A display screen color management method based on ambient light perception

Abstract

This application relates to the field of display technology and discloses a color management method for a display screen based on ambient light perception. The method includes: acquiring raw illumination data containing incident angle data and sensor spatial coordinates, and calculating the initial position of the light source; determining the light field orientation information by combining device posture data; constructing a three-dimensional light field model based on the light direction characteristics and intensity distribution characteristics of the light field orientation information; comparing the current light field with a standard light field distribution to generate a display parameter adjustment set including color temperature, brightness, and contrast; and converting the display parameter adjustment set into control command data to complete the dynamic adaptation processing of the screen display effect. This solves the problem of image distortion or unnaturalness caused by ambient light, achieving the effect of adjusting the display effect according to ambient light and improving display quality.

Description

Technical Field

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

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

[0003] The display effect of a screen is affected by ambient light, especially under complex lighting conditions, where screens often face severe challenges. Dynamic interference from ambient light can cause problems such as color distortion and decreased contrast, seriously affecting the accuracy of information transmission and the user's visual experience. To solve this problem, the industry commonly adopts a light intensity detection solution, which performs a one-time display adjustment based on static light intensity data.

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

[0005] This application provides a display screen color management method based on ambient light perception, which solves the problem of display screen display distortion or unnaturalness caused by ambient light.

[0006] This application provides a display screen color management method based on ambient light perception, the method comprising:

[0007] Collect the original illumination dataset and determine the initial estimate of the light source position based on the original illumination dataset;

[0008] Acquire the device attitude data of the sensor carrier, and determine the light field orientation information of the light source attitude by combining the initial estimate;

[0009] Based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, a three-dimensional light field model of ambient light is constructed, and light field distribution information is generated;

[0010] Based on the light field distribution information and preset mapping rules, calculate the display parameter adjustment values ​​that match the distribution of the current ambient light with that of the standard ambient light, and generate a display parameter adjustment set;

[0011] The set of display parameter adjustments is converted into corresponding control command data to complete the dynamic adaptation of the screen display effect.

[0012] Optionally, the step of acquiring the original illumination dataset and determining an initial estimate of the light source position based on the original illumination dataset includes:

[0013] The incident angle data in the original illumination dataset is preprocessed to generate light direction detection results;

[0014] Based on the light direction detection results, the preliminary position coordinate set of the light source in three-dimensional space is calculated using a triangulation algorithm;

[0015] Anomaly detection is performed on the preliminary location coordinate set. If anomaly coordinates are detected, data fusion is performed using sensor data from adjacent nodes to adjust the coordinates of the anomaly coordinates and generate the initial estimated value.

[0016] Optionally, the step of performing outlier detection on the preliminary location coordinate set, and if outlier coordinates are detected, performing data fusion using sensor data from adjacent nodes to adjust the outlier coordinates and generate the initial estimated value includes:

[0017] Detect whether there are any abnormal coordinates in the preliminary position coordinate set;

[0018] If it exists, then obtain the target sensor node associated with the abnormal coordinates;

[0019] Three reference nodes that are spatially adjacent to the target sensor node are selected, and the incident angle data and light intensity data of the target sensor node and the reference nodes are compared.

[0020] The abnormal coordinates are weighted and corrected based on the comparison results to obtain the initial estimated value.

[0021] Optionally, after 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, the method includes:

[0022] The ambient light light field data stream is continuously monitored. If the light field data stream exceeds the preset light field data threshold range, a real-time update mechanism is triggered and preliminary adjustment signal data is generated.

[0023] If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, then the parameters of the three-dimensional light field model are corrected according to the preliminary adjustment signal data to generate second light field distribution information;

[0024] The display parameter adjustment value is calibrated based on 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 command data is executed to complete the dynamic adaptation processing of the screen display effect.

[0025] Optionally, the step of acquiring the device attitude data of the sensor carrier and determining the light field orientation information of the light source attitude in combination with the initial estimate includes:

[0026] The device attitude data of the sensor carrier during the movement process is acquired, and the device attitude data and the initial estimated value are transformed by a spatial transformation matrix to obtain the relative attitude data of the sensor carrier with respect to the light source.

[0027] According to the preset distance-weight mapping relationship, each of the sensor carriers is assigned a corresponding weight value, and the weight value and the initial estimate are fused to generate the light field orientation information.

[0028] Optionally, the step of constructing a three-dimensional light field model of ambient light and generating light field distribution information based on the ray direction characteristics and intensity distribution characteristics in the light field orientation information includes:

[0029] Based on the aforementioned ray direction characteristics, a ray direction distribution model is established through spatial vector analysis;

[0030] Based on the intensity distribution characteristics, a three-dimensional light field intensity distribution model is constructed using a light field reconstruction algorithm;

[0031] The directional distribution model and the intensity distribution model are fused together to generate the light field distribution information that includes spatial orientation and intensity information.

[0032] Optionally, the step of calculating display parameter adjustment values ​​that match the distribution of the current ambient light and the standard ambient light based on the light field distribution information and preset mapping rules, and generating a display parameter adjustment set, includes:

[0033] 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. The display parameter adjustment value includes at least color temperature adjustment data, brightness adjustment data and contrast value.

[0034] Obtain the correlation information between the brightness adjustment data and the contrast value, and fuse the color temperature adjustment data with the correlation information to generate the display parameter adjustment set.

[0035] Optionally, the step of converting the display parameter adjustment set into corresponding control command data to complete the dynamic adaptation processing of the screen display effect includes:

[0036] Based on the set of display parameters, a predicted display effect is generated;

[0037] The matching degree between the predicted display effect and the expected target is evaluated using image analysis algorithms;

[0038] If the matching degree is greater than or equal to the preset matching degree threshold, the display parameter adjustment set is converted into the control instruction data;

[0039] The control command data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0040] In addition, to achieve the above objectives, embodiments of the present invention also provide a terminal device, including a memory, a processor, and an ambient light-sensing display color management program stored in the memory and executable on the processor. When the processor executes the ambient light-sensing display color management program, it implements the method described above.

[0041] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing an ambient light-sensing display screen color management program, which, when executed by a processor, implements the method described above.

[0042] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0043] (1) This invention collects raw illumination data including incident angle and sensor coordinates by multiple sensors and calculates the initial estimate of the light source position. Compared with related technologies that use a single light sensor to collect illumination data, this invention can more accurately locate the light source position and lay the foundation for subsequent light field modeling.

[0044] (2) This invention combines the device attitude data of the sensor carrier with the initial estimate of the light source to ensure that the position of the light source can still be accurately tracked when the device moves, thereby improving the stability of ambient light perception. At the same time, a three-dimensional light field model is constructed based on the characteristics of light direction and intensity distribution to accurately restore the spatial distribution characteristics of ambient light, making the display adaptation more consistent with real lighting conditions.

[0045] (3) This invention generates light field distribution information based on a three-dimensional light field model, and calculates display parameter adjustment values ​​that match the distribution of the current ambient light and the standard ambient light by combining preset mapping rules, thereby generating a display parameter adjustment set. Through the mapping calculation between the light field distribution information and the standard ambient light, the optimal display parameter adjustment values ​​are automatically generated, achieving precise matching between screen display and lighting environment.

[0046] (4) The present invention converts the display parameter adjustment set into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect. By dynamically adjusting parameters such as screen brightness and color temperature, it ensures that a comfortable visual experience can be provided under different lighting conditions. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an embodiment of the display screen color management method based on ambient light perception according to this application.

[0048] Figure 2 This is a flowchart illustrating Embodiment 2 of the display screen color management method based on ambient light perception according to this application;

[0049] Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application. Detailed Implementation

[0050] To address the issue of display distortion or unnatural image quality caused by ambient light, this solution deploys a multi-point photosensitive sensor array to collect ambient light data. Based on this dataset, an initial estimate of the light source's position is determined. Then, the device's attitude data is acquired from the sensor carrier. Combined with the initial estimate, the light field orientation information of the light source is determined. A three-dimensional light field model of the ambient light is constructed based on the ray direction and intensity distribution characteristics within this information, generating light field distribution information. Using pre-defined mapping rules, display parameter adjustment values ​​are calculated to match the ambient light with standard ambient light, generating a display parameter adjustment set. Finally, this set is converted into control command data, achieving dynamic adaptation of the screen display effect. This effectively adjusts the display effect according to ambient light, improving display quality.

[0051] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0052] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0053] Example 1

[0054] In this embodiment, a display screen color management method based on ambient light perception is provided.

[0055] Reference Figure 1The ambient light-sensing-based display screen color management method of this embodiment includes the following steps:

[0056] Step S100: Collect the original illumination dataset and determine the initial estimate of the light source position based on the original illumination dataset;

[0057] In this embodiment, by deploying a multi-point photosensitive sensor array, ambient light data is collected from multiple dimensions, and dynamic lighting changes are recorded by combining timestamps, thus achieving accurate analysis of complex lighting environments.

[0058] As an optional implementation, the raw illumination data may include data characterizing ambient light, such as light incidence angle, light incidence direction, and illumination intensity, as well as timestamp information. By integrating the feature values ​​and timestamp records of various sensor nodes, a raw illumination dataset containing multi-dimensional data is generated, providing a data foundation for subsequently generating complete spatial illumination distribution information.

[0059] For example, in a large indoor exhibition environment, multiple photosensitive sensor nodes are deployed to monitor light distribution. Assume the exhibition hall has multiple exhibition areas, with light sources including natural light, artificial lighting, and dynamic projection equipment, resulting in a complex and time-varying ambient light distribution. Each sensor node is deployed in different locations, such as corners, the central area, and near windows, collecting light intensity data from multiple angles and simultaneously recording timestamps to form a raw light dataset with timestamps. The sensor's built-in angle detection module can determine the direction of the light source and the angle of incidence, and by combining this with the light intensity data, the light source can be inferred. For instance, if the angle detection module of one node detects that the light mainly comes from a 45-degree angle above, it can be inferred from the light intensity that it might be direct sunlight from a ceiling light, while the light direction of another node is biased to the side, possibly due to the influence of natural light from a window. This multi-dimensional extraction method helps to construct a complete picture of dynamic light changes, especially when the exhibition hall lights switch or clouds move outside the windows, capturing subtle changes in light.

[0060] Optionally, after acquiring the raw illumination dataset, if the illumination intensity of a sensor node exceeds a preset threshold at a specific time point, cross-validation can be performed using illumination intensity data from adjacent nodes to obtain a more accurate illumination change trend and determine whether an abnormal illumination distribution phenomenon exists. For example, if 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, exceeding the preset threshold, the system automatically calls data from adjacent nodes for cross-validation. Assuming that the data from 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 sunlight from a projector, rather than a new light source. This phenomenon is marked as an abnormal illumination distribution phenomenon, and the outlier value is replaced with data from adjacent nodes. This cross-validation mechanism can distinguish between real ambient light changes and sensor malfunctions or local interference, effectively reducing the false positive rate and improving the reliability of monitoring.

[0061] Optionally, after generating the original dataset, the spatial light distribution information can be further analyzed. Suppose that the original dataset is processed and the discrete monitoring point data is transformed into a continuous spatial distribution image through intelligent algorithms, that is, the light intensity distribution in the exhibition hall is drawn into a heat map. It is found that the light intensity near the window area fluctuates more over time, while the central area is more 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 from strong light.

[0062] As another optional implementation, after acquiring the original illumination dataset, the incident angle data in the original illumination dataset is preprocessed, and the light direction detection results of each sensor are generated by the 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 by combining the triangulation algorithm.

[0063] For example, the incident angle data collected from multiple points undergoes preliminary processing. Since perfect alignment of the sensors during installation is difficult, leading to systematic offsets in the measured angles, a pre-established mapping table is needed to calibrate the sensor-collected data, obtaining a calibrated angle set. Then, combined with the three-dimensional coordinate data of each sensor node, a triangulation method is used to preliminarily calculate the light source's position, determining the initial coordinate set of the light source. For instance, if the coordinates of node A are collected at A(0,0,2) meters, the direction vector of the light source is measured. The corresponding elevation angle is 45° and the azimuth angle is 0°. The coordinates of node B are B(5,0,2) meters, and the measured direction vector of the light source is... The corresponding elevation angle is 45° and the azimuth angle is 180°. The preliminary position coordinates of the light source can be calculated using the triangulation method as (2.5, 0, 4.5) meters.

[0064] As another optional implementation, after establishing a preliminary set of position coordinates, outlier detection is performed on the preliminary set of position coordinates. If an abnormal coordinate is detected, data fusion is performed using sensor data from adjacent nodes to adjust the abnormal coordinates and generate the initial estimated value.

[0065] For example, the system detects whether there are anomalous coordinates in the initial location coordinate set. If so, it obtains the target sensor node associated with the anomalous coordinate, selects three spatially adjacent reference nodes of the target sensor node, and compares the incident angle data and illumination intensity data of the target sensor node with those of the reference nodes. Based on the comparison results, the anomalous coordinate is weighted and corrected. For example, the weight of each reference node can be calculated by combining three key factors: first, spatial distance weight (reference nodes closer to the anomalous coordinate have higher weights); second, optical similarity weight (nodes with similar illumination intensity and light direction have higher weights); and third, temporal stability weight (nodes with smaller data fluctuations have higher weights). A weighted average is then calculated based on the coordinates of the three reference nodes and the calculated weight values. Reference nodes that are close, have similar optical characteristics, and have stable data will have a greater impact on the final result. For example, an anomalous coordinate deviating by 2 meters may be adjusted to a range of only 0.2 meters from its true position after weighted correction by the surrounding three reference nodes. After weighted correction of the anomalous coordinate, the initial estimated value is obtained.

[0066] Step S200: Obtain the device attitude data of the sensor carrier, and determine the light field orientation information of the light source attitude by combining the initial estimate;

[0067] In this embodiment, sensor carriers are installed at different locations within the venue. The sensors record acceleration and angular velocity data in real time when the equipment tilts or rotates. This data initially reflects the motion state and directional changes of the equipment. Light field orientation information is 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.

[0068] As an optional implementation, after acquiring the device attitude data, the device attitude data and the initial estimate are transformed by a spatial transformation matrix to obtain the relative attitude data of the sensor carrier with respect to the light source.

[0069] For example, in an exhibition hall, inertial data containing acceleration and angular velocity is acquired from the inertial sensors built into the sensor carrier. This inertial data is the fundamental information for the device's motion state and attitude changes. This data is susceptible to various noise interferences, therefore, it needs to be filtered to remove noise interference. The denoised inertial data is the device's attitude data, reflecting its position and orientation in space, such as its tilt angle and rotation angle. The device attitude data and the initial estimate of the light source's position are then transformed into a unified coordinate system to obtain the relative attitude data of the sensor carrier with respect to the light source. For example, if the initial estimated position of the light source is a point within the exhibition hall, and the device attitude data shows that its orientation deviates from the light source direction by approximately 10 degrees, the relative attitude data of the device with respect to the light source can be calculated through matrix transformation.

[0070] Optionally, if the relative attitude data is detected to exceed a preset attitude threshold, sensor fusion processing is performed on the relative attitude data. The analysis can be performed from the perspective of multi-source data integration. If the attitude data deviation of the sensor carrier device in a certain dimension exceeds 5 degrees, the inertial sensor data is called and compared with other auxiliary information. For example, the weight of the attitude data is adjusted by combining the initial estimated distribution range of the light source, and finally the corrected relative attitude data is obtained, thereby improving the accuracy of the data.

[0071] As another optional implementation, after generating relative attitude data, corresponding weight values ​​are assigned to each sensor carrier according to a preset distance-weight mapping relationship, and the weight values ​​and the initial estimated values ​​are fused to generate the light field orientation information.

[0072] For example, a weighted average method can be used to integrate relative attitude data and initial estimates. Sensors are distributed at various locations within the exhibition hall, and different weights are assigned based on the distance between the sensor and the light source. Data that is closer may have a weight of 0.6, while data that is farther away may have a weight of 0.4. These weighted values ​​are then fused with the initial estimates. For direction vectors, a weighted summation and normalization method is used, while for position coordinates, a weighted arithmetic mean of the coordinates of each node is calculated. During the fusion process, the deviation of each sensor's data from the intermediate results is monitored in real time. If abnormal data is detected, such as when the deviation exceeds a threshold, its weight coefficient is automatically reduced, resulting in an adaptive filtering result. The final output light field orientation information retains the high accuracy of near-field data while maintaining spatial consistency through far-field data. Simultaneously, a confidence verification mechanism effectively suppresses interference from sensor noise and outliers. This fusion method, through dual optimization of distance weighting and dynamic adjustment, achieves a balance between positioning accuracy and system robustness in complex light field environments.

[0073] Step S300: Based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, construct a three-dimensional light field model of the ambient light and generate light field distribution information;

[0074] In this embodiment, the light field distribution information refers to the ambient light distribution in each area of ​​the exhibition environment, including the differences in the directionality and intensity distribution of the light.

[0075] As an alternative implementation, a directional distribution model of light rays can be established based on the directional characteristics of light rays through spatial vector analysis. Then, a three-dimensional light field intensity distribution model can be constructed using a light field reconstruction algorithm based on the intensity distribution characteristics. The directional distribution model and the intensity distribution model are then fused to generate light field distribution information that includes spatial orientation and intensity information.

[0076] For example, the ray direction characteristics include the incident angle and incident direction of the ray. The intensity distribution characteristics are the ray intensity at different locations and directions in the environment. By calculating the ray direction vector and performing clustering or interpolation, a ray direction distribution model is formed. The direction distribution model can be represented by a vector field or direction histogram to visually display the ray direction characteristics. 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 mesh or function to show the intensity changes of the light field at different spatial locations. Then, the direction information and intensity information are combined 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 simultaneously contains detailed information on both direction and intensity, outputting a three-dimensional visualization model.

[0077] Step S400: Calculate the 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 the preset mapping rules, and generate a display parameter adjustment set;

[0078] In this embodiment, the display parameter adjustment set includes a set of optimized parameters for brightness, contrast, and color temperature. The preset mapping rule is a light field feature-control parameter conversion rule, the core of which lies in transforming complex light field information into analyzable feature parameters. Assuming that the light field data in the exhibition hall contains illumination distribution information for multiple areas, the preset mapping rule can divide the data into high-brightness areas and low-brightness areas, extracting the illumination coverage and intensity change trend of each area, providing a data basis for generating display parameter adjustment values.

[0079] 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. The display parameter adjustment value includes at least color temperature adjustment data, brightness adjustment data, and contrast value. The correlation information between the brightness adjustment data and the contrast value is obtained, and the color temperature adjustment data and the correlation information are fused to generate the display parameter adjustment set.

[0080] For example, the core of the preset mapping rule lies in transforming complex light field information into analyzable feature parameters. Assuming the light field data within an exhibition hall contains illumination distribution information for multiple areas, the preset mapping rule can divide the data into high-brightness and low-brightness zones, extracting the illumination coverage and intensity variation trends for each area. If the standard ambient light distribution has different brightness requirements for different areas—the central area needs higher brightness to highlight exhibits, while the edge areas need softer light to create atmosphere—the light field distribution information is compared and analyzed with the standard ambient light distribution. Simultaneously, combined with the preset mapping rule, the characteristics of the environmental distribution are extracted in detail. The initial brightness adjustment requirements are then categorized to determine the preliminary adjustment range that meets the conditions. The initial adjustment range is then used to calibrate the matching degree between the contrast value and the environmental distribution. If the matching degree is lower than a preset matching threshold, a weighted calculation method is used for correction, resulting in the adjusted contrast adjustment data. Finally, the contrast adjustment data is combined with the business requirements for color temperature matching, and the data is processed layer by layer to determine whether the color temperature value is within the adaptive value range, obtaining color temperature adjustment data that conforms to the display parameters. By integrating the correlation information between brightness adjustment and contrast values ​​through color temperature adjustment data, the final parameter set is generated and the display parameter adjustment set is determined.

[0081] For example, a preset mapping rule divides the exhibition hall into high-brightness and low-brightness zones. Standard ambient light requires higher brightness in the central area and softer light in the edge areas. By comparing and analyzing the light field distribution information with the standard ambient light distribution and combining it with the preset mapping rule, the brightness adjustment range for the central area can be initially determined to be 70-90 units, and for the edge area, 30-50 units. The contrast value is then calibrated to match the environmental distribution using this initial adjustment range. If the matching degree is lower than the preset matching threshold, a weighted calculation method is used for correction. Assuming the contrast value in the central area is too low, a higher weight of 0.7 is assigned, and the edge area a weight of 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. Assuming different exhibition areas have different color temperature requirements—the central area needs a warmer color temperature, such as 3000K, and the edge area needs a cooler color temperature, such as 5000K—layer by layer analysis is performed. If the color temperature value is not within the suitable range, such as 2800K-5500K, fine-tuning is performed to obtain color temperature adjustment data that conforms to the display parameters. By comprehensively processing the initial brightness adjustment range, contrast adjustment data, and color temperature adjustment data, a display parameter adjustment set is generated. This process ensures that the brightness in the central area is 85 units, the contrast ratio is 75%, and the color temperature is 3200K, while the brightness in the edge area is 40 units, the contrast ratio is 60%, and the color temperature is 4800K. This integrated approach optimizes the overall harmony of the lighting environment.

[0082] Step S500: Convert the display parameter adjustment set into corresponding control instruction data to complete the dynamic adaptation processing of the screen display effect.

[0083] In this embodiment, the display parameter adjustment set is a set of numbers that needs to be converted into actual control command data and encapsulated into a formatted signal suitable for interface transmission. This signal is then sent to the display module through the device driver interface. The display module dynamically adjusts the screen display content based on the received signal stream and determines whether the adjusted display content matches the target, thus obtaining the final screen display output result.

[0084] As an optional implementation, a parameter mapping tool can be used to convert the display parameter adjustment set into corresponding control command data. This control command data is then compared to a preset data threshold range. If it exceeds the range, the data is calibrated to obtain a standard-compliant command dataset. The command dataset is encapsulated into a formatted signal suitable for interface transmission. This formatted signal is verified to determine if it meets the requirements of the transmission channel, obtaining a signal stream that can be directly transmitted. Transmission data is obtained from this signal stream, and using a standard interface transmission protocol, the signal stream is sent to the display module through the device driver interface. The transmission process is monitored in real time to ensure complete transmission. When the display module receives the signal stream, it dynamically adjusts the screen display content based on the signal stream content.

[0085] For example, the parameter mapping tool can be understood as a preset conversion rule library. It maps adjustment values, such as brightness of 80 units and contrast of 50 units, to control command signals that the device can recognize. The control command signals are compared and calibrated against preset threshold ranges. For instance, if the brightness command value for a certain area in an exhibition hall is 85 units, while the preset threshold upper limit is 80 units, exceeding the range, the excess command value is automatically calibrated to 80 units to ensure the command data conforms to the standard. The command data is packaged into signal packets suitable for interface transmission. The verification process checks whether the signal packets are complete and whether they meet the bandwidth requirements of the transmission channel, such as a data packet size of less than 2MB. If not, they are repackaged to ensure the transmissibility of the signal stream. During signal stream transmission, the start and end times are recorded, and data integrity is monitored. If a signal stream termination is detected during transmission, a retransmission mechanism is triggered to ensure the data is delivered completely to the display module. Finally, the screen brightness or hue is adjusted according to the signal stream content.

[0086] 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 by an image analysis algorithm; if the matching degree is greater than or equal to a preset matching degree threshold, the display parameter adjustment set can be converted into the control instruction data; the control instruction data can be sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0087] For example, the set of display parameter adjustments is transmitted to the corresponding simulation model of each display area to generate a predicted display effect. An image analysis algorithm is then used to evaluate the matching degree between the predicted display effect and the expected target. If the matching degree is 84%, which is higher than the preset 80% threshold, this set of display parameter adjustments is converted into control command data that the display driver module can recognize. The display is then dynamically adjusted according to the control command data. Here, the expected target refers to display data that ensures a clear image, natural colors, and harmony with ambient light.

[0088] In this embodiment, a multi-point photosensitive sensor array is deployed to collect illumination data in a complex environment. This data, combined with triangulation algorithms and inertial sensor data, is used to calculate the position and orientation of the light source, thereby constructing a three-dimensional light field model. Based on this model, display parameters matching the ambient light are calculated, and corresponding control commands are generated to dynamically adjust the screen display effect. Simultaneously, by comparing the actual output with the expected parameters, display control is optimized. This allows for precise perception of complex lighting environments and intelligent dynamic adjustment of the display effect, improving display quality.

[0089] Example 2

[0090] Based on Embodiment 1, another embodiment of this application is proposed, with reference to... Figure 2 After converting the display parameter adjustment set into corresponding control command data and completing the dynamic adaptation process of the screen display effect, the following steps are included:

[0091] Step S600: Continuously monitor the light field data stream of ambient light. If the light field data stream exceeds the preset light field data threshold range, trigger the real-time update mechanism and generate preliminary adjustment signal data.

[0092] In this embodiment, after completing the dynamic adaptation processing of the screen display effect, the ambient light light field data stream is continuously detected. When the light field data stream exceeds the preset light field data threshold range, real-time ambient light data is acquired 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.

[0093] As an optional implementation, ambient light data and sensor data are continuously monitored. If the change in the incident angle or direction of ambient light exceeds the preset light field data threshold range, the entire process from calculating the position of the light source to adjusting the display parameters is restarted, which triggers a real-time update mechanism.

[0094] 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 reference value by 28 degrees, exceeding the system's allowed fluctuation range of ±15 degrees. This anomaly triggers the real-time update mechanism.

[0095] As another optional implementation, after triggering the real-time update mechanism, relevant data on the direction of ambient light is acquired and processed to obtain a preliminary dynamic estimate of the light source's position. The relevant data on the direction of ambient light includes the incident light direction data detected by each sensor and the precise spatial coordinate information of the sensors. These data together constitute a set of direction vectors in three-dimensional space, providing a basis for locating the light source.

[0096] For example, these relevant data undergo quality screening 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 the location is verified by combining prior knowledge such as building layout. For instance, if the incident angle of light at the upper left corner of the display screen changes from 45 degrees to 70 degrees, real-time data from three surrounding sensors is retrieved. The first sensor shows light coming from a 30-degree direction at the upper right with an intensity of 850 lux; the second sensor detects incident light at a 65-degree direction at the upper left with an intensity of 1200 lux; and the third records light at a 15-degree direction 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 deemed unreliable due to potential obstruction or noise interference and is discarded. The remaining valid data is converted into direction vectors in three-dimensional space, and each vector is weighted according to the corresponding sensor signal-to-noise ratio. Knowing the precise installation coordinates of these sensors, the weighted direction vectors are input into a triangulation algorithm. Since light rays rarely converge perfectly at a single point in real-world environments, triangulation algorithms calculate the optimal approximation region for these vectors in three-dimensional space, resulting in a probability distribution range. For example, the calculation results show that the most likely convergence area of ​​these light rays is concentrated within an ellipsoidal space of approximately ±1.5 meters near the west window of the exhibition hall, shifting westward by about 3 meters compared to the previous light source position, thus obtaining a preliminary dynamic estimate of the light source's location.

[0097] 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, then the parameters of the three-dimensional light field model are corrected according to the preliminary adjustment signal data to generate second light field distribution information;

[0098] In this embodiment, when an anomaly in the light field data is detected, it is necessary to distinguish between continuous environmental changes and transient interference. The preset time threshold can be 5 seconds, which acts as a buffer filter to effectively avoid overreacting to brief interferences. For example, interference from natural light gradients and tourists' mobile phone flashes. The intensity peak caused by the flash usually lasts for less than 2 seconds, while real sunlight changes often last for more than 10 seconds.

[0099] As an alternative implementation, when ambient light anomalies are continuously detected, the three-dimensional light field model is iterated in conjunction with the initially generated adjustment signal. By analyzing the illumination change trajectory over the past 10 seconds, the trend for the next 5 seconds is predicted, and the light source radiation parameters and spatial attenuation coefficient in the model are dynamically corrected.

[0100] For example, when it is determined that the current light pollution mainly comes from natural light incident through side windows, the weight of artificial light sources in the three-dimensional light field model is reduced, and the calculation accuracy of diffuse reflection components is enhanced. The corrected second light field distribution information contains more accurate directional radiation patterns and intensity gradient data, providing targeted regional adjustment schemes for the display screen. For example, additional color temperature compensation and brightness enhancement are implemented in the upper left corner area exposed to strong direct light, while other areas remain fine-tuned, ensuring visual consistency while optimizing energy consumption. This dynamic model update mechanism can maintain high color accuracy even when facing continuously changing ambient light, while keeping the response latency within a reasonable range.

[0101] Step S800: Calibrate the display parameter adjustment value according to the second light field distribution information, update the display parameter adjustment set, and execute 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.

[0102] 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 commands, which are then sent to the display module. This ultimately achieves real-time dynamic adaptation of the screen display effect, such as automatically adjusting brightness or contrast to maintain the best visual experience.

[0103] As an alternative implementation, instead of directly generating new display parameter adjustment values, the original display parameter adjustment values ​​are calibrated using the second light field distribution information, which can maintain display consistency while reducing computational load.

[0104] For example, assuming the original indoor light source was a top LED light, the screen brightness was set to 300 nits and the color temperature adjusted to 6500 lux to match this lighting condition. Then, a new warm-colored light source with an intensity of 2000 lux was detected on the west side of the screen, while the intensity of the top LED light was relatively reduced to 800 lux. The calibration process was initiated, first analyzing that the incident angle of the new light source was approximately 30 degrees and had a clear directionality. Considering that this strong lateral light would create glare on the screen surface, the brightness of the western area of ​​the screen was increased to 400 nits to resist visual interference caused by direct light, while the overall color temperature was adjusted to 5800 Kelvin to balance the mixed effect of the cool light from the top and the warm light from the west. This calibration process is not a simple numerical replacement, but rather a multi-dimensional parameter optimization based on the spatial distribution, spectral characteristics, and incident angle of the new light field. The final output control commands will precisely fine-tune the brightness, contrast, and color temperature of different screen zones, ensuring that the display maintains a uniform visual effect and accurate color reproduction even in mixed lighting environments.

[0105] After updating the display parameter adjustment set, the display parameter adjustment set is converted into corresponding control command data to complete the dynamic adaptation of the screen display effect. This step is consistent with step S500 in Embodiment 1.

[0106] In this embodiment, the light field model can be updated in real time and the display parameter adjustment values ​​can be calibrated, maintaining the best display adaptation effect while significantly reducing system energy consumption. Precise compensation is only performed on local areas affected by changes in ambient light, avoiding unnecessary global brightness or color temperature adjustments, thus achieving sustainable energy-saving operation while ensuring visual quality.

[0107] Example 3

[0108] In this application embodiment, a display screen color management device based on ambient light perception is proposed.

[0109] Reference Figure 3 , Figure 3 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application.

[0110] 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 high-speed RAM or stable non-volatile memory, such as disk storage. Alternatively, the memory 1004 may be a storage device independent of the aforementioned processor 1001.

[0111] Those skilled in the art will understand that Figure 3 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0112] like Figure 3 As shown, the memory 1004, which serves 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.

[0113] exist Figure 3 In the hardware structure of the ambient light-sensing display color management device shown, the processor 1001 can call the ambient light-sensing display color management program stored in the memory 1004 and perform the following operations:

[0114] Collect the original illumination dataset and determine the initial estimate of the light source position based on the original illumination dataset;

[0115] Acquire the device attitude data of the sensor carrier, and determine the light field orientation information of the light source attitude by combining the initial estimate;

[0116] Based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, a three-dimensional light field model of ambient light is constructed, and light field distribution information is generated;

[0117] Based on the light field distribution information and preset mapping rules, calculate the display parameter adjustment values ​​that match the distribution of the current ambient light with that of the standard ambient light, and generate a display parameter adjustment set;

[0118] The set of display parameter adjustments is converted into corresponding control command data to complete the dynamic adaptation of the screen display effect.

[0119] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0120] The incident angle data in the original illumination dataset is preprocessed to generate light direction detection results;

[0121] Based on the light direction detection results, the preliminary position coordinate set of the light source in three-dimensional space is calculated using a triangulation algorithm;

[0122] Anomaly detection is performed on the preliminary location coordinate set. If anomaly coordinates are detected, data fusion is performed using sensor data from adjacent nodes to adjust the coordinates of the anomaly coordinates and generate the initial estimated value.

[0123] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0124] Detect whether there are any abnormal coordinates in the preliminary position coordinate set;

[0125] If it exists, then obtain the target sensor node associated with the abnormal coordinates;

[0126] Three reference nodes that are spatially adjacent to the target sensor node are selected, and the incident angle data and light intensity data of the target sensor node and the reference nodes are compared.

[0127] The abnormal coordinates are weighted and corrected based on the comparison results to obtain the initial estimated value.

[0128] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0129] The ambient light light field data stream is continuously monitored. If the light field data stream exceeds the preset light field data threshold range, a real-time update mechanism is triggered and preliminary adjustment signal data is generated.

[0130] If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, then the parameters of the three-dimensional light field model are corrected according to the preliminary adjustment signal data to generate second light field distribution information;

[0131] The display parameter adjustment value is calibrated based on 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 command data is executed to complete the dynamic adaptation processing of the screen display effect.

[0132] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0133] The device attitude data of the sensor carrier during the movement process is acquired, and the device attitude data and the initial estimated value are transformed by a spatial transformation matrix to obtain the relative attitude data of the sensor carrier with respect to the light source.

[0134] According to the preset distance-weight mapping relationship, each of the sensor carriers is assigned a corresponding weight value, and the weight value and the initial estimate are fused to generate the light field orientation information.

[0135] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0136] Based on the aforementioned ray direction characteristics, a ray direction distribution model is established through spatial vector analysis;

[0137] Based on the intensity distribution characteristics, a three-dimensional light field intensity distribution model is constructed using a light field reconstruction algorithm;

[0138] The directional distribution model and the intensity distribution model are fused together to generate the light field distribution information that includes spatial orientation and intensity information.

[0139] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0140] 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. The display parameter adjustment value includes at least color temperature adjustment data, brightness adjustment data and contrast value.

[0141] Obtain the correlation information between the brightness adjustment data and the contrast value, and fuse the color temperature adjustment data with the correlation information to generate the display parameter adjustment set.

[0142] Optionally, the processor 1001 may call the ambient light-sensing display color management program stored in the memory 1004, and further perform the following operations:

[0143] Based on the set of display parameters, a predicted display effect is generated;

[0144] The matching degree between the predicted display effect and the expected target is evaluated using image analysis algorithms;

[0145] If the matching degree is greater than or equal to the preset matching degree threshold, the display parameter adjustment set is converted into the control instruction data;

[0146] The control command data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

[0147] In addition, to achieve the above objectives, embodiments of the present invention also provide a terminal device, including a memory, a processor, and an ambient light-sensing display color management program stored in the memory and executable on the processor. When the processor executes the ambient light-sensing display color management program, it implements the ambient light-sensing display color management method as described above.

[0148] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing an ambient light-sensing display color management program. When the ambient light-sensing display color management program is executed by a processor, it implements the ambient light-sensing display color management method described above.

[0149] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, third, etc., does not indicate any order. These words can be interpreted as names.

[0154] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0155] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A display screen color management method based on ambient light perception, characterized in that, The display screen color management method based on ambient light perception includes: Collect the original illumination dataset and determine the initial estimate of the light source position based on the original illumination dataset; Acquire the device attitude data of the sensor carrier, and determine the light field orientation information of the light source attitude by combining the initial estimate; Based on the light direction characteristics and intensity distribution characteristics in the light field orientation information, a three-dimensional light field model of ambient light is constructed, and light field distribution information is generated; Based on the light field distribution information and preset mapping rules, calculate the display parameter adjustment values ​​that match the distribution of the current ambient light with that of the standard ambient light, and generate a display parameter adjustment set; The display parameter adjustment set is converted into corresponding control command data to complete the dynamic adaptation of the screen display effect; The step of acquiring the original illumination dataset and determining the initial estimate of the light source position based on the original illumination dataset includes: The incident angle data in the original illumination dataset is preprocessed to generate light direction detection results; Based on the light direction detection results, the preliminary position coordinate set of the light source in three-dimensional space is calculated using a triangulation algorithm; Anomaly detection is performed on the preliminary location coordinate set. If anomaly coordinates are detected, data fusion is performed using sensor data from adjacent nodes to adjust the coordinates of the anomaly coordinates and generate the initial estimated value.

2. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, The step of performing outlier detection on the preliminary location coordinate set, and if outlier coordinates are detected, then performing data fusion using sensor data from adjacent nodes to adjust the outlier coordinates and generate the initial estimated value includes: Detect whether there are any abnormal coordinates in the preliminary position coordinate set; If it exists, then obtain the target sensor node associated with the abnormal coordinates; Three reference nodes that are spatially adjacent to the target sensor node are selected, and the incident angle data and light intensity data of the target sensor node and the reference nodes are compared. The abnormal coordinates are weighted and corrected based on the comparison results to obtain the initial estimated value.

3. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, After the step of converting the display parameter adjustment set into corresponding control command data to complete the dynamic adaptation processing of the screen display effect, the following steps are included: The ambient light light field data stream is continuously monitored. If the light field data stream exceeds the preset light field data threshold range, a real-time update mechanism is triggered and preliminary adjustment signal data is generated. If the duration of the light field data stream exceeding the preset light field data threshold range is greater than a preset time, then the parameters of the three-dimensional light field model are corrected according to the preliminary adjustment signal data to generate second light field distribution information; The display parameter adjustment value is calibrated based on 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 command data is executed to complete the dynamic adaptation processing of the screen display effect.

4. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, The step of acquiring the device attitude data of the sensor carrier and determining the light field orientation information of the light source attitude in combination with the initial estimate includes: The device attitude data of the sensor carrier during the movement process is acquired, and the device attitude data and the initial estimated value are transformed by a spatial transformation matrix to obtain the relative attitude data of the sensor carrier with respect to the light source. According to the preset distance-weight mapping relationship, each of the sensor carriers is assigned a corresponding weight value, and the weight value and the initial estimate are fused to generate the light field orientation information.

5. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, The step of constructing a three-dimensional light field model of ambient light and generating light field distribution information based on the ray direction characteristics and intensity distribution characteristics in the light field orientation information includes: Based on the aforementioned ray direction characteristics, a ray direction distribution model is established through spatial vector analysis; Based on the intensity distribution characteristics, a three-dimensional light field intensity distribution model is constructed using a light field reconstruction algorithm; The directional distribution model and the intensity distribution model are fused together to generate the light field distribution information that includes spatial orientation and intensity information.

6. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, The step of calculating display parameter adjustment values ​​that match the distribution of the current ambient light with that of the standard ambient light based on the light field distribution information and preset mapping rules, and generating a display parameter adjustment set, includes: 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. The display parameter adjustment value includes at least color temperature adjustment data, brightness adjustment data and contrast value. Obtain the correlation information between the brightness adjustment data and the contrast value, and fuse the color temperature adjustment data with the correlation information to generate the display parameter adjustment set.

7. The display screen color management method based on ambient light perception as described in claim 1, characterized in that, The step of converting the display parameter adjustment set into corresponding control command data to complete the dynamic adaptation of the screen display effect includes: Based on the set of display parameters, a predicted display effect is generated; The matching degree between the predicted display effect and the expected target is evaluated using image analysis algorithms; If the matching degree is greater than or equal to the preset matching degree threshold, the display parameter adjustment set is converted into the control instruction data; The control command data is sent to the display driver module to complete the dynamic adaptation processing of the screen display effect.

8. A terminal device, characterized in that, The device includes a memory, a processor, and an ambient light-sensing display color management program stored in the memory and executable on the processor. When the processor executes the ambient light-sensing display color management program, it implements the method described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an ambient light-sensing display color management program, which, when executed by a processor, implements the method described in any one of claims 1-7.

Images