Display method and system of mini-computer host

Abstract

The invention discloses a display method and system for a mini-computer host, and relates to the technical field of computer display, and the method comprises the steps: monitoring and analyzing the ambient light intensity and color temperature in real time through a built-in ambient light sensor of the mini-computer host, and collecting ambient light data; the display brightness and the color temperature are intelligently calculated and adjusted through a pre-trained deep learning model by using the ambient light data; when video stream coding parameters are dynamically adjusted, network conditions and video stream complexity are analyzed in real time, and resolution and frame rate are adaptively adjusted; and combining the optimized display parameters with the adjusted video stream and the display. Through ambient light intelligent perception, network path optimization and video stream parameter dynamic adjustment, the problems of complex illumination adaptability and smooth video playing in the prior art are solved, and the user experience is greatly enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of computer display, and in particular to a display method and system for a mini computer host. Background Art

[0002] With the rapid development of Internet technology and the advancement of wireless communication technology, the impact of network fluctuations on user experience has become increasingly significant, especially in the field of mini computer host display technology. Traditional display technology mainly relies on fixed display parameter settings, which often cannot adapt to the ever-changing network environment. Later, with the rise of Internet of Things (IoT) technology and the enhancement of edge computing capabilities, display technology began to incorporate more intelligent elements, especially to improve the smoothness under network fluctuations. For example, the advancement of MIMO antenna technology enables devices to automatically select the best wireless transmission path in a complex and changing network environment, thereby reducing network latency and packet loss and improving the smoothness of data transmission. In addition, the development of intelligent coding technology also enables video streaming to maintain good transmission quality under dynamically changing network conditions.

[0003] Although the development of the above technologies has achieved certain results, there are still some shortcomings in practical applications. In terms of the smoothness of network fluctuations, existing technologies often focus on signal strength and ignore the impact of network fluctuations, resulting in network delays and packet loss even when the signal strength is good. In addition, in terms of dynamic adjustment of video stream encoding parameters, existing methods can usually only adjust the encoding parameters of the video stream based on simple network conditions (such as packet loss rate and delay), but fail to fully consider the interaction between network conditions and video content, limiting the smoothness of video playback and the quality of visual experience. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a display method and system for a mini computer host to solve the problem of insufficient smoothness of video stream transmission in a network fluctuation environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a display method for a mini computer host, which includes: using a built-in ambient light sensor in the mini computer host to monitor and analyze the ambient light intensity and color temperature in real time, and collect ambient light data; using the ambient light data to intelligently calculate and adjust the display brightness and color temperature through a pre-trained deep learning model; using the MIMO antenna array integrated in the mini computer host to automatically scan and analyze the current network environment, identify signal strength and stability, and intelligently select the best wireless transmission path; after selecting the wireless transmission path, dynamically adjust the encoding parameters of the video stream in the network condition through the video encoding unit integrated in the mini computer host; while dynamically adjusting the video stream encoding parameters, analyze the network condition and video stream complexity in real time, and adaptively adjust the resolution and frame rate; combining the optimized display parameters with the adjusted video stream, the display starts according to the latest settings and displays the optimized content.

[0008] As a preferred solution of the display method of the mini computer host of the present invention, wherein: the ambient light intensity and color temperature of the surrounding light are monitored and analyzed in real time by the built-in ambient light sensor of the mini computer host to collect ambient light data, the specific steps are:

[0009] Ambient light sensor using a high-sensitivity photodiode array for self-test and calibration;

[0010] The ambient light sensor collects and records the raw photocurrent data in real time at a predetermined frequency;

[0011] Use the photocurrent-light intensity relationship curve and color temperature estimation algorithm to convert the raw photocurrent data recorded in real time to obtain light intensity and color temperature data;

[0012] Based on the light intensity and color temperature data, the built-in microprocessor analyzes the changing trends of light intensity and color temperature in real time;

[0013] The standard deviation threshold method is applied to identify and filter out sudden light changes, and the stable and representative light intensity and color temperature data are integrated into a continuous time series data set;

[0014] The time series data set is processed by sliding window averaging method to form standard ambient light data.

[0015] As a preferred solution of the display method of the mini computer host of the present invention, wherein: the ambient light data is used to intelligently calculate and adjust the display brightness and color temperature through a pre-trained deep learning model, and the specific steps are:

[0016] Wavelet transform is applied to decompose the ambient light data and extract the characteristics of light intensity and color temperature in different frequency bands. The expression is:

[0017]

[0018] Where I(t) is the change of light intensity I with time t, ψ * is the complex conjugate of the wavelet function ψ, τ is the time translation parameter, s is the scale parameter, WT I (τ, s) is the wavelet coefficient of the light intensity value under the time translation parameter τ and the scale parameter s, and dt represents the time element of the integral variable;

[0019] Based on the wavelet coefficients of light intensity under the time shift parameter τ and scale parameter s, the ambient light data is converted into intelligently calculated display brightness and color temperature through a pre-trained deep learning model. The expressions are:

[0020]

[0021]

[0022] Where B(t) is the display brightness value at time t, C(t) is the display color temperature value at time t, C represents the display color temperature value, and D(t,t i ) is time t and historical time point t i Time distance, WT C (τ i ,s i ) is the wavelet coefficient of the color temperature value displayed under the time shift parameter τ and the scale parameter s, α is the display brightness time decay factor, β is the display color temperature time decay factor, and N is the number of historical time points;

[0023] The calculated display brightness value B(t) and color temperature value C(t) are applied to the display panel in real time to perform dynamic display parameter adjustment.

[0024] As a preferred solution of the display method of the mini computer host of the present invention, wherein: the intelligent selection of the best wireless transmission path comprises the following specific steps:

[0025] Initialize the MIMO antenna array to perform self-test and use the fast Fourier transform algorithm to perform spectrum scanning to identify channel conditions and available frequency bands;

[0026] Estimating the power spectrum density of the signal in each available frequency band and calculating the standard deviation of the signal strength to evaluate the signal strength and stability;

[0027] Use autocorrelation function to identify periodic and non-periodic interference sources and mark the detected interference source frequency bands;

[0028] Based on signal strength, stability and marked interference source frequency bands, a graph theory algorithm is used to establish the topology of the current network environment and assign node weights based on signal quality;

[0029] Use Dijkstra algorithm to update network topology in real time, recalculate paths based on signal strength and stability, and select the optimal path;

[0030] According to the path characteristics, the transmission parameters are dynamically adjusted and path monitoring is implemented. If path performance degradation is detected, the backup path is switched immediately.

[0031] As a preferred solution of the display method of the mini computer host of the present invention, the specific steps of dynamically adjusting the encoding parameters of the video stream in the network condition are as follows:

[0032] Set video encoding parameters, including starting bit rate, target bit rate range, maximum and minimum frame rate, resolution, and encoding format;

[0033] Continuously measure network latency and packet loss using the integrated network monitoring module;

[0034] Combine the signal strength and stability information of the MIMO antenna array to evaluate the quality of the current wireless path;

[0035] Adaptive bitrate algorithm is used to adjust the encoding bitrate of video stream in real time according to network delay and packet loss rate.

[0036] Dynamically adjust frame rate and resolution based on network conditions while maintaining video quality;

[0037] Use machine learning models to predict future changes in network conditions, adjust encoding parameters in advance, and use different encoding quality levels based on the complexity and importance of video content;

[0038] Applying the adjusted encoding parameters to a video encoding unit to encode the video stream in real time;

[0039] Establish a feedback loop between video stream encoding quality and network conditions, continuously monitor encoding effects and network changes, and make continuous adjustments and optimizations.

[0040] As a preferred solution of the display method of the mini computer host of the present invention, wherein: while dynamically adjusting the video stream encoding parameters, the network status and video stream complexity are analyzed in real time, and the resolution and frame rate are adaptively adjusted. The specific steps are:

[0041] Based on the information of network delay L, packet loss rate P and signal strength S, a network status evaluation function F is designed. net The expression is:

[0042]

[0043] Among them, F net(L, P, S) represents the network status score, α, β and γ are network status influencing factors, which are used to adjust the influence of network delay, packet loss rate and signal strength respectively, and e is the base of natural logarithm;

[0044] Apply the video content analysis algorithm to evaluate the complexity of the video stream and construct the video stream complexity function F vid The expression is:

[0045] F vid (E,T,C)=log(1+δE+∈T+ζΩ);

[0046] Among them, F vid (E, T, C) is the video complexity score, where E is the number of edges in the video frame, T is the mean of the coefficient of variation of the pixel grayscale values ​​in the video frame, C is the entropy of the color space distribution in the video frame, δ is the influencing factor of the edge number E, ∈ is the influencing factor of the texture complexity T, and ζ is the influencing factor of the color diversity Ω;

[0047] The network status evaluation function F net and the video complexity function F vid Combined to construct a comprehensive decision function Q ad The expression is:

[0048]

[0049] Among them, Q ad (F net ,F vid ) is expressed as the comprehensive decision score, R max The maximum resolution value of the video stream;

[0050] According to Q ad The resolution and frame rate of the video stream are adaptively adjusted based on the value of

[0051] As a preferred solution of the display method of the mini computer host of the present invention, wherein: the display parameters after optimization are combined with the adjusted video stream, the display starts according to the latest settings and displays the optimized content, the specific steps are:

[0052] When the display is turned on, it automatically adjusts the display brightness and color temperature based on the ambient light sensor data, and optimizes the video stream encoding parameters according to the network conditions;

[0053] Adopting adaptive streaming media method to intelligently match display parameters with video stream parameters;

[0054] Use machine learning algorithms to predict user interests and preload relevant content into the cache, adjusting loading priority based on network status and video complexity;

[0055] Dynamically adjust the layout according to the screen size and resolution, and adaptively adjust the video content;

[0056] After parameter adjustment and content preprocessing are completed, the optimized customized content is started and presented.

[0057] In a second aspect, the present invention provides a display system for a mini computer host, comprising an intelligent display adaptation module, a display parameter intelligent adjustment module, a network path management module, a video stream encoding parameter dynamic adjustment module, an adaptive optimization module and a content presentation module; the intelligent display adaptation module is used to monitor and analyze the ambient light intensity and color temperature in real time through the built-in ambient light sensor of the mini computer host, and collect ambient light data; the display parameter intelligent adjustment module is used to use the ambient light data to intelligently calculate and adjust the display brightness and color temperature through a pre-trained deep learning model; the network path management module is used to automatically scan and analyze the current network environment through the MIMO antenna array integrated in the mini computer host, identify the signal strength and stability, and intelligently select the best wireless transmission path; the video stream encoding parameter dynamic adjustment module is used to dynamically adjust the encoding parameters of the video stream in the network condition through the video encoding unit integrated in the mini computer host after selecting the wireless transmission path; the adaptive optimization module is used to analyze the network condition and the complexity of the video stream in real time while dynamically adjusting the video stream encoding parameters, and adaptively adjust the resolution and frame rate; the content presentation module is used to combine the optimized display parameters with the adjusted video stream, and the display starts according to the latest settings and displays the optimized content.

[0058] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the display method of the mini computer host as described in the first aspect of the present invention is implemented.

[0059] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the display method of the mini computer host as described in the first aspect of the present invention is implemented.

[0060] The beneficial effects of the present invention are as follows: the present invention realizes intelligent adjustment of display brightness and color temperature by deeply analyzing ambient light data and combining pre-trained deep learning models to provide a more comfortable and energy-saving viewing experience. At the same time, through the dynamic network path selection of the MIMO antenna array and the real-time optimization of video encoding parameters, high-quality video transmission can be ensured under various network conditions. In addition, the present invention also takes into account the complexity of the video content and the network conditions, realizes adaptive adjustment of resolution and frame rate, and further improves the smoothness and visual effect of video playback. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 This is a flow chart of the display method of the mini computer host in Example 1.

[0063] Figure 2 This is a flow chart of collecting ambient light data in Example 1. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a display method for a mini computer host, comprising the following steps:

[0068] S1. Use the built-in ambient light sensor of the mini computer host to monitor and analyze the surrounding light intensity and color temperature in real time and collect ambient light data.

[0069] Furthermore, an ambient light sensor with a high-sensitivity photodiode array is used for self-checking and calibration;

[0070] It should be noted that the specific implementation steps of the self-test and calibration in "using an ambient light sensor with a high-sensitivity photodiode array to perform self-test and calibration" are as follows:

[0071] When the mini computer host system starts up, the sensor driver is loaded and the photodiode array and related circuit components are initialized;

[0072] In a completely light-shielded environment, measure the dark current level of the photodiode array under no light conditions;

[0073] Use a calibrated light source of known light intensity and color temperature to provide stable lighting conditions for the photodiode array;

[0074] Under the calibrated light source, record the photocurrent generated by the photodiode array, compare it with the known light intensity value, and establish the relationship curve between photocurrent and light intensity;

[0075] By changing the color temperature of the calibration light source, repeating the above process, recording the relationship between the photocurrent change and the color temperature, and improving the comprehensive conversion model of photocurrent, light intensity and color temperature;

[0076] Compare the photocurrent measurement with the actual light intensity and color temperature values ​​and calculate the calibration coefficient to compensate for the non-ideal characteristics of the photodiode array;

[0077] The calculated calibration coefficients and dark current values ​​are stored in non-volatile memory as part of the sensor calibration. These parameters are loaded each time the system is started for pre-calibration before real-time data acquisition to ensure the consistency and reliability of the sensor output.

[0078] It should also be noted that the specific steps for applying the calibration coefficients to ensure the accuracy of the sensor output are as follows:

[0079] Furthermore, after the system is started, the previously calculated calibration coefficients and dark current values ​​are loaded from the non-volatile memory, and the ambient light sensor collects raw photocurrent data at a predetermined frequency;

[0080] Subtract the dark current value from the collected photocurrent data to eliminate the influence of the current conduction of the sensor itself under no light conditions;

[0081] Multiply the compensated photocurrent data by the light intensity calibration coefficient to correct the response deviation of the sensor under different light intensities. The expression is:

[0082] L adj =(L raw -L dark )×K illum ;

[0083] Among them, L adj is the adjusted photocurrent value, L raw is the original photocurrent value, L dark is the dark current value, K illum is the light intensity calibration factor;

[0084] If you need to calibrate the color temperature response at the same time, you also need to adjust L adj The result is multiplied by the color temperature calibration coefficient to ensure accurate conversion of photocurrent to light intensity at different color temperatures. The expression is:

[0085] L final =L adj ×K color ;

[0086] Among them, L final is the photocurrent value that is ultimately used to convert into light intensity, K color is the color temperature calibration factor;

[0087] Use the photocurrent-light intensity relationship curve and color temperature estimation algorithm to convert the final photocurrent value into light intensity and color temperature data;

[0088] The converted light intensity and color temperature data are applied in real time to subsequent processing processes such as intelligent adjustment of display brightness and color temperature, network environment analysis, etc.

[0089] The ambient light sensor collects and records the raw photocurrent data in real time at a predetermined frequency;

[0090] It should be noted that "collecting at a predetermined frequency" means that the data collection frequency is determined according to the needs of the application scenario and the capabilities of the processor. For example, for scenarios that require rapid response to environmental changes, a higher collection frequency may be required, such as 10 times per second (10Hz).

[0091] It should also be noted that the specific implementation steps of "real-time recording of raw photocurrent data" are as follows:

[0092] Based on the predetermined frequency acquisition, a loop program is written to ensure that the ambient light sensor continuously collects data at the set frequency; in each cycle, the photocurrent value generated by the photodiode array is read and written into the memory buffer in real time, and the raw photocurrent data is recorded in real time to prepare for subsequent processing steps.

[0093] Use the photocurrent-light intensity relationship curve and color temperature estimation algorithm to convert the raw photocurrent data recorded in real time to obtain light intensity and color temperature data;

[0094] It should be noted that the following are the specific implementation steps of how to use the photocurrent-light intensity relationship curve and the color temperature estimation algorithm to convert the raw photocurrent data recorded in real time to obtain the light intensity and color temperature data:

[0095] In a laboratory environment, a standard light source is used. By gradually adjusting the brightness of the light source, the photocurrent output of the photodiode array under different light intensities is measured to form a relationship curve.

[0096] Also in a laboratory environment, using a light source with a known color temperature, the photocurrent response of the photodiode array at different color temperatures was recorded;

[0097] The ambient light sensor collects and records the raw photocurrent data in real time at a predetermined frequency, which is the direct input for subsequent conversion to obtain a series of photocurrent data points that vary with time;

[0098] Substitute the obtained photocurrent data points into the relationship curve and calculate the corresponding light intensity value;

[0099] Substitute the photocurrent data at the same time into the calibrated color temperature estimation algorithm to calculate the color temperature of the light source;

[0100] Pair the obtained light intensity and color temperature values ​​to form a list of data pairs containing timestamps to generate a time series dataset, in which each data point contains light intensity and color temperature information;

[0101] Perform a preliminary check on the integrated data set to remove any possible outliers or invalid data points to ensure the accuracy and completeness of the data set in preparation for subsequent analysis and compression.

[0102] Furthermore, based on the light intensity and color temperature data, the changing trends of light intensity and color temperature are analyzed in real time through the built-in microprocessor;

[0103] It should be noted that the specific implementation steps of "real-time analysis of the changing trend of light intensity and color temperature through the built-in microprocessor" are as follows:

[0104] The microprocessor reads the pre-processed light intensity and color temperature data from the storage buffer within the recent period (such as the past 5 minutes) to ensure that the read data is stable data after abnormal detection and filtering, providing a basis for trend analysis;

[0105] Divide the read data into multiple fixed-size time windows, each containing a certain number of data points (for example, one data point per second), where window division facilitates subsequent statistical calculations and helps capture short-term and long-term trends;

[0106] The average value, standard deviation and other statistics are calculated for the light intensity and color temperature data in each time window. The calculation of statistics provides quantitative indicators for trend analysis, especially the standard deviation can be used to identify volatility.

[0107] Compare the statistics of adjacent time windows to identify whether the light intensity and color temperature have an upward, downward or stable trend. This step is based on the change of statistics to determine the trend direction and provide a basis for subsequent decision-making;

[0108] Use simple linear regression or other prediction models to predict the likely changes in light intensity and color temperature in the next time window based on the identified trends, where the prediction results will be used to adjust display parameters to ensure that the display effect matches the expected environment;

[0109] Feedback trend prediction results to the display parameter adjustment module to trigger automatic adjustment of brightness and color temperature, where the predicted ambient light changes will guide the dynamic adjustment of display parameters to maintain a comfortable viewing experience;

[0110] The microprocessor continuously monitors the ambient light data and repeats the above steps to ensure the real-time analysis and prediction.

[0111] Through the above steps, the microprocessor can analyze the changing trends of light intensity and color temperature in real time, providing key information for the intelligent adjustment of display parameters to ensure the best visual experience in a changing environment.

[0112] Furthermore, the standard deviation threshold method is applied to identify and filter out sudden illumination changes, and the stable and representative illumination intensity and color temperature data are integrated into a continuous time series dataset;

[0113] It should be noted that in the link of "applying the standard deviation threshold method to identify and filter out sudden lighting changes, and integrating stable and representative light intensity and color temperature data into a continuous time series data set", the specific implementation process is as follows:

[0114] Based on the light intensity and color temperature data from the most recent period (e.g., the past 1 minute), calculate the standard deviation (σ) of these data points. This step is used to quantify the degree of fluctuation in the data;

[0115] According to application requirements and data characteristics, set a multiple threshold of standard deviation (for example, 3σ). This threshold is used as a benchmark to determine whether a data point is abnormal;

[0116] Traverse each light intensity and color temperature data to check whether it exceeds the set threshold range (that is, whether it is greater than μ+3σ or less than μ-3σ, where μ is the mean). Data outside the range is considered an outlier;

[0117] Once the outliers are identified, they are removed from the dataset or replaced with the values ​​of the nearest neighboring data points to maintain the continuity of the dataset. This step ensures that the dataset is not affected by extreme values;

[0118] Combine the filtered light intensity and color temperature data to form a continuous time series data set. Ensure that the data is arranged in chronological order to facilitate subsequent analysis and processing.

[0119] The time series data set is processed by sliding window averaging method to form standard ambient light data;

[0120] Preferably, a sliding window averaging method (e.g., a window size of 5) is applied to smooth the integrated data set, which means that the new value of each data point will be the average of itself and the data points before and after it, and the window moves with the data stream;

[0121] It should be noted that the data set processed by the sliding window averaging method now has a lower noise level, forming more stable and representative standard ambient light data.

[0122] S2. Utilize ambient light data to intelligently calculate and adjust display brightness and color temperature through a pre-trained deep learning model.

[0123] Furthermore, wavelet transform is applied to decompose the ambient light data to extract the light intensity and color temperature characteristics of different frequency bands. The expression is:

[0124]

[0125] Where I(t) is the change of light intensity I with time t, ψ * is the complex conjugate of the wavelet function ψ, τ is the time translation parameter, s is the scale parameter, WT I (τ, s) is the wavelet coefficient of the light intensity value under the time translation parameter τ and the scale parameter s, and dt represents the time element of the integral variable;

[0126] Preferably, in the wavelet transform, the complex conjugate ψ * The use of is mainly reflected in the calculation of the inner product of the wavelet function to ensure the real number properties and physical meaning of the transformation result;

[0127] Preferably, wavelet transform is a time-frequency analysis tool that can decompose a signal into different time and frequency scales, thereby extracting local features of the signal.

[0128] It should be noted that here, ψ * Represents the complex conjugate of the wavelet function ψ. The role of the complex conjugate is that when the wavelet function ψ is a complex function, the use of the complex conjugate can ensure that the result of the inner product is a real number. This is because the wavelet function ψ and its complex conjugate ψ * The product of will produce a real value, even though ψ itself is complex.

[0129] It should also be noted that in actual wavelet transform, the main purpose of using complex conjugate is:

[0130] Maintain the real number nature of the result: In signal processing, it is usually hoped that the transformation result is a real value with physical meaning. When the wavelet function is complex, the use of complex conjugate can ensure that the inner product result is a real number.

[0131] Matching phase information: Complex wavelet functions can carry phase information, which is very important for certain types of data analysis. Using complex conjugate for matching can better preserve and analyze the phase characteristics of the signal.

[0132] Calculate energy distribution: The result of wavelet transform is usually used to analyze the energy distribution of the signal by taking the modulus square (i.e. |·| 2 Operation), the signal energy distribution at a given scale and position can be obtained.

[0133] Therefore, in wavelet transform, the use of complex conjugate is to ensure the correctness and physical meaning of the transform result, especially when processing complex signals or needing to preserve phase information.

[0134] Based on the wavelet coefficients of light intensity under the time shift parameter τ and scale parameter s, the ambient light data is converted into intelligently calculated display brightness and color temperature through a pre-trained deep learning model. The expressions are:

[0135]

[0136] Where B(t) is the display brightness value at time t, C(t) is the display color temperature value at time t, C represents the display color temperature value, and D(t,t i ) is time t and historical time point t i Time distance, WT C (τ i ,s i ) is the wavelet coefficient of the color temperature value displayed under the time shift parameter τ and the scale parameter s, α is the display brightness time decay factor, β is the display color temperature time decay factor, and N is the number of historical time points;

[0137] Preferably, α and β are defined as time decay factors of display brightness and color temperature, and their role is to control the degree of influence of historical illumination data on the adjustment of current display parameters (brightness and color temperature). These two factors determine the speed and degree of influence of past illumination conditions on current display settings, so their settings directly affect the smoothness and real-time performance of display response.

[0138] It should be noted that the size of the α factor determines the response speed of the display brightness B(t) to past changes in light intensity. A larger α means that the display brightness will "forget" old light data faster and respond more quickly to the latest light changes, which is suitable for scenes that require instant brightness adjustment, but may cause frequent brightness fluctuations. On the contrary, a smaller α makes the display brightness more stable because it tends to average historical light intensity, which is suitable for application scenarios where the ambient light does not change much or where brightness fluctuations need to be reduced.

[0139] It should also be noted that the size of the α factor determines how quickly the display brightness B(t) responds to past changes in light intensity. A larger α means that the display brightness will "forget" old light data more quickly and respond more quickly to the latest light changes. On the contrary, a smaller α makes the display brightness more stable because it tends to average historical light intensity, which is suitable for application scenarios where ambient light changes little or brightness fluctuations need to be reduced.

[0140] To further explain, the specific numerical settings of α and β are usually affected by the following factors:

[0141] Frequency and amplitude of ambient light changes: If the light changes frequently and dramatically, larger α and β may be required to quickly adapt to the changes; otherwise, smaller α and β should be selected to maintain the stability of display parameters.

[0142] User preference and visual comfort: Some users may prefer a more stable display setting, in which case smaller α and β should be selected; while for users who prefer real-time adaptation to the environment, larger α and β can be selected.

[0143] Characteristics of display devices: The response time and brightness adjustment capabilities of different display devices will also affect the selection of α and β. For example, a fast-responding display may allow the use of larger α and β, while a slower-responding display may require smaller α and β to avoid delays in display parameter adjustment.

[0144] In short, the time decay factors of α and β are key parameters to ensure intelligent adjustment of display parameters. Their setting needs to take into account many factors and requires repeated trials and optimization to obtain the most suitable values ​​for specific application scenarios.

[0145] It should also be noted that the association between the “pre-trained deep learning model” and the “wavelet coefficients of light intensity under the time translation parameter τ and the scale parameter s” is as follows:

[0146] First, wavelet transform is applied to decompose the ambient light data to extract the characteristics of different frequency bands of light intensity and color temperature. Here, the result of wavelet transform, i.e. WT I (τ i ,s i ) and WT C (τ i ,s i ), which provides the characteristic representation of light intensity and color temperature at different time scales;

[0147] These features obtained through wavelet transform are used to train a deep learning model. The learning goal of the model is to learn how to infer the most appropriate display brightness B(t) and color temperature C(t) from these features. During the training phase, the model will learn a set of parameters that can map wavelet coefficients to display parameters. This mapping relationship is the core of the deep learning model.

[0148] After the model training is completed, the effect of the deep learning model is no longer seen directly in the expression, because it has been internalized as the parameters of the model. When new ambient light data arrives, the wavelet transform is used again to extract features, and then these features are input into the trained model. The model will output the corresponding display brightness and color temperature based on the previously learned parameters.

[0149] In expressions B(t) and C(t), the “shadow” of the deep learning model is reflected in the following points:

[0150] Feature input: WT I (τ i ,s i ) and WT C (τ i ,s i ) are taken as the input of the model, which are the output of wavelet transform and represent the characteristics of light intensity and color temperature at different times and scales.

[0151] Parameter adjustment: Although the parameters learned during model training do not appear directly in the expression, these parameters determine WT I (τ i ,s i ) and WT C (τ i ,s i ) is converted to B(t) and C(t), which means that even if the expression does not directly contain model parameters, these parameters indirectly affect the calculation of B(t) and C(t) through model training.

[0152] Time decay factor: The time decay factors α and β in the expression can be regarded as a result of model training. They reflect how the model adjusts the model according to the time distance D(t,t i ) weight the importance of historical lighting data, and the values ​​of these factors are determined in part by the deep learning model during the training process to optimize the prediction of display brightness and color temperature.

[0153] Therefore, although the specific parameters of the "pre-trained deep learning model" do not appear directly in the final expression, the existence and role of the model runs through the entire technical process. From feature extraction, model training to model application, each link is closely connected, forming a system for intelligent calculation of display brightness and color temperature.

[0154] The calculated display brightness value B(t) and color temperature value C(t) are applied to the display panel in real time to perform dynamic display parameter adjustment.

[0155] It should be noted that the specific implementation steps of “applying the calculated display brightness value B(t) and color temperature value C(t) to the display panel in real time to adjust the dynamic display parameters” are as follows:

[0156] First, the calculated display brightness value B(t) and color temperature value C(t) are converted into control signals acceptable to the display. This process usually involves mapping the values ​​to the brightness level and color temperature range supported by the display;

[0157] These control signals are then formatted into an instruction set that the display hardware can understand. This may include, but is not limited to, converting the signals to specific voltage levels or digital signals to match the interface standard of the display panel;

[0158] For LCD screens, the B(t) value will be used to adjust the backlight intensity to achieve the desired brightness level. The backlight controller receives the brightness signal from the system and adjusts the power of the LED backlight source, thereby changing the overall brightness of the screen;

[0159] The C(t) value will guide the color temperature adjustment circuit to achieve the target color temperature by changing the ratio of red, green and blue LED backlights or by adjusting the position of the internal color filter.

[0160] To ensure the accuracy and response speed of display parameters, the system should include a closed-loop feedback mechanism. This means that after adjusting the display parameters, the display will measure the actual brightness and color temperature of the display panel again and compare these measurements with the target values ​​B(t) and C(t);

[0161] If the measured value deviates from the target value, the system will make corresponding adjustments until the actual performance of the display panel matches the calculated value. This process may involve rapid iterations to quickly converge to the ideal state;

[0162] When adjusting display parameters, user experience should be considered to avoid sudden changes in brightness and color temperature that may cause visual discomfort. Therefore, the adjustment process should be designed to be a smooth transition, especially in low-light environments, to reduce eye fatigue;

[0163] In addition, the system can also allow users to customize brightness and color temperature preferences, or adjust display parameters according to personal biological clocks to further optimize the visual experience;

[0164] Ensure that the display parameter adjustment mechanism is compatible with various types of monitors, considering that different brands and models of monitors may have different control protocols and response times;

[0165] When designing display parameter adjustment algorithms, energy consumption must also be considered, especially on mobile devices, where shortening of battery life due to frequent adjustments of brightness and color temperature should be avoided.

[0166] From the above detailed steps, it can be clearly seen that the real-time application of the calculated display brightness B(t) and color temperature C(t) to the display panel is a comprehensive process involving signal conversion, hardware response, real-time feedback and user experience optimization; this process not only improves the quality of the display effect, but also ensures the comfort of the user's visual experience and the overall efficiency of the system.

[0167] S3, through the MIMO antenna array integrated in the mini computer host, automatically scan and analyze the current network environment, identify signal strength and stability, and intelligently select the best wireless transmission path.

[0168] Furthermore, the MIMO antenna array is initialized to perform self-test and a spectrum scan is performed using a fast Fourier transform algorithm to identify channel conditions and available frequency bands to ensure the accuracy of signal measurement;

[0169] It should be noted that the specific implementation steps of "initializing the MIMO antenna array to perform self-test and use the fast Fourier transform algorithm to perform spectrum scanning to identify channel conditions and available frequency bands" are as follows:

[0170] Start all antenna elements in the MIMO antenna array and perform a hardware status check to ensure there are no hardware failures or connection anomalies.

[0171] Load the preset antenna configuration file, including parameters such as the number of antennas, arrangement mode, and operating frequency range, to ensure that it matches the hardware;

[0172] Run the calibration algorithm to compensate for the phase and gain differences between antennas and improve signal reception accuracy;

[0173] According to the Nyquist sampling theorem, the received signal of each antenna is sampled at a rate at least twice the highest frequency of the signal to obtain a discrete time signal;

[0174] Convert the sampled time domain signal into a frequency domain signal to obtain a spectrum diagram and identify the signal components and intensities of each frequency band;

[0175] Analyze the spectrum graph, identify the channel occupancy status, and determine which frequency bands have strong signals and which frequency bands are relatively idle and can be used for data transmission;

[0176] Based on the FFT results, the signal-to-noise ratio (SNR) of each frequency band is calculated to identify the channel conditions and distinguish between good, interfered, and unusable frequency bands;

[0177] The frequency bands with SNR higher than the preset threshold are marked as available frequency bands, and those below the threshold are regarded as interfered or unavailable frequency bands, in preparation for subsequent path selection;

[0178] Perform autocorrelation analysis on the signals in each frequency band to identify periodic interference (such as power frequency noise) and non-periodic interference (such as burst pulse signals);

[0179] By comparing the phase difference and strength of signals received by different antennas, the interference source can be located to assist in subsequent path planning and signal processing;

[0180] Mark the detected interfered frequency bands and avoid using them in the path selection process to reduce signal distortion and bit error rate;

[0181] Set periodic tasks to re-execute FFT spectrum scans regularly to respond to changes in channel conditions in a timely manner;

[0182] When a large fluctuation in signal strength or a degradation in network performance is detected, additional spectrum scans are immediately performed to quickly adapt to environmental changes.

[0183] Estimating the power spectrum density of the signal in each available frequency band and calculating the standard deviation of the signal strength to evaluate the signal strength and stability;

[0184] Furthermore, the autocorrelation function is used to identify periodic and non-periodic interference sources, and the detected interference source frequency bands are marked;

[0185] It should be noted that in the process of using the autocorrelation function to identify periodic and non-periodic interference sources, the specific implementation steps are as follows:

[0186] The received signal data is divided into multiple short data segments according to a certain length. The length of each segment needs to be long enough to capture the periodic characteristics of the signal, but not too long to avoid excessive calculation.

[0187] Apply a window function (such as a Hamming window, a Hamming window, or a rectangular window) to each segment of data to reduce spectrum leakage and Gibbs phenomenon and improve the accuracy of the autocorrelation function;

[0188] For each data segment, the autocorrelation function is used to calculate the similarity of the signal at different time delays. The expression of the autocorrelation function is:

[0189]

[0190] Among them, R x(τ) represents the autocorrelation function of the signal x at a time delay of τ. τ is the time delay and is an integer used to represent the offset of the signal in the autocorrelation function when it is compared with itself. When τ = 0, the autocorrelation function measures the energy (or power) of the signal. When τ ≠ 0, the autocorrelation function measures the similarity between the signal and its time-shifted version, that is, the similarity between the signal and itself (without time offset). x[n] represents the value of the signal x at time index n. x * [n+τ] represents the complex conjugate of the value of the signal x at time index n+τ. If the signal is a real signal, the complex conjugate operation can be ignored and x * [n+τ] is x[n+

[0191] τ], but if the signal contains complex components, then the use of the complex conjugate is necessary to ensure correct mathematical operation;

[0192] Analyze the peak position of the autocorrelation function. Periodic signals will produce periodic peaks in the autocorrelation function. You can identify significant peaks by setting a threshold. The time delay corresponding to these peaks is the period of the signal.

[0193] Check whether the distances between peaks show regular repetition to determine whether there is periodic interference;

[0194] For non-periodic interference, the degree of randomness of the signal can be evaluated by statistical methods (such as Shannon entropy or Ljung-Box test). Signals with high randomness are more likely to be non-periodic interference;

[0195] Use machine learning or pattern recognition algorithms, such as support vector machines (SVMs), neural networks, etc., to identify and classify signal patterns and distinguish between periodic and non-periodic features;

[0196] Map the identified periodic and non-periodic interferences to the frequency domain, use FFT to perform spectrum analysis on the signal, and determine the frequency range of the interference;

[0197] The identified interference frequency bands are marked, and the data of these frequency bands will be filtered or avoided in subsequent signal processing;

[0198] According to the results of interference identification, dynamically adjust the signal processing strategy, such as adjusting filter parameters, changing signal transmission frequency or power, to reduce the impact of interference;

[0199] In practical applications, the signal should be continuously monitored because the interference source may change over time and environment, and the above steps need to be repeated periodically or in real time to maintain the effectiveness of the system.

[0200] In summary, through this series of signal processing and data analysis, the frequency band of the interference source can be effectively identified and marked, providing important information for subsequent signal optimization and path selection.

[0201] Based on signal strength, stability and marked interference source frequency bands, a graph theory algorithm is used to establish the topology of the current network environment and assign node weights based on signal quality;

[0202] Furthermore, the Dijkstra algorithm is used to update the network topology map in real time, recalculate the path based on signal strength and stability, and select the optimal path;

[0203] It should be noted that the specific steps for real-time updating of the network topology map are as follows:

[0204] Set a reasonable period (for example, every 30 seconds) to regularly scan the network environment and update the topology to adapt to slow changes in the environment;

[0205] When the signal strength changes beyond a preset threshold (e.g., ±10dBm), a new interference source appears, or an existing interference source disappears, a topology update is immediately triggered to quickly respond to sudden changes in the environment.

[0206] Use Fast Fourier Transform (FFT) to scan the spectrum again to identify new channel conditions and available frequency bands;

[0207] Estimate the power spectral density (PSD) of the signal in each available frequency band, recalculate the standard deviation of the signal strength, and evaluate the signal stability;

[0208] Apply the autocorrelation function again to identify any new periodic and non-periodic interference sources and mark the frequency bands of newly detected interference sources;

[0209] Reconstruct the network topology using graph theory algorithms based on the latest signal strength, stability, and interference source labeling information;

[0210] Redistribute node weights based on signal quality (signal strength and stability) to ensure that the network topology reflects the true status of the current network environment;

[0211] Use the Dijkstra algorithm to recalculate the optimal path from the sender to the receiver based on the updated network topology and node weights;

[0212] Select the calculated optimal path as the current data transmission path;

[0213] Continuously monitor path performance, including metrics such as signal strength, latency, and packet loss;

[0214] Compare the current path performance with the preset performance standard to determine whether the path needs to be switched;

[0215] If the path performance is lower than the preset threshold, the path switching process is immediately initiated to switch to the backup path or another recalculated better path;

[0216] Record the reasons and effects of path switching for subsequent optimization of path selection algorithms and adjustment of preset thresholds.

[0217] In summary, through the above steps, the network topology map can be updated in real time according to environmental changes and performance requirements to ensure the stability and efficiency of data transmission.

[0218] According to the path characteristics, the transmission parameters are dynamically adjusted and path monitoring is implemented. If path performance degradation is detected, the backup path is switched immediately.

[0219] It should be noted that the specific steps of path performance evaluation, backup path maintenance and selection criteria in the link of "dynamically adjusting transmission parameters according to path characteristics, and implementing path monitoring at the same time, and immediately switching to the backup path if path performance degradation is detected" are as follows:

[0220] Define path performance indicators: throughput: the amount of data successfully transmitted per unit time; packet loss rate: the proportion of data packets lost, reflecting the stability of the path; delay: the average time from the sending to the receiving of a data packet; jitter: the statistical value of delay variation, measuring the consistency of the path; signal quality: comprehensive signal strength and signal-to-noise ratio, evaluating the physical layer quality of the path.

[0221] Set performance thresholds. For each performance indicator, set thresholds, such as minimum throughput, maximum packet loss rate, maximum latency and jitter range, and minimum signal quality requirements.

[0222] Continuous performance monitoring: Implement continuous monitoring and evaluate the performance indicators of the path regularly (such as every second or every few seconds); use the sliding window statistical method to calculate the performance average and change trend in the recent period of time.

[0223] Performance degradation judgment: when any performance indicator is lower than the preset threshold, it is considered as performance degradation; a comprehensive performance index can be set to weight the sum of various indicators or take the minimum value to simplify the performance judgment process.

[0224] Backup path selection and maintenance: During the network topology construction phase, multiple potential transmission paths are identified, not just the optimal path. Graph theory algorithms (such as the K-shortest path algorithm) are used to pre-calculate K suboptimal paths as backup options. Signal quality tests are performed regularly on these backup paths to maintain their effectiveness.

[0225] Instant path switching: when the performance of the primary path degrades and the switching conditions are met, a new path is quickly selected from the pre-maintained backup path list; before switching, the latest performance indicators of the backup path are compared to ensure that the new path is better than the current path; when performing path switching, data transmission is first suspended, and then transmission is resumed after the switch is quickly completed to minimize the interruption time.

[0226] Path performance feedback and optimization: record the cause and result of each path switching for later analysis; adjust performance thresholds and backup path selection strategies, and continuously optimize path selection and switching mechanisms.

[0227] In summary, through the above steps, real-time path performance monitoring and intelligent path switching can be achieved, thereby ensuring the stability and efficiency of wireless transmission.

[0228] S4. After the wireless transmission path is selected, the video encoding unit integrated in the mini computer host dynamically adjusts the encoding parameters of the video stream in the network condition.

[0229] Furthermore, video encoding parameters are set, including starting bit rate, target bit rate range, maximum and minimum frame rate, resolution, and encoding format (e.g., H.264, H.265);

[0230] Continuously measure network latency and packet loss using the integrated network monitoring module;

[0231] Combine the signal strength and stability information of the MIMO antenna array to evaluate the quality of the current wireless path;

[0232] According to network delay and packet loss rate, use adaptive bit rate algorithm (such as ABR algorithm) to adjust the encoding bit rate of video stream in real time to ensure smooth video stream transmission;

[0233] Preferably, the Adaptive Bitrate (ABR) algorithm is one of the core components for dynamically adjusting video stream encoding parameters, and is mainly used to optimize video transmission quality and efficiency.

[0234] It should be noted that the steps for implementing the adaptive bit rate algorithm are as follows:

[0235] Set the initial parameters for video encoding, including starting bit rate, target bit rate range, maximum and minimum frame rates, resolution, and encoding format (such as H.264, H.265); use the integrated network monitoring module to continuously measure network latency and packet loss rate, and combine the signal strength and stability information of the MIMO antenna array to evaluate the quality of the current wireless path.

[0236] Monitor network conditions in real time, including but not limited to network latency, packet loss rate, and signal strength changes; use time series analysis or machine learning models based on historical network data to predict future network conditions and estimate possible network fluctuations.

[0237] If the network condition is good (low latency, low packet loss rate), the bit rate is gradually increased to improve the video quality; if the network condition deteriorates (high latency, high packet loss rate), the bit rate is reduced to ensure the continuity of the video stream.

[0238] Through video content recognition technology, the complexity and importance of the video can be judged; based on the bit rate adjustment, the frame rate and resolution can be dynamically adjusted according to the complexity of the video content to provide the best visual experience under limited network resources.

[0239] Use historical network conditions and video streaming data to train machine learning models to predict changes in network conditions. Based on the prediction results, adjust encoding parameters in advance to cope with upcoming changes in network conditions, such as reducing the bit rate in advance when network congestion is expected.

[0240] The adjusted encoding parameters are applied to the video encoding unit to encode the video stream in real time; the video stream transmission strategy is optimized according to the adjusted parameters, such as selecting a suitable transmission protocol (TCP, UDP, etc.).

[0241] Establish a feedback loop between the video stream encoding quality and network conditions, and continuously monitor the encoding effect and network changes; based on the feedback information, dynamically adjust the encoding parameters to achieve the best balance between video quality and network efficiency.

[0242] By real-time monitoring of network status and video stream transmission quality, and using feedback control theory to adjust encoding parameters, the stability and efficiency of video stream transmission can be ensured. Machine learning algorithms are used to predict network conditions, and encoding parameters are adjusted in advance to enhance the system's foresight and adaptability. In the process of adjusting bit rate, frame rate, resolution and encoding format, multi-objective optimization strategies are used to balance video quality, network load and user experience.

[0243] Furthermore, under the premise of maintaining video quality, the frame rate and resolution are dynamically adjusted according to network conditions to match the best transmission efficiency and visual experience;

[0244] It should be noted that in the process of adjusting video encoding parameters, "while maintaining video quality" means ensuring that video quality does not deteriorate. This is a key challenge, especially when the network condition is unstable. In order to maintain or optimize video quality while dynamically adjusting encoding parameters, the following strategies can be adopted:

[0245] Use visual perceptual coding algorithms: These algorithms are based on the characteristics of the human visual system (HVS) and give priority to encoding video areas that are more sensitive to the human eye (such as edges and areas with rich textures). This allows high subjective video quality to be maintained even at low bit rates.

[0246] Use objective video quality assessment (VQA) models, such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) or other more advanced evaluation methods, to monitor the quality of encoded videos in real time. These models can help quantify video distortion and guide the adjustment of encoding parameters; Video complexity analysis: By analyzing the complexity of video content (such as motion, color changes, texture details, etc.), dynamically adjust encoding parameters to adapt to the characteristics of video content and ensure video quality in key scenes.

[0247] Based on network conditions and video content complexity, rate control algorithms (such as constant bit rate CBR, variable bit rate VBR, or quality adaptive coding QAM) are used to dynamically adjust the encoding bit rate to ensure the best video quality under bandwidth constraints; while maintaining video quality, the frame rate and resolution are dynamically adjusted according to network conditions. For example, when the network is congested, the frame rate or resolution is reduced to reduce bandwidth requirements, and when the network conditions are good, the frame rate or resolution is increased to improve video quality.

[0248] Use machine learning models (such as long short-term memory networks (LSTM) or convolutional neural networks (CNN)) to predict future network conditions and adjust encoding parameters in advance to avoid video quality degradation caused by network mutations. Based on historical data and real-time network conditions, use reinforcement learning (such as Q-learning or Deep Q-Network DQN) to optimize encoding strategies and find the best encoding parameter combination to achieve a balance between optimal video quality and network efficiency.

[0249] The video quality assessment results and network status monitoring data are input into the feedback control system to continuously adjust the encoding parameters to achieve the best balance between video quality and network efficiency.

[0250] When a sharp deterioration in network conditions is detected, the backup mechanism is immediately activated, such as reducing video quality or pausing encoding until the network conditions improve, to avoid long-term video freezes or losses.

[0251] In summary, the above strategies can effectively maintain video quality and provide a smooth video experience when network conditions fluctuate greatly. These methods combine visual perception, real-time quality assessment, rate control, machine learning prediction, and closed-loop control mechanisms to form a comprehensive video encoding parameter dynamic adjustment scheme that aims to optimize the efficiency and quality of video transmission.

[0252] Use machine learning models to predict future changes in network conditions, adjust encoding parameters in advance, reduce buffering time and video freezes, and use different encoding quality levels based on the complexity and importance of video content to prioritize the transmission quality of key images;

[0253] It should be noted that the key steps of “using machine learning models to predict future network status changes”, such as the source of model training data, prediction algorithm selection, and model deployment, are as follows:

[0254] Collect historical network performance data, including but not limited to network latency, packet loss rate, signal strength, stability information of MIMO antenna arrays, and encoding parameter adjustment records during past video streaming transmissions; the collected data should contain time series information to capture the changing patterns of network conditions over time; clean the collected data, remove outliers and missing values, and normalize the time series data to make it suitable for model training.

[0255] Select features that are highly correlated with changes in network conditions from the collected data, such as the network delay, packet loss rate, signal strength, etc. mentioned above; build a sliding time window and divide the network performance data by time window to capture short-term and long-term trends in network conditions; based on basic features, generate new features, such as moving average, standard deviation and other statistics to enhance the predictive ability of the model.

[0256] Select a machine learning model suitable for time series forecasting, such as long short-term memory network (LSTM), recurrent neural network (RNN) or Prophet, which are particularly good at processing sequence data and predicting trends; divide the processed data set into training set and validation set for model training and validation; use the training set data to train the model, and optimize the model parameters through the back propagation algorithm to minimize the prediction error; use grid search or random search and other methods to adjust the model's hyperparameters to obtain the best prediction performance.

[0257] Use the trained model to predict the network status in the future and output the predicted values ​​of key indicators such as network delay and packet loss rate. Based on the predicted network status, predict which encoding parameters (such as bit rate, frame rate, resolution) need to be adjusted, as well as the direction and magnitude of the adjustment. Deploy the trained model to the video encoding unit of the mini computer host so that it can receive network status data in real time, make predictions, and adjust the encoding parameters accordingly.

[0258] Continuously collect network status data and monitor the deviation between the actual network status and the model prediction; establish a feedback mechanism to feed back the difference between the actual network status and the predicted results to the model for online learning and dynamic optimization of the model, and continuously improve the prediction accuracy.

[0259] As time goes by, the network environment may change, so the model needs to be updated regularly and retrained to adapt to the latest network status data.

[0260] In summary, through the above steps, a machine learning model can be constructed that can effectively predict changes in network conditions, thereby realizing proactive adjustment of video stream encoding parameters and ensuring the smoothness and video quality of video transmission under changing network environments.

[0261] Applying the adjusted encoding parameters to a video encoding unit to encode the video stream in real time;

[0262] Establish a feedback loop between video stream encoding quality and network conditions, continuously monitor encoding effects and network changes, and make continuous adjustments and optimizations.

[0263] It should be noted that if the performance of the main path drops sharply, the pre-selected backup path will be immediately activated, and the encoding parameters will be adjusted to adapt to the new path; when the network is interrupted or there is severe packet loss, the video quality will be automatically reduced or the transmission will be suspended, and the video stream will be resumed after the network is restored. On the basis of ensuring video quality, the energy consumption of the video encoding unit is optimized, the equipment operation time is extended, and the smoothness, clarity and response speed of the video stream are taken into consideration to ensure that users get the best viewing experience.

[0264] It should also be noted that the specific implementation steps for "continuously monitoring coding effects and network changes" are as follows:

[0265] Define network status indicators: including but not limited to network latency, packet loss rate, signal strength, signal-to-noise ratio (SNR) of MIMO antenna arrays, bit error rate (BER), etc. Define video quality indicators: such as objective quality indicators of video streams (PSNR, SSIM), subjective quality assessment, coding efficiency (such as the relationship between coding bit rate and video clarity), video smoothness (number of freezes, rebuffering time), etc.

[0266] Continuously collect network status data and remove noise through algorithm processing (such as moving average and sliding window statistics) to ensure the accuracy and reliability of the data; Video quality assessment: Use video quality assessment tools or algorithms, such as VMAF (Video Multimethod Assessment Fusion), to calculate the quality score of the video stream in real time to reflect the video viewing experience.

[0267] Compare network status indicators with preset thresholds. Once they exceed the normal range, the encoding parameter adjustment is triggered immediately. Compare video quality indicators with target quality standards. If the video quality is lower than expected, adjust the encoding parameters to improve the video quality.

[0268] Integrate machine learning models and rule engines to intelligently decide whether encoding parameters need to be adjusted and the specific values ​​of adjustment based on the current network status and video quality indicators; based on the decision results, dynamically adjust video encoding parameters such as bit rate, frame rate, resolution, etc. to optimize video streaming and viewing experience.

[0269] Collect historical data, including network conditions, video quality, user behavior, etc., to train machine learning models so that they can more accurately predict network changes and user preferences; regularly evaluate the performance of the entire system, including video transmission efficiency, user satisfaction, etc., adjust optimization strategies based on evaluation results, and continuously improve system performance.

[0270] Implement anomaly detection algorithms, such as statistical-based methods or deep learning models, to identify abnormal patterns in network or video quality; when anomalies are detected, take immediate action, such as switching to alternate network paths, reducing video quality to ensure smoothness, until the network returns to normal.

[0271] Collect direct feedback on video quality through the user interface, such as clarity ratings, smoothness evaluations, etc.; incorporate user feedback into the decision-making process, adjust optimization goals, and ensure that video services always meet user expectations.

[0272] In summary, through the above-mentioned closed-loop monitoring and feedback system, it is possible to monitor the encoding effect and network changes in real time, and make continuous optimization adjustments according to actual conditions, thereby providing high-quality video transmission services.

[0273] S5. While dynamically adjusting the video stream encoding parameters, the network status and video stream complexity are analyzed in real time to adaptively adjust the resolution and frame rate.

[0274] Furthermore, based on the information of network delay L, packet loss rate P and signal strength S, a network status evaluation function F is designed net The expression is:

[0275]

[0276] Among them, F net (L, P, S) represents the network status score, α, β and γ are network status influencing factors, which are used to adjust the influence of network delay, packet loss rate and signal strength respectively, and e is the base of natural logarithm;

[0277] Apply the video content analysis algorithm to evaluate the complexity of the video stream and construct the video stream complexity function F vid The expression is:

[0278] F vid (E,T,C)=log(1+δE+∈T+ζΩ);

[0279] Among them, F vid (E, T, C) is the video complexity score, where E is the number of edges in the video frame, T is the mean of the coefficient of variation of the pixel grayscale values ​​in the video frame, C is the entropy of the color space distribution in the video frame, δ is the influencing factor of the edge number E, ∈ is the influencing factor of the texture complexity T, and ζ is the influencing factor of the color diversity Ω;

[0280] It should be noted that for the extraction of features in the video stream, such as the number of edges, the mean of the coefficient of variation of pixel grayscale values, and the entropy of color space distribution, the following are the specific algorithm steps:

[0281] Video frame edge quantity extraction:

[0282] Convert the video frames to grayscale images to simplify the color information and apply a Gaussian filter to smooth the image and reduce the effect of noise.

[0283] Use the Canny edge detection algorithm, which is a multi-stage algorithm that detects edges in an image by applying Gaussian filtering, calculating gradient strength and direction, non-maximum suppression, and double threshold detection; the output of the Canny algorithm is a binary image in which edges are marked in white and non-edge areas are black.

[0284] Count the number of white pixels in the binary image output by the Canny algorithm and use this as an approximation of the number of edges.

[0285] Mean calculation of pixel gray value variation coefficient:

[0286] The grayscale value of each pixel in the video frame is counted to obtain the distribution of the grayscale value.

[0287] The mean and standard deviation of the grayscale values ​​are calculated; the coefficient of variation is obtained by dividing the standard deviation by the mean, which reflects the degree of fluctuation of the grayscale value; the above calculation is performed on the pixels of the entire video frame to obtain the mean of the coefficient of variation, which reflects the texture complexity of the entire image.

[0288] Entropy calculation of color space distribution:

[0289] The color space of the video frame (such as RGB, HSV) is divided into several small areas, each area represents a color.

[0290] For each color region, calculate the probability of the color appearing in the image; use a histogram to count the number of pixels in each color region, and then divide the number of pixels by the total number of pixels to get the probability distribution of the color appearing.

[0291] The information entropy formula is used to calculate the entropy of color distribution, and the expression is:

[0292] H=-∑p i log2 p i ;

[0293] Among them, p i is the probability of the i-th color appearing, H is the entropy of the color space distribution, reflecting the color diversity and uncertainty;

[0294] In summary, the above steps can accurately extract the number of edges E, the mean value T of the pixel gray value variation coefficient, and the entropy C of the color space distribution from the video stream. The extraction of these features is the basis of the video content analysis algorithm, which can construct a more accurate video stream complexity function F vid , thereby optimizing video transmission and display effects.

[0295] Furthermore, the network status evaluation function F net and the video complexity function F vid Combined to construct a comprehensive decision function Q ad The expression is:

[0296]

[0297] Among them, Q ad (F net ,F vid ) is expressed as the comprehensive decision score, R max The maximum resolution value of the video stream;

[0298] According to Q ad The resolution and frame rate of the video stream are adaptively adjusted based on the value of

[0299] It should be noted that how to ad The specific implementation steps for adaptively adjusting the video stream resolution and frame rate are as follows:

[0300] S5.1. Initialization parameters and thresholds:

[0301] Set a series of thresholds T1, T2, ..., T n To divide Q ad Each threshold corresponds to a resolution and frame rate adjustment level. For example, T1 may correspond to a slight adjustment under slight network pressure, while T n Corresponding to the substantial adjustment under severe network pressure; for each threshold T i Assign a scaling factor p i , used to calculate the adjustment of resolution and frame rate, the scaling factor reflects Q ad The extent of the impact on resolution and frame rate;

[0302] S5.2 Analysis Q ad Values:

[0303] Determine Q ad In which threshold interval does it fall, that is, find the largest i that makes Q ad ≥T i ;

[0304] S5.3. Adjust resolution and frame rate:

[0305] Set the current resolution value to R c , the maximum resolution is R max , then the new resolution R new The calculation expression is:

[0306]

[0307] This means that the amount of resolution adjustment depends not only on the scaling factor p i , is also affected by the ratio of the current resolution to the maximum resolution to avoid the resolution dropping too quickly.

[0308] Assume the current frame rate is F c , the maximum frame rate is F max , then the new frame rate F new The calculation expression is:

[0309]

[0310] Similar to resolution, frame rate adjustment also takes into account the ratio of the current frame rate to the maximum frame rate to maintain a proper viewing experience.

[0311] S5.4. Implementation adjustments and handling of abnormal situations:

[0312] The calculated R new and F new Applied to video streams, effective immediately;

[0313] Repeat steps S5.2 and S5.3 periodically to dynamically respond to changes in network conditions and video complexity;

[0314] If the network condition improves, Q ad If the increase is significant, the resolution and frame rate should be increased slowly to avoid abrupt changes that negatively affect the viewing experience;

[0315] If the network condition continues to deteriorate, the system should maintain the current settings even if the minimum resolution and frame rate have been reached to avoid unnecessary attempts to adjust and save resources;

[0316] In summary, through the above detailed implementation steps, it can be ensured that the resolution and frame rate of the video stream can be intelligently adjusted according to the network conditions and video complexity, thereby providing the best video quality and viewing experience in different environments.

[0317] S6. Combining the optimized display parameters with the adjusted video stream, the display starts up according to the latest settings and displays the optimized content.

[0318] Furthermore, when the display is turned on, it automatically adjusts the display brightness and color temperature based on the ambient light sensor data to adapt to the current environment and improve viewing comfort. At the same time, it optimizes the video stream encoding parameters according to network conditions to ensure smooth video transmission and quality adaptation to network bandwidth;

[0319] Adopting adaptive streaming media method, intelligently matching display parameters with video stream parameters, ensuring that video content can be presented in the best effect under any lighting and network conditions;

[0320] Preferably, adaptive streaming technology aims to dynamically adjust the quality of the video stream according to the user's network conditions and device capabilities to provide the best viewing experience. When combined with ambient light sensor data, display brightness and color temperature parameters, the user experience can be further improved to ensure that the video stream quality matches the display environment. The following is the specific workflow:

[0321] The ambient light sensor continuously monitors the surrounding light intensity, and the sensor data is transmitted to the system in real time to evaluate the current ambient brightness level.

[0322] When the system receives data from the ambient light sensor, it adjusts the brightness of the display according to the ambient brightness to reduce eye fatigue and improve viewing comfort; the color temperature will also be adjusted according to changes in ambient light. For example, in a darker environment, the system will tend to use warm tones (lower color temperature), and in a bright environment, it will use cool tones (higher color temperature).

[0323] The adaptive streaming system intelligently selects the most suitable video encoding configuration based on the adjusted display brightness and color temperature parameters, as well as network conditions. In low-light environments, the system may select a lower bitrate video stream because the human eye is less sensitive to details in the dark. Conversely, in high-light environments, a higher bitrate video stream may be selected to ensure sufficient visibility of details. For changes in color temperature, the system can adjust video color correction to ensure that the colors on the screen look natural under different lighting conditions.

[0324] The entire process is a dynamic feedback loop. As the ambient light changes, the system continuously adjusts the display parameters and video stream settings; this real-time adjustment ensures that the video stream provides the best visual effects and viewing experience regardless of the ambient light conditions.

[0325] In summary, through the above steps and technical points, adaptive streaming can work together with ambient light sensor data, display brightness and color temperature parameters to ensure that the video stream quality always matches the display environment and provide the most suitable viewing experience.

[0326] Use machine learning algorithms to predict user interests and preload relevant content into the cache. Adjust loading priority based on network status and video complexity to ensure content loading speed and reduce waiting time.

[0327] It should be noted that using historical data and real-time network status to adjust content preloading strategies involves key links such as data analysis, predictive modeling, and network status assessment. The following is a specific strategy implementation step:

[0328] Systematically collect user viewing records, including viewing time, content type, viewing duration, preference settings, etc., as well as video metadata (such as size, resolution, bit rate, duration, and popularity), continuously monitor the real-time network connection status of user devices (upload / download speed, latency, packet loss rate), and save historical data on past network conditions for analysis, and extract key features from massive data, such as user viewing time preferences, content type preferences, and network bandwidth stability. Through feature selection technology, identify the factors that most affect content preloading decisions, eliminate redundant features, and improve model efficiency;

[0329] Use advanced machine learning algorithms, such as random forest, gradient boosting tree or neural network, and use historical data to train models to predict video content that users may be interested in. Optimize prediction accuracy by adjusting model parameters; the system monitors the user's network connection status in real time, dynamically evaluates network conditions, and sets preloading thresholds based on changes in network conditions to ensure effective use of bandwidth resources in different network environments;

[0330] Based on the model prediction results and real-time network evaluation, the content to be preloaded is prioritized. For long videos, a segmented preloading strategy is adopted, with the first few minutes or key frames loaded first, and subsequent preloading plans are dynamically adjusted based on network conditions. Based on the device storage space and current network conditions, the amount of cached content is intelligently determined, and expired or low-priority content is cleaned up in a timely manner to reserve space for new content.

[0331] According to the preloading strategy, the system starts pushing content to the user's device. At the same time, the preloading effect is continuously monitored, and indicators such as preloading success rate, user experience feedback, and device resource utilization are collected; based on the monitoring data, model parameters, network evaluation standards, and preloading strategies are dynamically adjusted to form a closed-loop mechanism of continuous iteration and optimization to continuously improve the efficiency of content preloading and user experience.

[0332] In summary, through the above closely related steps, a system is built that can intelligently adjust the content preloading strategy according to the user's historical behavior and real-time network status, ensuring efficient use of network resources while improving the user's viewing experience.

[0333] Dynamically adjust the layout according to the screen size and resolution, and adaptively adjust the video content to ensure that text and images are clear and readable at any resolution. The video content is adaptively adjusted, automatically cropped or filled to fit the display ratio and avoid black edges or stretching.

[0334] After parameter adjustment and content preprocessing are completed, the optimized customized content is started and presented to ensure smooth loading and display of the content, providing users with a seamless viewing experience.

[0335] It should be noted that after completing the step of "after parameter adjustment and content preprocessing, start and present the optimized customized content", a continuous optimization and feedback mechanism is still required. The specific steps are as follows: Feedback collection and analysis: Collect user feedback on display effects through the user interface or remote monitoring, including satisfaction with brightness, color temperature, video quality, etc., analyze feedback data, and continuously adjust and optimize the matching algorithm between display parameters and video stream parameters; System adaptive learning: Utilize machine learning algorithms to enable the system to self-learn based on long-term user feedback and usage patterns, optimize parameter adjustment strategies, gradually reduce human intervention, and improve the system's automation level and user experience.

[0336] The present embodiment also provides a display system for a mini computer host, including: an intelligent display adaptation module, a display parameter intelligent adjustment module, a network path management module, a video stream encoding parameter dynamic adjustment module, an adaptive optimization module and a content presentation module; the intelligent display adaptation module is used to monitor and analyze the ambient light intensity and color temperature in real time through the built-in ambient light sensor of the mini computer host, and collect ambient light data; the display parameter intelligent adjustment module is used to use the ambient light data to intelligently calculate and adjust the display brightness and color temperature through a pre-trained deep learning model; the network path management module is used to automatically scan and analyze the current network environment through the MIMO antenna array integrated in the mini computer host, identify the signal strength and stability, and intelligently select the best wireless transmission path; the video stream encoding parameter dynamic adjustment module is used to dynamically adjust the encoding parameters of the video stream in the network condition through the video encoding unit integrated in the mini computer host after selecting the wireless transmission path; the adaptive optimization module is used to analyze the network condition and the complexity of the video stream in real time while dynamically adjusting the video stream encoding parameters, and adaptively adjust the resolution and frame rate; the content presentation module is used to combine the optimized display parameters with the adjusted video stream, and the display starts according to the latest settings and displays the optimized content.

[0337] This embodiment also provides a computer device suitable for the display method of a mini computer host, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the display method of the mini computer host proposed in the above embodiment.

[0338] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0339] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the display method of the mini computer host proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0340] In summary, the present invention realizes accurate perception and intelligent adjustment of ambient light intensity and color temperature through the coordinated work of built-in ambient light sensors and deep learning models, ensuring that the display screen can provide the best visual effect in any lighting environment and improving viewing comfort; the combination of MIMO antenna array and intelligent path selection algorithm ensures that video transmission can remain stable and efficient even in complex and changeable network environments, avoiding video freezes caused by signal interference, and significantly enhancing the smoothness of video playback; the ability to dynamically adjust video stream encoding parameters, combined with real-time analysis of network conditions and video content complexity, achieves the optimal balance between video quality and network resource consumption. In summary, the present invention not only optimizes the user's visual experience, but also effectively improves the overall performance of the mini computer host in display and wireless transmission, providing users with a more intelligent, efficient and comfortable use experience.

[0341] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of the display method of the mini computer host are provided.

[0342] The mainstream mini computer host A and the new model mini computer host B (i.e., the device using the present invention) on the market were selected for comparative testing. The test was conducted in three typical environments: bright office, dim room and outdoor direct sunlight to simulate various lighting conditions in daily use. Under each environmental condition, continuous testing was carried out for one week, and data was recorded in four time periods every day: morning (8:00), noon (12:00), evening (18:00) and night (22:00).

[0343] First, both mini computer hosts were initialized and set up, including installing the same software and operating system versions to ensure that other factors except the display method were the same.

[0344] Secondly, mini computer hosts A and B are placed in the same environment, and the ambient light sensor is turned on for real-time monitoring. Host A adopts the traditional display brightness and color temperature adjustment method, while host B adopts the method of the present invention, that is, real-time analysis and adjustment of display parameters.

[0345] Then, during the test, the following data were recorded in detail: light intensity (lux), color temperature (K), display brightness (cd / m2), color temperature adjustment (K), video stream encoding parameter adjustment (kbps), resolution adjustment (px), frame rate adjustment (fps), and user satisfaction score (out of 10 points);

[0346] Finally, in order to quantify the video stream quality and network transmission efficiency, the network delay (ms) and packet loss rate (%) are also recorded. Through these data, the performance of the present invention in different environments can be fully evaluated.

[0347] The details are shown in Table 1:

[0348] Table 1 Comparative test results of mini computer host display methods

[0349]

[0350]

[0351] As can be seen from the table above, host B is closer to environmental changes in terms of display brightness and color temperature adjustment. For example, in a bright office, the display brightness of host B reaches 220cd / m 2 , which is higher than the 200cd / m 2 Brighter, which makes the image clearer in strong light environments. At the same time, the video stream encoding parameters of host B are adjusted more finely, and the encoding rate is reduced to 1800kbps, which not only reduces the consumption of network resources, but also reduces the network delay to 45ms and the packet loss rate to 1%, which is significantly better than the performance of host A.

[0352] In terms of user satisfaction score, host B is higher than host A in all test environments, especially in the outdoor direct sunlight environment, the user satisfaction score increased from 6.8 to 8.8, indicating that the present invention performs better in extreme environments.

[0353] Through the analysis of network delay and packet loss rate, it can be found that the network transmission efficiency of host B is higher, which is due to the strategy of intelligently selecting the best wireless transmission path and dynamically adjusting the video stream encoding parameters of the present invention. Under all test conditions, the network delay and packet loss rate of host B are lower than those of host A, which means that the video playback is smoother and the user experience is better.

[0354] In summary, the mini computer host display method of the present invention has shown obvious advantages in intelligent adjustment of display parameters, network transmission efficiency and user satisfaction, proving its innovation and practicality. This innovation not only improves the display quality, but also optimizes the use of network resources, providing users with a more comfortable and efficient use experience.

[0355] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A display method for a mini computer host, characterized in that: include, The built-in ambient light sensor of the mini computer host can monitor and analyze the surrounding light intensity and color temperature in real time and collect ambient light data; Using ambient light data, the display brightness and color temperature are intelligently calculated and adjusted through a pre-trained deep learning model; The MIMO antenna array integrated in the mini computer host can automatically scan and analyze the current network environment, identify signal strength and stability, and intelligently select the best wireless transmission path; After selecting the wireless transmission path, the video encoding unit integrated in the mini computer host dynamically adjusts the encoding parameters of the video stream in the network condition; While dynamically adjusting the video stream encoding parameters, it analyzes the network status and video stream complexity in real time and adjusts the resolution and frame rate adaptively. Combining the optimized display parameters with the adjusted video stream, the monitor boots up and displays optimized content based on the latest settings.

2. The display method of the mini computer host as claimed in claim 1, characterized in that: The mini computer host has a built-in ambient light sensor to monitor and analyze the ambient light intensity and color temperature in real time and collect ambient light data. The specific steps are: Ambient light sensor using a high-sensitivity photodiode array for self-test and calibration; The ambient light sensor collects and records the raw photocurrent data in real time at a predetermined frequency; Use the photocurrent-light intensity relationship curve and color temperature estimation algorithm to convert the raw photocurrent data recorded in real time to obtain light intensity and color temperature data; Based on the light intensity and color temperature data, the built-in microprocessor analyzes the changing trends of light intensity and color temperature in real time; The standard deviation threshold method is applied to identify and filter out sudden light changes, and the stable and representative light intensity and color temperature data are integrated into a continuous time series data set; The time series data set is processed by sliding window averaging method to form standard ambient light data.

3. The display method of the mini computer host as claimed in claim 2, characterized in that: The method utilizes ambient light data to intelligently calculate and adjust display brightness and color temperature through a pre-trained deep learning model. The specific steps are: Wavelet transform is applied to decompose the ambient light data and extract the characteristics of light intensity and color temperature in different frequency bands. The expression is: Among them, I(t) is the change value of light intensity I with time t, ψ * is the complex conjugate of the wavelet function ψ, τ is the time translation parameter, s is the scale parameter, WT I (τ, s) is the wavelet coefficient of the light intensity value under the time translation parameter τ and the scale parameter s, and dt represents the time element of the integral variable; Based on the wavelet coefficients of light intensity under the time shift parameter τ and scale parameter s, the ambient light data is converted into intelligently calculated display brightness and color temperature through a pre-trained deep learning model. The expressions are: Where B(t) is the display brightness value at time t, C(t) is the display color temperature value at time t, C represents the display color temperature value, and D(t,t i ) is time t and historical time point t i Time distance, WT C (τ i ,s i ) is the wavelet coefficient of the color temperature value displayed under the time shift parameter τ and the scale parameter s, α is the display brightness time decay factor, β is the display color temperature time decay factor, and N is the number of historical time points; The calculated display brightness value B(t) and color temperature value C(t) are applied to the display panel in real time to perform dynamic display parameter adjustment.

4. The display method of the mini computer host as claimed in claim 3, characterized in that: The specific steps of intelligently selecting the best wireless transmission path are: Initialize the MIMO antenna array to perform self-test and use the fast Fourier transform algorithm to perform spectrum scanning to identify channel conditions and available frequency bands; Estimating the power spectrum density of the signal in each available frequency band and calculating the standard deviation of the signal strength to evaluate the signal strength and stability; Use autocorrelation function to identify periodic and non-periodic interference sources and mark the detected interference source frequency bands; Based on signal strength, stability and marked interference source frequency bands, a graph theory algorithm is used to establish the topology of the current network environment and assign node weights based on signal quality; Use Dijkstra algorithm to update network topology in real time, recalculate paths based on signal strength and stability, and select the optimal path; According to the path characteristics, the transmission parameters are dynamically adjusted and path monitoring is implemented. If path performance degradation is detected, the backup path is switched immediately.

5. The display method of the mini computer host as claimed in claim 4, characterized in that: The specific steps of dynamically adjusting the encoding parameters of the video stream in the network condition are as follows: Set video encoding parameters, including starting bit rate, target bit rate range, maximum and minimum frame rate, resolution, and encoding format; Continuously measure network latency and packet loss using the integrated network monitoring module; Combine the signal strength and stability information of the MIMO antenna array to evaluate the quality of the current wireless path; Adaptive bitrate algorithm is used to adjust the encoding bitrate of video stream in real time according to network delay and packet loss rate. Dynamically adjust frame rate and resolution based on network conditions while maintaining video quality; Use machine learning models to predict future changes in network conditions, adjust encoding parameters in advance, and use different encoding quality levels based on the complexity and importance of video content; Applying the adjusted encoding parameters to a video encoding unit to encode the video stream in real time; Establish a feedback loop between video stream encoding quality and network conditions, continuously monitor encoding effects and network changes, and make continuous adjustments and optimizations.

6. The display method of the mini computer host as claimed in claim 5, characterized in that: While dynamically adjusting the video stream encoding parameters, the network status and video stream complexity are analyzed in real time, and the resolution and frame rate are adjusted adaptively. The specific steps are: Based on the information of network delay L, packet loss rate P and signal strength S, a network status evaluation function F is designed. net The expression is: Among them, F net (L, P, S) represents the network status score, α, β and γ are network status influencing factors, which are used to adjust the influence of network delay, packet loss rate and signal strength respectively, and e is the base of natural logarithm; Apply the video content analysis algorithm to evaluate the complexity of the video stream and construct the video stream complexity function F vid The expression is: F vid (E,T,C)=log(1+δE+∈T+ζΩ); Among them, F vid (E, T, C) is the video complexity score, where E is the number of edges in the video frame, T is the mean of the coefficient of variation of the pixel grayscale values ​​in the video frame, C is the entropy of the color space distribution in the video frame, δ is the influencing factor of the edge number E, ∈ is the influencing factor of the texture complexity T, and ζ is the influencing factor of the color diversity Ω; The network status evaluation function F net and the video complexity function F vid Combined to construct a comprehensive decision function Q ad The expression is: Among them, Q ad (F net ,F vid ) is expressed as the comprehensive decision score, R max The maximum resolution value of the video stream; According to Q ad The resolution and frame rate of the video stream are adaptively adjusted based on the value of 7. The display method of the mini computer host as claimed in claim 6, characterized in that: The optimized display parameters are combined with the adjusted video stream, and the display is started according to the latest settings and displays the optimized content. The specific steps are: When the display is turned on, it automatically adjusts the display brightness and color temperature based on the ambient light sensor data, and optimizes the video stream encoding parameters according to the network conditions; Adopting adaptive streaming media method to intelligently match display parameters with video stream parameters; Use machine learning algorithms to predict user interests and preload relevant content into the cache, adjusting loading priority based on network status and video complexity; Dynamically adjust the layout according to the screen size and resolution, and adaptively adjust the video content; After parameter adjustment and content preprocessing are completed, the optimized customized content is started and presented.

8. A display system for a mini computer host, based on the display method for a mini computer host according to any one of claims 1 to 7, characterized in that: It includes an intelligent display adaptation module, a display parameter intelligent adjustment module, a network path management module, a video stream encoding parameter dynamic adjustment module, an adaptive optimization module and a content presentation module; The intelligent display adaptation module is used to monitor and analyze the ambient light intensity and color temperature in real time and collect ambient light data through the built-in ambient light sensor of the mini computer host; The display parameter intelligent adjustment module is used to use the ambient light data to intelligently calculate and adjust the display brightness and color temperature through a pre-trained deep learning model; The network path management module is used to automatically scan and analyze the current network environment, identify signal strength and stability, and intelligently select the best wireless transmission path through the MIMO antenna array integrated in the mini computer host; The video stream encoding parameter dynamic adjustment module is used to dynamically adjust the encoding parameters of the video stream in the network condition through the video encoding unit integrated in the mini computer host after the wireless transmission path is selected; The adaptive optimization module is used to analyze the network status and video stream complexity in real time while dynamically adjusting the video stream encoding parameters, and adaptively adjust the resolution and frame rate; The content presentation module is used to combine the optimized display parameters with the adjusted video stream, and the display starts and displays the optimized content according to the latest settings.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the display method of the mini computer host according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the display method of the mini computer host according to any one of claims 1 to 7 are implemented.

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