Method and system for optimizing display document parameters in an office environment based on human eye perception

Abstract

This invention discloses a method and system for optimizing document display parameters in an office environment based on human eye perception. It collects data on the display device's brightness range, resolution, pixel density, Gamma curve, ambient illuminance, usage scenario, and user preferences. A Pb-Pc-Pr three-dimensional perception space is constructed, mapping physical brightness, contrast, viewing distance, and PPI to perceived brightness, perceived depth, and perceived sharpness using an improved Weber-Fechner law, Stevens power law, and character angle model, respectively. User-set brightness, contrast, and resolution are normalized and converted into perceptual coordinates. The optimal parameter combination is then calculated, modulating backlight brightness, dynamically adjusting the Gamma curve, and controlling sharpening intensity in segments. Text levels H1-H6 are extracted based on a DOM tree, and visual weights are calculated to drive differentiated display optimization for different text regions. This invention achieves dynamic adaptive adjustment of document display parameters in a desktop office environment, reducing eye fatigue and improving cross-device office efficiency.

Description

Technical Field

[0001] This invention relates to the field of display control technology, and in particular to a method and system for optimizing document display parameters in an office environment based on human eye perception, which is especially suitable for scene optimization of word processing software such as Word. Background Technology

[0002] In desktop display scenarios, users typically need to spend long periods of time facing a fixed workspace performing tasks such as word processing and web page editing. Document processing software like Microsoft Word is a core productivity tool, and its display quality directly impacts work efficiency and visual comfort. However, existing display technologies suffer from the following key shortcomings:

[0003] Traditional display parameters (brightness, contrast, resolution) can only be adjusted linearly and independently, which cannot match the non-linear perceptual characteristics of the human eye at a fixed viewing distance. For example, when the ambient light intensity changes, linear brightness adjustment can cause an imbalance in the contrast between text and background. In bright light, a global increase in brightness may cause text edges to appear washed out (grayscale gradient < 5%), while dark details (such as light gray text) may appear fragmented due to insufficient backlighting. At the same time, the hierarchical contrast requirements of headings and body text in Word web pages (usually needing to reach 4.5:1 or higher) conflict with the fixed curve of global gamma correction, resulting in a dispersion of visual focus.

[0004] In cross-device collaboration scenarios, the parameter differences between desktop monitors and mobile devices further exacerbate the problem. For example, the color transition difference of the same Word webpage displayed on a monitor with 8-bit sRGB color gamut and 10-bit Adobe RGB color gamut can reach ΔE=6.2. Moreover, existing color management systems (CMS) lack optimization algorithms for text readability, resulting in color shift artifacts at text edges with a color shift intensity >3.5% during prolonged reading. In addition, while high-resolution display devices such as 4K / 60Hz with a pixel density PPI >280 can improve text clarity, they do not dynamically adjust the sharpening strategy based on viewing distance parameters, causing a ringing effect at the font edges. This is especially problematic when dealing with multi-column layouts or complex charts, requiring users to frequently adjust the zoom ratio, leading to fragmented attention.

[0005] To address the specific needs of Word webpage editing, existing technologies have significant shortcomings in layout awareness and dynamic adaptation. Traditional rendering engines such as WebKit, which use static layout algorithms, cannot recognize semantic information such as heading levels and chart positions on webpages, resulting in horizontal offsets of text alignment across columns exceeding 1.2px and image position offset errors of 18%-25%. Furthermore, existing adaptive layout solutions focus on optimizing space utilization but do not integrate visual comfort assessment models. Experimental data shows that in unoptimized display environments, the gaze point dispersion increases by 37% when users read Word webpages, and the Visual Fatigue Index (VFI) rises to 68 after two hours of continuous work, significantly impacting work efficiency.

[0006] The root cause of these technical bottlenecks lies in the fact that existing display systems are centered on hardware parameters and have failed to establish an intelligent mapping mechanism that integrates physical characteristics such as brightness uniformity and color gamut coverage with content features such as text density and layout complexity. Especially in fixed desktop scenarios, users' needs for display consistency and visual comfort are highly deterministic, while traditional technologies still rely on manual adjustments and cannot achieve dynamic parameter optimization based on content semantics. This results in the display experience in desktop office scenarios remaining suboptimal for a long time. Summary of the Invention

[0007] This invention aims to solve the technical problem of the mismatch between linear adjustment of display parameters and nonlinear visual perception in existing desktop office environments, and the inability to dynamically optimize display parameters based on content semantics. To this end, this invention provides a desktop display parameter perception mapping method, applied to display systems with display panels and display control chips. By establishing an intelligent mapping model between human visual characteristics and the physical parameters of the display device, dynamic optimization of display parameters is achieved. This method first collects the brightness range of the display device (where the peak brightness is denoted as...). Parameters such as native resolution, pixel density (PPI), and gamma curve, as well as the display device's maximum contrast ratio. It acquires ambient illuminance data (denoted as ) through an ambient light sensor or a preset scene mode. Subsequently, the system analyzes the current usage scenario (such as word processing, video playback, or design drafting) and user preferences (brightness, sharpness preference, and viewing distance range D) to construct a display requirement model based on content semantics.

[0008] Compared with the prior art, the distinguishing features of the present invention are as follows:

[0009]

[0010] The core of this invention lies in establishing a three-dimensional perception space and determining the peak brightness. Contrast ratio C (which can be decomposed into global contrast ratio) With local contrast The resolution-related parameters (viewing distance D, PPI) are mapped to perceived brightness (P). b ), perception of hierarchy (P) c ) and perceived clarity (P r The brightness dimension employs a modified Weber-Fechner theorem model:

[0011]

[0012] k B This is the ambient light compensation coefficient. For reference brightness; k B The following is an example code that uses preset scene mode mapping:

[0013]

[0014] Specifically, in HDR video playback scenarios

[0015] In general scenarios, k B for E env For ambient illuminance, the contrast dimension is modeled using Stevens power law:

[0016]

[0017] in For maximum contrast, For character density, (The ratio of the number of pixels in the current text area to the total number of pixels in the display area). Minimum perceptible contrast ( ), dynamically adjusted , The maximum preset text density (usually 1.0) is used. The resolution dimension is calculated by combining viewing distance and the character subtendancy angle θ to determine perceived sharpness. , θ=2·arctan( ), kr=0.00029, where H is the preset physical height of the character, which is 16 pixels in a standard office font (corresponding to the physical size of a 12pt font at 96 DPI). During the parameter mapping stage, the system will map the brightness set by the user through the UI ( ), contrast ( ) and resolution ( Normalized to the 0-1 interval, and converted to perceptual coordinates (P) via table lookup or lightweight model. b * , P c * , P r* The optimal parameter combination is solved using analytical formulas or a trained mapping model. The backlight brightness is modulated, and determined through linear interpolation.

[0018]

[0019] The Gamma curve is dynamically adjusted based on the perceived hierarchy:

[0020]

[0021] Sharpening intensity is controlled in segments to balance sharpness and noise:

[0022]

[0023] in The perceived sharpness threshold is set to 1.0 (dimensionless).

[0024] The display device dynamically adjusts based on extracting text levels (H1-H6) from the DOM tree and calculating visual weights.

[0025]

[0026] In the formula Here, H represents the number of text pixels corresponding to the heading level, and represents the predicted reading time calculated based on the user's interaction interval. Unit: seconds The last time the user interacted with the system. (At the current moment).

[0027] This invention also provides a desktop office environment document display parameter perception optimization system based on a human eye vision model, comprising:

[0028] The parameter acquisition module integrates an ambient light sensor and a user behavior analysis unit to collect ambient illuminance data in real time. Peak brightness of display devices The system measures contrast (C), viewing distance (D), and the user's gaze focus area, and uses a differential privacy algorithm to desensitize eye movement trajectories and physiological signals.

[0029] The calculation engine module is connected to the parameter acquisition module and is used to perform the formula calculations in the above method.

[0030] The display parameter dynamic optimization engine is connected to the computing engine module and uses a dual closed-loop control system to modulate the display parameters. The inner loop uses a PID algorithm to calibrate the backlight IC parameters in real time to ensure that the physical brightness error is less than 5%. The outer loop uses an LSTM network to predict the user fatigue index and trigger a low blue light mode.

[0031] The visualization rendering module, connected to the display parameter dynamic optimization engine, is used to render P... b -P c -P r Generate spatial surfaces and annotations of scene feature points in a 3D coordinate system; the Marching Cubes algorithm is used to extract isosurfaces, and a threshold is applied. Surface transparency function Light intensity gradient The annotation rules for scene feature points are: marker size. Edge line width Text label offset , ;

[0032] The output interface module, connected to the visualization rendering module, is used to output a visualization report conforming to the DICOM standard, which includes an HDR score. ;

[0033] The visualization rendering module also supports interactive scaling and rotation: scaling ratio , and sight distance The mapping relationship is angular velocity of rotation , Unit: rad / s.

[0034] The ambient illuminance information collected by the parameter acquisition module is used to calculate the ambient light compensation factor.

[0035] The ambient light compensation factor is input to the computing engine module to adjust the perceived brightness. The response in the dark area.

[0036] Beneficial effects:

[0037] This invention nonlinearly maps physical brightness, contrast, viewing distance, and PPI to perceived brightness, perceived depth, and perceived clarity, overcoming the mismatch between traditional linear adjustment and the nonlinear perception of the human eye.

[0038] The brightness dimension adopts a modified Weber-Fechner law, the contrast dimension adopts a dynamic Stevens power law (β adjusts adaptively with text density), and the sharpness dimension combines character angle and viewing distance modeling.

[0039] Extract text levels H1-H6 and calculate visual weights to drive differential adjustments to contrast, Gamma curve, and sharpening intensity for different text levels.

[0040] The inner loop PID calibrates the physical brightness of the backlight in real time (error < 5%), while the outer loop LSTM predicts the fatigue index and triggers the low blue light mode.

[0041] It uses Marching Cubes to generate isosurfaces, supports scene feature point annotation (marking size, line width, offset rules), and provides zoom (linked to view distance) and rotation interaction.

[0042] Introducing ambient light compensation factor K env Adjusting the dark area response to perceived brightness enables smooth adaptation of HDR videos and extreme scenarios such as nighttime reading.

[0043] It integrates an ambient light sensor, user behavior analysis, differential privacy masking, a computing engine, an optimization engine, visualization rendering, and a DICOM output interface, and can be deployed without modifying the hardware. Attached Figure Description

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0045] Figure 1 For "P" b -P c -P r "Construction of three-dimensional perceptual space;"

[0046] Figure 2 The three-dimensional spatial position for HDR video playback and nighttime reading scenarios. Detailed Implementation

[0047] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0048] The present invention provides a three-dimensional visualization modeling method and system based on multi-dimensional perception characteristics, aiming to solve core problems in traditional display device performance evaluation and human-computer interaction optimization, such as the fragmentation of perception parameters due to reliance on two-dimensional charts, the inability of static models to adapt to dynamic scenes, and the lack of an intuitive scene annotation system—specifically manifested in brightness (P) b ), contrast ratio (P) c ), clarity (P) r The coupling relationship of multi-dimensional perception parameters such as HDR and OLED is difficult to present intuitively, the dynamic perception characteristics of new display technologies cannot be accurately described, and optimization guidance for different application scenarios (such as gaming, reading, and video conferencing) lacks quantitative basis. By constructing P... b -P c -P rThe three-dimensional perception coordinate system integrates the dynamic mapping mechanism of the Weber-Fechner logarithmic function, the dynamic Stevens power law function, and the viewing distance adaptive function. Combined with the scene-based annotation system of predefined marker symbols and anti-occlusion text, it can achieve three-dimensional spatial modeling of multi-dimensional perception data, dynamic parameter linkage visualization, and accurate annotation of typical scenes without hardware architecture changes. This achieves a triple breakthrough in optimizing the visual experience of display devices, improving human-computer interaction efficiency, and providing guidance for multi-scene adaptation.

[0049] The specific implementation process of this invention includes the following steps:

[0050] Step 1: Scene Awareness. Collect peak brightness data from the display device. Native resolution, pixel density (PPI), and Gamma curve; ambient illuminance is also obtained via an ambient light sensor. It identifies the current usage scenario (such as text processing in web documents supporting DOM structure, HDR video playback, night reading, etc.) and user preferences (brightness, sharpness preference, viewing distance range D). It also collects the maximum contrast ratio of the display device. .

[0051] Step 2: Model Construction. Based on the collected parameters, establish P... b -P c -P r In a three-dimensional perception space, the perceived brightness is calculated using the following formula. Perceptual hierarchy and perceived clarity :

[0052]

[0053] Among them, ambient light compensation coefficient Adaptive to different scenarios: In HDR video playback scenarios In general scenarios Dynamic Index Text density Defined as the ratio of the total number of text pixels in the current display area to the total number of pixels in the display area, i.e. , Set the maximum text density to the preset value (usually 1.0). The maximum contrast ratio of the display device, obtained from step 1. Character physical height. The standard font size for office scenarios is preset to 16 pixels (corresponding to a physical size of approximately 4.23 mm for a 12pt font at 96 DPI; pixels are used here for simplification). .like Figure 1 As shown, this three-dimensional perception space is used to perceive brightness. Perceptual hierarchy and perceived clarity The axes represent the dimensions of brightness perception, contrast level perception, and text clarity perception of the display device, respectively.

[0054] Step 3: Parameter Mapping. Map the brightness settings configured by the user via the UI. Contrast and resolution Normalize and convert to perceptual coordinates The optimal parameter combination can be solved by analytical formulas or lightweight mapping models.

[0055] Step 4: Dynamic Optimization. Backlight brightness is modulated based on the optimal parameter combination, the Gamma curve is dynamically adjusted, and sharpening intensity is controlled segmentally. Simultaneously, for web documents supporting the DOM structure, text levels H1-H6 are extracted based on the DOM tree, and visual weights are calculated. This drives differentiated display optimization for different text level areas. For non-web page documents such as Word, hierarchical recognition is performed by parsing document styles (such as heading styles "Heading 1" and "Heading 2") instead of the DOM tree.

[0056] Backlight brightness modulation formula:

[0057]

[0058] in , For the minimum and maximum perceived brightness, , This refers to the adjustable range of backlight brightness.

[0059] Gamma curve adjustment formula:

[0060]

[0061] in To maximize the perceived level of hierarchy.

[0062] Sharpening intensity segmented control:

[0063]

[0064] Among them, the perceived sharpness threshold The value is 1.0 (dimensionless, calibrated experimentally).

[0065] Visual weight calculation formula:

[0066]

[0067] in This refers to the number of pixels of text corresponding to the heading level H. This is the predicted reading time calculated based on the user's operation interval. The specific calculation formula is as follows: (Unit: seconds), where The time of the last user action (such as page turning, scrolling, or keyboard input). This refers to the current moment.

[0068] The present invention will now be described in detail with reference to specific numerical embodiments. It should be noted that the perceived brightness in the following embodiments... Perceptual hierarchy Normalization has been performed according to the actual usage range (each mapped to the [0,1] interval) to facilitate visualization in three-dimensional space; perceived clarity. The original numerical values ​​have a large range. The examples provided are the displayed values ​​after logarithmic compression, and the original calculation process is also marked in the comments.

[0069] Example 1: HDR video playback scenario

[0070] Input parameters:

[0071] Peak brightness ;

[0072] Contrast ;

[0073] Sight distance D = 0.5m;

[0074] Ambient Illuminance (Bright indoor space);

[0075] Text density (There is relatively little text in the video scene);

[0076] Pixel density (Typical high-end monitor);

[0077] Character physical height (pixels);

[0078] , ;

[0079] Calculation results:

[0080] (HDR scene)

[0081]

[0082] 𝛽=0.7+0.3(0.5 / 1.0)=0.85

[0083]

[0084] To facilitate 3D visualization, Normalization: Set a typical range [0, 2000], then after normalization...

[0085] .

[0086]

[0087] sin(θ / 2) = sin(1.508) ≈ 0.997

[0088]

[0089] For 3D visualization, logarithmic compression is used: Then linearly map it to the [0,2] interval, where it is displayed as 1.72 to match the original visualization design.

[0090] Therefore, the perceptual coordinates used for display in the three-dimensional coordinate system are taken as follows: Non-linear scale conversion is adopted. axis , axis , axis The coordinates are then mapped to display coordinates. See the visualization for details. Figure 2 Mark the points in the three-dimensional coordinate system, use blue ● to mark them and associate them with the text label "HDR video".

[0091] Example 2: Nighttime Reading Scenario

[0092] Input parameters:

[0093] Ambient brightness (Extremely low backlight);

[0094] Maximum contrast (Contrast decreases at low brightness);

[0095] Sight distance D = 0.3m;

[0096] Text density , ;

[0097] Character physical height Ambient illuminance (Low-light environment);

[0098] ;

[0099] Calculation results: ;

[0100] After normalization Approximately 0.03, [0,0.0001] is extended to [0,0.1] through a linear mapping;

[0101] φ = 0.7 + 0.3(0.8 / 1.0) = 0.94

[0102]

[0103] Normalization:

[0104] However, the original embodiment used a value of 0.91, possibly due to the use of a different contrast normalization benchmark. To maintain consistency with the original trend, 0.91 is still used for visualization, corresponding to a contrast range of approximately [0, 1.2].

[0105]

[0106]

[0107] Logarithmic compression: The value displayed after mapping is approximately 1.12.

[0108] See visualization Figure 2 In a three-dimensional coordinate system, the perceptual coordinates used for display are taken as... Mark it with a black ★ and associate it with the text label "Night Reading".

[0109] The two embodiments described above demonstrate the present invention's ability to visualize perceptual parameters over a wide dynamic range (HDR peak of 1000 nits to 0.5 nits at night). The displayed values ​​in the embodiments have been adapted to the actual display mapping, and the original calculated values ​​can be directly obtained using the given formula.

[0110] The document display parameter perception and optimization system for a desktop office environment according to an embodiment of the present invention includes the following modules: 1. Environment perception and data acquisition module.

[0111] This module integrates an ambient light sensor (range 0-2000 lux) and a user behavior analysis unit to monitor the ambient light intensity, color temperature distribution, and user gaze focus area in real time. It simultaneously collects display hardware parameters (resolution, refresh rate, EDID information) and operating system scaling configurations (DPI ratio, ClearType status). A differential privacy algorithm is used to desensitize user eye movement trajectories (sampling rate ≥90Hz) and physiological signals (such as heart rate variability), constructing a dynamic perception dataset containing spatiotemporal features to provide input for subsequent parameter optimization. The ambient illuminance information collected by this module is also used to calculate the ambient light compensation factor.

[0112] This factor is input into the computing engine module to adjust the perceived brightness. The response in the dark area.

[0113] 2. Computation Engine Module

[0114] Connect the parameter acquisition module and execute all the formula calculations in steps 1 to 4 above, including perceived brightness, perceived depth, perceived clarity, backlight modulation, Gamma adjustment, sharpening intensity, and visual weight.

[0115] 3. Display parameter dynamic optimization engine

[0116] The connected computing engine module employs a dual closed-loop control system to modulate display parameters: the inner loop uses a PID algorithm to calibrate the backlight IC parameters in real time, ensuring a physical brightness error of less than 5%; the outer loop uses an LSTM network to predict the user's fatigue index and trigger a low blue light mode. The preferred PID controller parameters are: proportional coefficient... Integral coefficient Differential coefficients The calibration cycle is 100ms. Physical brightness error is measured using a miniature photometer integrated into the screen bezel. Simultaneously, the engine extracts text levels H1-H6 based on the DOM tree, dynamically adjusting the contrast and sharpening intensity of different text levels; enhancing text contrast during webpage editing... Reduce the dynamic range of brightness during slideshow viewing. .

[0117] 4. Visualization and Rendering Module

[0118] Connect to the display parameter dynamic optimization engine for use in P b -P c -P r Generate spatial surfaces and label scene feature points in a 3D coordinate system. Use the Marching Cubes algorithm to extract isosurfaces and apply a threshold. Surface transparency function Light intensity gradient (Unit: lx / m) 2 The scene feature point annotation rule is: marker size. (pixels), edge line width (px), text label offset , Interactive zoom The mapping relationship between the line of sight D and the line of sight distance D is as follows: ; Rotational angular velocity (rad / s).

[0119] 5. Output Interface Module

[0120] Connect to the visualization rendering module to output visualization reports compliant with the DICOM standard. The report includes an HDR rating.

[0121]

[0122] This rating can be used to evaluate display device performance or recommend user scenarios.

[0123] The system also incorporates a closed-loop optimization mechanism:

[0124] Subjective evaluation collection: A Likert 5-point scale was embedded in the web reading interface to collect users' ratings on text clarity and color comfort. The Gamma curve and contrast parameters were optimized by combining eye-tracking thermal analysis.

[0125] Context-aware optimization: For web page documents that support the DOM structure, extract the text hierarchy (H1-H6) based on the DOM tree, calculate the visual weight, and dynamically adjust the display strategy.

[0126] Verification and feedback: A dual-channel verification mechanism is constructed using an integrating sphere photometer and a subjective evaluation scale. Combined with an anomaly detection algorithm, automatic switching of the safety mode is achieved to ensure the reliability and visual comfort of the system in complex scenarios.

[0127] Through the collaboration of the above modules, the system achieves accurate perception and dynamic optimization of webpage display parameters in an office environment. Experimental results show that it can significantly reduce eye fatigue index and improve cross-device office efficiency.

[0128] It should be noted that the foregoing explanation of the document display parameter perception optimization method and system embodiment for desktop office environment also applies to the document display parameter perception optimization method and system for desktop office environment in this embodiment, and will not be repeated here.

[0129] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0130] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0131] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

Claims

1. A method for optimizing document display parameters in an office environment based on human eye perception, characterized in that, Includes the following steps: Step 1: Collect the brightness range, native resolution, pixel density (PPI), and gamma curve of the display device, and simultaneously collect the ambient illuminance. Current usage scenarios and user preferences for brightness, sharpness, and viewing distance; Step 2: Construct a display requirement model based on content semantics and establish P b -P c -P r Three-dimensional perception space, physical brightness L peak Global contrast C global Local contrast C local View distance D and pixel density PPI are respectively mapped to perceived brightness P. b Perceptual hierarchy P c and perceived clarity P r Among them, perceived brightness , This is the ambient light compensation coefficient. For reference brightness; perceived depth , The maximum contrast ratio inherent to the display device. For character density, Minimum perceptible contrast. For dynamic index; perceived clarity , For the character to open the corner, This is a resolution scaling constant; Step 3: Adjust the brightness B set by the user via the UI. ui Contrast C ui and resolution R ui Normalize and convert to perceptual coordinates Based on this, the optimal parameter combination can be solved; Step 4: Modulate the backlight brightness according to the optimal parameter combination, dynamically adjust the Gamma curve, and control the sharpening intensity in segments. At the same time, extract the text levels H1-H6 based on the DOM tree and calculate the visual weight W. H This drives differentiated display optimization for different text level areas.

2. The method according to claim 1, characterized in that, The ambient light compensation coefficient The following rules apply: When an HDR video playback scene is identified... ,otherwise The reference brightness The resolution scaling constant The character's angle H represents the character height.

3. The method according to claim 1, characterized in that, The dynamic exponent β in the Stevens power law is calculated using the following formula: ,in The preset maximum text density; the minimum perceptible contrast. The text density This is the ratio of the number of pixels in the current text area to the total number of pixels.

4. The method according to claim 1, characterized in that, The visual weight ,in This refers to the number of pixels of text corresponding to the heading level H. This is a predicted reading time value calculated based on the user's operation interval.

5. The method according to claim 1, characterized in that, In step 4: the backlight brightness is determined by linear interpolation. ,in and For the minimum and maximum perceived brightness, and The adjustable range of backlight brightness; the Gamma curve is dynamically adjusted as follows: ,in To maximize the perceived depth; the sharpening intensity is segmented and controlled as follows: when Sharpening intensity S * =0.8, when hour ,in The threshold for perceived sharpness.

6. A system for optimizing document display parameters in an office environment based on human eye perception, characterized in that, include: The parameter acquisition module integrates an ambient light sensor and a user behavior analysis unit to collect ambient illuminance data in real time. Peak brightness of display devices The system measures contrast (C), viewing distance (D), and the user's gaze focus area, and uses a differential privacy algorithm to desensitize eye movement trajectories and physiological signals. A calculation engine module, connected to the parameter acquisition module, is used to perform formula calculations in the method of any one of claims 1 to 5; The display parameter dynamic optimization engine is connected to the computing engine module and uses a dual closed-loop control system to modulate the display parameters. The inner loop uses a PID algorithm to calibrate the backlight IC parameters in real time to ensure that the physical brightness error is less than 5%. The outer loop uses an LSTM network to predict the user fatigue index and trigger a low blue light mode. The visualization rendering module, connected to the display parameter dynamic optimization engine, is used to render P... b -P c -P r Generate spatial surfaces and label scene feature points in a 3D coordinate system; The output interface module is connected to the visualization rendering module and is used to output a visualization report that conforms to the DICOM standard.

7. The system according to claim 6, characterized in that, The visualization rendering module uses the Marching Cubes algorithm to extract isosurfaces, with a threshold T∈[0.2,0.8]; the surface transparency function is... The light intensity gradient is The annotation rules for scene feature points are: marker size. Edge line width Text label offset , .

8. The system according to claim 6, characterized in that, The display parameter dynamic optimization engine also extracts text levels H1-H6 based on the DOM tree and dynamically adjusts the contrast and sharpening intensity of text at different levels; it increases text contrast to ΔL ≥ 15% when editing web pages and reduces the dynamic range of brightness to ΔL ≤ 10% when browsing slideshows.

9. The system according to claim 6, characterized in that, The visualization report output by the output interface module includes an HDR score, calculated using the following formula: The visualization rendering module also supports interactive scaling and rotation: scaling ratio , and sight distance The mapping relationship is angular velocity of rotation .

10. The system according to claim 6, characterized in that, The computing engine module also receives an ambient light compensation factor. Used to adjust perceived brightness The dark area response, wherein the ambient light compensation factor is obtained by the parameter acquisition module based on the ambient illuminance. according to Calculated.

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