Intelligent multimedia equipment and display method thereof

Abstract

The invention relates to the technical field of multimedia equipment, in particular to intelligent multimedia equipment and a display method thereof. The method comprises the following steps of obtaining equipment use environment data and performing multi-dimensional fusion, constructing an environment description model, analyzing and generating an environment light field, performing refraction light intensity analysis and calculating mixed light intensity data to obtain the reflection light intensity of a display screen, performing missing value filling and brightness habit analysis on historical use data of a user, and obtaining the brightness habit of the user. The method comprises the steps of generating personalized brightness habit data, calculating a light supplement rate of a display screen based on the data, performing curve fitting, forming a dynamic light supplement rate curve, processing the curve through piecewise linear interpolation, generating a continuous brightness adjustment instruction sequence, regulating and controlling the brightness of multimedia equipment in real time, and improving user experience. The display method of the intelligent multimedia equipment is more comprehensive and more personalized.

Description

Technical Field

[0001] The present invention relates to the technical field of multimedia devices, and in particular to an intelligent multimedia device and a display method thereof. Background Art

[0002] With the rapid development of intelligent multimedia devices, users have an increasing demand for display effects. The performance of devices under different ambient light conditions, especially the accuracy of brightness and color, directly affects the user's visual experience. However, existing multimedia devices often cannot adapt to changes in ambient light in real time, resulting in poor display effects, frequent brightness adjustments and visual fatigue during user use. In addition, users' personalized needs and usage habits are often not fully considered. Different users have significant differences in their preferences for brightness in similar environments. Traditional brightness setting methods lack flexibility and intelligence and cannot effectively meet the unique needs of each user. This limits the adaptability of the device and makes it difficult to improve overall usage satisfaction. Finally, although some smart devices have begun to explore technologies for automatically adjusting brightness based on ambient light intensity, they are still insufficient in data acquisition and processing. The complexity of the ambient light field and the lack and inconsistency of user historical usage data make it difficult for existing algorithms to achieve accurate dynamic adjustment. Summary of the invention

[0003] Based on this, it is necessary to provide a display method for an intelligent multimedia device to solve at least one of the above technical problems.

[0004] To achieve the above object, a display method of an intelligent multimedia device includes the following steps:

[0005] Step S1: Acquire device usage environment data; perform multi-dimensional fusion on the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate a device usage environment light field;

[0006] Step S2: Analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; perform mixed light intensity calculation on the reflected light intensity data of the display screen based on the preset external brightness data of the device to generate mixed light intensity data of the display screen;

[0007] Step S3: Obtain the user's historical usage data; fill in missing values ​​in the user's historical usage data to obtain completed historical data; perform user habit brightness analysis on the completed historical data to generate user device brightness habit data;

[0008] Step S4: Calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the display screen fill light rate data; perform curve fitting on the display screen fill light rate data to generate a dynamic fill light rate curve;

[0009] Step S5: performing piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; dynamically adjusting the brightness of the multimedia device according to the continuous brightness adjustment instruction sequence to execute the display method of the intelligent multimedia device.

[0010] The present invention integrates environmental usage data in multiple dimensions to construct an environmental description model that can accurately reflect the lighting changes in the usage environment, ensure that the display effect matches the environmental conditions, and improve the user's visual experience. The combination of refracted light intensity analysis and mixed light intensity calculation can accurately obtain the reflected light intensity data of the display screen, provide reliable basic data for dynamic brightness adjustment, and optimize the display effect. The missing value filling technology is used to process the user's historical usage data, which can more comprehensively reflect the user's usage habits, thereby generating personalized device brightness habit data and improving the adaptability of the device. The device fill light rate is calculated and curve fitting is performed. The generated dynamic fill light rate curve can effectively reflect the user's brightness requirements under different ambient light conditions and ensure that the display screen can provide ideal brightness under various conditions. The piecewise linear interpolation processing technology makes the brightness adjustment instruction sequence continuous and smooth, avoids mutations, improves the dynamic brightness control capability of the device, and reduces user eye discomfort.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: deploying environmental sensors on multimedia devices and collecting all-round environmental data to obtain device usage environment data;

[0013] Step S12: performing principal component analysis on the device use environment data to obtain reduced-dimensional device use environment data; performing multi-dimensional fusion of features on the reduced-dimensional device use environment data to obtain fused environment feature data;

[0014] Step S13: reconstructing the simulation environment model based on the fused environment feature data to generate an environment description model;

[0015] Step S14: parsing the ambient light state of the environment description model to obtain device ambient light state data;

[0016] Step S15: Perform light field simulation on the device ambient light state data to generate the device usage ambient light field.

[0017] The present invention improves the real-time perception of the surrounding environment of the device through the all-round deployment and data collection capabilities of environmental sensors, ensures the accuracy and comprehensiveness of the data, and provides a solid data foundation for subsequent analysis. Principal component analysis optimizes the complexity of environmental data through dimensionality reduction processing, retains the most representative features, reduces the computational burden, improves data processing efficiency, and makes subsequent feature fusion more efficient. Feature multi-dimensional fusion integrates environmental features of different dimensions, enhances the comprehensiveness and information content of the data, provides a rich source of information for the construction of environmental models, and promotes the accurate understanding of environmental changes. The simulation reconstruction of the environmental description model provides a theoretical basis for the performance of the device in different usage scenarios by simulating real environmental features, and improves the adaptability of the device in various environments. The analysis capability of ambient light state data enables the device to accurately identify the current lighting conditions, ensure the optimization of display effects, and improve the user's visual experience in different lighting environments. The device uses the ambient light field generated by light field simulation, which provides a basis for the dynamic adjustment of the display effect, enables the device to intelligently adapt to environmental changes, and improves the user's interactive experience and convenience of use.

[0018] Preferably, step S2 comprises the following steps:

[0019] Step S21: performing display screen reflection and refraction analysis on the multimedia device based on the ambient light field used by the device to obtain display screen reflected light intensity distribution data;

[0020] Step S22: performing spatial filtering on the reflected light intensity distribution to obtain interference-free reflected light intensity distribution data;

[0021] Step S23: performing refracted light intensity analysis on the interference-removed reflected light intensity distribution data to obtain display screen reflected light intensity data;

[0022] Step S24: performing mixed light intensity calculation on the display screen reflected light intensity data based on the preset device external brightness data to generate display screen mixed light intensity data.

[0023] The present invention enhances the device's understanding of the impact of ambient light by performing reflection and refraction analysis of the display screen based on the ambient light field, ensures the accuracy and realism of the display effect, and improves the user's visual experience. The spatial filtering processing of the reflected light intensity distribution effectively removes noise and interference, improves the quality of the light intensity data, makes subsequent analysis more reliable, and reduces the impact of errors on the final results. The refracted light intensity analysis is based on the interference-free light intensity distribution data, which ensures the accuracy of the reflected light intensity data, provides a scientific basis for the brightness adjustment of the display screen, and optimizes the display effect. The mixed light intensity calculation is combined with the preset external brightness data to ensure the reasonable fusion of the reflected light intensity of the display screen and the ambient light source, making the display effect more natural and comfortable under different environmental conditions. The generation of mixed light intensity data of the display screen lays the foundation for dynamic brightness control, enabling the device to respond to environmental changes in real time, improving the user experience and the adaptability of the device.

[0024] Preferably, step S21 includes the following steps:

[0025] Step S211: performing light source decomposition on the ambient light field of the device to obtain ambient light data; performing ray tracing on the ambient light field of the device based on the ambient light data to obtain an incident light distribution map;

[0026] Step S212: Acquire multimedia device display screen data; perform microstructure simulation on the multimedia device display screen data to generate a display screen microstructure model;

[0027] Step S213: performing light interaction analysis on the display screen microstructure model based on the incident light distribution diagram to generate a reflection and refraction light model;

[0028] Step S214: Calculate the light intensity distribution of the reflection and refraction light model to obtain the display screen reflection light intensity distribution data.

[0029] The present invention decomposes the light source by using the ambient light field of the device. The acquisition of ambient light source data improves the comprehensive understanding of the external light source, helps to accurately simulate the display effect, and thus enhances the user experience. Ray tracing is performed based on the ambient light source data, and the generated incident light distribution map provides a detailed basis for the subsequent light interaction analysis, ensuring the high accuracy and reliability of the optical model. The generated display screen microstructure model can reflect the real physical properties of the display screen through microstructure simulation, making the light interaction analysis more realistic and effective. The light interaction analysis is combined with the incident light distribution map, and the generated reflection and refraction light model provides an intuitive perspective for understanding the propagation behavior of light in the display screen, improving the scientific nature of the display effect. The light intensity distribution calculation results of the reflection and refraction light model provide important data support for the brightness control of the display screen, ensuring the visibility and comfort of the displayed content under different environmental conditions.

[0030] Preferably, step S213 includes the following steps:

[0031] Pair the incident light distribution diagram and the display screen microstructure model with interaction points to obtain a light surface interaction point set;

[0032] Perform local coordinate transformation on each interaction point set on the light surface to generate interaction point coordinate data;

[0033] Performing ray path tracing on the incident ray distribution diagram based on the interaction point coordinate data to obtain ray path data;

[0034] Calculate the light reflectivity of the interaction point according to the light path data to obtain light reflectivity data; calculate the light transmittance of the interaction point according to the light path data to obtain light transmittance data; calculate the reflection-transmission ratio of the light reflectivity data and the light transmittance data to generate reflection-transmission ratio data;

[0035] Based on the reflection and transmission ratio data, a path model is constructed for the light path data to generate a light path model; based on the light path model, the light intensity attenuation of the display screen microstructure model is calculated to obtain attenuated light data;

[0036] Perform multiple scattering simulation on the attenuated light data to obtain scattering-enhanced light data; perform optical path difference compensation processing on the scattering-enhanced light data to generate phase-corrected light data;

[0037] The characteristic parameters of the phase-corrected light data are aggregated to obtain the key characteristic data of the light; the simulation model of the key characteristic data of the light is reshaped to generate a reflection and refraction light model.

[0038] The present invention generates a light surface interaction point set by pairing the incident light distribution diagram and the display screen microstructure model, which enhances the interactive understanding between the light and the display screen surface and lays the foundation for subsequent optical analysis. The interaction point coordinate data generated by the local coordinate transformation ensures the accuracy of the light path tracing and improves the accuracy and practicality of the optical model. The light path tracing results provide detailed light path information for the subsequent reflectivity and transmittance calculations, supporting a comprehensive evaluation of the display screen performance. By calculating the light reflectivity and transmittance, the reflection and transmission ratio data obtained provide key data for the optical property analysis of the display screen, which helps to optimize the display effect. The light path model constructed based on the reflection and transmission ratio data provides an in-depth analysis perspective for the optical performance of the display screen, making the light intensity attenuation calculation more scientific. The scattering enhanced light data generated by the multiple scattering simulation results significantly improves the realism of the display effect and provides a richer visual expression for the display content. The light data generated after the phase correction process ensures the consistency of the light path under different optical conditions and improves the stability and reliability of the display content. The aggregation of the key characteristic data of the light and the reshaping of the simulation model provide a systematic basis for the generation of the reflection and refraction light model, ensuring the comprehensiveness and practicality of the model.

[0039] Preferably, step S24 includes the following steps:

[0040] Step S241: performing pixel-level calibration on preset device external brightness data to obtain calibrated external brightness data; performing spatial alignment on the calibrated external brightness data and display screen reflected light intensity data to obtain spatially aligned light intensity data;

[0041] Step S242: adding and superimposing the spatially aligned light intensity data pixel by pixel to generate a mixed light intensity matrix;

[0042] Step S243: performing color correction on the mixed light intensity matrix to obtain a color-corrected light intensity matrix; performing spatial uniformity analysis on the color-corrected light intensity matrix to obtain a uniformity evaluation result;

[0043] Step S244: performing local uniformity adjustment on the color correction light intensity matrix based on the uniformity evaluation result to generate display screen mixed light intensity data.

[0044] The present invention ensures the accuracy of the calibrated external brightness data through the implementation of pixel-level calibration, and improves the reliability and consistency of the display effect. The generation of spatially aligned light intensity data ensures the relative accuracy between different light intensity data, and provides a solid foundation for subsequent light intensity analysis. The mixed light intensity matrix generated by the process of pixel-by-pixel addition and superposition provides comprehensive data support for the optical characteristics of the display screen and enhances the authenticity of the display effect. The color correction process ensures the color accuracy in the mixed light intensity matrix, optimizes the visual experience, and reduces the color cast. The results of the spatial uniformity analysis provide a specific basis for subsequent uniformization adjustments, ensure the consistency of the brightness and color of the display screen, and enhance the user's visual experience. The mixed light intensity data of the display screen generated by the local uniformization adjustment further optimizes the display effect, making the brightness and color distribution of different areas more uniform, and improving the overall visual performance.

[0045] Preferably, step S3 comprises the following steps:

[0046] Step S31: obtaining the user's historical usage data; performing time series analysis on the user's historical usage data to obtain historical usage time series data; identifying missing points on the historical usage time series data to generate historical data missing points;

[0047] Step S32: performing linear interpolation to fill in the user's historical usage data based on the missing points in the historical data to obtain complete historical data;

[0048] Step S33: grouping the completed historical data into time periods to obtain time-grouped historical data; calculating the average brightness of the time-grouped historical data to obtain time-period average brightness data; calculating the average standard deviation of the time-grouped historical data to generate the time-period average standard deviation;

[0049] Step S34: multi-dimensionally fuse the time period average brightness data and the time period average standard deviation to obtain time period brightness feature data; perform brightness habit clustering on the time period brightness feature data to generate user device brightness habit data.

[0050] The present invention provides an in-depth understanding of user behavior through the acquisition and analysis of historical usage time series data, laying a data foundation for the construction of the subsequent brightness habit model. The process of missing point identification and linear interpolation filling ensures the integrity of the historical usage data, reduces the analysis error caused by missing data, and improves the reliability of the data. The implementation of time period grouping makes the analysis of user behavior more targeted and facilitates the identification of changes in user brightness requirements in different time periods. The calculation of the average brightness and standard deviation of the time period provides a quantitative basis for the extraction of brightness features, which helps to identify the brightness preferences of users in specific time periods. The multi-dimensional fusion operation effectively integrates the brightness feature data of the time period, enhances the comprehensiveness and applicability of the data, and provides a comprehensive perspective for subsequent clustering analysis. The result of brightness habit clustering generates user device brightness habit data, clarifies the brightness requirements of different users, helps to optimize the automatic brightness adjustment strategy of the device, and improves the user experience.

[0051] Preferably, step S4 comprises the following steps:

[0052] Step S41: Calculate the brightness value of each light intensity point based on the brightness habit data of the user device to obtain an ideal brightness value; perform a difference comparison between the ideal brightness value and the mixed light intensity data of the display screen to generate brightness difference data;

[0053] Step S42: Calculating the fill light amount for the display screen mixed light intensity data based on the brightness difference data to obtain the display screen fill light rate data;

[0054] Step S43: distinguishing the display screen fill light rate data by brightness intervals to generate segmented fill light rate data; performing linear regression analysis of each interval on the segmented fill light rate data to obtain segmented linear fitting parameters;

[0055] Step S44: performing curve fitting on the segmented fill light rate data based on the segmented linear fitting parameters to generate a dynamic fill light rate curve.

[0056] The present invention ensures that the device accurately adjusts the brightness according to user needs by calculating the ideal brightness value, providing users with a visual experience that is more in line with personal preferences. The generation of brightness difference data provides a clear basis for the calculation of the amount of fill light for the display screen, ensuring the pertinence and effectiveness of the fill light measures. The generation of the display screen fill light rate data makes the dynamic brightness adjustment more precise, effectively improving the readability and comfort of the screen under different ambient light conditions. The implementation of brightness interval differentiation optimizes the fill light strategy, making the adjustment under different light intensity conditions more flexible and efficient, meeting the needs of diverse usage scenarios. Segmented linear regression analysis provides a reasonable mathematical model for the segmented fill light rate data, making the fill light adjustment process more predictable and stable. The generation of the dynamic fill light rate curve provides an intuitive form of expression for real-time brightness control, allowing the device to automatically adapt to lighting changes in different usage environments, thereby improving user satisfaction.

[0057] Preferably, step S42 includes the following steps:

[0058] Step S421: positioning the difference area of ​​the display screen mixed light intensity data based on the brightness difference data to obtain fill light requirement area data;

[0059] Step S422: performing brightness value compensation calculation on the fill light requirement area data and the display screen mixed light intensity data to generate actually required fill light data;

[0060] Step S423: performing loss simulation on the actually required fill light data based on the reflection-transmission ratio data to generate simulated loss data; performing logical inversion on the actually required fill light data according to the simulated loss data to obtain actually required brightness value enhancement data;

[0061] Step S424: performing brightness value compensation calculation on the display screen mixed light intensity data according to the actually required brightness value enhancement data to obtain the display screen fill light rate data.

[0062] The present invention accurately identifies specific areas that require fill light by analyzing brightness difference data, ensuring that fill light measures are targeted. This feature optimizes the use efficiency of the light source, reduces the range of light intensity adjustment, avoids waste of resources, and thus improves overall energy efficiency and environmental friendliness. Based on the combination of fill light demand area and display screen mixed light intensity data, the actual required fill light value can be dynamically generated. This process makes the fill light process more in line with the actual use needs of users, thereby significantly improving visual comfort and user satisfaction. By analyzing the reflection and transmission ratio data, the light intensity loss is simulated, providing a scientific basis for the actual required fill light data. This simulation takes into account environmental impact and equipment characteristics, making the fill light design more scientific and reasonable, significantly reducing the problem of insufficient fill light caused by changes in external conditions, and ensuring the user experience under different lighting conditions. The simulated loss data is used to perform logical inversion on the actual required fill light data to generate brightness value enhancement data. This feature ensures the effectiveness of the fill light result through the inversion process, so that the final fill light solution can not only meet environmental requirements, but also accurately reflect the user's visual habits and preferences, providing a more personalized user experience. After obtaining the brightness enhancement data, the brightness of the display screen is finely adjusted by calculating the fill light rate. This adjustment process ensures that the device can adjust the brightness in real time according to the changes in external light, thereby maintaining the best display effect under various lighting conditions, improving the clarity and visual effect of information presentation, and enhancing the intelligence and adaptability of smart multimedia devices.

[0063] The present invention further provides an intelligent multimedia device, including a controller, for executing the display method of the intelligent multimedia device as described above, wherein the controller of the intelligent multimedia device includes:

[0064] The environment acquisition module is used to obtain the device usage environment data; perform multi-dimensional fusion of the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate the device usage environment light field;

[0065] The light intensity analysis module is used to analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; based on the preset device external brightness data, the mixed light intensity of the reflected light intensity data of the display screen is calculated to generate the mixed light intensity data of the display screen;

[0066] The preference acquisition module is used to obtain the user's historical usage data; fill in the missing values ​​of the user's historical usage data to obtain the completed historical data; analyze the user's habitual brightness of the completed historical data to generate the user's device brightness habit data;

[0067] The fill light calculation module is used to calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the initial fill light rate data; perform curve fitting on the initial fill light rate data to generate a dynamic fill light rate curve;

[0068] The brightness control module is used to perform piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; according to the continuous brightness adjustment instruction sequence, the multimedia device is dynamically adjusted to execute the display method of the intelligent multimedia device.

[0069] The present invention has multi-dimensional fusion capability through the environment acquisition module, and can accurately collect various data such as light, temperature and humidity in the environment where the device is used. This comprehensive data collection ensures that the device can adapt to different use environments in real time, thereby optimizing its performance and enhancing the user's visual experience. The light intensity analysis module obtains more accurate display screen reflected light intensity data by analyzing the refracted light intensity of the ambient light field. Based on these data, mixed light intensity calculation is performed with the preset device external brightness, and the generated display screen mixed light intensity data can effectively improve the visual effect in various environments, ensure that the content is clearly visible, and reduce visual fatigue. The preference acquisition module uses historical usage data to fill in missing values ​​and generate complete historical data. Then, through the user's habitual brightness analysis, personalized user device brightness habit data is formed. This process strengthens the device's understanding of user preferences, makes the display effect more in line with the user's usage habits, and improves the user's overall satisfaction. The fill light calculation module calculates the fill light rate based on the user's device brightness habit data, and the generated dynamic fill light rate curve can reflect the changes in ambient light in real time. This dynamic adjustment ensures that the device always maintains the best display effect under different lighting conditions, thereby improving the efficiency of the device. The brightness control module generates a continuous brightness adjustment instruction sequence by performing piecewise linear interpolation processing on the dynamic fill light rate curve. This process realizes intelligent brightness control of multimedia devices, ensuring that users can enjoy a smooth and consistent visual experience when using the device regardless of changes in the external environment, thus improving the intelligence and interactivity of the device.

[0070] Figures

[0071] Figure 1 A schematic diagram of the steps of a display method for an intelligent multimedia device;

[0072] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0073] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve this, please refer to Figures 1 to 3 , a display method of an intelligent multimedia device comprises the following steps:

[0079] Step S1: Acquire device usage environment data; perform multi-dimensional fusion on the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate a device usage environment light field;

[0080] Step S2: Analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; perform mixed light intensity calculation on the reflected light intensity data of the display screen based on the preset external brightness data of the device to generate mixed light intensity data of the display screen;

[0081] Step S3: Obtain the user's historical usage data; fill in missing values ​​in the user's historical usage data to obtain completed historical data; perform user habit brightness analysis on the completed historical data to generate user device brightness habit data;

[0082] Step S4: Calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the display screen fill light rate data; perform curve fitting on the display screen fill light rate data to generate a dynamic fill light rate curve;

[0083] Step S5: performing piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; dynamically adjusting the brightness of the multimedia device according to the continuous brightness adjustment instruction sequence to execute the display method of the intelligent multimedia device.

[0084] The present invention integrates environmental usage data in multiple dimensions to construct an environmental description model that can accurately reflect the lighting changes in the usage environment, ensure that the display effect matches the environmental conditions, and improve the user's visual experience. The combination of refracted light intensity analysis and mixed light intensity calculation can accurately obtain the reflected light intensity data of the display screen, provide reliable basic data for dynamic brightness adjustment, and optimize the display effect. The missing value filling technology is used to process the user's historical usage data, which can more comprehensively reflect the user's usage habits, thereby generating personalized device brightness habit data and improving the adaptability of the device. The device fill light rate is calculated and curve fitting is performed. The generated dynamic fill light rate curve can effectively reflect the user's brightness requirements under different ambient light conditions and ensure that the display screen can provide ideal brightness under various conditions. The piecewise linear interpolation processing technology makes the brightness adjustment instruction sequence continuous and smooth, avoids mutations, improves the dynamic brightness control capability of the device, and reduces user eye discomfort.

[0085] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the display method of the intelligent multimedia device of the present invention. In this example, the display method of the intelligent multimedia device includes the following steps:

[0086] Step S1: Acquire device usage environment data; perform multi-dimensional fusion on the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate a device usage environment light field;

[0087] In this embodiment, comprehensive data collection is performed on the environment in which the device is used, and environmental sensors (such as temperature and humidity sensors, light sensors, etc.) are used to obtain real-time environmental data. These sensors send data to the central processing unit through a wireless communication module. During the data processing process, a data fusion algorithm, such as a Kalman filter algorithm, is used to perform multi-dimensional fusion of various environmental data, eliminate noise and redundancy, and form a high-precision environmental description model. Next, the model is analyzed for light states, and the characteristics of the light source in the environment are modeled using optical analysis software, and finally the device use environment light field is generated, which is used for subsequent light intensity analysis.

[0088] Step S2: Analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; perform mixed light intensity calculation on the reflected light intensity data of the display screen based on the preset external brightness data of the device to generate mixed light intensity data of the display screen;

[0089] In this embodiment, the simulated data of the ambient light field used by the device is used to perform refracted light intensity analysis. Using a ray tracing algorithm, by establishing a light propagation model, the refraction path of light on the display screen is analyzed to obtain the reflected light intensity distribution data of the display screen. Next, based on the preset external brightness data of the device (which can be obtained through the built-in brightness adjustment module), the reflected light intensity data of the display screen is calculated for mixed light intensity. The two are combined through a weighted average algorithm to generate mixed light intensity data for the display screen, ensuring that the data accurately reflects the light intensity conditions in the current display environment.

[0090] Step S3: Obtain the user's historical usage data; fill in missing values ​​in the user's historical usage data to obtain completed historical data; perform user habit brightness analysis on the completed historical data to generate user device brightness habit data;

[0091] In this embodiment, historical data is obtained by recording the usage log of the user device (such as brightness adjustment records, usage time, etc.). Subsequently, the missing values ​​are filled using an interpolation algorithm, and the linear interpolation method is used to ensure that the generated supplementary historical data is smooth and continuous. Next, the user's habitual brightness is analyzed, the historical data is grouped through cluster analysis, the average brightness and standard deviation of each group are calculated, and finally the user device brightness habit data is generated. These data reflect the user's brightness preferences in different time periods and provide a basis for the subsequent calculation of the fill light rate.

[0092] Step S4: Calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the display screen fill light rate data; perform curve fitting on the display screen fill light rate data to generate a dynamic fill light rate curve;

[0093] In this embodiment, the fill light rate is calculated for the mixed light intensity data of the display screen according to the brightness habit data of the user device. The fill light rate required for the display screen is calculated using the light intensity difference formula, and mathematical operations are performed using data analysis tools (such as the NumPy library in Python) to ensure data accuracy. Next, the calculated fill light rate data is curve fitted, and a polynomial fitting algorithm is used to generate a dynamic fill light rate curve. This curve can accurately reflect the fill light change trend required by the device under different environmental conditions, providing guidance for subsequent brightness control.

[0094] Step S5: performing piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; dynamically adjusting the brightness of the multimedia device according to the continuous brightness adjustment instruction sequence to execute the display method of the intelligent multimedia device.

[0095] In this embodiment, data of a dynamic fill light rate curve is received, and the curve contains fill light rate information required under different environmental conditions. Next, an interpolation algorithm (such as piecewise linear interpolation) is used to process the dynamic fill light rate curve. The specific operation steps include: first, the dynamic fill light rate curve is decomposed into multiple discrete points, and piecewise linear interpolation is performed using a data analysis tool (such as Python's SciPy library). The process involves identifying the key points of the curve and calculating interpolation between adjacent points to generate a series of continuous fill light rate values. After the interpolation is completed, the obtained continuous brightness adjustment instruction sequence is further processed to ensure the smoothness and accuracy of the instruction sequence. At this time, a filtering algorithm (such as Kalman filtering) is applied to reduce the existing noise to ensure the stability of the instruction during transmission. The continuous brightness adjustment instruction sequence is input into the controller of the multimedia device. After receiving the instruction, the controller adjusts the backlight module and the image processing unit of the display screen through the driving circuit to achieve dynamic brightness control. Specifically, the backlight module accurately controls the brightness output through a PWM (pulse width modulation) signal to ensure that the brightness is adjusted in different time periods so that the display effect reaches the best state. Monitor the changes in ambient light in real time, and based on the real-time data fed back by the ambient light sensor, continuously update and optimize the fill light rate instruction sequence to achieve adaptive brightness adjustment. Throughout the process, the device maintains high responsiveness and accuracy to ensure that users can get a comfortable visual experience under various lighting conditions.

[0096] Preferably, step S1 comprises the following steps:

[0097] Step S11: deploying environmental sensors on multimedia devices and collecting all-round environmental data to obtain device usage environment data;

[0098] Step S12: performing principal component analysis on the device use environment data to obtain reduced-dimensional device use environment data; performing multi-dimensional fusion of features on the reduced-dimensional device use environment data to obtain fused environment feature data;

[0099] Step S13: reconstructing the simulation environment model based on the fused environment feature data to generate an environment description model;

[0100] Step S14: parsing the ambient light state of the environment description model to obtain device ambient light state data;

[0101] Step S15: Perform light field simulation on the device ambient light state data to generate the device usage ambient light field.

[0102] In this embodiment, the multimedia device is deployed with environmental sensors, and a plurality of environmental sensors are installed at suitable locations, including temperature sensors, humidity sensors, light sensors, and noise sensors. These sensors are arranged at even intervals to ensure that all environmental changes around the device are covered. Through the real-time data acquisition module of the sensor, the device collects environmental data at a certain time interval (for example, per second). All collected data are transmitted to the central processing unit through a wireless communication module (such as Wi-Fi or Bluetooth), stored in a data storage system, and form preliminary equipment use environment data. The collected environmental data are processed using the principal component analysis (PCA) algorithm. At this time, the input data is a high-dimensional environmental parameter, including temperature, humidity, light intensity, etc. Professional statistical analysis tools (such as scikit-learn libraries in MATLAB or Python) are used to perform PCA, extract the main components, and reduce the dimension of the data. The analysis process includes calculating the covariance matrix, extracting eigenvalues ​​and eigenvectors, and selecting the first few principal components (for example, the first two or the first three) as the dimensionality reduction result. Then, the data after dimensionality reduction is subjected to feature multi-dimensional fusion, and different environmental features are fused using a weighted algorithm to generate comprehensive environmental feature data for subsequent analysis. Through data visualization tools (such as MATLAB's graphical interface or Python's Matplotlib library), the environmental feature data is visualized and the relationship between the features is intuitively analyzed. Then, appropriate simulation modeling software (such as ANSYS or COMSOL Multiphysics) is selected to build the environmental model and set boundary conditions and initial conditions. The model is solved through numerical calculation methods (such as finite element analysis) to generate a simulation environment model that matches the actual environment and reflects the dynamic change characteristics of the environment. Optical analysis tools (such as optical simulation software Zemax or TracePro) are used to simulate the lighting conditions of the environmental model. Input light source parameters, including the type, intensity and position of the light source, and perform light propagation simulation. Through the simulation calculation of the light field, the ambient light state data is obtained, and the light intensity distribution under different times and conditions is recorded. Quantitatively analyze the ambient light state and extract key lighting features for subsequent light field simulation. Select a special light field simulation tool (such as LightTools or OpticStudio), build a light field model, and input ambient light state data. Define the geometry and optical properties of the light source in the model, and use the ray tracing algorithm to simulate the propagation and scattering of light in the environment. Perform light field simulation by setting simulation parameters (such as simulation accuracy and number of iterations) to obtain the light intensity distribution diagram of the ambient light field used by the device. Finally, save the generated light field data and prepare to provide basic data support for subsequent display adjustment and optimization.

[0103] Preferably, step S2 comprises the following steps:

[0104] Step S21: performing display screen reflection and refraction analysis on the multimedia device based on the ambient light field used by the device to obtain display screen reflected light intensity distribution data;

[0105] Step S22: performing spatial filtering on the reflected light intensity distribution to obtain interference-free reflected light intensity distribution data;

[0106] Step S23: performing refracted light intensity analysis on the interference-removed reflected light intensity distribution data to obtain display screen reflected light intensity data;

[0107] Step S24: performing mixed light intensity calculation on the display screen reflected light intensity data based on the preset device external brightness data to generate display screen mixed light intensity data.

[0108] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0109] Step S21: performing display screen reflection and refraction analysis on the multimedia device based on the ambient light field used by the device to obtain display screen reflected light intensity distribution data;

[0110] In this embodiment, the data related to the ambient light field used by the device is obtained to analyze the interaction between the display screen surface of the multimedia device and the light field. Optical simulation software (such as LightTools) is used to analyze the reflection and refraction of the display screen. The ambient light field parameters, including information such as the position of the light source, light intensity, and incident angle, are input, and the interaction between the light and the display screen surface is calculated by ray tracing technology. The generated output data includes the distribution of reflected light intensity in each area of ​​the display screen, forming a light intensity distribution map, recording the reflected light intensity values ​​at different positions, and the data will be used for subsequent light intensity processing and analysis.

[0111] Step S22: performing spatial filtering on the reflected light intensity distribution to obtain interference-free reflected light intensity distribution data;

[0112] In this embodiment, a digital signal processing tool (such as MATLAB or Python's SciPy library) is used to perform a spatial filtering operation on the reflected light intensity distribution data. A suitable filter, such as a Gaussian filter or a median filter, is selected, and the parameters of the filter, such as the window size and the standard deviation, are defined. The filtering operation is performed, the original reflected light intensity data is input into the filter, and the interference-free reflected light intensity distribution data is output. By comparing the data before and after filtering, it is ensured that the interference noise is effectively removed and the true light intensity distribution information is retained.

[0113] Step S23: performing refracted light intensity analysis on the interference-removed reflected light intensity distribution data to obtain display screen reflected light intensity data;

[0114] In this embodiment, the de-interferenced reflected light intensity distribution data is subjected to refracted light intensity analysis. First, optical computing software (such as Zemax) is used to simulate the process of light refraction on the display surface. The refracted light intensity distribution is calculated based on the material properties of the display (such as the refractive index) and the incident angle of the light. The refraction interface is set, the de-interferenced reflected light intensity data is input, and the light path is traced to obtain the refracted light intensity distribution data. Finally, the analysis results are integrated to form the reflected light intensity data of the display, including the refracted light intensity values ​​of each area, to provide the necessary data basis for subsequent calculations.

[0115] Step S24: performing mixed light intensity calculation on the display screen reflected light intensity data based on the preset device external brightness data to generate display screen mixed light intensity data.

[0116] In this embodiment, a mixed light intensity calculation is performed on the reflected light intensity data of the display screen based on the preset external brightness data of the device. First, the parameters of the external brightness of the device are obtained, including the brightness values ​​under different display modes. A calculation tool (such as Excel or Python) is used to perform a mixed light intensity calculation, and the reflected light intensity data of the display screen is added point by point to the external brightness data to form a mixed light intensity data set. The calculation process includes comparing the reflected light intensity value with the preset brightness value one by one to generate the final mixed light intensity data of the display screen. The generated mixed light intensity data is used for subsequent display adjustment and optimization to realize dynamic brightness control of intelligent multimedia devices.

[0117] Preferably, step S21 includes the following steps:

[0118] Step S211: performing light source decomposition on the ambient light field of the device to obtain ambient light data; performing ray tracing on the ambient light field of the device based on the ambient light data to obtain an incident light distribution map;

[0119] Step S212: Acquire multimedia device display screen data; perform microstructure simulation on the multimedia device display screen data to generate a display screen microstructure model;

[0120] Step S213: performing light interaction analysis on the display screen microstructure model based on the incident light distribution diagram to generate a reflection and refraction light model;

[0121] Step S214: Calculate the light intensity distribution of the reflection and refraction light model to obtain the display screen reflection light intensity distribution data.

[0122] In this embodiment, the ambient light field is used to decompose the light source of the device to extract the ambient light source data. This operation is performed using optical analysis software (such as OptiFDTD), and the parameters of the ambient light field, such as the type of light source, light intensity, and incident angle, are input. The light intensity distribution information of multiple light sources is generated through the light source decomposition algorithm. Then, based on the ambient light source data, a ray tracing operation is performed, and a Ray Tracing tool (such as POV-Ray) is used to simulate the propagation of light. The ambient light source data is used as input, and a ray tracing algorithm is used to obtain a distribution diagram of incident light, and the intensity and direction information of different light rays are recorded to provide basic data for subsequent optical analysis. The display screen data of the multimedia device is obtained, including information such as the size, resolution, material, and microstructure of the display screen. The display screen is modeled using computer-aided design software (such as SolidWorks) to create a geometric model of the display screen. Then, the data of the display screen is simulated for microstructure, and structural analysis is performed using Finite Element Analysis software (such as ANSYS). Set material properties, boundary conditions, and loading conditions to generate a microstructure model of the display screen, ensuring that the model truly reflects the microscopic features of the display screen surface, such as pixel arrangement and material distribution, and providing an accurate geometric basis for subsequent light interaction analysis. Perform light interaction analysis on the display screen microstructure model based on the incident light distribution map. Use optical simulation software (such as LightTools) to do this, and input the generated incident light distribution map and microstructure model. Use the ray tracing algorithm to simulate the interaction between light and the display screen surface, including reflection, refraction, and absorption. Set appropriate boundary conditions and optical parameters (such as refractive index and reflectivity) to generate a reflection and refraction light model, and record the intensity and interaction position of each light. Through this analysis, complete light interaction data is obtained to form a light reflection and refraction model, laying the foundation for subsequent light intensity distribution calculations. Use optical calculation tools (such as Zemax) to import the reflection and refraction light model to calculate the light intensity. Set the calculation parameters, including the light source type and incident angle, and integrate the intensity of each light through the light intensity distribution formula to obtain the reflected light intensity distribution. The output data includes the light intensity values ​​of different areas, forming a display screen reflected light intensity distribution data set. It ensures that multiple reflections and refractions of light are taken into account during the calculation process, improves the accuracy of the data, facilitates subsequent display adjustment and optimization, and supports the functional realization of smart multimedia devices.

[0123] Preferably, step S213 includes the following steps:

[0124] Pair the incident light distribution diagram and the display screen microstructure model with interaction points to obtain a light surface interaction point set;

[0125] Perform local coordinate transformation on each interaction point set on the light surface to generate interaction point coordinate data;

[0126] Performing ray path tracing on the incident ray distribution diagram based on the interaction point coordinate data to obtain ray path data;

[0127] Calculate the light reflectivity of the interaction point according to the light path data to obtain light reflectivity data; calculate the light transmittance of the interaction point according to the light path data to obtain light transmittance data; calculate the reflection-transmission ratio of the light reflectivity data and the light transmittance data to generate reflection-transmission ratio data;

[0128] Based on the reflection and transmission ratio data, a path model is constructed for the light path data to generate a light path model; based on the light path model, the light intensity attenuation of the display screen microstructure model is calculated to obtain attenuated light data;

[0129] Perform multiple scattering simulation on the attenuated light data to obtain scattering-enhanced light data; perform optical path difference compensation processing on the scattering-enhanced light data to generate phase-corrected light data;

[0130] The characteristic parameters of the phase-corrected light data are aggregated to obtain the key characteristic data of the light; the simulation model of the key characteristic data of the light is reshaped to generate a reflection and refraction light model.

[0131] In this embodiment, the incident light distribution diagram and the display screen microstructure model are paired with interaction points to determine the specific location where the light interacts with the display screen surface. Optical simulation software (such as OptiFDTD) is used for interaction analysis, and the interaction points between the light and the display screen are calculated by setting the incident angle of the light and the geometric shape of the display screen surface. The program automatically generates an interaction point set, which contains the coordinates of each light interacting with the display screen and its light intensity information. The local coordinate transformation of each interaction point on the light surface interaction point set is performed to uniformly calculate the impact of the light on the display screen. The coordinate system of each interaction point is transformed through a mathematical transformation algorithm (such as homogeneous coordinate transformation). This process is implemented using computing software (such as MATLAB), and the original coordinates and transformation matrix of the interaction point are input to generate interaction point coordinate data. The incident light distribution diagram is traced based on the interaction point coordinate data. Ray tracing software (such as LightTools) is used to perform this operation, and the interaction point coordinates and light distribution information are input to execute the ray path tracing algorithm. The program calculates the propagation path of the light one by one, including each interaction point passed by the light and its light intensity change, and generates detailed light path data. The light reflectivity of the interaction point is calculated based on the light path data. The reflectivity is calculated by optical calculation software (such as Zemax), the light intensity information of the interaction point and the refractive index of the material are input, and the reflectivity is calculated by applying the Fresnel equations. At the same time, the light transmittance is calculated using the same data to obtain complete light interaction performance data. The reflection-transmission ratio is calculated for the light reflectivity data and the light transmittance data. A calculation program is written using mathematical analysis tools (such as Python's NumPy library) to analyze the proportional relationship between reflectivity and transmittance. The input data includes the reflectivity and transmittance of each interaction point, and a reflection-transmission ratio data set is generated through simple division and addition operations. A path model is constructed for the light path data based on the reflection-transmission ratio data. The light path model is generated using model building software (such as MATLAB Simulink), and the reflection-transmission ratio data and light path information are input. A complete light path model is formed by building a network model. The light intensity attenuation is calculated for the display screen microstructure model based on the light path model. The light intensity attenuation analysis is performed using optical simulation software (such as TracePro), and the light path model and the display screen microstructure data are input to calculate the attenuation of light when propagating in the microstructure. Perform multiple scattering simulation on the attenuated light data. Use optical simulation software (such as COMSOL Multiphysics) to perform scattering analysis, input the attenuated light data and the material properties of the display screen, and use the Monte Carlo method to simulate the multiple scattering process of light in the display screen. Perform optical path difference compensation on the scattering enhancement light data.Use signal processing tools (such as MATLAB) to perform phase correction, input the scattered enhanced light data, and use the phase compensation algorithm to calculate the optical path difference of each light. Aggregate the characteristic parameters of the phase-corrected light data. Use data analysis tools (such as Python's Pandas library) to aggregate data, input the phase-corrected light data, and extract key optical characteristic parameters, such as light intensity distribution, scattering angle, and optical path. The program integrates the characteristic parameters into a key characteristic data set of light through statistical analysis to provide data support for subsequent simulation model reshaping. Reshape the simulation model of the key characteristic data of light. Use 3D modeling software (such as Blender) to reshape, input the key characteristic data of light, build a new optical model, and generate a reflection and refraction light model. By adjusting the geometric shape and optical parameters of the model, ensure that the model can truly reflect the reflection and refraction behavior of light on the surface of the display screen, and provide support for the display method of smart multimedia devices.

[0132] Preferably, step S24 includes the following steps:

[0133] Step S241: performing pixel-level calibration on preset device external brightness data to obtain calibrated external brightness data; performing spatial alignment on the calibrated external brightness data and display screen reflected light intensity data to obtain spatially aligned light intensity data;

[0134] Step S242: adding and superimposing the spatially aligned light intensity data pixel by pixel to generate a mixed light intensity matrix;

[0135] Step S243: performing color correction on the mixed light intensity matrix to obtain a color-corrected light intensity matrix; performing spatial uniformity analysis on the color-corrected light intensity matrix to obtain a uniformity evaluation result;

[0136] Step S244: performing local uniformity adjustment on the color correction light intensity matrix based on the uniformity evaluation result to generate display screen mixed light intensity data.

[0137] In this embodiment, the preset device external brightness data is calibrated at the pixel level to ensure that the brightness of each pixel meets the standard. Use image processing software (such as Adobe Photoshop) to open the brightness data graph, apply the brightness adjustment tool, fine-tune each pixel according to the standard brightness value, and generate calibrated external brightness data. Next, the calibrated external brightness data is aligned with the display screen reflected light intensity data using a spatial alignment algorithm to ensure that the two completely overlap in spatial coordinates. Use an image processing library (such as OpenCV) for spatial alignment, locate the corresponding pixel points through a feature matching algorithm, and generate spatial alignment light intensity data. The spatial alignment light intensity data is added and superimposed pixel by pixel to generate a mixed light intensity matrix. This process is implemented by a programming language (such as Python), and the spatial alignment light intensity data and the calibrated external brightness data are stored in a matrix form using the NumPy library. Using element-level addition operations, the light intensity values ​​of each corresponding pixel are added to generate a new mixed light intensity matrix. When designing a program, it should be ensured that the input data dimensions are consistent to avoid errors caused by inconsistent dimensions. This process forms the basis for the final light intensity output of the display screen. The mixed light intensity matrix is ​​color corrected to obtain a color-corrected light intensity matrix. This process is performed using color correction software (such as ColorMunki), the mixed light intensity matrix is ​​input, and the color correction algorithm is applied to adjust the light intensity data. Specifically, a standard color model (such as sRGB) is used to compare with the mixed light intensity matrix, and pixel-by-pixel color adjustment is performed according to the error analysis to generate a color-corrected light intensity matrix. At the same time, the color-corrected light intensity matrix is ​​analyzed using a spatial uniformity analysis tool (such as Matlab), the uniformity of the light intensity distribution in different regions is calculated, and the uniformity evaluation result is obtained. Based on the uniformity evaluation result, the color-corrected light intensity matrix is ​​locally uniformized to generate the mixed light intensity data of the display screen. First, the color-corrected light intensity matrix is ​​opened using image processing software (such as GIMP), and the light intensity is adjusted by applying a local uniformization filter (such as a Gaussian filter) based on the uniformity evaluation. This process involves processing the local area of ​​the light intensity matrix to ensure that the change of the light intensity value is smooth and continuous. After the processing is completed, the generated mixed light intensity data of the display screen is saved as a new image file, ready for further display applications. This process ensures that the final output light intensity data remains consistent and beautiful in visual effects.

[0138] Preferably, step S3 comprises the following steps:

[0139] Step S31: obtaining the user's historical usage data; performing time series analysis on the user's historical usage data to obtain historical usage time series data; identifying missing points on the historical usage time series data to generate historical data missing points;

[0140] Step S32: performing linear interpolation to fill in the user's historical usage data based on the missing points in the historical data to obtain complete historical data;

[0141] Step S33: grouping the completed historical data into time periods to obtain time-grouped historical data; calculating the average brightness of the time-grouped historical data to obtain time-period average brightness data; calculating the average standard deviation of the time-grouped historical data to generate the time-period average standard deviation;

[0142] Step S34: multi-dimensionally fuse the time period average brightness data and the time period average standard deviation to obtain time period brightness feature data; perform brightness habit clustering on the time period brightness feature data to generate user device brightness habit data.

[0143] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0144] Step S31: obtaining the user's historical usage data; performing time series analysis on the user's historical usage data to obtain historical usage time series data; identifying missing points on the historical usage time series data to generate historical data missing points;

[0145] In this embodiment, the user's historical usage data is obtained, including the timestamp of device usage and the corresponding brightness setting. Use data analysis tools (such as the Pandas library in Python) to import historical data sets, and use time series analysis functions to extract time information and convert it into a time series format. Next, identify missing points in the historical usage time series data, use interpolation methods (such as linear interpolation) to identify gaps in the time series, and generate historical data missing points. This process will provide the basis for subsequent data filling and analysis, ensuring a complete understanding of historical data.

[0146] Step S32: performing linear interpolation to fill in the user's historical usage data based on the missing points in the historical data to obtain complete historical data;

[0147] In this embodiment, for the identified missing points in historical data, the user's historical usage data is filled using a linear interpolation method. The interpolation function in Python's Scikit-learn library is used to compare the position of the missing value with the adjacent data to generate smooth filled data. For example, for the brightness data at the missing time point, the algorithm will perform linear calculations based on the known values ​​before and after to ensure that the interpolation result is consistent with the overall trend. The final generated completed historical data is stored as a new data set as input for subsequent analysis.

[0148] Step S33: grouping the completed historical data into time periods to obtain time-grouped historical data; calculating the average brightness of the time-grouped historical data to obtain time-period average brightness data; calculating the average standard deviation of the time-grouped historical data to generate the time-period average standard deviation;

[0149] In this embodiment, the completed historical data is grouped by time period, and the grouping function of the Pandas library is used to classify the data by hour, day or week to form time grouped historical data. Then, the brightness values ​​in each time period are averaged to generate the average brightness data for the time period. Through the aggregation function in Pandas, the average brightness value of each time period can be quickly calculated. At the same time, the average standard deviation of the time grouped historical data is calculated, and the standard deviation function in the NumPy library is used to obtain the average standard deviation of the time period. This step will provide the necessary statistical information for the subsequent analysis of brightness fluctuations.

[0150] Step S34: multi-dimensionally fuse the time period average brightness data and the time period average standard deviation to obtain time period brightness feature data; perform brightness habit clustering on the time period brightness feature data to generate user device brightness habit data.

[0151] In this embodiment, multi-dimensional fusion is performed based on the average brightness data of the time period and the average standard deviation of the time period to generate the brightness feature data of the time period. Through the NumPy library in Python, the two data sets are combined by column to form a feature matrix containing the average brightness and its standard deviation. Then, a machine learning tool (such as Scikit-learn) is used to perform cluster analysis on the brightness feature data of the time period, and the K-Means algorithm is used to group the data according to similarity to generate the brightness habit data of the user device. The clustering results can clearly show the brightness preferences of users in different time periods, form the final brightness habit characteristics, and provide a basis for the intelligent regulation of subsequent devices.

[0152] Preferably, step S4 comprises the following steps:

[0153] Step S41: Calculate the brightness value of each light intensity point based on the brightness habit data of the user device to obtain an ideal brightness value; perform a difference comparison between the ideal brightness value and the mixed light intensity data of the display screen to generate brightness difference data;

[0154] Step S42: Calculating the fill light amount for the display screen mixed light intensity data based on the brightness difference data to obtain the display screen fill light rate data;

[0155] Step S43: distinguishing the display screen fill light rate data by brightness intervals to generate segmented fill light rate data; performing linear regression analysis of each interval on the segmented fill light rate data to obtain segmented linear fitting parameters;

[0156] Step S44: performing curve fitting on the segmented fill light rate data based on the segmented linear fitting parameters to generate a dynamic fill light rate curve.

[0157] In this embodiment, the brightness habit data of the user device is analyzed to calculate the ideal brightness value of each light intensity point. Using Python's NumPy library, mathematical operations are performed for each brightness habit data point to generate an ideal brightness value. Next, the ideal brightness value is compared with the mixed light intensity data of the display screen, and the absolute difference formula is used to calculate the brightness difference of each point, and the brightness difference data is stored through a data frame (such as Pandas). This process provides basic data for the subsequent calculation of the fill light amount. Based on the generated brightness difference data, the fill light amount of the display screen is calculated. Using the formula "fill light amount = ideal brightness value-display screen mixed light intensity data", the Pandas library in Python can be used to calculate each data point to obtain the display screen fill light rate data. By processing and storing the results point by point, the fill light rate data of each light intensity point is ensured to be accurate and logical, laying a data foundation for subsequent analysis. The display screen fill light rate data is distinguished by brightness intervals. First, set multiple brightness intervals (such as 0-50, 51-100, etc.), and use Python's NumPy library to segment the fill light rate data according to the set intervals to generate segmented fill light rate data. Next, perform linear regression analysis on the data in each brightness interval, and use the linear regression function of the Scikit-learn library to generate piecewise linear fitting parameters by fitting the fill light rate data in each interval. This step ensures effective modeling of fill light requirements in different brightness intervals. Based on the piecewise linear fitting parameters, curve fitting is performed on the segmented fill light rate data. Use the polynomial fitting tool in Scikit-learn to fuse the fill light rate data in different intervals to generate a dynamic fill light rate curve. By adjusting the order of the fitted polynomial, the fitting effect is optimized to ensure that the curve can accurately reflect the changing trend of the fill light demand. Finally, the dynamic fill light rate curve is visualized to facilitate subsequent display adjustment and intelligent control, forming a complete display method.

[0158] Preferably, step S42 includes the following steps:

[0159] Step S421: positioning the difference area of ​​the display screen mixed light intensity data based on the brightness difference data to obtain fill light requirement area data;

[0160] Step S422: performing brightness value compensation calculation on the fill light requirement area data and the display screen mixed light intensity data to generate actually required fill light data;

[0161] Step S423: performing loss simulation on the actually required fill light data based on the reflection-transmission ratio data to generate simulated loss data; performing logical inversion on the actually required fill light data according to the simulated loss data to obtain actually required brightness value enhancement data;

[0162] Step S424: performing brightness value compensation calculation on the display screen mixed light intensity data according to the actually required brightness value enhancement data to obtain the display screen fill light rate data.

[0163] In this embodiment, the brightness difference data is analyzed to identify the fill light demand area in the mixed light intensity data of the display screen. The image processing library OpenCV is used to perform threshold processing on the brightness difference data, set a suitable threshold, and extract the area with significant brightness difference. The fill light demand area is located by a contour detection algorithm (such as Canny edge detection) to generate fill light demand area data. The data will include the coordinate information of the area to facilitate the subsequent fill light calculation. The generated fill light demand area data is used to perform brightness value compensation calculation on the mixed light intensity data of the display screen. By applying the coordinate information of the fill light demand area, the light intensity value in each area is adjusted, and the calculation formula is "actual required fill light = ideal brightness value - display screen mixed light intensity data". This process can be vectorized using the NumPy library to improve the calculation efficiency, and finally the actual required fill light data is generated and stored in the data frame for subsequent processing. Using the previously generated reflection and transmission ratio data, the actual required fill light data of each fill light demand area is calculated for loss, using the formula "loss = actual required fill light × reflection and transmission ratio". The simulation results will be saved in array form. Subsequently, the actual required fill light data is logically inverted according to the simulated loss data, and the calculation formula is "actual required brightness value enhancement = actual required fill light - loss". This process can also use NumPy for array operations to ensure the efficiency and accuracy of the calculation. According to the actual required brightness value enhancement data, the brightness value compensation calculation is performed on the display mixed light intensity data. The formula "display fill light rate = actual required fill light + display mixed light intensity data" is used to perform point-by-point calculations to generate the display fill light rate data. This calculation process can use the data framework of the Pandas library to integrate the calculation results and facilitate subsequent display adjustments. At the same time, the data visualization can use the Matplotlib library to present the fill light rate data in graphical form for easy analysis and adjustment. Through these steps, the precise adjustment of the display brightness is completed to ensure the best display effect of the device.

[0164] The present invention further provides an intelligent multimedia device, including a controller, for executing the display method of the intelligent multimedia device as described above, wherein the controller of the intelligent multimedia device includes:

[0165] The environment acquisition module is used to obtain the device usage environment data; perform multi-dimensional fusion of the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate the device usage environment light field;

[0166] The light intensity analysis module is used to analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; based on the preset device external brightness data, the mixed light intensity of the reflected light intensity data of the display screen is calculated to generate the mixed light intensity data of the display screen;

[0167] The preference acquisition module is used to obtain the user's historical usage data; fill in the missing values ​​of the user's historical usage data to obtain the completed historical data; analyze the user's habitual brightness of the completed historical data to generate the user's device brightness habit data;

[0168] The fill light calculation module is used to calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the initial fill light rate data; perform curve fitting on the initial fill light rate data to generate a dynamic fill light rate curve;

[0169] The brightness control module is used to perform piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; according to the continuous brightness adjustment instruction sequence, the multimedia device is dynamically adjusted to execute the display method of the intelligent multimedia device.

[0170] The present invention has multi-dimensional fusion capability through the environment acquisition module, and can accurately collect various data such as light, temperature and humidity in the environment where the device is used. This comprehensive data collection ensures that the device can adapt to different use environments in real time, thereby optimizing its performance and enhancing the user's visual experience. The light intensity analysis module obtains more accurate display screen reflected light intensity data by analyzing the refracted light intensity of the ambient light field. Based on these data, mixed light intensity calculation is performed with the preset device external brightness, and the generated display screen mixed light intensity data can effectively improve the visual effect in various environments, ensure that the content is clearly visible, and reduce visual fatigue. The preference acquisition module uses historical usage data to fill in missing values ​​and generate complete historical data. Then, through the user's habitual brightness analysis, personalized user device brightness habit data is formed. This process strengthens the device's understanding of user preferences, makes the display effect more in line with the user's usage habits, and improves the user's overall satisfaction. The fill light calculation module calculates the fill light rate based on the user's device brightness habit data, and the generated dynamic fill light rate curve can reflect the changes in ambient light in real time. This dynamic adjustment ensures that the device always maintains the best display effect under different lighting conditions, thereby improving the efficiency of the device. The brightness control module generates a continuous brightness adjustment instruction sequence by performing piecewise linear interpolation processing on the dynamic fill light rate curve. This process realizes intelligent brightness control of multimedia devices, ensuring that users can enjoy a smooth and consistent visual experience when using the device regardless of changes in the external environment, thus improving the intelligence and interactivity of the device.

[0171] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0172] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A display method of an intelligent multimedia device, characterized in that: The following steps are involved: Step S1: Acquire device usage environment data; perform multi-dimensional fusion on the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate a device usage environment light field; Step S2: Analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; perform mixed light intensity calculation on the reflected light intensity data of the display screen based on the preset external brightness data of the device to generate mixed light intensity data of the display screen; Step S3: Obtain the user's historical usage data; fill in missing values ​​in the user's historical usage data to obtain completed historical data; perform user habit brightness analysis on the completed historical data to generate user device brightness habit data; Step S4: Calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the display screen fill light rate data; perform curve fitting on the display screen fill light rate data to generate a dynamic fill light rate curve; Step S5: performing piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; dynamically adjusting the brightness of the multimedia device according to the continuous brightness adjustment instruction sequence to execute the display method of the intelligent multimedia device.

2. The display method of the intelligent multimedia device according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: deploying environmental sensors on multimedia devices and collecting all-round environmental data to obtain device usage environment data; Step S12: performing principal component analysis on the device use environment data to obtain reduced-dimensional device use environment data; performing multi-dimensional fusion of features on the reduced-dimensional device use environment data to obtain fused environment feature data; Step S13: reconstructing the simulation environment model based on the fused environment feature data to generate an environment description model; Step S14: parsing the ambient light state of the environment description model to obtain device ambient light state data; Step S15: Perform light field simulation on the device ambient light state data to generate the device usage ambient light field.

3. The display method of the intelligent multimedia device according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing display screen reflection and refraction analysis on the multimedia device based on the ambient light field used by the device to obtain display screen reflected light intensity distribution data; Step S22: performing spatial filtering on the reflected light intensity distribution to obtain interference-free reflected light intensity distribution data; Step S23: performing refracted light intensity analysis on the interference-removed reflected light intensity distribution data to obtain display screen reflected light intensity data; Step S24: performing mixed light intensity calculation on the display screen reflected light intensity data based on the preset device external brightness data to generate display screen mixed light intensity data.

4. The display method of the intelligent multimedia device according to claim 3, characterized in that: Step S21 includes the following steps: Step S211: performing light source decomposition on the ambient light field of the device to obtain ambient light data; performing ray tracing on the ambient light field of the device based on the ambient light data to obtain an incident light distribution map; Step S212: Acquire multimedia device display screen data; perform microstructure simulation on the multimedia device display screen data to generate a display screen microstructure model; Step S213: performing light interaction analysis on the display screen microstructure model based on the incident light distribution diagram to generate a reflection and refraction light model; Step S214: Calculate the light intensity distribution of the reflection and refraction light model to obtain the display screen reflection light intensity distribution data.

5. The display method of the intelligent multimedia device according to claim 4, characterized in that: Step S213 includes the following steps: Pair the incident light distribution diagram and the display screen microstructure model with interaction points to obtain a light surface interaction point set; Perform local coordinate transformation on each interaction point set on the light surface to generate interaction point coordinate data; Performing ray path tracing on the incident ray distribution diagram based on the interaction point coordinate data to obtain ray path data; Calculate the light reflectivity of the interaction point according to the light path data to obtain light reflectivity data; calculate the light transmittance of the interaction point according to the light path data to obtain light transmittance data; calculate the reflection-transmission ratio of the light reflectivity data and the light transmittance data to generate reflection-transmission ratio data; Based on the reflection and transmission ratio data, a path model is constructed for the light path data to generate a light path model; based on the light path model, the light intensity attenuation of the display screen microstructure model is calculated to obtain attenuated light data; Perform multiple scattering simulation on the attenuated light data to obtain scattering-enhanced light data; perform optical path difference compensation processing on the scattering-enhanced light data to generate phase-corrected light data; The characteristic parameters of the phase-corrected light data are aggregated to obtain the key characteristic data of the light; the simulation model of the key characteristic data of the light is reshaped to generate a reflection and refraction light model.

6. The display method of the intelligent multimedia device according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing pixel-level calibration on preset device external brightness data to obtain calibrated external brightness data; performing spatial alignment on the calibrated external brightness data and display screen reflected light intensity data to obtain spatially aligned light intensity data; Step S242: adding and superimposing the spatially aligned light intensity data pixel by pixel to generate a mixed light intensity matrix; Step S243: performing color correction on the mixed light intensity matrix to obtain a color-corrected light intensity matrix; performing spatial uniformity analysis on the color-corrected light intensity matrix to obtain a uniformity evaluation result; Step S244: performing local uniformity adjustment on the color correction light intensity matrix based on the uniformity evaluation result to generate display screen mixed light intensity data.

7. The display method of the intelligent multimedia device according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: obtaining the user's historical usage data; performing time series analysis on the user's historical usage data to obtain historical usage time series data; identifying missing points on the historical usage time series data to generate historical data missing points; Step S32: performing linear interpolation to fill in the user's historical usage data based on the missing points in the historical data to obtain complete historical data; Step S33: grouping the completed historical data into time periods to obtain time-grouped historical data; calculating the average brightness of the time-grouped historical data to obtain time-period average brightness data; calculating the average standard deviation of the time-grouped historical data to generate the time-period average standard deviation; Step S34: multi-dimensionally fuse the time period average brightness data and the time period average standard deviation to obtain time period brightness feature data; perform brightness habit clustering on the time period brightness feature data to generate user device brightness habit data.

8. The display method of the intelligent multimedia device according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Calculate the brightness value of each light intensity point based on the brightness habit data of the user device to obtain an ideal brightness value; perform a difference comparison between the ideal brightness value and the mixed light intensity data of the display screen to generate brightness difference data; Step S42: Calculating the fill light amount for the display screen mixed light intensity data based on the brightness difference data to obtain the display screen fill light rate data; Step S43: distinguishing the display screen fill light rate data by brightness intervals to generate segmented fill light rate data; performing linear regression analysis of each interval on the segmented fill light rate data to obtain segmented linear fitting parameters; Step S44: performing curve fitting on the segmented fill light rate data based on the segmented linear fitting parameters to generate a dynamic fill light rate curve.

9. The display method of the intelligent multimedia device according to claim 8, characterized in that: Step S42 includes the following steps: Step S421: positioning the difference area of ​​the display screen mixed light intensity data based on the brightness difference data to obtain fill light requirement area data; Step S422: performing brightness value compensation calculation on the fill light requirement area data and the display screen mixed light intensity data to generate actually required fill light data; Step S423: performing loss simulation on the actually required fill light data based on the reflection-transmission ratio data to generate simulated loss data; performing logical inversion on the actually required fill light data according to the simulated loss data to obtain actually required brightness value enhancement data; Step S424: performing brightness value compensation calculation on the display screen mixed light intensity data according to the actually required brightness value enhancement data to obtain the display screen fill light rate data.

10. An intelligent multimedia device, characterized in that: A controller is included, which is used to execute the display method of the intelligent multimedia device according to claim 1, and the controller of the intelligent multimedia device includes: The environment acquisition module is used to obtain the device usage environment data; perform multi-dimensional fusion of the device usage environment data to obtain an environment description model; perform ambient light state analysis on the environment description model to generate the device usage environment light field; The light intensity analysis module is used to analyze the refracted light intensity of the ambient light field of the device to obtain the reflected light intensity data of the display screen; based on the preset device external brightness data, the mixed light intensity of the reflected light intensity data of the display screen is calculated to generate the mixed light intensity data of the display screen; The preference acquisition module is used to obtain the user's historical usage data; fill in the missing values ​​of the user's historical usage data to obtain the completed historical data; analyze the user's habitual brightness of the completed historical data to generate the user's device brightness habit data; The fill light calculation module is used to calculate the device fill light rate of the display screen mixed light intensity data according to the user device brightness habit data to obtain the initial fill light rate data; perform curve fitting on the initial fill light rate data to generate a dynamic fill light rate curve; The brightness control module is used to perform piecewise linear interpolation processing on the dynamic fill light rate curve to obtain a continuous brightness adjustment instruction sequence; according to the continuous brightness adjustment instruction sequence, the multimedia device is dynamically adjusted to execute the display method of the intelligent multimedia device.

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