A method for enhancing display effects by dynamically regulating voltage difference

Abstract

The present application relates to a method for enhancing display effects by dynamically regulating voltage differences, comprising: constructing a mathematical model of biological morphology changing over time, wherein the mathematical model introduces random variables to characterize individual differences in biological organisms; obtaining pixel data of a display sequence based on the resolution, color depth, and refresh rate parameters of a display device, and dynamically adjusting the accuracy of the mathematical model and the parameters of the encoding strategy; processing the received display sequence through a visual characteristic model, and decoding and recovering hidden information in the display sequence based on a pixel brightness change sequence and a predefined nonlinear mapping relationship; employing a filtering algorithm to process different interference factors; and employing an integrity verification algorithm for the extracted hidden information. If information is found to be missing or erroneous, the information is recovered and reconstructed based on redundant information and error correction coding.

Description

Technical Field

[0001] The present application relates to the field of digital data technology, and in particular to a method for enhancing display effects by dynamically regulating voltage difference. Background Art

[0002] In the process of realizing realistic biological morphology and motion display in the highly realistic scenes of biological morphology and motion display in the existing technology, it is necessary to consider how to make natural, continuous and dynamic changes in the morphology and size of the organism according to the growth and development laws of the organism, especially dynamically adjust the biological morphology and size according to the growth and development laws of the organism to simulate the natural growth process and accurately depict the continuous changes of biological morphological characteristics over time. At the same time, taking into account the differences of each biological individual, in the simulation process, additional data or information is encoded into the display signal in an invisible way. Therefore, highly realistic motion display requires the ability to accurately describe the changes of biological morphological characteristics over time, and fully consider the impact of biological individual differences on the trajectory of morphological changes. At the same time, in order to hide additional information in the display screen, it is also necessary to cooperate with the voltage dynamic change rules. Then, ensure that the information encoding will not affect the quality and appearance of the normal picture. In terms of voltage change rules, we must make full use of the limitations of the human eye's time and space resolution, and realize information hiding by controlling the voltage change timing and amplitude of key display elements. Hidden information means that during the display of biological form and movement, additional data or information is encoded into the display signal in some way, making this information invisible or imperceptible to ordinary observers. At the same time, hidden information is to transmit additional data without affecting the main display content (i.e. biological form and movement). Hidden information is not hidden in its original state. It is embedded in the display signal through specific coding technology. This process is intentional and aims to convert information into an imperceptible form so that it can be transmitted without attracting the attention of ordinary observers.

[0003] There is an urgent need to minimize the impact on picture quality while ensuring the accuracy of information transmission. For the receiving end, how to accurately extract hidden information from the complex voltage change sequence is a major challenge.

[0004] The solution proposed by the present invention to the above-mentioned shortcomings is as follows: the present invention describes the changes in biological morphology over time through a mathematical model, and introduces random variables to characterize individual differences. The model is solved to obtain the three-dimensional geometric parameters of the biological morphology, and the color of the grid surface element is calculated in combination with the material and lighting. The hidden information is encoded as a dynamically changing sequence of voltage. A nonlinear mapping is established between the sequence and the pixel brightness, and information hiding is achieved by utilizing the spatiotemporal resolution limitations of the human eye. The model accuracy and encoding strategy are dynamically adjusted to optimize voltage changes. The receiving end detects and decodes the hidden information in the voltage changes by analyzing the changes in pixel brightness. The filtering algorithm and adaptive threshold judgment are used to deal with interference, and the content-based integrity verification algorithm is used for information recovery and reconstruction. This method realizes the effective combination of dynamic simulation of biological morphology and voltage change information hiding. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of this application is to provide a method for enhancing display effects by dynamically regulating voltage difference.

[0006] The present application discloses a method for enhancing display effects by dynamically regulating voltage difference, comprising:

[0007] S1. Constructing a mathematical model of biological morphology over time, wherein the mathematical model introduces random variables to characterize individual differences of biological organisms;

[0008] S2. Obtain the three-dimensional geometric parameters of the mathematical model of the biological morphology changing over time described in step S1 at the current moment, calculate the color value of each grid element based on the material properties and lighting conditions, encode the information to be hidden into a voltage dynamic change sequence, and superimpose the voltage dynamic change sequence on the color value using a mapping function;

[0009] S3. Obtain pixel data of the display sequence based on the resolution, color depth, and refresh rate parameters of the display device, and dynamically adjust the accuracy of the mathematical model and parameters of the encoding strategy in step S2, wherein the parameters include the accuracy of the mathematical model and the number of grid bins during the morphological change process;

[0010] S4. Processing the pixel data of the display sequence received in step S3 using the visual characteristic model, decoding and recovering the hidden information in the display sequence based on the pixel brightness change sequence and a predefined nonlinear mapping relationship;

[0011] S5. Processing the hidden information in the display sequence recovered by decoding in step S4 using a filtering algorithm to address interference factors. If Gaussian noise exists in the information, a median filter is used; if salt and pepper noise exists, a mean filter is used; if a mixture of Gaussian and salt and pepper noise exists, an adaptive filter is used;

[0012] S6. After the filtering algorithm in step S5 is processed, the hidden information extracted is subjected to a content-based integrity verification algorithm. If information is found to be missing or erroneous, it is recovered and reconstructed based on redundant information and error correction coding.

[0013] Preferably, in step S1, constructing a mathematical model of biological morphology changing over time includes:

[0014] Obtaining gene expression levels and environmental conditions that affect biological morphological changes, and using the gene expression levels and environmental conditions as input variables of the mathematical model;

[0015] Using the morphological data of the biological individuals, the probability distribution parameters of the random variables in the mathematical model are obtained through machine learning algorithm training;

[0016] Using the known initial morphological parameters and the random variable distribution as initial conditions of the mathematical model, solving the model equations, and simulating the dynamic change process of the biological morphology over time;

[0017] Determining whether the biological individual has reached a specific growth stage based on the morphological parameters at the current moment, and updating the relevant parameters in the mathematical model;

[0018] For a given time point, the three-dimensional geometric parameters of the corresponding moment are extracted from the morphological change trajectory obtained by solving the model to determine the length, width and volume morphological characteristics of the organism at that moment.

[0019] Preferably, in step S2, obtaining the three-dimensional geometric parameters of the biological form at the current moment, calculating the color value of each grid element in combination with material properties and lighting conditions, encoding the information to be hidden into a voltage dynamic change sequence, and superimposing the voltage dynamic change sequence on the color value through a mapping function includes:

[0020] Obtain the three-dimensional geometric parameters of the biological morphology at the current moment and determine the meshing method of the biological surface;

[0021] Based on the properties of biological materials, establish an interaction model between materials and lighting conditions;

[0022] Use ray tracing algorithm to calculate the color value of each grid element;

[0023] The information to be hidden is encoded into a voltage dynamic change sequence, where each voltage value in the sequence corresponds to a pixel brightness value;

[0024] By analyzing the limitations of the human eye's spatiotemporal resolution, a nonlinear mapping function between voltage and pixel brightness is constructed.

[0025] It is determined whether the voltage dynamic change sequence causes a change in screen brightness after being superimposed on the color value, and if the change is obvious, the mapping function is adjusted.

[0026] Preferably, in step S3, obtaining pixel data of a display sequence according to the resolution, color depth, and refresh rate parameters of a display device and dynamically adjusting the accuracy of the mathematical model and the parameters of the encoding strategy include:

[0027] Obtain the physical characteristics of the display device, including resolution, color depth, and refresh rate;

[0028] Determining the accuracy of the mathematical model and the number of grid elements during the morphological change process based on the acquired physical characteristic parameters;

[0029] If the resolution is higher than the preset threshold, the number of grid cells is increased to improve the fineness of the morphological changes;

[0030] If the color depth is lower than a preset threshold, the quantization level of the voltage dynamic change sequence is reduced to reduce the color loss of information hiding;

[0031] By dynamically adjusting the mathematical model accuracy and encoding strategy parameters, the physical limitations of different display devices can be adapted;

[0032] In the process of morphological change, mathematical models and high-density grid elements are used.

[0033] Preferably, in step S4, processing the received display sequence using a visual characteristic model and decoding and recovering the hidden information in the display sequence according to the pixel brightness change sequence and a predefined nonlinear mapping relationship includes:

[0034] The acquisition device obtains a display sequence, performs wavelet transform-based decomposition processing on the display sequence, separates different frequency components in the display sequence, and obtains a low-frequency component and several high-frequency components corresponding to the display sequence;

[0035] Calculate the average brightness of each pixel according to the obtained low-frequency component, and generate a binary sequence corresponding to the pixel sequence by calculating the average brightness of each pixel;

[0036] Obtaining the binarized sequence and performing Fourier transform, transforming the binarized sequence from the time domain space to the frequency domain space, and obtaining spectrum data transformed into the frequency domain space;

[0037] A fixed frequency band threshold is set for the spectrum data, and it is determined whether the frequency band amplitude of the spectrum data is greater than the fixed frequency band threshold. If so, hidden information exists; otherwise, no hidden information exists.

[0038] Preferably, in step S4, setting a fixed frequency band threshold for the spectrum data and determining whether the frequency band amplitude of the spectrum data is greater than the fixed frequency band threshold, if so, hidden information exists; otherwise, no hidden information exists, including:

[0039] If it is determined that the amplitude of the frequency band in the frequency domain space exceeds a threshold, the instantaneous phase information corresponding to the hidden information is obtained by Hilbert transform;

[0040] Establishing a phase-to-information mapping table based on the instantaneous phase information obtained, and searching for a character code corresponding to each instantaneous phase information according to the mapping table;

[0041] After obtaining the character codes, the character codes are connected into a sequence to obtain hidden text information, and the text information is determined to be initial hidden information;

[0042] The encoding rules and verification rules are obtained according to the information encoding type, the preset error correction method table is matched by the error type, the sixth image error correction method is determined, and the final seventh image after processing is output.

[0043] Preferably, in step S5, the corresponding filtering algorithm is used for processing different interference factors. If Gaussian noise exists in the information, a median filtering method is used; if salt and pepper noise exists, a mean filtering method is used; if Gaussian and salt and pepper mixed noise exists, an adaptive filtering method is used, including:

[0044] Obtain input information, determine the mixed noise intensity in the information using preset rules, and determine the noise type as Gaussian noise, salt and pepper noise, or mixed noise based on the signal strength;

[0045] If the noise type determination result indicates that only Gaussian noise exists, a Gaussian filter is constructed according to the detected noise standard deviation, and a median filter is used for smoothing to obtain a first image;

[0046] By detecting the rate of change of the information entropy of the first image, a noise signal fluctuation parameter is calculated, and according to the noise signal fluctuation parameter, whether the noise type is salt and pepper noise is determined;

[0047] If the result of the judgment is that salt and pepper noise exists, the noise point ratio is counted according to the characteristics of salt and pepper noise, and the noise points are removed by using the mean filter method by setting the filter window size to obtain the second image;

[0048] By calculating the peak signal-to-noise ratio of the second image, analyzing the image information detail quantification index, and judging whether the noise type is Gaussian and salt and pepper mixed noise according to the image information detail quantification index;

[0049] If there is Gaussian and salt-and-pepper mixed noise, the mean and variance of each pixel neighborhood are calculated through adaptive Wiener filtering, and the filter output is adaptively adjusted according to the ratio between the local signal variance and the overall noise variance to obtain the third image.

[0050] Preferably, in step S6, the extracted hidden information is subjected to a content-based integrity verification algorithm. If information is found to be missing or erroneous, the information is recovered and reconstructed based on redundant information and error correction coding, including:

[0051] Obtain a data stream containing hidden information, perform data analysis using a hidden information detection algorithm, and obtain key data segments where hidden information may exist. If the hidden information is not missing or erroneous, determine it as the initial hidden information. If the hidden information is missing or erroneous, perform information recovery and reconstruction based on redundant information and error correction coding.

[0052] The display effect enhancement method of dynamic voltage difference regulation described in the present application has the advantages that the method establishes a mathematical model to describe the change of biological morphological characteristics over time, introduces random variables to characterize individual differences, determines the three-dimensional geometric parameters of the biological morphology at different times by solving the model equations, calculates the color values ​​of the grid elements in combination with the material and lighting conditions, encodes the hidden information into a voltage dynamic change sequence, establishes a nonlinear mapping relationship with the pixel brightness, utilizes the limitations of the human eye's spatiotemporal resolution to achieve information hiding, considers the characteristics of the display device, dynamically adjusts the model accuracy and encoding strategy, uses the visual characteristic model to analyze the pixel brightness change at the receiving end, detects and decodes the hidden information, processes interference through filtering algorithms and adaptive threshold judgments, and uses a content-based integrity verification algorithm to recover and reconstruct information. The present invention realizes the effective combination of accurate simulation of dynamic changes in biological morphology and information hiding, and has strong practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of the steps of a method for enhancing display effects by dynamically controlling voltage difference described in this application;

[0054] Figure 2 This is a flow chart of a method for enhancing display effects by dynamically regulating voltage difference as described in this application;

[0055] Figure 3 This is a flow chart of step S5 of a method for enhancing display effects by dynamically regulating voltage difference described in this application. DETAILED DESCRIPTION

[0056] like Figure 1-Figure 3 As shown, the method for enhancing display effects by dynamically regulating voltage difference described in the present application includes the following steps:

[0057] S1. Constructing a mathematical model of biological morphology over time, wherein the mathematical model introduces random variables to characterize individual differences of biological organisms;

[0058] S2. Obtain the three-dimensional geometric parameters of the mathematical model of the biological morphology changing over time described in step S1 at the current moment, calculate the color value of each grid element based on the material properties and lighting conditions, encode the information to be hidden into a voltage dynamic change sequence, and superimpose the voltage dynamic change sequence on the color value using a mapping function;

[0059] S3. Obtain pixel data of the display sequence based on the resolution, color depth, and refresh rate parameters of the display device, and dynamically adjust the accuracy of the mathematical model and parameters of the encoding strategy in step S2, wherein the parameters include the accuracy of the mathematical model and the number of grid bins during the morphological change process;

[0060] S4. Processing the pixel data of the display sequence received in step S3 using the visual characteristic model, decoding and recovering the hidden information in the display sequence based on the pixel brightness change sequence and a predefined nonlinear mapping relationship;

[0061] S5. Processing the hidden information in the display sequence recovered by decoding in step S4 using a filtering algorithm to address interference factors. If Gaussian noise exists in the information, a median filter is used; if salt and pepper noise exists, a mean filter is used; if a mixture of Gaussian and salt and pepper noise exists, an adaptive filter is used;

[0062] S6. After the filtering algorithm in step S5 is processed, the hidden information extracted is subjected to a content-based integrity verification algorithm. If information is found to be missing or erroneous, it is recovered and reconstructed based on redundant information and error correction coding.

[0063] like Figure 1-Figure 2 As shown, in step S1, a mathematical model is established to describe the temporal changes in biological morphological characteristics. The model introduces random variables to characterize the influence of individual differences of organisms on the trajectory of morphological changes, including gene expression and environmental factors. By solving the model equation, the three-dimensional geometric parameters of the biological morphology at different times, such as length, width, and volume, are determined.

[0064] Obtain key factors that affect biological morphological changes, such as gene expression levels and environmental conditions, and use them as input variables for the model. Using morphological data from a large number of individual organisms, machine learning algorithms such as support vector machines or neural networks are used to train the probability distribution parameters of random variables in the model.

[0065] Taking the known initial morphological parameters and random variable distribution as the initial conditions of the model, the model equations are solved to simulate the dynamic changes of biological morphology over time;

[0066] During the simulation, the morphological parameters at the current moment, such as length and width, are used to determine whether the organism has reached a specific growth stage, and the relevant parameters in the model are updated;

[0067] At a given time point, the three-dimensional geometric parameters of the corresponding moment are extracted from the morphological change trajectory obtained by solving the model to determine the length, width and volume morphological characteristics of the organism at that moment;

[0068] Integrate morphological feature data at different times to analyze the overall change trend of biological morphology over time, evaluate individual differences and the impact of environmental factors on growth and development, and dynamically adjust the mathematical model accuracy and the number of grid elements based on the physical characteristics of the display device, including resolution, color depth, and refresh rate, to adapt to the display capabilities of different devices;

[0069] If the resolution is higher than the preset threshold, the number of grid cells is increased to improve the fineness of the morphological changes;

[0070] If the color depth is lower than a preset threshold, the quantization level of the voltage dynamic change sequence is reduced to reduce the color loss of information hiding;

[0071] During the morphological change process, high-precision mathematical models and high-density grid elements are used to improve the realism of the morphological change. During the information hiding process, a voltage dynamic change sequence with a low quantization level is used to reduce color distortion and improve the visual quality of the hidden information.

[0072] Specifically, in step S1, gene expression level data, including RNA sequencing results, were obtained, and key gene expression levels were extracted, including the growth hormone gene expression value of 2.5×10³ TPM;

[0073] Environmental condition data include temperature and humidity, including temperature set to 25°C and humidity to 60%;

[0074] Using these data as input variables, a neural network algorithm was used to input the morphological data of 1,000 biological individuals. The random variable distribution parameters were trained, including a gene expression coefficient of variation of 0.15. The initial morphological parameters, including length 10mm, width 5mm, and random variable distribution were input into the model, and the differential equation was solved to simulate morphological changes.

[0075] During the simulation, if the current length reaches 15 mm, it is judged to enter growth stage II, and the growth rate parameter is updated to 0.8 mm / day. At time point t = 30 days, the three-dimensional geometric parameters output by the model are extracted, and the length, width, and volume are 18 mm, 7 mm, and 125 mm³.

[0076] Data from different time points were integrated to analyze morphological trends and assess the effects of gene expression and environmental factors, including a 10% increase in growth rate under high temperature conditions;

[0077] According to the display device parameters, such as resolution 1920×1080, color depth 24 bits, and refresh rate 60Hz, the model accuracy was adjusted, including the number of mesh elements set to 5000;

[0078] If the resolution is increased to 4K, the number of grid elements is increased to 10,000 to improve the fineness;

[0079] If the color depth is reduced to 16 bits, the quantization level of the voltage dynamic change sequence is reduced to 4 levels to reduce color loss;

[0080] In the simulation of morphological changes, high-precision models and dense grids are used to ensure the realism of the morphology; in information hiding, a low-quantization-level voltage dynamic change sequence is used to reduce color distortion and improve visual quality.

[0081] like Figure 1-Figure 2 As shown, in step S2, the three-dimensional geometric parameters of the biological morphology at the current moment are obtained, and the color value of each grid element is calculated in combination with the material properties and lighting conditions. For each display screen, the information to be hidden is encoded as a voltage dynamic change sequence. A nonlinear mapping relationship is established between each voltage value in the sequence and the brightness value of the corresponding pixel in the display screen. Through the mapping function, the limitations of the human eye's spatiotemporal resolution are exploited to make the brightness change of the screen imperceptible after the voltage dynamic change sequence is superimposed. The encoded voltage dynamic change sequence is superimposed on the color value for hidden information embedding;

[0082] Obtain the three-dimensional geometric parameters of the current biological form, including vertex coordinates and normal vectors, and determine the meshing method of the biological surface;

[0083] Based on biological material properties such as reflectivity, transmittance, and roughness, a model of the interaction between materials and lighting conditions is established. A ray tracing algorithm is used to combine 3D geometric parameters, material properties, and lighting conditions to calculate the color value of each mesh element.

[0084] The information to be hidden is encoded into a voltage dynamic change sequence, where each voltage value in the sequence corresponds to a pixel brightness value;

[0085] By analyzing the limitations of the human eye's spatiotemporal resolution, a nonlinear mapping function between voltage and pixel brightness is constructed.

[0086] Determine whether the voltage dynamic change sequence superimposed on the color value causes a change in the brightness of the image. If the change is obvious, adjust the mapping function.

[0087] The voltage dynamic change sequence that meets the hiding requirements is superimposed on the color value of the grid element to achieve hidden embedding of information in the display screen;

[0088] Obtain the physical characteristic parameters of the display device, including resolution, color depth, and refresh rate. Based on the obtained physical characteristic parameters, dynamically adjust the mathematical model accuracy and encoding strategy parameters to adapt to the physical limitations of different display devices;

[0089] During the morphological change process, high-precision mathematical models and high-density grid elements are used to improve the realism of the morphological change. During the information hiding process, a voltage dynamic change sequence with a low quantization level is used to reduce color distortion and improve the visual quality of the hidden information.

[0090] Specifically, in step S2, when obtaining the three-dimensional geometric parameters of the biological morphology at the current moment, a high-precision three-dimensional scanner is used to obtain the vertex coordinates and normal vectors of the biological surface with an accuracy of 0.1 mm, and the Delaunay triangulation algorithm is used to divide the biological surface into 10,000 grid elements;

[0091] Based on the reflectivity, transmittance, and roughness properties of biological materials, the Cook-Torrance lighting model was used to establish an interaction model between materials and lighting conditions. A Monte Carlo ray tracing algorithm was then used to calculate the color value of each mesh element using 10,000 rays, resulting in highly realistic biomorphic rendering results.

[0092] The information to be hidden is encoded as a 256-level quantized voltage dynamic change sequence. Based on the Weber-Fechner law of the human eye's spatiotemporal resolution, a logarithmic mapping function between voltage values ​​and pixel brightness values ​​is constructed. When the brightness change caused by the superposition of the voltage dynamic change sequence is less than 10%, the hiding requirement is considered met. The voltage dynamic change sequence is then superimposed on the color value of the grid element to achieve information hiding and embedding.

[0093] After obtaining the physical characteristic parameters of the display device:

[0094] If the resolution is higher than 2K, increase the number of mesh elements to 20,000;

[0095] If the color depth is lower than 8 bits, the voltage dynamic change sequence quantization level is reduced to 128;

[0096] By adaptively adjusting the accuracy of the mathematical model and the encoding strategy, 20,000 grid elements are used in the morphological change process, and a 128-level quantized voltage dynamic change sequence is used in the information hiding process, ultimately achieving highly realistic and low-distortion biological morphological changes and information hiding on different display devices.

[0097] like Figure 1-Figure 2In step S3, during the morphological change and information hiding process, the physical characteristics and limitations of the display device are taken into consideration, and the accuracy of the mathematical model and the parameters of the encoding strategy are dynamically adjusted according to the resolution, color depth, and refresh rate parameters of different devices. For high-resolution devices, the number of grid bins is increased; for low-color-depth devices, the number of quantization levels of the voltage dynamic change sequence is reduced;

[0098] Obtaining physical characteristic parameters of the display device, including resolution, color depth, and refresh rate; determining the accuracy of the mathematical model and the number of mesh elements during the morphological change process based on the obtained physical characteristic parameters;

[0099] If the resolution is higher than the preset threshold, the number of grid cells is increased to improve the fineness of the morphological changes;

[0100] If the color depth is lower than a preset threshold, the quantization level of the voltage dynamic change sequence is reduced to reduce the color loss of information hiding;

[0101] Adapt to the physical limitations of different display devices by dynamically adjusting the mathematical model accuracy and encoding strategy parameters;

[0102] In the process of morphological change, high-precision mathematical models and high-density grid elements are used to improve the realism of morphological changes;

[0103] During the information hiding process, a low-quantization voltage dynamic change sequence is used to reduce color distortion and improve the visual quality of the hidden information. Based on the current morphological parameters, including length and width, it is determined whether the biological individual has reached a specific growth stage and the relevant parameters in the model are updated.

[0104] At a given time point, the three-dimensional geometric parameters of the corresponding moment are extracted from the morphological change trajectory obtained by solving the model to determine the length, width and volume morphological characteristics of the organism at that moment;

[0105] By integrating morphological characteristic data at different times, analyzing the overall changing trend of biological morphology over time, and evaluating the impact of individual differences and environmental factors on growth and development, we can provide a basis for further optimizing the model and predicting biological growth and development.

[0106] Specifically, in step S3, the resolution of the display device is obtained through the system API as 1920x1080 pixels, the color depth is 8 bits, and the refresh rate is 60 Hz;

[0107] Based on these parameters, it was determined that a high-precision mathematical model with an accuracy of 0.01 mm would be used in the morphological change simulation, and the number of mesh elements was set to 10,000.

[0108] Because the resolution is higher than the preset threshold of 1080p, the system automatically increases the number of grid cells to 15,000 to improve the fineness of morphological changes. At the same time, because the color depth is lower than the preset threshold of 10 bits, the quantization level of the voltage dynamic change sequence is reduced from 256 to 128 levels to reduce color loss during the information hiding process.

[0109] By dynamically adjusting the mathematical model accuracy to 0.005mm and reducing the quantization level of the encoding strategy to 64 levels, the system successfully adapted to the physical limitations of the display device;

[0110] When simulating biological morphological changes, an ultra-high-precision mathematical model with an accuracy of 0.001mm and 20,000 grid elements are used to significantly enhance the realism of morphological changes. At the same time, during the information hiding process, an ultra-low voltage dynamic change sequence with 32-level quantization is used to effectively reduce color distortion, achieving a visual quality of over 95% for the hidden information.

[0111] According to the simulation results, when the length of an organism reaches 10 cm and the width reaches 5 cm, it is judged to have entered the mature stage and the growth rate parameters in the model are automatically updated;

[0112] For the growth state on the 30th day, the system extracts from the morphological change trajectory that the organism's length at that moment is 8.5 cm, width is 4.2 cm, and volume is 95.3 cm³;

[0113] Finally, a comprehensive analysis of the morphological characteristic data from 0 to 60 days showed that the average growth rate of the organism was 0.2 cm / day, the standard deviation of the growth rate due to individual differences was 0.05 cm / day, and the growth rate increased by 0.01 cm / day for every 1°C increase in ambient temperature, providing a key basis for further optimization of the growth model.

[0114] like Figure 1-Figure 2 As shown, in step S4, at the receiving end, the visual characteristic model is used to process the received display sequence, and the temporal variation characteristics of the pixel brightness are analyzed to determine whether there is hidden information. If hidden information is detected, the original hidden information is decoded and restored based on the pixel brightness variation sequence and the predefined nonlinear mapping relationship.

[0115] Receive pixel data of the display sequence, obtain the corresponding brightness information matrix through the information coding algorithm, use the brightness information matrix to preprocess the brightness information, perform background modeling based on the brightness distribution of the neighborhood around the pixel point, and obtain the background brightness component;

[0116] Decompose the brightness information matrix into high-frequency components and low-frequency components. If the amplitude of the high-frequency component is above a specific threshold, the pixel is marked as belonging to the significant change area.

[0117] Compare the change frequency of the significant change area in the time domain with the preset human visual frequency range. If there is a difference between the two, it is preliminarily determined that there is hidden information;

[0118] Based on the nonlinear relationship between the change amplitude of the significant change area and the visual sensitivity of the human eye, a mapping algorithm is used to construct a visual characteristic model that reflects the perception characteristics of the human eye.

[0119] Select a time window containing complete information, process the brightness change data within the time window through the visual characteristic model, and obtain the time point and information value of the information hiding;

[0120] Based on the dynamic range of voltage change and display refresh rate, a nonlinear mapping relationship function from voltage value to pixel brightness value is predefined, and multiple discrete mapping relationship tables are constructed. After obtaining the time point of information hiding and the size of information value, the hidden information is restored using a predetermined codeword decoding scheme in combination with the nonlinear mapping relationship table. The restored hidden information is parsed using a text analysis tool. If the hidden information syntax or logic is correct, the complete and correct original hidden information is obtained.

[0121] Specifically, in step S4, after receiving the pixel data of the display sequence, the brightness value of each pixel is first collected. The display sequence is preset to 1080p resolution, and the brightness value range of each pixel is 0-255;

[0122] The brightness value is converted into 8-bit binary data through the information coding algorithm to form a brightness information matrix. Then, the brightness information matrix is ​​preprocessed. The brightness values ​​of the 3x3 neighborhood around each pixel are selected, and their mean and standard deviation are calculated to construct a background brightness component model. For example, if the brightness value of a pixel is 150, the neighborhood mean is 145, and the standard deviation is 5, then the background brightness component is 145±5;

[0123] The brightness information matrix is ​​decomposed into high-frequency and low-frequency components. The discrete wavelet transform (DWT) algorithm is used. If the amplitude of the high-frequency component exceeds the preset threshold of 30, it is marked as a significant change area.

[0124] Compare the frequency of changes in the time domain of the significant change area. The preset human visual frequency range is 20-60Hz. If the frequency of change in a certain area is 25Hz, it is preliminarily judged that hidden information exists.

[0125] According to the human visual sensitivity curve, a nonlinear mapping function f(x)=a*x^2+b*x+c is constructed, where a=0.1, b=0.5, and c=0, reflecting the human eye's perception of brightness changes.

[0126] Select 10 consecutive frames as the time window and process them through the visual characteristic model to obtain the time points t1, t2, t3 of information hiding and the corresponding information values ​​v1, v2, v3;

[0127] Based on the voltage dynamic range of 0-5V and the display refresh rate of 60Hz, a nonlinear mapping relationship table is predefined, including a voltage of 3V corresponding to a brightness value of 180;

[0128] Combined with the mapping table, the Huffman coding and decoding scheme is used to restore the time point and information value to the original hidden information "01011001";

[0129] Finally, natural language processing tools are used to perform grammatical and logical analysis on the restored information. If the information conforms to the preset grammatical rules and logical structure, it is confirmed that the original hidden information is complete and correct.

[0130] In the nonlinear mapping function f(x)=ax^2+bx+c, the physical meaning of each character is as follows:

[0131] f(x): The output of the function, which represents the human eye's perception of brightness changes. Here, it is the perceived amount of brightness change, which is related to the change in brightness value, but not a simple linear relationship;

[0132] x: The input of the function, representing the change in the brightness value of the pixel. In this context, x is the change in brightness value, which can be the original brightness value or the brightness value after some processing (including adjustment of the background brightness component model);

[0133] a: quadratic term coefficient, which determines the nonlinear degree of brightness change perception. Here, a=0.1, which means that the perceived intensity of brightness change increases with the amount of brightness change, but the rate of increase gradually slows down;

[0134] b: linear term coefficient, which affects the linear growth of perceived intensity with the change of brightness. Here, b=0.5, indicating that the change of brightness has a direct positive impact on the perceived intensity.

[0135] c: constant term, indicating that even if the brightness does not change, the human eye has a basic perception intensity. Here, c=0 means that the perception intensity is zero when there is no brightness change.

[0136] like Figure 3 In step S5, during the information analysis process, corresponding filtering algorithms are used to process different interference factors. For Gaussian noise, median filtering is used; for salt and pepper noise, mean filtering is used; for mixed noise, adaptive filtering is used, through adaptive threshold judgment and error correction coding measures;

[0137] Obtain input information, determine the mixed noise intensity in the information using preset rules, and determine the noise type as Gaussian noise, salt and pepper noise, or mixed noise based on the signal strength;

[0138] If the noise type determination result indicates that only Gaussian noise exists, a Gaussian filter is constructed according to the detected noise standard deviation, and a median filter is used for smoothing to obtain a first image;

[0139] By detecting the rate of change of the information entropy of the first image, a noise signal fluctuation parameter is calculated, and according to the noise signal fluctuation parameter, whether the noise type is salt and pepper noise is determined;

[0140] If the result of the judgment is that salt and pepper noise exists, the noise point ratio is counted according to the characteristics of salt and pepper noise, and the noise points are removed by using the mean filter method by setting the filter window size to obtain the second image;

[0141] By calculating the peak signal-to-noise ratio of the second image, analyzing the image information detail quantification index, and judging whether the noise type is Gaussian and salt and pepper mixed noise according to the image information detail quantification index;

[0142] If there is mixed Gaussian and salt-and-pepper noise, the mean and variance of each pixel neighborhood are calculated through adaptive Wiener filtering, and the filter output is adaptively adjusted according to the ratio between the local signal variance and the overall noise variance to obtain the third image;

[0143] The third image is compared using pre-set threshold intervals for segmenting different regions, and multiple threshold comparison results are used for judgment and processing, thereby obtaining a fourth image through threshold processing;

[0144] Obtaining encoding rules and verification rules according to the information encoding type, and determining a fourth image error correction method by matching the error type with a preset error correction method table;

[0145] The fourth image is processed by calling the error correction function interface, and the processed final fifth image is output.

[0146] Specifically, in step S5, the intensity of mixed noise in the information is determined by a preset rule, including determining it as high-intensity noise when the signal-to-noise ratio is lower than 10dB;

[0147] If the Gaussian noise standard deviation is greater than 30, a 3x3 Gaussian filter is constructed to perform median filtering;

[0148] By calculating the rate of change of information entropy, it is determined that salt and pepper noise exists when the rate of change exceeds 0.5;

[0149] When the percentage of statistical noise points exceeds 10%, a 5x5 filter window is set to perform mean filtering to remove noise points;

[0150] If the peak signal-to-noise ratio of the image is lower than 35dB, it is judged as mixed noise and an adaptive Wiener filter is used. The filter strength is increased when the local variance is more than 1.5 times the overall noise variance.

[0151] Use the threshold intervals [-30,30] and [-60,60] to perform threshold decision processing on the image region;

[0152] According to the BCH (15,11) coding rule and CRC-8 check rule, a secondary retransmission method is used to correct errors when the BER is above 10^-4 by looking up the table, and the processed image is finally output.

[0153] like Figure 1-Figure 2 As shown, in step S6,

[0154] Step 1: For the extracted hidden information, a content-based integrity verification algorithm is used to determine whether the information is complete and correct by analyzing the semantic characteristics and contextual relevance of the information. If the information is found to be missing or incorrect, it is recovered and reconstructed based on redundant information and error correction coding;

[0155] Step 2: Obtain the extracted hidden information and use a content-based integrity verification algorithm to analyze the semantic features and contextual relevance of the hidden information;

[0156] Step 3: Based on the analysis results, determine whether the hidden information is complete and correct. If the information is complete and correct, it is determined to be the initial hidden information;

[0157] Step 4: If it is determined that the hidden information is missing or erroneous, the information is recovered and reconstructed based on the redundant information and error correction code;

[0158] Step 5: Through information recovery and reconstruction, the restored hidden information is obtained and determined as the initial hidden information;

[0159] Step 6: Process the initial hidden information using a visual characteristic model to analyze the temporal variation characteristics of pixel brightness. Based on the temporal variation characteristics of pixel brightness, determine whether there is further hidden information in the initial hidden information.

[0160] Step 7: If it is determined that further hidden information exists in the initial hidden information, decoding is performed to recover the further hidden original information based on the pixel brightness change sequence and the predefined nonlinear mapping relationship;

[0161] Step 8: Obtain the recovered further hidden original information, and perform integrity verification, recovery, and reconstruction on it using the same methods as steps 1 to 6 in step S6;

[0162] Step 9: Through integrity verification, recovery and reconstruction, it is finally determined that the hidden information is the complete and correct original hidden information.

[0163] Specifically, in step S6, the hidden information of the extracted 200 characters is obtained, and a semantic feature extraction algorithm based on Word2Vec and a context relevance analysis algorithm based on a graph are used to determine the semantic relevance by calculating the cosine similarity of the word vectors, and a semantic association graph is constructed. A comprehensive analysis reveals that there are 5 missing pieces of information and 2 errors. The complete initial hidden information of 210 characters is recovered and reconstructed using the RS (255, 223) error correction coding scheme and the 3-fold redundancy mechanism.

[0164] According to the temporal variation characteristics of pixel brightness, wavelet transform is used to extract high-frequency components, and an energy threshold of 0.2 is set to determine whether there is hidden information. The results show that there is further hidden information.

[0165] According to the nonlinear mapping relationship of pixel brightness change y=ax^3+bx^2+cx+d (where a=0.5, b=-1.2, c=2.0, d=-1.5), the decoding recovers an additional 50 characters of hidden information;

[0166] The recovered 50 characters are then subjected to integrity verification, recovery, and reconstruction again, ultimately confirming the complete original hidden message of 260 characters.

[0167] In the nonlinear mapping relationship y=ax^3+bx^2+cx+d of pixel brightness changes, the physical meaning of each character is as follows:

[0168] y: The output of the function, which represents the pixel brightness value after nonlinear mapping. This is the new brightness value calculated based on the pixel brightness change and is used to decode hidden information;

[0169] x: the input of the function, representing the pixel brightness change, which is the change in the original pixel brightness value due to the hidden information;

[0170] a: cubic coefficient, which determines the degree of high-order nonlinearity in the perception of brightness changes. Here, a = 0.5 means that a large increase in brightness change will lead to a large change in the perceived brightness value;

[0171] b: quadratic term coefficient, which affects the nonlinear change of brightness perception. Here, b=-1.2, indicating that a certain range of brightness changes will lead to a decrease in the perceived brightness value;

[0172] c: linear term coefficient, which represents the effect of the linear part of the brightness change on the perceived brightness. Here, c = 2.0, indicating that the brightness change has a positive linear effect on the perceived brightness.

[0173] d: constant term, representing the baseline output value of the mapping function when the pixel brightness does not change. Here, d=-1.5, which means that when there is no brightness change, the output of the mapping function has a negative baseline value;

[0174] This nonlinear mapping relationship is used to convert the change in pixel brightness into a new brightness value so that hidden information can be decoded from the pixel brightness change. This relationship takes into account the nonlinear perception characteristics of the human eye to brightness changes, making the encoding and decoding process of hidden information more complex and covert.

[0175] Those skilled in the art can make various other corresponding changes and deformations based on the technical solutions and concepts described above, and all of these changes and deformations should fall within the scope of protection of the claims of this application.

Claims

1. A method for enhancing display effects by dynamically regulating voltage difference, characterized in that: The following steps are involved: S1. Constructing a mathematical model of biological morphology over time, wherein the mathematical model introduces random variables to characterize individual differences of biological organisms; S2. Obtaining the three-dimensional geometric parameters of the mathematical model of the biological morphology changing with time in step S1 at the current moment, calculating the color value of each grid element in combination with material properties and lighting conditions, encoding the information to be hidden into a voltage dynamic change sequence, and superimposing the voltage dynamic change sequence on the color value through a mapping function, specifically including: Obtain the three-dimensional geometric parameters of the biological morphology at the current moment to determine the meshing method of the biological surface; Based on the properties of biological materials, establish an interaction model between materials and lighting conditions; Use ray tracing algorithm to calculate the color value of each grid element; The information to be hidden is encoded as a sequence of dynamically changing voltages. Each voltage value in the sequence corresponds to a pixel brightness value. By analyzing the Weber-Fechner law of the temporal and spatial resolution of the human eye, a nonlinear mapping function between voltage values ​​and pixel brightness values ​​is constructed. After the voltage dynamic change sequence is superimposed on the color value, it is determined whether it causes a change in the brightness of the screen. If so, the mapping function is adjusted; if not, the hiding requirement is met; S3. Obtain pixel data of the display sequence based on the resolution, color depth, and refresh rate parameters of the display device, and dynamically adjust the accuracy of the mathematical model and parameters of the encoding strategy in step S2, wherein the parameters include the accuracy of the mathematical model and the number of grid bins during the morphological change process; S4. Processing the pixel data of the display sequence received in step S3 using the visual characteristic model, decoding and recovering the hidden information in the display sequence based on the pixel brightness change sequence and a predefined nonlinear mapping relationship; S5. Processing the hidden information in the display sequence recovered by decoding in step S4 using a filtering algorithm to address interference factors. If Gaussian noise exists in the information, a median filter is used; if salt and pepper noise exists, a mean filter is used; if a mixture of Gaussian and salt and pepper noise exists, an adaptive filter is used; S6. After the filtering algorithm in step S5 is processed, the hidden information extracted is subjected to a content-based integrity verification algorithm. If information is found to be missing or erroneous, it is recovered and reconstructed based on redundant information and error correction coding.

2. The method for enhancing display effects by dynamically regulating voltage difference according to claim 1, characterized in that: Step S1 includes the following steps: The method of constructing a mathematical model of biological morphology changing over time includes: Obtaining gene expression levels and environmental conditions that affect biological morphological changes, and using the gene expression levels and environmental conditions as input variables of the mathematical model; Using the morphological data of the biological individuals, the probability distribution parameters of the random variables in the mathematical model are obtained through machine learning algorithm training; Using the known initial morphological parameters and the random variable distribution as initial conditions of the mathematical model, solving the model equation to simulate the dynamic change process of the biological morphology over time; Based on the morphological parameters at the current moment, it is used to determine whether the biological individual has reached a specific growth stage and to update the morphological parameters in the mathematical model; For a given time point, the three-dimensional geometric parameters at the corresponding moment are extracted from the morphological change trajectory obtained by solving the model to determine the length, width and volume morphological characteristics of the organism at that moment.

3. The method for enhancing display effects by dynamically regulating voltage difference according to claim 1, characterized in that: Step S3 includes the following steps: According to the resolution, color depth, and refresh rate parameters of the display device, pixel data of the display sequence is obtained, and the accuracy of the mathematical model and the parameters of the encoding strategy in step S2 are dynamically adjusted. The parameters include the accuracy of the mathematical model and the number of grid bins during the morphological change process, including: Obtain the physical characteristics of the display device, including resolution, color depth, and refresh rate; Determining the accuracy of the mathematical model and the number of grid elements during the morphological change process based on the acquired physical characteristic parameters; If the resolution is higher than a preset threshold, the number of grid cells is increased; If the color depth is lower than a preset threshold, the quantization level of the voltage dynamic change sequence is reduced.

4. The method for enhancing display effects by dynamically regulating voltage difference according to claim 1, characterized in that: Step S4 includes the following steps: The pixel data of the display sequence received in step S3 is processed by the visual characteristic model, and hidden information in the display sequence is decoded and restored according to the pixel brightness change sequence and the predefined nonlinear mapping relationship, including: The acquisition device obtains a display sequence, performs wavelet transform-based decomposition processing on the display sequence, separates different frequency components in the display sequence, and obtains low-frequency components and high-frequency components corresponding to the display sequence; Calculate the average brightness of each pixel according to the obtained low-frequency component, and generate a binary sequence corresponding to the pixel sequence by calculating the average brightness of each pixel; Obtaining the binarized sequence and performing Fourier transform, transforming the binarized sequence from the time domain space to the frequency domain space, and obtaining spectrum data transformed into the frequency domain space; A fixed frequency band threshold is set for the spectrum data, and it is determined whether the frequency band amplitude of the spectrum data is greater than the fixed frequency band threshold. If so, hidden information exists; otherwise, no hidden information exists.

5. The method for enhancing display effects by dynamically regulating voltage difference according to claim 1, characterized in that: Step S5 includes the following steps: A filtering algorithm is used to process the hidden information in the display sequence recovered by decoding in step S4 in response to interference factors. If Gaussian noise exists in the information, a median filtering method is used; if salt and pepper noise exists, a mean filtering method is used; if a mixed Gaussian and salt and pepper noise exists, an adaptive filtering method is used, including: Obtain input information, determine the mixed noise intensity in the information using preset rules, and determine the noise type as Gaussian noise, salt and pepper noise, or mixed noise based on the signal strength; If the noise type determination result indicates that Gaussian noise exists, a Gaussian filter is constructed according to the detected noise standard deviation, and a median filter is used for smoothing to obtain a first image; By detecting the rate of change of the information entropy of the first image, a noise signal fluctuation parameter is calculated, and according to the noise signal fluctuation parameter, whether the noise type is salt and pepper noise is determined; If the result of the judgment is that salt and pepper noise exists, the noise point ratio is counted according to the characteristics of salt and pepper noise, and the noise points are removed by using the mean filter method by setting the filter window size to obtain the second image; By calculating the peak signal-to-noise ratio of the second image, analyzing the image information detail quantification index, and judging whether the noise type is Gaussian and salt and pepper mixed noise according to the image information detail quantification index; If there is Gaussian and salt-and-pepper mixed noise, the mean and variance of each pixel neighborhood are calculated through adaptive Wiener filtering, and the filter output is adaptively adjusted according to the ratio between the local signal variance and the overall noise variance to obtain the third image.

6. The method for enhancing display effects by dynamically regulating voltage difference according to claim 1, characterized in that: Step S6 includes the following steps: After the filtering algorithm processing in step S5, the hidden information extracted is subjected to a content-based integrity verification algorithm. If information is found to be missing or erroneous, it is recovered and reconstructed based on redundant information and error correction coding, including: Obtain a data stream containing hidden information, perform data analysis using a hidden information detection algorithm, and obtain key data segments containing hidden information. If the hidden information is not missing or erroneous, determine it as the initial hidden information. If the hidden information is missing or erroneous, perform information recovery and reconstruction based on redundant information and error correction coding.

7. The method for enhancing display effects by dynamically regulating voltage difference according to claim 4, characterized in that: The spectrum data, setting a fixed frequency band threshold, and determining the frequency band amplitude of the spectrum data include the following steps: If it is determined that the amplitude of the frequency band in the frequency domain space exceeds a threshold, the instantaneous phase information corresponding to the hidden information is obtained by Hilbert transform; A mapping table of phase information is established based on the instantaneous phase information obtained, and a character code corresponding to each instantaneous phase information is searched according to the mapping table.

8. The method for enhancing display effects by dynamically regulating voltage difference according to claim 7, characterized in that: The mapping table searches for the character code corresponding to each instantaneous phase information. After obtaining the character code, the character codes are connected into a sequence to obtain the hidden text information, determine that the text information is the initial hidden information, obtain the encoding rules and verification rules according to the information encoding type, match the preset error correction method table through the error type, determine the sixth image error correction method, and output the processed final seventh image.

9. The method for enhancing display effects by dynamically regulating voltage difference according to claim 6, characterized in that: The hidden information detection algorithm performs data analysis using a Word2Vec-based semantic feature extraction algorithm and a graph-based context relevance analysis algorithm. It determines semantic relevance by calculating the cosine similarity of word vectors and constructs a semantic association graph.

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