the position coordinates in the second distribution data, use the density - peak detection algorithm to identify the core aggregation area, combine the Gaussian mixture model to analyze the pixel - distribution characteristics, and obtain the active - pixel distribution characteristics within the attention area to obtain the region activity; By comparing the region activity with the viewing distance value, determine the dynamic adjustment coefficient of the display screen to obtain the adjusted boundary range; Redefine the region of interest and the non - region of interest using the adjusted boundary range.

[0009] In an alternative embodiment, battery power data is obtained and the maximum threshold for pixel activation is calculated based on the battery power data through an energy distribution model. The number of pixel activations is obtained based on the distribution range of active pixels, including: Collect the current power status of the battery through a sensor to obtain battery power data; Based on the battery power data and the energy required for each pixel to be activated, calculate the maximum number of pixels that can be activated, denoted as the maximum threshold for pixel activation; Determine the distribution range of active pixels and, based on the distribution range of active pixels, use a density estimation algorithm to analyze the activity level of pixels in the region and combine it with a Gaussian distribution model to determine the core active region; identify the number of highly active pixels in the core active region; Extract pixel data within the region of interest from the distribution range of active pixels to obtain the number of pixel activations; among them, use an energy optimization algorithm to preferentially allocate energy to highly active pixels to make their activation rate reach a first preset ratio, and control the activation rate of low - active pixels within a second preset ratio to ensure that the number of pixel activations in the region of interest is stable around a preset number.

[0010] In an alternative embodiment, if the number of pixel activations is greater than the maximum threshold, a dynamic pixel compression algorithm is used to adjust the pixel density of the region of interest to obtain an adjusted pixel activation scheme, including: When the number of pixel activations is greater than the maximum threshold, set an initial pixel density based on the current screen resolution; Analyze the pixel activity within the region of interest, identify the number of highly active pixels in the region of interest, and determine the number of pixels exceeding the maximum threshold; Use a pixel density optimization method based on the K - means clustering algorithm to divide the region of interest into several sub - regions; Adjust the pixel density according to the pixel activity of each sub - region; for highly active sub - regions with activity greater than the first activity level, increase the pixel density to the first density; for low - active sub - regions with activity less than the second activity level, reduce the pixel density to the second density; among them, combine the Laplace operator to detect pixel edges to ensure that the image quality after compression does not decrease significantly and obtain a preliminarily adjusted distribution feature; For the preliminarily adjusted distribution feature, obtain the regional data of pixel activation and determine whether it meets the requirements of the preset activation scheme; if not, perform secondary processing on the dynamic pixels using adjustment parameters to obtain an adjusted pixel density distribution; According to the adjusted pixel density distribution, extract the change characteristics of the activation number from the region of interest. Determine the adjusted pixel activation state; among them, use statistical tools to analyze the matching degree between the distribution characteristics and the threshold range, and obtain the dynamically adjusted activation scheme; Integrate the pixel point density and the activation quantity within the region of interest to obtain the final optimized distribution result; according to the final optimized distribution result, obtain the pixel activation characteristics within the region of interest, and determine the complete pixel activation scheme.

[0011] In an alternative embodiment, based on the adjusted pixel activation scheme, extract the display content within the region of interest to obtain the thickness and density data of the contour line, and determine the contour line parameters, including: Based on the adjusted pixel activation scheme, for the display content within the region of interest, use an adaptive contour extraction algorithm to generate the thickness and density data of the contour line, and obtain the preliminary contour distribution characteristics; According to the preliminary contour distribution characteristics, obtain the boundary data of the display content from the region of interest, and determine the initial drawing range of the contour line; If the initial drawing range exceeds the preset threshold range, adjust the parameters of the adaptive contour extraction algorithm, generate the updated thickness and density data, and obtain the adjusted contour distribution characteristics; For the adjusted contour distribution characteristics, analyze the matching degree between the density data of the contour line and the drawing parameters, and determine the adjusted drawing range; Through the adjusted drawing range, extract the spatial distribution data of the contour line from the display content, and obtain the final adjusted value of the drawing parameters; According to the final adjusted value of the drawing parameters, generate a complete contour line drawing scheme for the pixel activation state within the region of interest.

[0012] In an alternative embodiment, based on the battery power data and the contour line parameters, adjust the pixel activation quantity and the contour density of the non-region of interest to obtain an energy-saving optimized layout for full-screen display, including: Calculate the current available energy through the battery power data combined with the device power consumption model; Set the energy allocation ratio of the non-region of interest; Use the Sobel operator to extract the edge features of the non-region of interest to generate a gradient magnitude map; perform binaryzation on it through the Otsu adaptive threshold algorithm to extract effective edge pixels; Obtain the contour density data from the contour line parameters, and adjust the contour density of the non-region of interest according to the energy allocation ratio; among them, use the Laplacian operator to detect the edge curvature, and increase the sampling point density in the area where the radius of curvature is less than the preset pixel to ensure the contour smoothness; Based on the regional energy distribution result, adjust the number of pixel activations in the non - concerned area. Set the pixel activation rate in the high - energy area to the first ratio, the medium - energy area to the second ratio, and the low - energy area to the third ratio to obtain the initial energy - saving distribution; According to the initial energy - saving distribution, determine the display boundary of the non - concerned area; if the display boundary exceeds the preset threshold range, adjust the energy mapping parameters to generate the updated number of pixel activations; For the updated number of pixel activations, use statistical tools to analyze the matching degree between the contour density and the activation number to obtain the adjusted layout range.

[0013] Through the adjusted layout range, extract the spatial distribution data of the contour lines from the non - concerned area to determine the adjustment parameters for full - screen display; According to the adjustment parameters, integrate the battery power and the energy mapping data, and through RGB color space conversion, match the low - power consumption colors in combination with the regional content type; Fuse the adjusted pixel activation data with the contour parameters and output them to the rendering pipeline to generate the final energy - saving optimized layout.

[0014] In an alternative embodiment, the method further includes: Real - time update the changes in the line - of - sight focus and viewing distance, and combine the trend with the energy - saving optimized layout to dynamically adjust the distribution of active pixels and contour lines through a fast - refresh algorithm; If the change trend exceeds the preset threshold, extract the key - area features from the full - screen display data, and use the local redrawing algorithm to recalculate the pixel activation and contour parameters to obtain a display scheme adapted to the new interaction behavior.

[0015] In an alternative embodiment, real - time update the changes in the line - of - sight focus and viewing distance, and combine the trend with the energy - saving optimized layout to dynamically adjust the distribution of active pixels and contour lines through a fast - refresh algorithm, including: Real - time update the user's line - of - sight focus coordinates and viewing distance value; Analyze the change trend of the user's concerned area in combination with the device screen resolution and refresh rate; based on the Kalman filtering algorithm, predict the movement trajectory of the line - of - sight focus within the next N seconds and calculate the dynamic range of the concerned area; For the concerned area, use the fast - refresh algorithm to increase the pixel refresh frequency to the first frequency, and at the same time reduce the refresh frequency of the non - concerned area to the second frequency, and adjust the number of active pixels in combination with the energy - saving optimized layout to obtain the distribution adjustment data; Fuse the adjusted display data with the dynamic refresh parameters and dynamically adjust the distribution of active pixels and contour lines through the fast - refresh algorithm; If the change trend exceeds the preset threshold, extract the key area features from the full-screen display data, and use the local redrawing algorithm to recalculate the pixel activation and contour parameters to obtain a display solution adapted to the new interaction behavior, including: By monitoring the user's interaction behavior in real time; Use the SIFT feature extraction algorithm to identify the key area from the full-screen display data and extract the key area features to obtain the preliminarily divided data; Locate the key area according to the preliminarily divided data, adopt the local redrawing algorithm, increase the pixel activation rate of the key area to 95% and decrease the non-key area to 30%, calculate the number of pixel activations, and determine the activation distribution range; According to the activation distribution range, adjust the contour density of the key area and the non-key area to obtain the adjusted contour line data; If the matching degree between the adjusted contour line data and the interaction behavior is insufficient, use the preset template to compare the adjustment parameters and judge the corrected distribution state; Update the display solution according to the corrected distribution state; Obtain the pixel layout data adapted to the interaction behavior, fuse the adjusted display data with the local redrawing parameters, and output to the rendering engine to obtain a display solution adapted to the new interaction behavior.

[0016] In a second aspect, the present invention provides a UPS energy-saving control system with a low-voltage DC and an LCD screen display, including: The first processing module is used to: collect the user's eye image data and obtain the line-of-sight focus coordinates and viewing distance values based on the eye image data; The second processing module is used to: divide the display screen into a concerned area and a non-concerned area based on the line-of-sight focus coordinates and the viewing distance value; determine the distribution range of active pixels based on the boundary range of the concerned area; The third processing module is used to: obtain the battery power data and calculate the maximum threshold of pixel activation through the energy distribution model based on the battery power data; obtain the number of pixel activations based on the distribution range of active pixels; The fourth processing module is used to: if the number of pixel activations is greater than the maximum threshold, adopt a dynamic pixel compression algorithm to adjust the pixel point density of the concerned area to obtain an adjusted pixel activation scheme; The fifth processing module is used to: based on the adjusted pixel activation scheme, extract the thickness and density data of the contour lines of the display content in the concerned area to determine the contour line parameters; The sixth processing module is used to: based on the battery power data and the contour line parameters, adjust the number of pixel activations and the contour density of the non-concerned area to obtain an energy-saving optimized layout for full-screen display.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: collecting user eye data, calculating the coordinates of the line-of-sight focus and the viewing distance, and accurately positioning the user's attention area. Based on this information, the present invention divides the display screen into attention and non-attention areas, and combines the battery power data to dynamically adjust the number and density of pixel activations. For the attention area, the system uses an adaptive contour extraction algorithm to generate clear display content; for the non-attention area, the display quality is reduced through an energy-display mapping model to save power. The present invention can also adjust the display strategy in real time according to the change trend of the user's line of sight, ensuring the maximization of energy-saving effect while guaranteeing the viewing experience. This intelligent display solution not only improves the energy efficiency of the device, but also provides a more personalized and comfortable visual experience for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic flowchart of a UPS energy-saving control method with a low-voltage DC and an LCD screen display provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a UPS energy-saving control system with a low-voltage DC and an LCD screen display provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Referring to Figure 1 , the first embodiment of the present invention provides a UPS energy-saving control method with a low-voltage DC and an LCD screen display, including the following steps: S101, collecting the user's eye image data and obtaining the coordinates of the line-of-sight focus and the viewing distance value based on the eye image data; S102, dividing the display screen into an attention area and a non-attention area based on the coordinates of the line-of-sight focus and the viewing distance value; determining the distribution range of active pixels based on the boundary range of the attention area; S103, obtaining the battery power data and calculating the maximum threshold of pixel activation through an energy distribution model based on the battery power data; obtaining the number of pixel activations based on the distribution range of active pixels; S104, if the number of pixel activations is greater than the maximum threshold, using a dynamic pixel compression algorithm to adjust the pixel density of the attention area to obtain an adjusted pixel activation scheme; S105. Based on the adjusted pixel activation scheme, extract the thickness and density data of the contour lines from the display content within the region of interest, and determine the contour line parameters. S106. Based on the battery power data and the contour line parameters, adjust the pixel activation quantity and the contour density of the non-region of interest to obtain an energy-saving optimized layout for full-screen display.

[0021] Specifically, by collecting the user's eye image data to obtain the line-of-sight focus coordinates and the viewing distance value, and based on this, dividing the region of interest and the non-region of interest, it is possible to accurately identify the user's focus of attention, and perform more detailed processing on the region of interest, such as determining the range of active pixel distribution, etc., so that the display content better meets the user's visual needs, and the user can more clearly see the information they are interested in, improving the pertinence and effectiveness of the display, thereby enhancing the user experience. Determining the contour line parameters based on the adjusted pixel activation scheme can make the contour of the display content within the region of interest clearer and more reasonable, further enhancing the display effect. Obtaining the battery power data and calculating the maximum threshold of pixel activation, and combining the active pixel distribution range to obtain the pixel activation quantity, and adjusting it if it exceeds the threshold. This method can reasonably control the pixel activation quantity according to the battery power, avoid unnecessary energy consumption, and achieve energy conservation while ensuring the display effect. Based on the battery power data and the contour line parameters, adjust the pixel activation quantity and the contour density of the non-region of interest to obtain an energy-saving optimized layout for full-screen display. Operations such as reducing the pixel activation rate and the contour density for the non-region of interest effectively reduce the overall energy consumption, extend the power supply time, and improve the energy utilization efficiency. Using a dynamic pixel compression algorithm to adjust the pixel density of the region of interest and making reasonable adjustments when the pixel activation quantity exceeds the threshold can not only ensure the display quality of the region of interest but also avoid resource waste caused by excessive pixel activation, optimizing the display performance. The adjustment of the non-region of interest is processed differently according to the importance and user attention of different regions, maintaining a good display effect while saving energy and enhancing the overall display performance.

[0022] The entire scheme performs comprehensive analysis and control based on various information such as the user's eye image data and battery power data, achieving intelligent energy-saving control and display optimization.

[0023] In one implementation, collecting the user's eye image data and obtaining the line-of-sight focus coordinates and the viewing distance value based on the eye image data includes: Collecting the user's eye image data through an infrared camera at a preset frequency; Using an ellipse detection algorithm based on the Hough transform to extract the pupil contour from the eye image data; When the user is looking at the screen, a three-dimensional coordinate system is established based on the corneal reflection points formed by the dual-infrared light sources, and the line-of-sight vector is calculated; Based on the established three-dimensional coordinate system and the line-of-sight vector, the straight-line equation of the line of sight in space is determined. Combining with the equation of the plane where the screen is located, the intersection point of the line of sight and the screen plane is obtained by solving the system of equations, and the coordinates of the intersection point are the coordinates of the line-of-sight focus in three-dimensional space; The intersection point is transformed into the screen coordinate system through the space mapping matrix to obtain the coordinates of the line-of-sight focus in the screen coordinate system; Monitor the change rate of the pupil diameter; combining with the preset focal length adjustment parameters of the system, input the time series data containing the pupil diameter change rate and the focal length adjustment parameters into the deep learning model for analysis, so as to calculate the viewing distance between the user and the screen.

[0024] Specifically, when acquiring the user's eye data, the built-in infrared camera collects the user's eye image data at a preset frequency. In this embodiment, the infrared camera collects data at a frequency of 120 frames per second. After collecting the eye image data, the ellipse detection algorithm based on the Hough transform is used to extract the pupil contour from these data. The Hough transform is a feature extraction technique widely used in the field of image processing, which can transform a specific shape in the image space (such as the pupil ellipse shape here) into the parameter space for analysis and detection. Through this algorithm, the contour of the pupil in the eye image can be accurately located, and its positioning accuracy reaches 1 millimeter. When the user is looking at the screen, a three-dimensional coordinate system is established by means of the corneal reflection point (Purkinje spot) formed by the dual infrared light sources. The Purkinje spot refers to the reflected light spot generated on the surfaces of intraocular tissues such as the cornea and lens when light irradiates the eye. Using the specific Purkinje spot formed by the dual infrared light sources, a three-dimensional coordinate system that can accurately reflect the position and direction of the eye can be constructed. Under this three-dimensional coordinate system, an improved 3D line-of-sight estimation algorithm is used to calculate the line-of-sight vector. This algorithm combines a polynomial regression model and a geometric optical model, and can achieve an angular error of ±5 degrees at a viewing distance of 60 centimeters, ensuring the accuracy of the line-of-sight vector calculation. Based on the established three-dimensional coordinate system and the calculated line-of-sight vector, the straight-line equation of the line of sight in space is determined. The plane where the screen is located also has its corresponding plane equation. By solving the system of equations composed of these two equations, the intersection point of the line of sight and the screen plane can be obtained, and the coordinates of this intersection point are the coordinates of the line-of-sight focus in the three-dimensional space. The coordinates of the obtained line-of-sight focus in the three-dimensional space cannot be directly applied to screen display control. The intersection point needs to be transformed to the screen coordinate system through a spatial mapping matrix, so as to obtain the coordinates of the line-of-sight focus in the screen coordinate system. The spatial mapping matrix is a mathematical tool for coordinate transformation, which can accurately transform the three-dimensional space coordinates into the two-dimensional coordinates on the screen according to factors such as the size, resolution of the screen, and the relative position relationship with the user. In practical applications, Kalman filtering is also used to smooth the original coordinates (such as (x = 326, y = 183)), eliminating the ±2 pixel jitter caused by eye micro-vibrations, and further improving the accuracy of the line-of-sight focus coordinates. When calculating the viewing distance, it is necessary to monitor the change rate of the pupil diameter. The pupil diameter changes with factors such as the distance between the user and the screen and the ambient light. By monitoring its change rate, distance-related information can be obtained. At the same time, in combination with the preset focal length adjustment parameters of the system, the time series data containing the pupil diameter change rate and the focal length adjustment parameters is input into the deep learning model for analysis. The deep learning model used in this embodiment is the LSTM network (Long Short-Term Memory network), which is a special type of recurrent neural network that can effectively process time series data and capture the long-term dependence relationships in the data.Through the analysis of the input data by the LSTM network, a ranging accuracy of ±1 cm can be achieved within the range of 40 - 80 cm, thereby accurately calculating the viewing distance between the user and the screen.

[0025] Through the above series of steps, the eye image data of the user can be accurately collected, and based on these data, the high-precision line-of-sight focus coordinates and viewing distance values can be obtained, for subsequent dividing the display screen into a focus area and a non-focus area.

[0026] In one implementation, dividing the display screen into a focus area and a non-focus area based on the line-of-sight focus coordinates and the viewing distance value includes: Using the region growing algorithm based on the line-of-sight focus coordinates and the viewing distance value, expanding outward with the line-of-sight focus as the center and combining the pixel density threshold to process the display screen to obtain the preliminary boundary range of the focus area and the non-focus area; Based on the pixel distribution within the preliminary boundary range, calculate the gray value distribution of the pixel area within a preset range around the line-of-sight focus; use the K-means clustering algorithm to divide the display screen into a high-activity area and a low-activity area, and calculate the number of active pixels to determine the first distribution data; If the number of active pixels in the first distribution data is lower than the preset threshold, adjust the region segmentation parameters to obtain the second distribution data again; According to the position coordinates in the second distribution data, use the density peak detection algorithm to identify the core aggregation area, combine the Gaussian mixture model to analyze the pixel distribution characteristics, and obtain the active pixel distribution characteristics within the focus area to obtain the region activity; By comparing the region activity with the viewing distance value, determine the dynamic adjustment coefficient of the display screen to obtain the adjusted boundary range; Use the adjusted boundary range to re-divide the focus area and the non-focus area.

[0027] Specifically, based on the obtained line-of-sight focus coordinates and viewing distance value, the region growing algorithm is used to process the display screen. The region growing algorithm is an image segmentation algorithm that starts from one or more seed points and gradually merges adjacent pixel points according to pre-set growth criteria until certain stop conditions are met. In this embodiment, it expands outward with the line-of-sight focus as the center, just like gradually expanding the search range with a location as the center on a map. At the same time, the pixel density threshold is combined (for example: 100 pixels per square centimeter), and this threshold is an important criterion for judging whether a certain region belongs to the attention area. If during the expansion process, the pixel density of a certain region reaches or exceeds this threshold, it is more likely to be classified as the attention area, and thus the preliminary boundary range between the attention area and the non-attention area is obtained. Then, based on the pixel distribution within the preliminary boundary range, the gray value distribution of the pixel area within a preset range (10×10 pixel area in this embodiment) around the line-of-sight focus is calculated. The gray value is a numerical value representing the pixel brightness of the image, usually ranging from 0 (black) to 255 (white). By analyzing the gray value distribution of the pixels in this area, the activity level of the pixels can be understood. For example, in a region where the gray value changes frequently, it indicates that the pixels in this region may be constantly changing, and its activity level is relatively high. Then, the K-means clustering algorithm is used to divide the display screen into a high-activity region and a low-activity region. The K-means clustering algorithm is a commonly used clustering analysis algorithm whose purpose is to divide data points into K clusters, so that the data points within the same cluster have a high degree of similarity, while the data points in different clusters have a low degree of similarity. In this embodiment, K is taken as 2, that is, the display screen pixels are divided into two clusters: high-activity and low-activity. Through this algorithm, the set of pixels with a gray value change rate greater than 5% per second is defined as the high-activity region, and the rest are the low-activity regions. Calculate the number of active pixels, and these data constitute the first distribution data, which reflects the distribution of active pixels on the display screen after the preliminary division. If the number of active pixels in the first distribution data is lower than the preset threshold, this indicates that the preliminary division may not be accurate enough, and the region segmentation parameters need to be adjusted to obtain the second distribution data again.

[0028] The preset threshold is a reference value set based on practical experience and a large number of tests, and is used to determine whether the current division result is reasonable. The parameters to be adjusted may include the expansion rule in the region growing algorithm, the cluster center in the K-means clustering algorithm, etc., so that the division result is more in line with the actual situation. Then, according to the position coordinates in the second distribution data, the density peak detection algorithm is used to identify the core aggregation region. The density peak detection algorithm is a clustering algorithm based on the density of data points. It can quickly identify the core points in the data set with relatively high density and relatively low density of surrounding points, and the region composed of these core points is the core aggregation region. Combining the Gaussian mixture model to analyze the pixel distribution characteristics, the Gaussian mixture model is a probability model that assumes that the data is composed of a mixture of multiple Gaussian distributions. By estimating the parameters of these Gaussian distributions, the pixel distribution can be described more accurately. Through the combination of these two algorithms, the active pixel distribution characteristics in the region of interest are obtained, and then the region activity is obtained. The region activity reflects the activity degree of the pixels in the region of interest and is an important basis for subsequent adjustment of the division range. Then, by comparing the region activity with the viewing distance value, the dynamic adjustment coefficient of the display screen is determined. The viewing distance value reflects the distance between the user and the screen. Generally speaking, the closer the viewing distance, the higher the user's requirement for the screen details may be, and the range of the region of interest may need to be adjusted accordingly. When the region activity is high and the viewing distance is close, the range of the region of interest may need to be expanded; otherwise, the range of the region of interest may be reduced. The dynamic adjustment coefficient determined according to this relationship is used to adjust the preliminary boundary range to obtain the adjusted boundary range. Finally, the adjusted boundary range is used to re-divide the region of interest and the non-region of interest.

[0029] In one implementation, battery power data is obtained and the maximum threshold for pixel activation is calculated based on the battery power data through an energy allocation model. The number of pixel activations is obtained based on the distribution range of active pixels, including: Collect the current power state of the battery through a sensor to obtain battery power data; Based on the battery power data and the energy required for each pixel activation, calculate the maximum number of pixels that can be activated, denoted as the maximum threshold for pixel activation; Determine the distribution range of active pixels and analyze the activity degree of pixels in the region based on the distribution range of active pixels. Combine the Gaussian distribution model to determine the core active region; identify the number of highly active pixels in the core active region; Extract the pixel data in the region of interest from the active pixel distribution range to obtain the number of pixel activations; among them, the energy optimization algorithm is used to preferentially allocate energy to highly active pixels so that their activation rate reaches the first preset ratio, and the activation rate of low-active pixels is controlled within the second preset ratio to ensure that the number of pixel activations in the region of interest is stable around the preset number.

[0030] Specifically, the current battery charge status is collected by a sensor to obtain battery charge data. The sensor can monitor the battery charge in real time and accurately obtain the remaining charge of the current battery. For example, in a certain monitoring, the remaining charge of the current battery is obtained as 85%. Based on the obtained battery charge data and the energy required for each pixel to be activated, the maximum threshold for pixel activation is calculated. In this embodiment, based on the relationship between battery capacity and energy consumption, the energy required for each pixel to be activated is set to 1 millijoule. Assuming that the total current battery energy is 5000 millijoules, through a simple division operation (total energy ÷ energy required for each pixel to be activated), the maximum number of pixels that can be activated is calculated to be 50,000, and this value is recorded as the maximum threshold for pixel activation. It represents the maximum number of pixels that can be theoretically activated under the current battery charge.

[0031] In an actual application scenario, the distribution range of active pixels may be a specific area on the screen. For example, in this embodiment, it is a rectangular area from the upper left corner (150, 75) to the lower right corner (450, 325) of the screen. Based on this distribution range, a density estimation algorithm is used to analyze the activity level of pixels in the area. The density estimation algorithm is a method for estimating the probability density function of data distribution. Through it, the density and activity of pixels in different areas can be understood. The core active area is determined by combining with a Gaussian distribution model. The Gaussian distribution model is a common probability distribution model and is used in this scenario to more accurately describe the distribution of pixel activity levels, thereby determining the core active area, that is, the area where pixel activity is relatively high and concentrated.

[0032] After determining the core active area, the number of highly active pixels in this area is identified. In this embodiment, pixels with a gray value change rate greater than 4% per second are set as highly active pixels, and based on this standard, the system identifies that the number of highly active pixels in this area is 8000.

[0033] Pixel data in the area of interest is extracted from the active pixel distribution range, and then the number of pixel activations is obtained. In this process, in order to reasonably allocate energy and ensure that the number of pixel activations in the area of interest is stable around a preset number, an energy optimization algorithm is used. The core of this algorithm is to preferentially allocate energy to highly active pixels to make their activation rate reach a first preset ratio, while controlling the activation rate of low-active pixels within a second preset ratio. In this embodiment, the first preset ratio is set to 95%, and the second preset ratio is set to 30%, that is, to ensure that the activation rate of highly active pixels reaches 95% and the activation rate of low-active pixels is controlled within 30%. In this way, both the display effect of the important area (the area where highly active pixels are located) can be ensured and excessive energy consumption can be avoided, so as to ensure that the number of pixel activations in the area of interest is stable around 10,000.

[0034] In one implementation, if the number of pixel activations is greater than the maximum threshold, a dynamic pixel compression algorithm is used to adjust the pixel density of the region of interest to obtain an adjusted pixel activation scheme, including: When the number of pixel activations is greater than the maximum threshold, set the initial pixel density based on the current screen resolution; Analyze the pixel activity within the region of interest, identify the number of highly active pixels in the region of interest, and determine the number of pixels exceeding the maximum threshold; Adopt a pixel density optimization method based on the K-means clustering algorithm to divide the region of interest into several sub-regions; Adjust the pixel density according to the pixel activity of each sub-region; for highly active sub-regions with activity greater than the first activity degree, increase the pixel density to the first density; for low-active sub-regions with activity less than the second activity, reduce the pixel density to the second density; wherein, combine the Laplace operator to detect the pixel edges to ensure that the quality of the compressed image does not decrease significantly to obtain the preliminarily adjusted distribution characteristics; Obtain the regional data of pixel activation for the preliminarily adjusted distribution characteristics, and determine whether it meets the requirements of the preset activation scheme; if not, perform secondary processing on the dynamic pixels using adjustment parameters to obtain the adjusted pixel density distribution; According to the adjusted pixel density distribution, extract the change characteristics of the activation number from the region of interest, determine the adjusted pixel activation state; wherein, use statistical tools to analyze the matching degree between the distribution characteristics and the threshold range to obtain the dynamically adjusted activation scheme; Integrate the pixel density and activation number within the region of interest to obtain the final optimized distribution result; according to the final optimized distribution result, obtain the pixel activation characteristics within the region of interest and determine the complete pixel activation scheme.

[0035] Specifically, when the system detects that the number of pixel activations is greater than the maximum threshold, first set the initial pixel density based on the current screen resolution. For example, in an actual scenario, if the current screen resolution is 1920×1080, the system will set the initial pixel density to 1000 pixels per square centimeter.

[0036] Through specific algorithms and metrics, identify the number of highly active pixels in the region of interest and determine the number of pixels exceeding the maximum threshold. Take a specific example. Suppose the region of interest is the area from the upper left corner (200, 100) to the lower right corner (600, 400) of the screen. After analysis, it is found that the number of highly active pixels in this region is 12,000, and according to the previously calculated maximum threshold of 10,000, the number of pixels exceeding the maximum threshold is 2,000. Then, adopt a pixel density optimization method based on the K-means clustering algorithm to divide the region of interest into several sub-regions. The K-means clustering algorithm is a common data clustering algorithm. Its core idea is to divide data points into K clusters, making the data points within the clusters have a high degree of similarity and the data points between the clusters have a low degree of similarity. In this scenario, through this algorithm, the pixels in the region of interest are grouped according to characteristics such as activity to form different sub-regions. After that, adjust the pixel density according to the pixel activity of each sub-region. For the highly active sub-regions with an activity greater than the first activity (set to 5% per second in this embodiment), in order to ensure the clarity of image details, increase the pixel density to the first density (1,200 pixels per square centimeter); for the low active sub-regions with an activity less than the second activity (set to 2% per second in this embodiment), to reduce energy consumption, reduce the pixel density to the second density (800 pixels per square centimeter).

[0037] During the process of adjusting the pixel density, in order to ensure that the quality of the compressed image does not decrease significantly, detect the pixel edges by combining the Laplace operator. The Laplace operator is a second-order derivative operator commonly used for image edge detection. It can highlight the edge information in the image. By detecting the edges, the system can avoid destroying the edge details of the image when adjusting the pixel density, thus ensuring the image quality. After this series of operations, obtain the preliminarily adjusted distribution characteristics. For the preliminarily adjusted distribution characteristics, obtain the regional data of pixel activation and judge whether it meets the requirements of the preset activation scheme. The requirements of the preset activation scheme are a series of criteria set according to actual application needs and experience, used to measure whether the current pixel activation state meets the requirements of display and energy saving. If it does not meet the requirements, use adjustment parameters to perform secondary processing on the dynamic pixels. These adjustment parameters may involve the amplitude of pixel density adjustment, the parameters of the clustering algorithm, etc. Obtain the adjusted pixel point density distribution through readjustment. According to the adjusted pixel point density distribution, extract the change characteristics of the activation quantity from the region of interest to determine the adjusted pixel activation state. In order to further optimize the activation scheme, use statistical tools to analyze the matching degree between the distribution characteristics and the threshold range. Common statistical tools such as probability statistical analysis software, etc. Obtain the dynamically adjusted activation scheme through analysis.

[0038] Integrate the pixel point density and the activation quantity within the concerned area to obtain the final optimized distribution result. According to this final optimized distribution result, obtain the pixel activation features within the concerned area, including the activity and distribution law of pixels, etc., so as to determine the complete pixel activation scheme. This takes into account both the energy-saving requirements and ensures the display effect, achieving a balance between the two.

[0039] In one implementation manner, based on the adjusted pixel activation scheme, extract the thickness and density data of the contour lines from the display content within the concerned area, and determine the contour line parameters, including: Based on the adjusted pixel activation scheme, for the display content within the concerned area, use an adaptive contour extraction algorithm to generate the thickness and density data of the contour lines, and obtain the preliminary contour distribution features; According to the preliminary contour distribution features, obtain the boundary data of the display content from the concerned area, and determine the initial drawing range of the contour lines; If the initial drawing range exceeds the preset threshold range, then adjust the parameters of the adaptive contour extraction algorithm to generate the updated thickness and density data, and obtain the adjusted contour distribution features; For the adjusted contour distribution features, analyze the matching degree between the density data of the contour lines and the drawing parameters, and determine the adjusted drawing range; Through the adjusted drawing range, extract the spatial distribution data of the contour lines from the display content, and obtain the final adjusted value of the drawing parameters; According to the final adjusted value of the drawing parameters, generate a complete contour line drawing scheme for the pixel activation state within the concerned area.

[0040] Specifically, based on the adjusted pixel activation scheme, for the display content within the concerned area, use an adaptive contour extraction algorithm to generate the thickness and density data of the contour lines, and further obtain the preliminary contour distribution features. In this embodiment, the Canny edge detection algorithm is used as the adaptive contour extraction algorithm. The Canny edge detection algorithm is a classic edge detection algorithm, which extracts the edge information in the image through multiple steps. When processing the display content within the concerned area, first set the Gaussian filter kernel size to 5×5 and the standard deviation σ = 2.

[0041] Gaussian filtering is a linear smoothing filter, which is used to smooth the image, remove noise interference, and at the same time retain the effective edge information of the image. After Gaussian filtering, double-threshold processing is performed, with the high threshold set to 120 and the low threshold set to 60. In this process, pixels greater than the high threshold are determined as strong edge pixels, pixels less than the low threshold are excluded, and pixels between the two are judged whether they are edge pixels according to their connection with strong edge pixels. In this way, strong and weak edge pixels are screened out to generate an initial contour binary map, thereby obtaining the initial contour distribution characteristics.

[0042] According to the initial contour distribution characteristics, boundary data of the display content is obtained from the region of interest to determine the initial drawing range of the contour line. This initial drawing range is determined based on the initially extracted contour information, which roughly delimits the area where the contour line needs to be drawn. If the initial drawing range exceeds the preset threshold range, it indicates that the current contour extraction effect may not meet the expectations, and the parameters of the adaptive contour extraction algorithm need to be adjusted to generate more appropriate contour lines. In this embodiment, if the initial drawing range does not meet the requirements, the thresholds, filtering parameters, etc. in the Canny edge detection algorithm will be adjusted to generate updated thickness and density data, and obtain the adjusted contour distribution characteristics. For the adjusted contour distribution characteristics, analyze the matching degree between the density data of the contour line and the drawing parameters to determine the adjusted drawing range. The drawing parameters here include the width, color, style, etc. of the line. By analyzing the matching degree between the two, the drawing area of the contour line can be further optimized to make it more in line with the characteristics of the display content and the visual needs of users. For example, if the density of the contour line is large, but the line width in the drawing parameters is thin, it may lead to poor display effects, and at this time, the drawing range needs to be adjusted according to the analysis results.

[0043] With the adjusted drawing range, extract the spatial distribution data of the contour lines from the display content to obtain the final adjusted value of the drawing parameters. The spatial distribution data reflects information such as the position and orientation of the contour lines in the display content. By analyzing and processing these data, key information such as the starting point, ending point, and degree of curvature of the lines can be determined, and then the final adjusted value of the drawing parameters can be obtained. These values will be used to accurately draw the contour lines. Finally, according to the final adjusted value of the drawing parameters, a complete contour line drawing scheme is generated for the pixel activation status in the region of interest. When generating the drawing scheme, the analysis results of the regional activity are also combined to adjust the thickness of the contour lines. For example, the line width in the highly active sub-region (activity ≥ 8 times / second) is set to 2 pixels, the medium active region (activity 3 - 7 times / second) is set to 5 pixels, and the low active region (activity ≤ 2 times / second) is set to 1 pixel. At the same time, the line density is dynamically calculated based on the Bresenham algorithm, and additional sampling points are inserted in the local area where the radius of curvature is less than 10 pixels to ensure the smoothness of the contour. In addition, the system extracts the contour hue value through HSV color space conversion and automatically matches the complementary color in combination with the regional content type (such as text, graphics). The text area uses blue with a saturation of 70% and a brightness of 90% (H = 240°), and the graphic area uses red with a saturation of 50% and a brightness of 80% (H = 0°). In this way, the complete contour line drawing scheme generated by considering various factors can make the contour of the display content in the region of interest clearer and more vivid, improving the display effect and the user's visual experience.

[0044] In one implementation, based on the battery power data and the contour line parameters, adjust the pixel activation number and the contour density of the non - region of interest to obtain an energy - saving optimized layout for full - screen display, including: Calculate the current available energy by combining the battery power data with the device power consumption model; Set the energy distribution ratio of the non - region of interest; Use the Sobel operator to extract the edge features of the non - region of interest to generate a gradient magnitude map; perform binarization on it through the Otsu adaptive threshold algorithm to extract the effective edge pixels; Obtain the contour density data from the contour line parameters, and adjust the contour density of the non - region of interest according to the energy distribution ratio; among them, use the Laplacian operator to detect the edge curvature, and increase the sampling point density in the region where the radius of curvature is less than the preset pixel to ensure the smoothness of the contour; Based on the regional energy distribution result, adjust the pixel activation number of the non - region of interest, set the pixel activation rate of the high - energy region to the first ratio, the medium - energy region to the second ratio, and the low - energy region to the third ratio to obtain the initial energy - saving distribution; Determine the display boundary of the non - concerned area according to the initial energy - saving distribution; if the display boundary exceeds the preset threshold range, adjust the energy mapping parameters to generate the updated pixel activation quantity. For the updated pixel activation quantity, use statistical tools to analyze the matching degree between the contour density and the activation quantity to obtain the adjusted layout range.

[0045] Through the adjusted layout range, extract the spatial distribution data of the contour lines from the non - concerned area to determine the adjustment parameters for full - screen display. According to the adjustment parameters, integrate the battery power and the energy mapping data, through RGB color - space conversion, and match the low - power consumption colors in combination with the regional content type. Fuse the adjusted pixel activation data with the contour parameters and output them to the rendering pipeline to generate the final energy - saving optimized layout.

[0046] Specifically, calculate the current available energy by combining the battery power data with the device power - consumption model. The battery power data reflects the current remaining power of the battery, and the device power - consumption model is a mathematical model established based on the energy - consumption situation of the device in different operating states. For example, assuming that the current remaining battery capacity is 3000 mAh, by combining the power - consumption data of the device in different display modes and function operations, the current energy available for display can be accurately calculated.

[0047] Set the energy - distribution ratio of the non - concerned area. This ratio is determined according to the balance between energy - saving requirements and display effects. For example, in this embodiment, the energy - distribution ratio of the non - concerned area is set to 30%. This ratio determines the share of the non - concerned area in the total display energy consumption. Then, use the Sobel operator to extract the edge features of the non - concerned area and generate a gradient - magnitude map. The Sobel operator is an operator for edge detection, which determines the position and intensity of the edge by calculating the gradient of the pixel points in the image. When applying the Sobel operator, a convolution operation is performed on the image of the non - concerned area to obtain the gradient magnitude of each pixel point, thus generating a gradient - magnitude map. After that, use the Otsu adaptive threshold algorithm to binarize the gradient - magnitude map and extract the effective edge pixels. The Otsu algorithm is a method for automatically determining the image threshold, which can find an optimal threshold according to the gray - scale distribution characteristics of the image, divide the image into foreground and background parts, and thus extract the effective edge pixels.

[0048] Obtain the contour density data from the contour line parameters, and adjust the contour density of the non - concerned area according to the energy distribution ratio. In this embodiment, the contour density of the high - energy area (energy ratio ≥ 20%) is set to 8 per pixel, the medium - energy area (energy ratio 10% - 19%) is set to 5 per pixel, and the low - energy area (energy ratio ≤ 9%) is set to 2 per pixel. At the same time, use the Laplacian operator to detect the edge curvature. The Laplacian operator is a second - order derivative operator used to detect edges and details in an image, and through it, the curvature of the edge can be calculated. Increase the sampling point density in the area where the radius of curvature is less than the preset pixel (15 pixels in this embodiment), so as to ensure that during the process of adjusting the contour density, the smoothness of the contour is not affected and avoid jagged or discontinuous contours.

[0049] Based on the regional energy distribution result, adjust the number of pixel activations in the non - concerned area. Set the pixel activation rate of the high - energy area to the first ratio (such as 80%), the medium - energy area to the second ratio (such as 50%), and the low - energy area to the third ratio (such as 20%), thus obtaining the initial energy - saving distribution. This way of allocating the activation rate according to the energy area can minimize the energy consumption of the non - concerned area while ensuring a certain display effect. According to the initial energy - saving distribution, determine the display boundary of the non - concerned area. If the display boundary exceeds the preset threshold range, it indicates that the current energy distribution and pixel activation settings may be unreasonable, and it is necessary to adjust the energy mapping parameters and regenerate the updated number of pixel activations. The energy mapping parameters are the parameters used to describe the relationship between the battery energy and the number of pixel activations. By adjusting these parameters, the activation situation of the pixels can be changed to meet different display requirements and energy - saving requirements.

[0050] For the updated number of pixel activations, a statistical tool is used to analyze the matching degree between the contour density and the number of activations, and an adjusted layout range is obtained. The statistical tool can be various data analysis software or algorithms. By analyzing the correlation between the contour density and the number of activations, it is judged whether the current layout is reasonable, so as to determine a more optimized layout range. Through the adjusted layout range, the spatial distribution data of the contour lines are extracted from the non - concerned area, and the adjustment parameters for full - screen display are determined. These spatial distribution data contain information such as the position and direction of the contour lines in the non - concerned area. Based on these data, key parameters such as the starting point, ending point, and bending degree of the lines can be calculated, and these parameters will be used for further adjustment of the full - screen display. According to the adjustment parameters, the battery power and energy mapping data are integrated, and through RGB color space conversion, low - power colors are matched in combination with the regional content type. The RGB color space is a commonly used color representation method, and different colors are represented by adjusting the values of the three color channels of red (R), green (G), and blue (B). In this embodiment, the background area uses gray with a brightness value of 30% (R = 77, G = 77, B = 77), and the decoration area uses light green with a brightness value of 40% (R = 102, G = 153, B = 102). Such a low - power color setting can further reduce the energy consumption of the non - concerned area while ensuring a certain visual effect. Finally, the adjusted pixel activation data and contour parameters are fused and output to the rendering pipeline. The rendering pipeline is a set of processing steps that convert graphic data into the final display image. After being processed by the rendering pipeline, a final energy - saving optimized layout is generated, realizing effective energy - saving control of the non - concerned area while ensuring the display effect, and improving the energy utilization efficiency of the entire display system.

[0051] In one implementation, the method further includes: Real - time update the line - of - sight focus and viewing distance changes, combine the trend with the energy - saving optimized layout, and dynamically adjust the distribution of active pixels and contour lines through a fast - refresh algorithm; If the change trend exceeds the preset threshold, extract the key - area features from the full - screen display data, and use a local redrawing algorithm to recalculate the pixel activation and contour parameters to obtain a display solution adapted to the new interaction behavior.

[0052] In one implementation, real - time update the line - of - sight focus and viewing distance changes, combine the trend with the energy - saving optimized layout, and dynamically adjust the distribution of active pixels and contour lines through a fast - refresh algorithm, including: Real - time update the user's line - of - sight focus coordinates and viewing distance value; Combine the device screen resolution and refresh rate to analyze the change trend of the user's concerned area; based on the Kalman filtering algorithm, predict the line - of - sight focus movement trajectory within the next N seconds, and calculate the dynamic range of the concerned area; Adopt a fast refresh algorithm for the area of interest to increase the pixel refresh frequency to the first frequency, while reducing the refresh frequency of the non - area of interest to the second frequency, and combine energy - saving optimization to adjust the number of active pixels in the layout to obtain distribution adjustment data; Fuse the adjusted display data with dynamic refresh parameters, and dynamically adjust the distribution of active pixels and contour lines through the fast refresh algorithm; If the change trend exceeds the preset threshold, extract the key - area features from the full - screen display data, and use the local redrawing algorithm to recalculate the pixel activation and contour parameters to obtain a display solution adapted to the new interaction behavior, including: By monitoring the user's interaction behavior in real - time; Use the SIFT feature extraction algorithm to identify the key area from the full - screen display data and extract the key - area features to obtain the preliminary division data; Locate the key area according to the preliminary division data, adopt the local redrawing algorithm, increase the pixel activation rate of the key area to 95%, reduce the non - key area to 30%, calculate the number of activated pixels, and determine the activation distribution range; According to the activation distribution range, adjust the contour density of the key area and the contour density of the non - key area to obtain the adjusted contour line data; If the matching degree between the adjusted contour line data and the interaction behavior is insufficient, use a preset template to compare the adjustment parameters and judge the corrected distribution state; Update the display solution according to the corrected distribution state; Obtain the pixel layout data adapted to the interaction behavior, fuse the adjusted display data with the local redrawing parameters, and output to the rendering engine to obtain a display solution adapted to the new interaction behavior.

[0053] Specifically, the system will update the user's line-of-sight focus coordinates and viewing distance values in real time. This relies on the infrared camera mentioned above to collect eye image data, which is processed through a series of algorithms to obtain accurate line-of-sight focus coordinates and viewing distances. For example, by collecting 120 frames of eye images per second, continuously calculating and updating relevant data. Analyze the changing trend of the user's attention area in combination with the device screen resolution and refresh rate. The screen resolution determines the display fineness, while the refresh rate affects the smoothness of the picture. By analyzing the relationship between these parameters and the user's line-of-sight focus, it is possible to understand how the user's attention area changes dynamically. At the same time, based on the Kalman filter algorithm, predict the movement trajectory of the line-of-sight focus within the next N seconds (N = 5 in this embodiment), and calculate the dynamic range of the attention area. The Kalman filter algorithm is an optimal algorithm for estimating the system state, which can accurately predict the future state based on historical data and current measurements. In this scenario, it can predict the movement direction and distance of the line-of-sight focus, thereby determining the dynamic range of the attention area within a certain period in the future. For the attention area, adopt a fast refresh algorithm to increase the pixel refresh frequency to the first frequency (such as 120Hz), which can ensure the smoothness and real-time nature of the picture in the attention area, enabling the user to clearly see the changes in the picture. At the same time, reduce the refresh frequency of the non-attention area to the second frequency (such as 30Hz) to reduce unnecessary energy consumption. Combine energy-saving optimization layout to adjust the number of active pixels to obtain distribution adjustment data. The energy-saving optimization layout was previously determined based on factors such as battery power and contour line parameters. By adjusting the number of active pixels, the energy-saving goal can be achieved while ensuring the display effect.

[0054] Fuse the adjusted display data with the dynamic refresh parameters, and dynamically adjust the distribution of active pixels and contour lines through the fast refresh algorithm. The display data contains information such as the color and brightness of pixels, and the dynamic refresh parameters specify the refresh frequency and method. By fusing these two, the system can real-time adjust the positions of active pixels on the screen and the thickness and density of contour lines, etc., to adapt to the changes in the user's line-of-sight focus.

[0055] If the changing trend exceeds the preset threshold, it indicates that the user's attention area has changed significantly and the display scheme needs to be readjusted. At this time, extract the key area features from the full-screen display data, and use the local redrawing algorithm to recalculate the pixel activation and contour parameters to obtain a display scheme adapted to the new interaction behavior.

[0056] The specific steps are as follows: Key area recognition: By monitoring user interaction behaviors in real time, such as mouse clicks, keyboard inputs, etc. Use the SIFT feature extraction algorithm to identify key areas from the full-screen display data and extract key area features, obtaining the preliminary partition data. The SIFT (Scale-Invariant Feature Transform) feature extraction algorithm is an algorithm for detecting and describing local features of images. It has scale, rotation, and illumination invariance and can accurately identify key areas in images.

[0057] Local redrawing calculation: Locate the key areas according to the preliminary partition data, adopt the local redrawing algorithm, raise the pixel activation rate of the key areas to 95% and lower that of the non-key areas to 30%, calculate the number of activated pixels, and determine the activation distribution range. The local redrawing algorithm only redraws the key areas, avoiding unnecessary refreshing of the entire screen, thus saving energy.

[0058] Contour density adjustment: Adjust the contour density of the key areas and the non-key areas according to the activation distribution range to obtain the adjusted contour line data. For example, increase the contour density of the key areas to make the contours of the key areas clearer; reduce the contour density of the non-key areas to reduce unnecessary display details.

[0059] Match degree judgment and correction: If the match degree between the adjusted contour line data and the interaction behavior is insufficient, use a preset template to compare the adjustment parameters and judge the corrected distribution state. The preset template is a standard display mode established based on a large number of experiments and user feedback. By comparing with the preset template, problems in the current display scheme can be found and corresponding adjustments can be made.

[0060] Display scheme update: Update the display scheme according to the corrected distribution state. Obtain the pixel layout data adapted to the interaction behavior, fuse the adjusted display data with the local redrawing parameters, and output them to the rendering engine to obtain a display scheme adapted to the new interaction behavior. The rendering engine is responsible for converting these data into the final display image and presenting it to the user.

[0061] Refer to Figure 2 , the second embodiment of the present invention provides a UPS energy-saving control system with a low-voltage DC and an LCD screen display, including: The first processing module is used for: collecting the eye image data of the user and obtaining the line-of-sight focus coordinates and viewing distance values based on the eye image data; The second processing module is used for: dividing the display screen into a concerned area and a non-concerned area based on the line-of-sight focus coordinates and the viewing distance values; determining the distribution range of active pixels based on the boundary range of the concerned area; A third processing module, configured to: obtain battery power data and calculate a maximum threshold for pixel activation based on the battery power data through an energy distribution model; obtain the number of pixel activations based on the distribution range of active pixels; A fourth processing module, configured to: if the number of pixel activations is greater than the maximum threshold, adopt a dynamic pixel compression algorithm to adjust the pixel density of the region of interest to obtain an adjusted pixel activation scheme; A fifth processing module, configured to: based on the adjusted pixel activation scheme, extract the display content within the region of interest to obtain the thickness and density data of the contour lines, and determine the contour line parameters; A sixth processing module, configured to: based on the battery power data and the contour line parameters, adjust the number of pixel activations and the contour density of the non-region of interest to obtain an energy-saving optimized layout for full-screen display.

[0062] It should be noted that a low-voltage DC UPS energy-saving control system with an LCD screen provided by an embodiment of the present invention is used to execute all the process steps of a low-voltage DC UPS energy-saving control method in the above embodiment. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0063] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0064] The specific embodiments described above further elaborate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A low voltage DC UPS energy saving control method with LCD display, characterized in that: include: Collecting the user's eye image data and obtaining the sight focus coordinates and viewing distance value based on the eye image data; Divide the display screen into a focus area and a non-focus area based on the sight focus coordinates and the viewing distance value; Determine the distribution range of active pixels based on the boundary range of the focus area; Acquire battery power data and calculate a maximum threshold for pixel activation through an energy allocation model based on the battery power data; The number of pixel activations is obtained based on the distribution range of active pixels; If the number of pixel activations is greater than the maximum threshold, a dynamic pixel compression algorithm is used to adjust the pixel density of the focus area to obtain an adjusted pixel activation scheme; Based on the adjusted pixel activation scheme, the display content in the focus area is extracted to obtain the thickness and density data of the contour line, and the contour line parameters are determined; as well as, The number of pixel activations and the contour density of the non-interest area are adjusted based on the battery power data and the contour line parameters to obtain an energy-saving optimized layout for full-screen display.

2. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: Collecting the user's eye image data and obtaining the sight focus coordinates and viewing distance value based on the eye image data, including: Collecting user's eye image data through an infrared camera at a preset frequency; Extracting pupil contour from the eye image data using an ellipse detection algorithm based on Hough transform; When the user looks at the screen, a three-dimensional coordinate system is established through the corneal reflection points formed by the dual infrared light sources and the sight vector is calculated; Based on the established three-dimensional coordinate system, the line equation of the line of sight in space is determined with the line of sight vector, and the intersection of the line of sight and the screen plane is obtained by solving the equation group in combination with the equation of the plane where the screen is located. The coordinates of the intersection are the coordinates of the line of sight focus in the three-dimensional space; The intersection point is converted to a screen coordinate system through a space mapping matrix to obtain the sight focus coordinates in the screen coordinate system; Monitor the pupil diameter change rate; combine the system's preset focal length adjustment parameters, and input the time series data containing the pupil diameter change rate and focal length adjustment parameters into the deep learning model for analysis, so as to calculate the viewing distance between the user and the screen.

3. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: The display screen is divided into a focus area and a non-focus area based on the sight focus coordinates and viewing distance values, including: Based on the coordinates of the sight focus and the viewing distance, a regional growing algorithm is used to expand outward from the sight focus and combined with the pixel density threshold to process the display screen to obtain the preliminary boundary range of the attention area and the non-attention area; Based on the pixel distribution within the preliminary boundary range, the grayscale value distribution of the pixel area within the preset range around the sight focus is calculated; the display screen is divided into a high-activity area and a low-activity area using a K-means clustering algorithm, and the number of active pixels is calculated to determine the first distribution data; If the number of active pixels in the first distribution data is lower than a preset threshold, adjusting the region segmentation parameters to obtain the second distribution data again; According to the position coordinates in the second distribution data, a density peak detection algorithm is used to identify the core aggregation area, and the pixel distribution characteristics are analyzed in combination with a Gaussian mixture model to obtain the distribution characteristics of active pixels in the focus area to obtain the regional activity; By comparing the regional activity and the viewing distance value, the dynamic adjustment coefficient of the display screen is determined to obtain the adjusted boundary range; The adjusted boundary range is used to redivide the area of ​​concern and the area of ​​no concern.

4. The energy-saving control method for a low-voltage DC UPS with LCD display according to claim 1 is characterized in that: Obtaining battery power data and calculating a maximum threshold of pixel activation based on the battery power data through an energy allocation model, and obtaining the number of pixel activations based on a distribution range of active pixels, including: The current power state of the battery is collected by the sensor to obtain the battery power data; Calculate the maximum number of activatable pixels based on the battery power data and the energy required to activate each pixel, and record it as the maximum threshold of pixel activation; Determine the distribution range of active pixels and analyze the activity of pixels in the region using a density estimation algorithm based on the distribution range of active pixels and combine with a Gaussian distribution model to determine a core active region; identify the number of highly active pixels in the core active region; Pixel data within the focus area is extracted from the active pixel distribution range to obtain the number of pixel activations; wherein, an energy optimization algorithm is used to preferentially allocate energy to highly active pixels so that their activation rate reaches a first preset ratio, and the activation rate of low-active pixels is controlled within a second preset ratio to ensure that the number of pixel activations in the focus area is stable around a preset number.

5. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: If the number of pixel activations is greater than the maximum threshold, a dynamic pixel compression algorithm is used to adjust the pixel density of the focus area to obtain an adjusted pixel activation scheme, including: When the number of pixel activations is greater than the maximum threshold, the initial pixel density is set based on the current screen resolution; Analyze the activity of pixels in the region of interest, identify the number of highly active pixels in the region of interest, and determine the number of pixels exceeding a maximum threshold; The pixel density optimization method based on K-means clustering algorithm is used to divide the area of ​​interest into several sub-areas; The pixel density is adjusted according to the pixel activity of each sub-region; for the activity greater than the first activity For a high-activity sub-region with a degree of activity lower than the second activity, the pixel density is increased to the first density; for a low-activity sub-region with a degree of activity lower than the second activity, the pixel density is reduced to the second density; wherein the pixel edge is detected in combination with the Laplace operator to ensure that the image quality after compression is not significantly reduced to obtain the distribution characteristics after preliminary adjustment; For the initially adjusted distribution features, obtain the pixel activated area data and determine whether it meets the preset If the activation scheme does not meet the requirements, the dynamic pixels are processed again using the adjustment parameters to obtain the adjusted pixel density distribution; According to the adjusted pixel density distribution, the change characteristics of the number of activations are extracted from the focus area. Determine the adjusted pixel activation state; wherein, a statistical tool is used to analyze the matching degree between the distribution characteristics and the threshold range to obtain a dynamically adjusted activation scheme; The pixel density and activation number in the region of interest are integrated to obtain the final optimized distribution result; based on the final optimized distribution result, the pixel activation features in the region of interest are obtained to determine the complete pixel activation scheme.

6. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: Based on the adjusted pixel activation scheme, the display content in the focus area is extracted to obtain the thickness and density data of the contour line, and the contour line parameters are determined, including: Based on the adjusted pixel activation scheme, an adaptive contour extraction algorithm is used to generate the thickness and density data of contour lines for the displayed content in the focus area, and the preliminary contour distribution characteristics are obtained; According to the preliminary contour distribution characteristics, the boundary data of the displayed content is obtained from the focus area to determine the initial drawing range of the contour line; If the initial drawing range exceeds the preset threshold range, the parameters of the adaptive contour extraction algorithm are adjusted to generate updated thickness and density data to obtain adjusted contour distribution characteristics; According to the adjusted contour distribution characteristics, the matching degree between the density data of the contour lines and the drawing parameters is analyzed to determine the adjusted drawing range; By using the adjusted drawing range, the spatial distribution data of the contour lines are extracted from the displayed content to obtain the final drawing parameter adjustment value; According to the final drawing parameter adjustment value, a complete contour line drawing scheme is generated for the pixel activation state within the focus area.

7. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: Adjusting the number of pixel activations and the contour density of the non-interest area based on the battery power data and the contour line parameters to obtain an energy-saving optimized layout for full-screen display includes: Calculate the current available energy by combining battery power data with the device power consumption model; Set the energy allocation ratio of non-concern areas; The Sobel operator is used to extract the edge features of the non-interest area to generate a gradient amplitude map; the Otsu adaptive threshold algorithm is used to binarize it to extract effective edge pixels; The contour density data is obtained from the contour line parameters, and the contour density of the non-interest area is adjusted according to the energy distribution ratio; the Laplacian operator is used to detect the edge curvature, and the sampling point density is increased in the area where the curvature radius is less than the preset pixel to ensure the contour smoothness; Based on the regional energy distribution result, the number of pixel activations in the non-focus area is adjusted, the pixel activation rate in the high energy area is set to the first ratio, the medium energy area is set to the second ratio, and the low energy area is set to the third ratio, and the initial energy saving distribution is obtained; Determine the display boundary of the non-interest area according to the initial energy-saving distribution; if the display boundary exceeds the preset threshold range, adjust the energy mapping parameters to generate an updated number of pixel activations; For the updated number of pixel activations, statistical tools are used to analyze the matching degree between the contour density and the number of activations to obtain the adjusted layout range. Extracting spatial distribution data of contour lines from the non-interest area through the adjusted layout range, and determining adjustment parameters for full-screen display; According to the adjustment parameters, the battery power and energy mapping data are integrated, and the low-power color is matched by combining the regional content type through RGB color space conversion; The adjusted pixel activation data is fused with the contour parameters and output to the rendering pipeline to generate the final energy-saving optimized layout.

8. The energy-saving control method for a low-voltage direct current UPS with LCD display according to claim 1 is characterized in that: The method further comprises: Real-time update of sight focus and viewing distance changes, trend combined with energy-saving optimization layout, and dynamic adjustment of active pixels and contour line distribution through fast refresh algorithm; If the change trend exceeds the preset threshold, key area features are extracted from the full-screen display data, and the pixel activation and contour parameters are recalculated using a local redrawing algorithm to obtain a display solution that adapts to the new interactive behavior.

9. The energy-saving control method for a low-voltage DC UPS with LCD display according to claim 8, characterized in that: Real-time update of sight focus and viewing distance changes, trend combined with energy-saving optimization layout, dynamic adjustment of active pixels and contour line distribution through fast refresh algorithm, including: Update the user's sight focus coordinates and viewing distance value in real time; Analyze the changing trend of the user's attention area by combining the device screen resolution and refresh rate; predict the movement trajectory of the sight focus in the next N seconds based on the Kalman filter algorithm, and calculate the dynamic range of the attention area; A fast refresh algorithm is used to increase the pixel refresh frequency to the first frequency for the focus area, and the refresh frequency of the non-focus area is reduced to the second frequency. The number of active pixels is adjusted in combination with energy-saving optimization layout to obtain distribution adjustment data; The adjusted display data is integrated with the dynamic refresh parameters, and the distribution of active pixels and contour lines is dynamically adjusted through a fast refresh algorithm; If the change trend exceeds the preset threshold, the key area features are extracted from the full-screen display data, and the pixel activation and contour parameters are recalculated using the local redrawing algorithm to obtain a display solution that adapts to the new interactive behavior, including: By monitoring user interaction behavior in real time; Use SIFT feature extraction algorithm to identify key areas from full-screen display data and extract key area features to obtain preliminary segmentation data; Based on the preliminary segmentation data, the key areas are located, and the local redrawing algorithm is used to increase the pixel activation rate in the key areas to 95%, and reduce it to 30% in the non-key areas. The number of pixel activations is calculated to determine the activation distribution range; According to the activation distribution range, the contour density of the key area and the contour density of the non-key area are adjusted to obtain adjusted contour line data; If the adjusted contour line data does not match the interactive behavior well enough, the preset template is used to compare and adjust the parameters to determine the distribution state after correction; Update the display scheme according to the corrected distribution status; Pixel layout data adapted to the interactive behavior is obtained, the adjusted display data is merged with the local redrawing parameters, and output to the rendering engine to obtain a display solution adapted to the new interactive behavior.

10. A low voltage DC UPS energy saving control system with LCD display, characterized in that: include: The first processing module is used to: collect eye image data of the user and obtain the sight focus coordinates and viewing distance value based on the eye image data; The second processing module is used to: divide the display screen into a focus area and a non-focus area based on the sight focus coordinates and the viewing distance value; determine the distribution range of active pixels based on the boundary range of the focus area; The third processing module is used to: obtain battery power data and calculate the maximum threshold of pixel activation based on the battery power data through an energy allocation model; obtain the number of pixel activations based on the distribution range of active pixels; A fourth processing module is used to: if the number of pixel activations is greater than the maximum threshold, use a dynamic pixel compression algorithm to adjust the pixel density of the focus area to obtain an adjusted pixel activation scheme; A fifth processing module is used to: extract the display content in the focus area based on the adjusted pixel activation scheme to obtain the thickness and density data of the contour line, and determine the contour line parameters; The sixth processing module is used to adjust the number of pixel activations and the contour density of the non-focus area based on the battery power data and the contour line parameters to obtain an energy-saving optimized layout for full-screen display.

Images