Display screen color management method based on ambient light perception

Abstract

The invention relates to the technical field of display, and discloses a display screen color management method based on ambient light perception, and the method comprises the steps: collecting original illumination data containing incident angle data and sensor space coordinates, and calculating the initial position of a light source; determining light field azimuth information in combination with the equipment attitude data; constructing a three-dimensional light field model based on light direction features and intensity distribution features of the light field azimuth information; comparing the current light field distribution with the standard light field distribution, and generating a display parameter adjustment set containing color temperature, brightness and contrast; the display parameter adjustment set is converted into control instruction data, and dynamic adaptation processing of the screen display effect is completed. The problem that picture display of the display screen is distorted or unnatural under the influence of ambient light is solved, and the effects of adjusting the display effect of the display screen according to the ambient light and improving the display quality are achieved.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a display screen color management method based on ambient light perception. Background Art

[0002] Display screens are the core components of electronic devices for outputting visual information. They present images and colors through pixel arrays and are widely used in all scenarios including smartphones, computers, commercial large screens, and wearable devices.

[0003] Display screens are affected by ambient light, especially in complex lighting conditions, where they often face significant challenges. Dynamic interference from ambient light can lead to color distortion and reduced contrast, severely impacting the accuracy of information transmission and the user's visual comfort. To address this issue, the industry's widely adopted light intensity detection solution uses static light intensity data for one-time display adjustments.

[0004] However, this method cannot effectively cope with complex scenarios such as the interweaving of multiple light sources or dynamic lighting changes, resulting in key parameters such as the brightness and color temperature of the display being difficult to match with the ambient light in real time, making the picture appear distorted or unnatural. Summary of the Invention

[0005] The embodiments of the present application provide a display color management method based on ambient light perception, thereby solving the problem of distorted or unnatural display caused by the display screen under the influence of ambient light.

[0006] The embodiment of the present application provides a display screen color management based on ambient light perception. The display screen color management method based on ambient light perception includes: Collecting an original lighting data set, and determining an initial estimate of a light source position based on the original lighting data set; Acquire device posture data of the sensor carrier, and determine light field orientation information of the light source posture in combination with the initial estimated value; constructing a three-dimensional light field model of the ambient light based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, and generating light field distribution information; Calculating display parameter adjustment values ​​that match the distribution of current ambient light and standard ambient light according to the light field distribution information and preset mapping rules, and generating a display parameter adjustment set; The display parameter adjustment set is converted into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect.

[0007] Optionally, the step of collecting an original illumination data set and determining an initial estimate of the light source position according to the original illumination data set includes: Preprocessing the incident angle data in the original illumination data set to generate a light direction detection result; Calculate the initial position coordinate set of the light source in three-dimensional space based on the light direction detection result and the triangulation positioning algorithm; An outlier detection is performed on the preliminary position coordinate set. If an abnormal coordinate is detected, data fusion is performed on the sensor data of adjacent nodes to adjust the coordinates of the abnormal coordinate to generate the initial estimated value.

[0008] Optionally, the step of performing outlier detection on the preliminary position coordinate set, and if an abnormal coordinate is detected, performing data fusion on the abnormal coordinate by using sensor data of adjacent nodes, and adjusting the coordinates of the abnormal coordinate to generate the initial estimate includes: Detecting whether there are abnormal coordinates in the preliminary position coordinate set; If so, obtaining the target sensor node associated with the abnormal coordinates; Selecting three reference nodes that are adjacent to the target sensor node in spatial distribution, and comparing the incident angle data and light intensity data of the target sensor node with those of the reference nodes; The abnormal coordinates are weightedly corrected based on the comparison result to obtain the initial estimated value.

[0009] Optionally, after converting the display parameter adjustment set into corresponding control instruction data to complete the step of dynamically adapting the screen display effect, the following steps are included: Continuously monitoring the light field data stream of ambient light, and if it is detected that the light field data stream exceeds a preset light field data threshold range, triggering a real-time update mechanism and generating preliminary adjustment signal data; If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, modifying parameters of the three-dimensional light field model according to the preliminary adjustment signal data to generate second light field distribution information; The display parameter adjustment value is calibrated according to the second light field distribution information, the display parameter adjustment set is updated, and the step of converting the display parameter adjustment set into corresponding control instruction data is executed to complete the dynamic adaptation processing of the screen display effect.

[0010] Optionally, the step of acquiring the device posture data of the sensor carrier and determining the light field orientation information of the light source posture in combination with the initial estimated value includes: Acquiring the device posture data of the sensor carrier during the movement process, and performing coordinate transformation on the device posture data and the initial estimated value using a space transformation matrix to obtain relative posture data of the sensor carrier relative to the light source; A corresponding weight value is allocated to each of the sensor carriers according to a preset distance-weight mapping relationship, and the weight value and the initial estimation value are fused to generate the light field orientation information.

[0011] Optionally, the step of constructing a three-dimensional light field model of ambient light according to light direction characteristics and intensity distribution characteristics in the light field orientation information to generate light field distribution information includes: Based on the light direction characteristics, establishing a light direction distribution model through space vector analysis; Based on the intensity distribution characteristics, a light field reconstruction algorithm is used to construct a three-dimensional light field intensity distribution model; The direction distribution model and the intensity distribution model are fused to generate the light field distribution information including spatial orientation and intensity information.

[0012] Optionally, the step of calculating display parameter adjustment values ​​that match the distribution of the current ambient light and the standard ambient light according to the light field distribution information and a preset mapping rule, and generating a display parameter adjustment set includes: Comparing and analyzing the light field distribution information with the standard ambient light distribution, and calculating the display parameter adjustment value in combination with the preset mapping rule, the display parameter adjustment value including at least color temperature adjustment data, brightness adjustment data, and contrast value; The brightness adjustment data and the correlation information of the contrast value are acquired, and the color temperature adjustment data and the correlation information are subjected to parameter fusion to generate the display parameter adjustment set.

[0013] Optionally, the step of converting the display parameter adjustment set into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect includes: generating a predicted display effect according to the display parameter adjustment set; Evaluating the degree of matching between the predicted display effect and the expected target through an image analysis algorithm; If the matching degree is greater than or equal to a preset matching degree threshold, converting the display parameter adjustment set into the control instruction data; The control instruction data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0014] In addition, to achieve the above-mentioned purpose, an embodiment of the present invention also provides a terminal device, including a memory, a processor, and a display color management program based on ambient light perception stored in the memory and runnable on the processor. When the processor executes the display color management program based on ambient light perception, the method described above is implemented.

[0015] In addition, to achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer-readable storage medium, on which a display screen color management program based on ambient light perception is stored. When the display screen color management program based on ambient light perception is executed by a processor, the method described above is implemented.

[0016] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: (1) The present invention uses multiple sensors to collect raw illumination data including incident angles and sensor coordinates, and calculates the initial estimated value of the light source position. Compared with the related art that uses a single light sensor to collect illumination data, the present invention can locate the light source position more accurately, laying the foundation for subsequent light field modeling.

[0017] (2) This invention combines the device posture data and initial light source estimation of the sensor carrier to ensure that the light source position can be accurately tracked even when the device is moving, thereby improving the stability of ambient light perception. At the same time, a three-dimensional light field model is constructed based on the light direction characteristics and intensity distribution characteristics, accurately restoring the spatial distribution characteristics of ambient light, so that the display adaptation is more consistent with real lighting conditions.

[0018] (3) The present invention generates light field distribution information based on a three-dimensional light field model and, in combination with preset mapping rules, calculates display parameter adjustment values ​​that match the distribution of the current ambient light with the standard ambient light distribution, thereby generating a display parameter adjustment set. By mapping the light field distribution information with the standard ambient light, the optimal display parameter adjustment values ​​are automatically generated, achieving a precise match between the screen display and the lighting environment.

[0019] (4) The present invention converts the display parameter adjustment set into corresponding control instruction data, completes the dynamic adaptation processing of the screen display effect, and ensures a comfortable visual experience under different lighting conditions by dynamically adjusting parameters such as screen brightness and color temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of Example 1 of the display color management method based on ambient light perception of this application; Figure 2 This is a flow chart of Example 2 of the display screen color management method based on ambient light perception of this application; Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in an embodiment of the present application. DETAILED DESCRIPTION

[0021] To address the problem of distorted or unnatural display images caused by ambient light, this solution deploys a multi-point photosensor array to collect an ambient light illumination dataset. Based on the original illumination dataset, an initial estimate of the light source position is determined. The device posture data of the sensor carrier is then acquired, and the light field orientation information of the light source posture is determined based on the initial estimate. A three-dimensional light field model of the ambient light is constructed based on the light direction characteristics and light intensity distribution characteristics in the light field orientation information. This light field distribution information is then generated. Based on preset mapping rules, display parameter adjustment values ​​are calculated to match the ambient light with the standard ambient light, generating a display parameter adjustment set. Finally, the display parameter adjustment set is converted into control command data, completing the dynamic adaptation of the screen display effect. This achieves the goal of adjusting the display effect according to the ambient light, improving display quality.

[0022] To better understand the above technical solutions, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] Example 1 In this embodiment, a display screen color management method based on ambient light perception is provided.

[0025] Reference Figure 1 The display screen color management method based on ambient light perception of this embodiment includes the following steps: Step S100: collecting an original illumination data set, and determining an initial estimate of the light source position based on the original illumination data set; In this embodiment, a multi-point photosensor array is deployed to collect ambient light data in multiple dimensions, and dynamic lighting changes are recorded in combination with timestamps to achieve accurate analysis of complex light environments.

[0026] As an optional implementation, raw illumination data can include ambient light information such as incident angle, direction, and intensity, as well as timestamp information. By integrating the characteristic values ​​and timestamp records of each sensor node, a raw illumination dataset containing multi-dimensional data is generated, providing the data foundation for the subsequent generation of complete spatial illumination distribution information.

[0027] For example, in a large indoor exhibition environment, multiple light-sensitive sensor nodes are deployed to monitor light distribution. Assume the exhibition hall has multiple exhibition areas, and the light sources include natural light, artificial lighting, and dynamic projection equipment. The ambient light distribution is complex and varies over time. Each sensor node is deployed in different locations, such as corners, central areas, and near windows, collecting light intensity data from multiple angles and simultaneously recording timestamps to form a time-stamped raw light dataset. The sensor's built-in angle detection module can determine the direction of the light source and the angle of incidence. Combined with the light intensity data, it can infer the source. For example, if the angle detection module of one node detects that the light is primarily coming from a 45-degree angle above, combined with the light intensity, it can be inferred that it is direct overhead lighting. However, at another node, the light direction is biased to the side, possibly due to natural light from a window. This multi-dimensional extraction method helps to construct a comprehensive picture of dynamic lighting changes, especially capturing subtle changes in lighting such as switching lights in the exhibition hall or moving clouds outside the window.

[0028] Optionally, after collecting the original illumination data set, if it is detected that the illumination intensity of a certain sensor node at a specific time point exceeds the preset illumination threshold, cross-validation can be performed through the illumination intensity data of adjacent nodes to obtain a more accurate illumination change trend and determine whether there is an abnormal illumination distribution phenomenon. For example, the normal illumination intensity range is 600-800 lux, and a sensor node detects an illumination intensity of 1000 lux at a specific time point, which exceeds the preset threshold. The system automatically calls the data of adjacent nodes for cross-validation. Assuming that the data of adjacent nodes are all within the normal range, it is determined that there is a temporary strong light source near the sensor node, such as direct light from a projector, rather than a new light source. This phenomenon is marked as an abnormal illumination distribution phenomenon, and the abnormal value is replaced by the data of adjacent nodes. This cross-validation mechanism can distinguish between real ambient light changes and sensor failures or local interference, effectively reducing the misjudgment rate and improving the reliability of monitoring.

[0029] Optionally, after generating the original data set, the spatial lighting distribution information can be further analyzed. Assuming that the original data set is processed and the discrete monitoring point data is converted into a continuous spatial distribution image through an intelligent algorithm, the light intensity distribution in the exhibition hall is plotted as a heat map. It is found that the light intensity near the window area fluctuates greatly over time, while the central area is relatively stable. At this time, the placement of the exhibits can be adjusted according to the changes in light intensity and the characteristics of the exhibits to avoid damage to the exhibits due to strong light.

[0030] As another optional implementation, after collecting the original illumination data set, the incident angle data in the original illumination data set is first preprocessed, and the light direction detection results of each sensor are generated by a spatial vector calculation method. Based on the light direction detection results, the preliminary position coordinate set of the light source in three-dimensional space is calculated in combination with the triangulation positioning algorithm.

[0031] For example, the incident angle data collected at multiple points are preliminarily processed. Since it is difficult to align the sensors completely during installation, resulting in a systematic offset in the measured angles, it is necessary to calibrate the data collected by the sensors through a pre-established mapping table to obtain a calibrated angle set. Then, combined with the three-dimensional coordinate data of each sensor node, the triangulation method is used to preliminarily calculate the position of the light source and determine the preliminary position coordinate set of the light source. For example, the coordinates of node A (0, 0, 2) meters are collected, and the light source direction vector is measured. , corresponding to a pitch angle of 45° and an azimuth angle of 0°. The coordinates of node B are B(5,0,2) meters, and the light source direction vector is measured , corresponding to a pitch angle of 45° and an azimuth angle of 180°. The preliminary position coordinates of the light source calculated by triangulation are (2.5, 0, 4.5) meters.

[0032] As another optional implementation, after establishing the preliminary position coordinate set, outlier detection is performed on the preliminary position coordinate set. If abnormal coordinates are detected, data fusion is performed through sensor data of adjacent nodes, and the coordinates of the abnormal coordinates are adjusted to generate the initial estimate.

[0033] Exemplarily, the initial location coordinate set is detected for abnormal coordinates. If so, the target sensor node associated with the abnormal coordinate is obtained. Three spatially adjacent reference nodes are selected for the target sensor node. The incident angle data and light intensity data of the target sensor node and the reference nodes are compared. Based on the comparison results, a weighted correction is performed on the abnormal coordinate. For example, the weight of each reference node can be calculated by combining three key factors: a spatial distance weight, where reference nodes closer to the abnormal coordinate have higher weights; an optical similarity weight, where nodes with similar light intensity and light direction have higher weights; and a temporal stability weight, where nodes with less data fluctuation have higher weights. A weighted average is then calculated based on the coordinates of the three reference nodes and the calculated weights. Reference nodes with closer proximity, similar optical characteristics, and stable data have a greater impact on the final result. For example, an abnormal coordinate that deviates by 2 meters may be adjusted to within 0.2 meters of the true location after weighted correction by the three surrounding reference nodes. After weighted correction of the abnormal coordinate, the initial estimate is obtained.

[0034] Step S200: Acquire device posture data of the sensor carrier, and determine light field orientation information of the light source posture in combination with the initial estimation value; In this embodiment, sensor carriers are installed at various locations within the venue. These sensors record real-time acceleration and angular velocity data as the device tilts or rotates. This data provides a preliminary understanding of the device's motion and directional changes. Light field orientation information provides a precise geometric description of the light source in three-dimensional space, constructing a complete spatial representation of the light source through multi-dimensional parameters.

[0035] As an optional implementation, after acquiring the device posture data, coordinate transformation is performed on the device posture data and the initial estimation value using a space transformation matrix to obtain relative posture data of the sensor carrier relative to the light source.

[0036] For example, in an exhibition hall, inertial data including acceleration and angular velocity are obtained from the inertial sensor built into the sensor carrier. These inertial data are the basic information of the motion state and posture change of the device. These data will be affected by various noise interferences, so the inertial data need to be filtered first to remove the noise interference. The denoised inertial data is the device posture data, which reflects the position of the device in space, such as the tilt angle, rotation angle, etc. of the device. The device posture data and the initial estimated value of the light source position are converted into a unified coordinate system to obtain the relative posture data of the sensor carrier relative to the light source. For example, the initial estimated position of the light source is a point in the exhibition hall, and the device posture data shows that its orientation deviates from the direction of the light source by about 10 degrees. Through matrix transformation, the relative posture data of the device relative to the light source can be calculated.

[0037] Optionally, if it is detected that the relative posture data exceeds a preset posture threshold, the relative posture data is subjected to sensor fusion processing, and can be analyzed from the perspective of multi-source data integration. Assuming that the posture data deviation of the sensor carrier device in a certain dimension exceeds 5 degrees, the inertial sensor data is called for comparison with other auxiliary information, for example, the weight of the posture data is adjusted in combination with the initial estimated distribution range of the light source, and finally the corrected relative posture data is obtained to improve the accuracy of the data.

[0038] As another optional implementation, after the relative posture data is generated, a corresponding weight value is assigned to each sensor carrier according to a preset distance-weight mapping relationship, and the weight value and the initial estimation value are fused to generate the light field orientation information.

[0039] For example, a weighted averaging method can be used to integrate relative pose data and initial estimates. Sensors are distributed throughout the exhibition hall and assigned different weights based on their distance from the light source. Data with closer distances may have a weight of 0.6, while data with farther distances may have a weight of 0.4. These weights are then fused with the initial estimate. Direction vectors are weighted summed and then normalized, while position coordinates are calculated using the weighted arithmetic mean of each node's coordinates. During the fusion process, the deviation between each sensor data and the intermediate results is monitored in real time. If abnormal data is detected, such as deviations exceeding a threshold, its weight coefficient is automatically reduced to form an adaptive filtering result. The final output of light field orientation information retains the high precision of near-field data while maintaining spatial consistency through far-field data. A confidence verification mechanism also effectively suppresses interference from sensor noise and outliers. This fusion method achieves a balance between positioning accuracy and system robustness in complex light field environments through the dual optimization of distance weighting and dynamic adjustment.

[0040] Step S300: constructing a three-dimensional light field model of ambient light according to the light direction characteristics and intensity distribution characteristics in the light field orientation information, and generating light field distribution information; In this embodiment, the light field distribution information refers to the ambient light distribution in each area of ​​the exhibition environment, including the directionality and intensity distribution differences of the light.

[0041] As an optional implementation, a directional distribution model can be established using spatial vector analysis based on the light's directional characteristics. A light field reconstruction algorithm can then be used to construct a 3D light field intensity distribution model based on the intensity distribution characteristics. The directional and intensity distribution models are then integrated to generate light field distribution information containing both spatial orientation and intensity information.

[0042] Exemplarily, the directional characteristics of light include the incident angle and incident direction of the light. The intensity distribution feature is the intensity of light at different positions and directions in the environment. By calculating the direction vector of the light and performing clustering or interpolation processing, a directional distribution model of the light is formed. The directional distribution model can be represented by a vector field or a directional histogram to intuitively display the directional characteristics of the light. Based on the collected intensity information, a three-dimensional light field intensity distribution model is constructed using a light field reconstruction algorithm. The intensity distribution model is represented by a three-dimensional voxel grid or function to show the intensity changes of the light field at different spatial positions. The directional information is then combined with the intensity information to generate a comprehensive three-dimensional light field model. Weighted averaging, interpolation or other fusion techniques can be used to ensure that the fused light field distribution information contains detailed information on both direction and intensity, and a three-dimensional visualization model is output.

[0043] Step S400: Calculating display parameter adjustment values ​​that match the distribution of current ambient light and standard ambient light according to the light field distribution information and a preset mapping rule to generate a display parameter adjustment set; In this embodiment, the display parameter adjustment set includes optimized parameter sets for brightness, contrast, and color temperature. The preset mapping rules are light field feature-to-control parameter conversion rules. Their core purpose is to transform complex light field information into analyzable characteristic parameters. Assuming the light field data within an exhibition hall contains illumination distribution information for multiple regions, the preset mapping rules can be used to divide the data into high-brightness and low-brightness areas. The illumination coverage and intensity variation trends for each region can be extracted, providing a data foundation for generating display parameter adjustment values.

[0044] As an optional implementation, the light field distribution information is compared and analyzed with the standard ambient light distribution, and the display parameter adjustment value is calculated in combination with the preset mapping rule, where the display parameter adjustment value includes at least color temperature adjustment data, brightness adjustment data, and contrast value; associated information of the brightness adjustment data and the contrast value is obtained, and the color temperature adjustment data and the associated information are parameter-fused to generate the display parameter adjustment set.

[0045] For example, the core of the preset mapping rules lies in converting complex light field information into analyzable characteristic parameters. Assuming that the light field data within an exhibition hall contains illumination distribution information for multiple regions, the preset mapping rules can be used to divide the data into high-brightness and low-brightness areas, extracting the illumination coverage and intensity variation trends for each region. If the standard ambient light distribution specifies different brightness requirements for different areas, with higher brightness required in the center to highlight exhibits and softer light to create an atmosphere in the periphery, the light field distribution information is compared and analyzed with the standard ambient light distribution. Simultaneously, the preset mapping rules are used to extract detailed characteristics of the ambient distribution. Initial brightness adjustment requirements are classified and processed to determine a preliminary adjustment range that meets the requirements. The contrast value is then calibrated within the preliminary adjustment range to determine the degree of match between the contrast value and the ambient distribution. If the match falls below a preset matching threshold, a weighted calculation method is used to make corrections, resulting in adjusted contrast adjustment data. The contrast adjustment data is then combined with the business requirement of color temperature matching, and the data is processed layer by layer to determine whether the color temperature value is within the adaptive value range, thereby obtaining color temperature adjustment data that meets the display parameters. By integrating the correlation information of brightness adjustment and contrast value through color temperature adjustment data, the final parameter set is generated and the display parameter adjustment set is determined.

[0046] For example, a preset mapping rule divides an exhibition hall into high-brightness and low-brightness zones. Standard ambient light requires higher brightness in the center and softer light in the periphery. Comparing the light field distribution with the standard ambient light distribution and combining it with the preset mapping rule, a preliminary brightness adjustment range of 70-90 units can be determined for the center and 30-50 units for the periphery. The contrast value is then calibrated to match the ambient light distribution using the initial adjustment range. If the match falls below the preset threshold, a weighted correction is applied. For example, assuming the center contrast value is low, a higher weight of 0.7 is assigned, while the periphery weight is 0.3, resulting in corrected contrast adjustment data. This corrected contrast adjustment data is then processed layer by layer based on color temperature matching requirements. For example, assuming different exhibition areas within the exhibition hall require different color temperatures, with the center requiring a warmer color temperature, such as 3000K, and the periphery requiring a cooler color temperature, such as 5000K. Layer by layer analysis reveals that if the color temperature value falls outside the adaptive range, such as 2800K-5500K, fine-tuning is performed to obtain color temperature adjustment data that meets the display parameters. The initial brightness adjustment range, contrast adjustment data, and color temperature adjustment data are integrated to generate a display parameter adjustment set. This process ensures that the center area has a brightness of 85 units, a contrast of 75%, and a color temperature of 3200K, while the edge areas have a brightness of 40 units, a contrast of 60%, and a color temperature of 4800K. This integrated approach optimizes the harmony of the overall lighting environment.

[0047] Step S500: converting the display parameter adjustment set into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect.

[0048] In this embodiment, the display parameter adjustment set is a digital set, which needs to be converted into actual control instruction data and encapsulated into a formatted signal suitable for interface transmission, and sent to the display module through the device driver interface. When the display module receives the signal stream, it dynamically adjusts the screen display content according to the content of the signal stream, determines whether the adjusted display content is consistent with the target, and obtains the final screen display output result.

[0049] As an optional implementation, the display parameter adjustment set can be converted into corresponding control instruction data through a parameter mapping tool, and the control instruction data can be compared with the preset data threshold range. If it exceeds the range, the data is calibrated to obtain an instruction data set that meets the standard. The instruction data set is encapsulated into a formatted signal suitable for interface transmission, and the formatted signal is verified to determine whether it meets the requirements of the transmission channel, and a signal stream that can be directly transmitted is obtained. The transmission data is obtained from the signal stream, and the standard interface transmission protocol is used to send the signal stream to the display module through the device driver interface. The transmission process is monitored in real time to ensure that the transmission is complete. When the display module receives the signal stream, the screen display content is dynamically adjusted according to the content of the signal stream.

[0050] For example, the parameter mapping tool can be understood as a preset conversion rule library, which maps adjustment values ​​such as brightness 80 units, contrast 50 units, etc. into control command signals that can be recognized by the device, and compares and calibrates the control command signals with the preset threshold range. Suppose the brightness command value of a certain area in the exhibition hall is 85 units, and the preset threshold upper limit is 80 units, which exceeds the range. At this time, the exceeded command value is automatically calibrated to 80 units to ensure that the command data meets the standard. The command data is packaged into a signal packet suitable for interface transmission. The verification process checks whether the signal packet is complete and whether it meets the bandwidth requirements of the transmission channel, such as the data packet size must be less than 2MB. If it does not meet the requirements, it is repackaged to ensure the transmittability of the signal stream. During the signal stream transmission process, the start time and end time of the transmission are recorded to monitor the integrity of the data. If the signal stream terminal is detected during the transmission process, the retransmission mechanism is triggered to ensure that the data is delivered to the display module in its entirety. The screen brightness or hue is then adjusted according to the content of the signal stream.

[0051] As another optional implementation, a predicted display effect can be generated based on the display parameter adjustment set; the matching degree between the predicted display effect and the expected target can be evaluated through an image analysis algorithm; if the matching degree is greater than or equal to a preset matching degree threshold, the display parameter adjustment set is converted into the control instruction data; and the control instruction data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0052] For example, the display parameter adjustment set is transmitted to the simulation model corresponding to each regional display screen to generate a predicted display effect. An image analysis algorithm is then used to evaluate the match between the predicted display effect and the desired target. If the evaluation match is 84%, exceeding the preset 80% threshold, the display parameter adjustment set is converted into control command data recognizable by the display driver module, and the display screen is dynamically adjusted based on this control command data. The desired target refers to display data that ensures a clear image, natural colors, and coordination with ambient light.

[0053] In this embodiment, a multi-point photosensor array is deployed to collect illumination data in complex environments. The position and orientation of the light sources are inferred using a triangulation algorithm and inertial sensor data. This in turn constructs a three-dimensional light field model. Based on this model, display parameters are calculated to match the ambient light, generating corresponding control instructions to dynamically adjust the screen display. Furthermore, by comparing actual output with expected parameters to optimize display control, the system accurately perceives complex lighting environments and enables intelligent dynamic adjustment of display effects, improving display quality.

[0054] Example 2 Based on the first embodiment, another embodiment of the present application is proposed, referring to Figure 2 After converting the display parameter adjustment set into corresponding control instruction data and completing the step of dynamic adaptation processing of the screen display effect, the following steps are included: Step S600: continuously monitoring the light field data flow of the ambient light, and if it is detected that the light field data flow exceeds a preset light field data threshold range, triggering a real-time update mechanism and generating preliminary adjustment signal data; In this embodiment, after the dynamic adaptation processing of the screen display effect is completed, the light field data stream of the ambient light is continuously detected. When it is detected that the light field data stream exceeds the preset light field data threshold range, real-time ambient light data is obtained and the display parameter adjustment set is updated. Finally, the dynamic adaptation processing of the screen display effect is completed according to the display parameter adjustment set.

[0055] As an optional implementation, ambient light data and sensor data are continuously monitored. If the change in the incident angle or direction of the ambient light exceeds the preset light field data threshold range, the entire process from inferring the light source position to adjusting the display parameters is restarted, which triggers the real-time update mechanism.

[0056] For example, a sensor detects a light intensity of 1500 units, while the preset light intensity threshold is 1200 units. At the same time, the incident angle deviates from the baseline value by 28 degrees, exceeding the system's allowed fluctuation range of plus or minus 15 degrees. This anomaly triggers the real-time update mechanism.

[0057] As another optional implementation, after the real-time update mechanism is triggered, ambient light direction data is acquired and processed to obtain a dynamic preliminary estimate of the light source's position. This ambient light direction data includes the incident light direction data detected by each sensor and the sensor's precise spatial coordinate information. Together, these data form a set of directional vectors in three-dimensional space, providing the basis for locating the light source.

[0058] For example, this relevant data is quality-screened to remove obvious outliers. Then, based on valid sensor data, the optimal intersection point of light vectors in three-dimensional space is calculated. Finally, the rationality of this location is verified by combining prior knowledge such as building layout. For example, if the incident angle of light in the upper left corner of a display screen changes from 45 degrees to 70 degrees, real-time data from three surrounding sensors is retrieved. The first sensor shows that the light is coming from a direction 30 degrees above and right with an intensity of 850 lux. The second sensor detects incident light at 65 degrees above and left with an intensity of 1200 lux. The third sensor records incident light at 15 degrees directly above with an intensity of 200 lux. Based on the standard deviation threshold of the intensity distribution, the data from the third sensor is determined to be unreliable due to possible occlusion or noise interference and is discarded. The remaining valid data is converted into directional vectors in three-dimensional space. Each vector is weighted according to the corresponding sensor's signal-to-noise ratio. Given the precise installation coordinates of these sensors, the weighted directional vectors are input into the triangulation algorithm. Because light rarely intersects perfectly at a single point in real-world environments, the triangulation algorithm calculates the optimal approximation of these vectors in three-dimensional space, yielding a probability distribution. For example, the calculations show that the most likely intersection of these light rays is concentrated within an ellipsoid approximately ±1.5 meters near the west window of the exhibition hall. This is approximately 3 meters west of the previous light source position, providing a preliminary estimate of the light source's dynamic position.

[0059] Step S700: If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, modifying parameters of the three-dimensional light field model according to the preliminary adjustment signal data to generate second light field distribution information; In this embodiment, when an anomaly in light field data is detected, it is necessary to distinguish whether it is a sustained environmental change or a transient disturbance. A preset time threshold of 5 seconds can act as a buffer filter, effectively avoiding overreaction to brief disturbances. For example, the intensity peak caused by the gradual change in natural light and the interference of tourists' mobile phone flashes typically lasts less than 2 seconds, while real daylight changes often last for more than 10 seconds.

[0060] As an optional implementation, when ambient light anomalies are continuously detected, the three-dimensional light field model is iterated in combination with the adjustment signal generated initially. By analyzing the illumination change trajectory over the past 10 seconds, the trend over the next 5 seconds is predicted, and the light source radiation parameters and spatial attenuation coefficient in the model are dynamically corrected.

[0061] For example, when it is determined that the current light pollution mainly comes from natural light incident from the side windows, the weight of the artificial light source in the three-dimensional light field model is reduced, and the calculation accuracy of the diffuse reflection component is enhanced. The corrected second light field distribution information contains more accurate directional radiation patterns and intensity gradient data, providing targeted regional adjustment solutions for the display. For example, additional color temperature compensation and brightness enhancement are implemented in the upper left corner area directly exposed to strong light, while other areas remain fine-tuned, ensuring visual consistency and optimizing energy consumption performance. This dynamic model update mechanism can maintain high color accuracy in the face of continuously changing ambient light, while keeping the response delay time within a reasonable range.

[0062] Step S800: calibrating the display parameter adjustment value according to the second light field distribution information, updating the display parameter adjustment set, and converting the display parameter adjustment set into corresponding control instruction data to complete the step of dynamic adaptation of the screen display effect.

[0063] In this embodiment, the original display parameter adjustment values ​​are calibrated based on the second light field distribution information to ensure that the adjustment values ​​more accurately match the current lighting conditions. The display parameter adjustment set is updated and converted into specific control instructions, which are sent to the display module. This ultimately enables real-time dynamic adaptation of the screen display, such as automatic brightness and contrast adjustment, to maintain an optimal visual experience.

[0064] As an optional implementation, the original display parameter adjustment value is calibrated using the second light field distribution information instead of directly generating a new display parameter adjustment value. This can maintain display consistency while reducing the computational load.

[0065] For example, suppose the room's original lighting source is an overhead LED. The screen sets its brightness to 300 nits and its color temperature to 6500 lux to match these lighting conditions. Then, a new warm light source with an intensity of 2000 lux is detected on the west side of the screen, while the intensity of the overhead LED is relatively reduced to 800 lux. The calibration process begins, first analyzing the incident angle of the new light source, which is approximately 30 degrees and has a significant directionality. Considering that this strong sidelight can cause glare on the screen surface, the brightness of the west area of ​​the screen is increased to 400 nits to mitigate the visual interference caused by direct light. At the same time, the overall color temperature is adjusted to 5800 Kelvin to balance the mixed effect of the cool overhead light and the warm west light. This calibration process is not a simple numerical substitution; rather, it involves multi-dimensional parameter optimization based on the spatial distribution, spectral characteristics, and incident angle of the new light field. The resulting control commands are precisely adjusted to fine-tune the brightness, contrast, and color temperature of different screen sections, ensuring uniform visual quality and accurate color reproduction in mixed lighting environments.

[0066] After the display parameter adjustment set is updated, the display parameter adjustment set is converted into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect. This step is consistent with step S500 of the first embodiment.

[0067] In this embodiment, the light field model is updated in real time and display parameter adjustment values ​​are calibrated, maintaining optimal display adaptation while significantly reducing system energy consumption. Accurate compensation is applied only to the local areas affected by ambient light changes, avoiding unnecessary global brightness or color temperature adjustments. This ensures sustainable energy-saving operation while maintaining visual quality.

[0068] Example 3 In an embodiment of the present application, a display screen color management device based on ambient light perception is proposed.

[0069] Reference Figure 3 , Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in an embodiment of the present application.

[0070] like Figure 3 As shown, the control terminal may include: a processor 1001, such as a CPU, a network interface 1003, a memory 1004, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The network interface 1003 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1004 may be a high-speed RAM memory or a non-volatile memory, such as a disk drive. The memory 1004 may also be a storage device independent of the processor 1001.

[0071] Those skilled in the art will understand that Figure 3 The terminal structure shown in the figure does not constitute a limitation to the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0072] like Figure 3 As shown, the memory 1004 as a computer storage medium may include an operating system, a network communication module, and a display screen color management program based on ambient light perception.

[0073] exist Figure 3 In the hardware structure of the display screen color management device based on ambient light perception shown, the processor 1001 can call the display screen color management program based on ambient light perception stored in the memory 1004 and perform the following operations: Collecting an original lighting data set, and determining an initial estimate of a light source position based on the original lighting data set; Acquire device posture data of the sensor carrier, and determine light field orientation information of the light source posture in combination with the initial estimated value; constructing a three-dimensional light field model of the ambient light based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, and generating light field distribution information; Calculating display parameter adjustment values ​​that match the distribution of current ambient light and standard ambient light according to the light field distribution information and preset mapping rules, and generating a display parameter adjustment set; The display parameter adjustment set is converted into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect.

[0074] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Preprocessing the incident angle data in the original illumination data set to generate a light direction detection result; Calculate the initial position coordinate set of the light source in three-dimensional space based on the light direction detection result and the triangulation positioning algorithm; An outlier detection is performed on the preliminary position coordinate set. If an abnormal coordinate is detected, data fusion is performed on the sensor data of adjacent nodes to adjust the coordinates of the abnormal coordinate to generate the initial estimated value.

[0075] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Detecting whether there are abnormal coordinates in the preliminary position coordinate set; If so, obtaining the target sensor node associated with the abnormal coordinates; Selecting three reference nodes that are adjacent to the target sensor node in spatial distribution, and comparing the incident angle data and light intensity data of the target sensor node with those of the reference nodes; The abnormal coordinates are weightedly corrected based on the comparison result to obtain the initial estimated value.

[0076] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Continuously monitoring the light field data stream of ambient light, and if it is detected that the light field data stream exceeds a preset light field data threshold range, triggering a real-time update mechanism and generating preliminary adjustment signal data; If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, modifying parameters of the three-dimensional light field model according to the preliminary adjustment signal data to generate second light field distribution information; The display parameter adjustment value is calibrated according to the second light field distribution information, the display parameter adjustment set is updated, and the step of converting the display parameter adjustment set into corresponding control instruction data is executed to complete the dynamic adaptation processing of the screen display effect.

[0077] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Acquiring the device posture data of the sensor carrier during the movement process, and performing coordinate transformation on the device posture data and the initial estimated value using a space transformation matrix to obtain relative posture data of the sensor carrier relative to the light source; A corresponding weight value is allocated to each of the sensor carriers according to a preset distance-weight mapping relationship, and the weight value and the initial estimation value are fused to generate the light field orientation information.

[0078] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Based on the light direction characteristics, establishing a light direction distribution model through space vector analysis; Based on the intensity distribution characteristics, a light field reconstruction algorithm is used to construct a three-dimensional light field intensity distribution model; The direction distribution model and the intensity distribution model are fused to generate the light field distribution information including spatial orientation and intensity information.

[0079] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: Comparing and analyzing the light field distribution information with the standard ambient light distribution, and calculating the display parameter adjustment value in combination with the preset mapping rule, the display parameter adjustment value including at least color temperature adjustment data, brightness adjustment data, and contrast value; The brightness adjustment data and the correlation information of the contrast value are acquired, and the color temperature adjustment data and the correlation information are subjected to parameter fusion to generate the display parameter adjustment set.

[0080] Optionally, the processor 1001 may call a display screen color management program based on ambient light perception stored in the memory 1004, and further perform the following operations: generating a predicted display effect according to the display parameter adjustment set; Evaluating the degree of matching between the predicted display effect and the expected target through an image analysis algorithm; If the matching degree is greater than or equal to a preset matching degree threshold, converting the display parameter adjustment set into the control instruction data; The control instruction data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0081] In addition, to achieve the above-mentioned purpose, an embodiment of the present invention also provides a terminal device, including a memory, a processor, and a display screen color management program based on ambient light perception stored in the memory and runnable on the processor. When the processor executes the display screen color management program based on ambient light perception, the display screen color management method based on ambient light perception as described above is implemented.

[0082] In addition, to achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer-readable storage medium, on which a display screen color management program based on ambient light perception is stored. When the display screen color management program based on ambient light perception is executed by a processor, the display screen color management method based on ambient light perception as described above is implemented.

[0083] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0087] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second and third etc. does not indicate any order. These words may be interpreted as names.

[0088] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0089] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims and their equivalents, the present application is intended to include such modifications and variations.

Claims

1. A display color management method based on ambient light perception, characterized in that: The display screen color management method based on ambient light perception includes: Collecting an original lighting data set, and determining an initial estimate of a light source position based on the original lighting data set; Acquire device posture data of the sensor carrier, and determine light field orientation information of the light source posture in combination with the initial estimated value; constructing a three-dimensional light field model of the ambient light based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, and generating light field distribution information; Calculating display parameter adjustment values ​​that match the distribution of current ambient light and standard ambient light according to the light field distribution information and preset mapping rules, and generating a display parameter adjustment set; The display parameter adjustment set is converted into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect.

2. The display screen color management method based on ambient light perception according to claim 1, characterized in that: The step of collecting an original illumination data set and determining an initial estimate of the light source position according to the original illumination data set includes: Preprocessing the incident angle data in the original illumination data set to generate a light direction detection result; Calculate the initial position coordinate set of the light source in three-dimensional space based on the light direction detection result and the triangulation positioning algorithm; An outlier detection is performed on the preliminary position coordinate set. If an abnormal coordinate is detected, data fusion is performed on the sensor data of adjacent nodes to adjust the coordinates of the abnormal coordinate to generate the initial estimated value.

3. The display screen color management method based on ambient light perception according to claim 2, wherein: The step of performing outlier detection on the preliminary position coordinate set, and if an abnormal coordinate is detected, performing data fusion on the abnormal coordinate by using sensor data of adjacent nodes, and adjusting the coordinates of the abnormal coordinate to generate the initial estimate includes: Detecting whether there are abnormal coordinates in the preliminary position coordinate set; If so, obtaining the target sensor node associated with the abnormal coordinates; Selecting three reference nodes that are adjacent to the target sensor node in spatial distribution, and comparing the incident angle data and light intensity data of the target sensor node with those of the reference nodes; The abnormal coordinates are weightedly corrected based on the comparison result to obtain the initial estimated value.

4. The display screen color management method based on ambient light perception according to claim 1, wherein: After the step of converting the display parameter adjustment set into corresponding control instruction data and completing the dynamic adaptation processing of the screen display effect, the method further includes: Continuously monitoring the light field data stream of ambient light, and if it is detected that the light field data stream exceeds a preset light field data threshold range, triggering a real-time update mechanism and generating preliminary adjustment signal data; If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, modifying parameters of the three-dimensional light field model according to the preliminary adjustment signal data to generate second light field distribution information; The display parameter adjustment value is calibrated according to the second light field distribution information, the display parameter adjustment set is updated, and the step of converting the display parameter adjustment set into corresponding control instruction data is executed to complete the dynamic adaptation processing of the screen display effect.

5. The display screen color management method based on ambient light perception according to claim 1, wherein: The step of obtaining the device posture data of the sensor carrier and determining the light field orientation information of the light source posture in combination with the initial estimated value includes: Acquiring the device posture data of the sensor carrier during the movement process, and performing coordinate transformation on the device posture data and the initial estimated value using a space transformation matrix to obtain relative posture data of the sensor carrier relative to the light source; A corresponding weight value is allocated to each of the sensor carriers according to a preset distance-weight mapping relationship, and the weight value and the initial estimation value are fused to generate the light field orientation information.

6. The display screen color management method based on ambient light perception according to claim 1, wherein: The step of constructing a three-dimensional light field model of ambient light based on the light direction characteristics and intensity distribution characteristics in the light field orientation information and generating light field distribution information includes: Based on the light direction characteristics, establishing a light direction distribution model through space vector analysis; Based on the intensity distribution characteristics, a light field reconstruction algorithm is used to construct a three-dimensional light field intensity distribution model; The direction distribution model and the intensity distribution model are fused to generate the light field distribution information including spatial orientation and intensity information.

7. The display screen color management method based on ambient light perception according to claim 1, wherein: The step of calculating the display parameter adjustment value that matches the distribution of the current ambient light and the standard ambient light according to the light field distribution information and the preset mapping rule, and generating the display parameter adjustment set includes: Comparing and analyzing the light field distribution information with the standard ambient light distribution, and calculating the display parameter adjustment value in combination with the preset mapping rule, the display parameter adjustment value including at least color temperature adjustment data, brightness adjustment data, and contrast value; The brightness adjustment data and the correlation information of the contrast value are acquired, and the color temperature adjustment data and the correlation information are subjected to parameter fusion to generate the display parameter adjustment set.

8. The display screen color management method based on ambient light perception according to claim 1, wherein: The step of converting the display parameter adjustment set into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect includes: generating a predicted display effect according to the display parameter adjustment set; Evaluating the degree of matching between the predicted display effect and the expected target through an image analysis algorithm; If the matching degree is greater than or equal to a preset matching degree threshold, converting the display parameter adjustment set into the control instruction data; The control instruction data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

9. A terminal device, characterized in that: The invention comprises a memory, a processor and a display color management program based on ambient light perception stored in the memory and executable on the processor. When the processor executes the display color management program based on ambient light perception, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a display screen color management program based on ambient light perception. When the display screen color management program based on ambient light perception is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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