Intelligent brightness adjustment system and method for LED displays incorporating environmental perception

Abstract

This invention discloses an intelligent brightness adjustment system and method for LED displays that incorporates environmental perception, belonging to the field of LED adjustment technology. The system includes: a brightness analysis unit for generating a dynamic brightness adjustment parameter set; a brightness adjustment unit for generating initial brightness adjustment results; and a brightness compensation unit for intelligently adjusting the brightness of the LED display. This application solves the technical problems of traditional LED display brightness adjustment methods being singular and unable to dynamically adapt to the display characteristics of small-pitch LEDs according to ambient light, resulting in poor display effects and excessive energy consumption. It achieves adaptive brightness optimization of small-pitch LED displays through environmental perception and dynamic compensation strategies, significantly improving display consistency, reducing energy consumption, and extending device lifespan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of LED regulation, in particular to an LED display screen brightness intelligent regulation system and method combined with environment sensing. BACKGROUND

[0002] Under the background of rapid development of LED display screen technology, small-pitch light-emitting diodes have been widely used in indoor command and dispatch, virtual broadcasting, stage performance and commercial display scenes due to their high pixel density and self-luminous characteristics. However, the brightness regulation of small-pitch LED display screens in complex environments still faces significant challenges: on the one hand, high-density pixel arrangement leads to prominent local brightness unevenness, especially in dynamic light changes (such as direct sunlight, night light interference) or densely populated areas, and traditional static brightness control strategies are difficult to achieve precise adaptation; on the other hand, the light decay effect and color temperature drift of LED devices under long-time operation will further exacerbate the decline in display quality, affecting visual consistency and increasing energy consumption. Existing technologies rely on fixed threshold or manual intervention adjustment methods, lack real-time sensing and dynamic feedback capabilities for environmental data, and cannot effectively respond to sudden weather changes (such as heavy rain, smog) or dynamic distribution of human flow interference on display effects. In addition, the high integration characteristics of small-pitch light-emitting diodes require more precise control of the driving circuit, and traditional methods are difficult to balance brightness uniformity, energy efficiency optimization, and device life extension. Therefore, an intelligent brightness regulation system is urgently needed to integrate environment sensing, dynamic compensation and device aging analysis to achieve adaptive optimization of small-pitch LED display screens, thereby improving display stability and user experience in complex application scenarios. SUMMARY

[0003] The present application provides an LED display screen brightness intelligent regulation system and method combined with environment sensing, aiming to solve the technical problems of single brightness regulation method of traditional LED display screens, inability to dynamically adapt small-pitch light-emitting diode display characteristics according to environmental light, resulting in poor display effect and high energy consumption, and achieve adaptive brightness optimization of small-pitch light-emitting diode display screens through environment sensing and dynamic compensation strategies, significantly improve display consistency, reduce energy consumption and prolong device life.

[0004] In view of the above problems, the present application provides an LED display screen brightness intelligent regulation system and method combined with environment sensing.

[0005] The first aspect disclosed in this application provides an intelligent brightness adjustment system for an LED display screen that incorporates environmental perception. This system includes: a brightness analysis unit, configured to perform real-time perception of a target area of ​​the LED display screen via an environmental perception module to obtain an environmental perception dataset, perform multi-dimensional brightness analysis on the environmental perception dataset, and generate a dynamic brightness adjustment parameter set; a brightness adjustment unit, configured to retrieve a set of device parameters for the LED display screen, correct the dynamic brightness adjustment parameter set, generate a brightness control instruction set, execute the brightness control instruction set to adjust the brightness of the LED display screen, and generate an initial brightness adjustment result; and a brightness compensation unit, configured to continuously verify the initial brightness adjustment result based on the initial brightness adjustment result and the environmental perception dataset, compensate the initial brightness adjustment result based on verification feedback data, determine a brightness adjustment strategy, and intelligently adjust the brightness of the LED display screen using the brightness adjustment strategy.

[0006] Another aspect of this application discloses a method for intelligent brightness adjustment of an LED display screen incorporating environmental perception. This method includes: real-time sensing of a target area of ​​the LED display screen using an environmental perception module to obtain an environmental perception dataset; performing multi-dimensional brightness analysis on the environmental perception dataset to generate a dynamic brightness adjustment parameter set; retrieving a set of device parameters from the LED display screen, correcting the dynamic brightness adjustment parameter set, generating a brightness control instruction set, executing the brightness control instruction set to adjust the brightness of the LED display screen, and generating an initial brightness adjustment result; continuously verifying the initial brightness adjustment result in conjunction with the environmental perception dataset, compensating the initial brightness adjustment result based on verification feedback data, determining a brightness adjustment strategy, and intelligently adjusting the brightness of the LED display screen using the brightness adjustment strategy.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] In the brightness analysis unit, the target area of ​​the LED display screen is sensed in real time by the environmental perception module to obtain an environmental perception dataset. Multi-dimensional brightness analysis is performed on the environmental perception dataset to generate a dynamic brightness adjustment parameter set. In the brightness adjustment unit, the device parameter set of the LED display screen is retrieved, the dynamic brightness adjustment parameter set is corrected, a brightness control command set is generated, and the brightness of the LED display screen is adjusted by executing the brightness control command set, generating an initial brightness adjustment result. In the brightness compensation unit, the initial brightness adjustment result is continuously verified in conjunction with the environmental perception dataset. The initial brightness adjustment result is compensated based on the verification feedback data to determine a brightness adjustment strategy. The brightness of the LED display screen is intelligently adjusted according to this strategy. This solves the technical problems of traditional LED display screens having a single brightness adjustment method and being unable to dynamically adapt to the display characteristics of small-pitch LEDs according to ambient light, resulting in poor display effects and excessive energy consumption. It achieves adaptive brightness optimization of small-pitch LED displays through environmental perception and dynamic compensation strategies, significantly improving display consistency, reducing energy consumption, and extending device lifespan.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0010] Figure 1 This application provides a schematic diagram of a structure for an intelligent brightness adjustment system for an LED display screen that incorporates environmental perception.

[0011] Figure 2 This application provides a flowchart illustrating a method for intelligent brightness adjustment of an LED display screen that incorporates environmental perception.

[0012] Explanation of reference numerals in the attached figures: Brightness resolution unit 11, brightness adjustment unit 12, brightness compensation unit 13. Detailed Implementation

[0013] This application provides an intelligent brightness adjustment system and method for LED displays that incorporates environmental perception. This solves the technical problems of traditional LED displays having a single brightness adjustment method and being unable to dynamically adapt to the display characteristics of small-pitch LEDs according to ambient light, resulting in poor display effects and excessive energy consumption. It achieves the technical effect of adaptive brightness optimization of small-pitch LED displays through environmental perception and dynamic compensation strategies, significantly improving display consistency, reducing energy consumption, and extending device life.

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0015] Example 1, as Figure 1 As shown in the embodiment of this application, an intelligent brightness adjustment system for an LED display screen incorporating environmental perception is provided. This system includes:

[0016] The brightness analysis unit is used to perceive the target area of ​​the LED display screen in real time through the environmental perception module, obtain the environmental perception dataset, perform multi-dimensional brightness analysis on the environmental perception dataset, and generate a set of dynamic brightness adjustment parameters.

[0017] Specifically, in the brightness analysis unit, the environmental sensing module performs real-time sensing of the target area of ​​the LED display screen, collecting data such as light intensity, pedestrian density, and meteorological conditions. This data collectively constitutes the environmental sensing dataset. The LED display screen can be composed of Mini LEDs, whose high-precision pixels and delicate brightness adjustment capabilities enable it to accurately respond to environmental changes. Subsequently, multi-dimensional brightness analysis is performed on the obtained environmental sensing dataset. Specifically, light intensity data is combined with pedestrian density data and meteorological data. Through environmental change response analysis, the required brightness adjustment parameters for the LED display screen under the current environment are determined. These brightness adjustment parameters reflect the impact of changes in ambient light, pedestrian flow, and meteorological factors on display brightness, thus deriving a set of dynamic brightness adjustment parameters. This set of dynamic brightness adjustment parameters is used in subsequent brightness control processes to ensure that the LED display screen's brightness adapts to the current environmental conditions, providing the best display effect. In this way, the LED display screen can respond to environmental changes in real time, optimize its display effect, avoid affecting the viewing experience due to insufficient or excessive light, and leverage the advantages of Mini LEDs in precise control and brightness adjustment.

[0018] Furthermore, the brightness resolution unit includes:

[0019] The environmental sensing module senses the light intensity of the target area of ​​the LED display screen to obtain spectral distribution data. Based on this spectral distribution data, multi-channel filtering is performed to generate effective light intensity data. The environmental sensing module also captures pedestrian movement trajectories within the target area of ​​the LED display screen, calculating pedestrian trajectory density data. A meteorological monitoring terminal is connected to the environmental sensing module to construct meteorological environmental data. The effective light intensity data, pedestrian trajectory density data, and meteorological environmental data are aligned according to spatiotemporal characteristics to obtain an environmental sensing feature vector group. This environmental sensing feature vector group is then added to the environmental sensing dataset.

[0020] In a preferred embodiment, an environmental sensing module uses an integrated light sensor to sense the illumination of the target area of ​​the LED display screen, measuring the illumination intensity in real time to obtain spectral distribution data. This spectral distribution data contains light intensity information within different wavelength ranges, reflecting the distribution characteristics of illumination within the area. Subsequently, based on the acquired spectral distribution data, multi-channel filtering is performed. The filtering channels are typically set according to predetermined wavelength ranges (such as short wavelength, medium wavelength, and long wavelength), each corresponding to different illumination information. Each filtering channel focuses on a specific spectral range, thereby extracting different components of the illumination. For each channel, a filtering algorithm is used to process the spectral distribution data. Common filtering methods include low-pass filtering (removing high-frequency noise and retaining low-frequency components, suitable for removing unnecessary high-frequency signals), band-pass filtering (allowing only light of specific wavelengths to pass through), and high-pass filtering (removing low-frequency components and retaining high-frequency information, suitable for emphasizing rapidly changing illumination signals). Each channel uses an appropriate filtering method to extract and enhance the illumination data for the target wavelength band. After multi-channel filtering, the filtering results of each channel are merged. The light intensity data extracted from each channel is used to reflect the lighting conditions of that wavelength band. The merged data is the effective light intensity data, representing the true light intensity of the area under specific lighting conditions, after noise or irrelevant light signals have been eliminated. Next, a high-definition camera integrated into the environmental perception module captures pedestrian flow information within the target area of ​​the LED display screen. Pedestrians in the image or video stream are detected using computer vision algorithms such as YOLO and OpenCV to obtain their location and movement information. The target area is then divided into multiple small blocks using a preset grid. By dividing the number of pedestrians in each block by the area of ​​that block, the pedestrian trajectory density of each block is obtained. This pedestrian trajectory density data helps the system understand the distribution of people in the area, further influencing the brightness adjustment strategy. In addition, the environmental perception module connects to a meteorological monitoring terminal to receive external meteorological data (such as temperature, humidity, and wind speed) to construct meteorological environmental data. This data helps identify the potential impact of meteorological changes (such as strong winds or humidity changes) on the display screen brightness. Then, the collected effective light intensity data, pedestrian trajectory density data, and meteorological environmental data are aligned according to their temporal and spatial characteristics. This process synchronizes the timestamps and geographic location information of various data types, ensuring that all data can be compared and analyzed at the same time and within the same region, forming consistent spatiotemporal data. After data alignment, these processed effective light intensity data, pedestrian trajectory density data, and meteorological environmental data are combined into an environmental perception feature vector group. This vector group includes all relevant data obtained by the environmental perception module and provides detailed input information for subsequent brightness adjustment calculations.Finally, the obtained environmental perception feature vector set is added to the environmental perception dataset to support the subsequent dynamic brightness adjustment process. In summary, this process, through real-time perception and processing of multi-dimensional data, can more comprehensively reflect current environmental changes and provide LED displays with more accurate and intelligent brightness adjustment criteria, thereby achieving automated brightness optimization and improving the visibility and comfort of the display effect.

[0021] Furthermore, the brightness resolution unit includes:

[0022] Based on the effective light intensity data, brightness constraints are set, and environmental brightness correlation analysis is performed on the pedestrian trajectory density data and the meteorological environment data according to the brightness constraints to construct an environment-brightness mapping network. Brightness prediction is performed based on the environmental perception dataset to obtain an initial brightness value matrix. Environmental change response is performed on the initial brightness value matrix according to the environment-brightness mapping network to generate brightness switching data. Distributed brightness control of the LED display screen is performed through the brightness switching data to generate the dynamic brightness adjustment parameter set.

[0023] In one feasible implementation, firstly, based on effective light intensity data, a set of brightness constraints are set. These constraints are based on the ambient light intensity, typically determined by real-time collected light intensity data. For example, in strong sunlight during the day, a higher upper limit for brightness is set; while at night or in environments with weak light, the upper limit is lowered to avoid energy waste and visual fatigue. Subsequently, pedestrian flow density data, meteorological environment data, and effective light intensity data are combined. By selecting historical pedestrian flow density data, historical meteorological environment data, historical light intensity, and historical brightness data that meet the constraints, a bidirectional neural network is trained. The bidirectional neural network can be a Bi-LSTM (Bidirectional Long Short-Term Memory network), and training is performed iteratively through steps such as forward propagation, loss calculation (e.g., mean squared error), backpropagation, and parameter optimization (e.g., Adam). After training, an environment-luminance mapping network is obtained to represent the relationship between environmental data and luminance. For example, when the pedestrian density is high, it means the audience of the display screen is increasing, and the luminance needs to be increased to ensure clarity. Under high temperature and strong sunlight, the LED display screen may face stronger ambient light effects, and the luminance needs to be increased to ensure display clarity. Then, based on the environmental perception dataset, the established environment-luminance mapping network is used to predict the luminance value that should be set in the current environment, obtaining an initial luminance value matrix. This matrix contains the preliminary luminance adjustment results for different areas. On this basis, environmental change response is performed on the areas involved in the initial luminance value matrix. When the rate of change of illumination between two adjacent areas exceeds 50 Lux / s (instantaneous strong light), a luminance gradient transition algorithm, such as an S-curve smoothing algorithm, is activated to smoothly transition the luminance change and avoid affecting the viewing experience due to excessively drastic luminance changes. On the other hand, if the current environment is rainy, an anti-glare strategy is activated by increasing the proportion of the blue channel to 35% and reducing the overall luminance fluctuation amplitude (ensuring the standard deviation is ≤15 cd / ㎡) to reduce the impact of reflection and glare in rainy environments, ensuring clear and stable display effects. After processing the environmental change response, a set of brightness switching data will be obtained based on the generated environmental change response data. This data indicates how the LED display should adjust its brightness under various environmental changes (such as strong light, rain, etc.). Through this switching data, the brightness of the display can be adjusted in a timely manner under rapidly changing environmental conditions.Finally, based on the brightness switching data, distributed brightness control is implemented for the LED display screen. This means that by controlling the brightness of different areas, the display effect of each area is optimized. Each area adjusts its brightness individually according to actual environmental changes and needs, thereby obtaining a brightness adjustment strategy that meets the needs of the entire screen. This strategy includes a dynamic brightness adjustment parameter set, which specifically includes maximum brightness setting, minimum brightness setting, color temperature difference, LED drive current, LED drive voltage, brightness adjustment coefficient, etc. This set provides complete parameter support for subsequent brightness adjustment of the LED display screen under different environmental changes, ensuring that the best display effect is always maintained under various dynamic environmental conditions, thus improving the user experience.

[0024] The brightness adjustment unit is used to retrieve the set of device parameters of the LED display screen, correct the set of dynamic brightness adjustment parameters, generate a set of brightness control instructions, execute the set of brightness control instructions to adjust the brightness of the LED display screen, and generate an initial brightness adjustment result.

[0025] Specifically, in the brightness adjustment unit, the first step is to retrieve a set of device parameters from the LED display screen. This includes information such as the LED display screen's hardware characteristics, current operating status, and historical brightness data. By retrieving these parameters, a comprehensive understanding of the LED display screen's current performance can be obtained, providing foundational data for subsequent brightness adjustment. After obtaining the device parameter set, the previously generated dynamic brightness adjustment parameter set is corrected. This correction process considers the current display device performance and historical brightness data to ensure that the brightness adjustment accurately adapts to the actual situation. Subsequently, based on the corrected adjustment parameters, a set of brightness control instructions is generated. These instructions specify how to precisely adjust the brightness values ​​of various areas of the display screen to achieve overall screen brightness optimization. Finally, these control instructions are executed to adjust the brightness of the LED display screen, generating an initial brightness adjustment result. This result represents the optimal brightness configuration required by the display screen under the current environmental conditions, providing a basis for subsequent continuous adjustments and compensation.

[0026] Furthermore, the brightness adjustment unit includes:

[0027] Based on the set of device parameters of the LED display screen, device aging analysis is performed to obtain device aging parameters, which include a historical brightness dataset. Light decay analysis is then performed by traversing the historical brightness dataset to generate light decay compensation data. The dynamic brightness adjustment parameter set is corrected based on the light decay compensation data to determine the brightness light decay correction result. Color temperature correction is performed based on the brightness light decay correction result to obtain the full-screen color temperature difference value of the LED display screen. A color temperature-brightness lookup table is constructed based on the brightness light decay correction result and the full-screen color temperature difference value. The LED display screen is then divided into regions according to the color temperature-brightness lookup table to generate multiple pixel blocks. Each pixel block contains multiple control instruction packets, and these control instruction packets correspond to the pixel blocks. The multiple control instruction packets are then encapsulated to generate the brightness control instruction set.

[0028] In one feasible implementation, an aging analysis is first performed based on the device parameter set of the LED display screen. This step aims to assess the aging phenomena that occur during the use of the LED display screen. The aging analysis obtains a set of device aging parameters by detecting factors such as the display screen's historical operating conditions, usage time, and ambient temperature. These parameters include a historical brightness dataset, which records the brightness performance of the display screen at different time points, helping the system determine the performance degradation of the display screen. Subsequently, the historical brightness dataset is iterated through. For the iterated data, linear regression is used to fit the trend of brightness changes over time. In this fitting process, the least squares method is used to calculate a specific regression equation. Then, the brightness after a certain usage time is subtracted from the factory brightness of the LED display screen, and the difference is divided by the factory brightness to obtain the light decay ratio. The brightness after a certain usage time is calculated by inputting the current time into the regression equation. Finally, 1 is divided by the difference between 1 and the light decay ratio to obtain light decay compensation data. This light decay compensation data is used to correct the brightness decrease caused by aging, ensuring that subsequent brightness adjustments still achieve the expected results. Based on the generated light decay compensation data, the previously obtained dynamic brightness adjustment parameter set is corrected. During this correction process, the light decay compensation data is directly multiplied by each parameter in the dynamic brightness adjustment parameter set to obtain the brightness light decay correction result. This result reflects the corrected brightness adjustment parameters, enabling the LED display to maintain efficient display performance even under decay conditions. Then, based on the brightness light decay correction result, color temperature correction is performed through steps such as pixel scanning, gain compensation, and color temperature gradient correction to ensure that the display's color temperature remains consistent during adjustment, avoiding color temperature changes caused by aging. After color temperature correction, the full-screen color temperature difference is calculated, ensuring color temperature balance across different areas. After obtaining the full-screen color temperature difference, a color temperature-brightness lookup table is constructed based on the brightness light decay correction result and the full-screen color temperature difference. This lookup table records the optimal brightness parameters at different color temperatures, used to dynamically adjust the brightness of each area to ensure a balance between color temperature and brightness. Furthermore, the LED display is segmented into regions based on the color temperature-brightness lookup table. Specifically, pixels with the same color temperature are merged to obtain multiple pixel blocks. The color temperature of each pixel block is then compared with a color temperature-brightness lookup table to obtain the corresponding brightness attenuation correction result. This matched result serves as the control instruction package for that pixel block, with each package containing specific brightness and color temperature adjustment parameters. Finally, all control instruction packages are encapsulated to generate a complete brightness control instruction set. This set includes the adjustment parameters for each pixel block, ensuring precise brightness adjustment and color temperature control for the entire LED display under various environmental conditions and equipment aging states.In summary, this process can accurately compensate for the brightness decay caused by equipment aging and intelligently adjust the brightness according to the real-time environment and equipment status, ensuring that the LED display screen always maintains the best display effect under different usage conditions.

[0029] Furthermore, when the brightness adjustment unit performs color temperature correction, it includes:

[0030] The LED display screen is pixel-scanned to obtain the initial brightness of the RGB three-color LEDs; based on the brightness decay correction result, the initial brightness of the RGB three-color LEDs is compensated for gain to generate RGB gain coefficients; target color temperature data is set, and the target color temperature data is mapped to the color space according to the RGB gain coefficients to construct a dynamic mixing matrix; based on the dynamic mixing matrix, a color temperature correction command is initiated to perform color temperature gradient correction to obtain the full-screen color temperature difference of the LED display screen.

[0031] In one feasible implementation, the LED display screen is first scanned pixel by pixel. This process involves scanning each pixel of the display screen one by one to obtain the initial brightness data of the RGB three-color LEDs at each pixel. The initial brightness of the RGB three-color LEDs refers to the brightness values ​​of the red, green, and blue light sources in each pixel. These brightness values ​​are the initial output values ​​of the LED display screen under normal operating conditions. Through this process, the initial brightness information of the entire screen can be obtained, providing basic data for subsequent light decay compensation and color temperature correction. After obtaining the initial brightness of the RGB three-color LEDs, a compensation gain is applied to the initial brightness of the RGB three-color LEDs based on the previous brightness and light decay correction results. Light decay compensation is to compensate for the brightness attenuation caused by equipment aging. By dividing the brightness value corresponding to the brightness and light decay correction results by the initial brightness of the RGB three-color LEDs, an RGB gain coefficient is generated. This RGB gain coefficient can adjust the initial brightness of the RGB three-color LEDs to ensure color accuracy and brightness consistency. Subsequently, target color temperature data is set, and chromaticity space mapping is performed on the target color temperature data using RGB gain coefficients. The purpose of chromaticity space mapping is to convert the target color temperature data into the color gamut achievable by the LED display and ensure that the mapped color temperature matches the color gamut of the display. The mapping process is performed by multiplying the RGB gain coefficients by the target color temperature data. Based on this mapping, a dynamic blending matrix is ​​further constructed. This matrix contains the actual color temperature corresponding to each pixel and can compensate for color temperature display anomalies caused by aging. Then, based on the constructed dynamic blending matrix, a color temperature correction command is initiated. According to this command, color temperature gradient correction is performed, that is, the current color temperature of the LED display is smoothly adjusted to the actual color temperature corresponding to the dynamic blending matrix, so that the color temperature change of different areas on the screen is consistent and avoids color temperature unevenness. Through this process, the color temperature of the LED display can be optimized, ensuring that its color performance is more natural and uniform in different display scenarios. Finally, the standard deviation is used to calculate the full-screen color temperature difference, which reflects the color temperature difference between different areas of the entire LED display after color temperature correction. The system will further fine-tune based on this color temperature difference to ensure the color temperature of the display screen is balanced across the entire screen, thereby providing a more consistent and high-quality display effect. Through this series of steps, the brightness and color temperature of the LED display screen can be precisely adjusted according to light decay correction, color temperature correction, and chromaticity mapping to ensure that the display effect is always at its best.

[0032] Furthermore, the brightness adjustment unit includes:

[0033] Based on the environmental perception dataset, a distribution analysis is performed on the multiple pixel blocks to generate spatial distribution features; a block priority weight matrix is ​​constructed for the multiple pixel blocks according to the spatial distribution features; the multiple control instruction packets are sorted according to the block priority weight matrix to generate an instruction execution sequence; the multiple pixel blocks are independently controlled according to the instruction execution sequence to obtain multiple area brightness feedback data; and the multiple area brightness feedback data are added to the initial brightness adjustment result.

[0034] In one feasible implementation, based on the obtained environmental perception dataset, the distribution of multiple pixel blocks on the LED display screen is first analyzed. Effective illuminance data, pedestrian flow density data, and meteorological data for each pixel block are obtained from the environmental perception dataset, and these environmental perception data are stored separately to obtain the spatial distribution characteristics of each block. Subsequently, the spatial distribution characteristics of each pixel block are processed using a maximum-minimum normalization method to ensure each feature is on the same scale. Then, the normalized features of each pixel block are weighted to obtain corresponding priority weights. By summing these priority weights, a block priority weight matrix is ​​constructed. This priority weight matrix contains the brightness adjustment priority of each pixel block on the entire display screen. Typically, pixel blocks in areas with weaker illumination or where the user's gaze is concentrated may have higher priority and therefore need to be considered first during adjustment. Afterwards, based on the block priority weight matrix, all control command packets are sorted to generate an instruction execution sequence. This instruction execution sequence ensures that high-priority areas receive brightness adjustment first and effectively avoids insufficient brightness or adjustment delays in high-demand areas. Then, based on the generated instruction execution sequence, multiple pixel blocks are independently controlled. Each pixel block adjusts its brightness according to its specific control instruction package to meet its environmental perception requirements. During this process, each block of the display adjusts its brightness according to the instructions. Simultaneously, the system collects regional brightness feedback data from each block. This data reflects the brightness change of each block after the actual brightness adjustment, ensuring that the brightness of each block meets the expected target. Finally, the regional brightness feedback data collected from each block is integrated into the initial brightness adjustment result. Through this process, the system updates the display's brightness adjustment status and optimizes the initial brightness adjustment result based on the feedback data. This update ensures more precise brightness adjustment in different areas of the display, thereby improving the overall display quality.

[0035] The brightness compensation unit is used to continuously verify the initial brightness adjustment result in combination with the environmental perception dataset, compensate the initial brightness adjustment result according to the verification feedback data, determine the brightness adjustment strategy, and intelligently adjust the brightness of the LED display screen through the brightness adjustment strategy.

[0036] The brightness compensation unit is used to continuously verify the initial brightness adjustment result in combination with the environmental perception dataset, compensate the initial brightness adjustment result according to the verification feedback data, determine the brightness adjustment strategy, and intelligently adjust the brightness of the LED display screen through the brightness adjustment strategy.

[0037] Specifically, in the brightness compensation unit, after the initial brightness adjustment is completed, continuous verification is performed based on the initial brightness adjustment result and an environmental perception dataset. This verification process compares the actual display effect with the expected brightness, detecting the impact of environmental changes on the brightness adjustment effect in real time. For example, when the light intensity or pedestrian density changes, it checks whether the initial brightness adjustment still meets the display requirements. After verification, verification feedback data is collected and analyzed. This data reflects possible brightness deviations or mismatches that may occur during the actual adjustment process. Based on this feedback data, corresponding compensation is performed on the initial brightness adjustment result. This compensation process may include adjusting brightness, color temperature, or other display parameters to correct display inaccuracies caused by environmental changes or equipment performance fluctuations. Through this series of compensations, a brightness adjustment strategy is determined. This strategy includes how to dynamically adjust the brightness of the display screen under different environmental conditions to ensure that the display effect remains optimal at all times. Finally, the brightness of the LED display screen is intelligently adjusted according to this brightness adjustment strategy. By continuously optimizing the adjustment process, real-time brightness optimization based on environmental changes is ultimately achieved, ensuring that the display screen maintains suitable brightness and color temperature in different environments.

[0038] Furthermore, the brightness compensation unit includes:

[0039] Based on the environmental perception dataset, the brightness feedback data of the multiple regions is continuously verified to generate verification feedback data; based on the verification feedback data, the expected brightness data of the LED display screen is extracted, and the expected brightness data is compared with the initial brightness adjustment result to construct a brightness deviation distribution map; combined with the device aging parameters, the brightness deviation distribution map is traversed and weighted to determine the deviation source weight coefficient; based on the deviation source weight coefficient, the initial brightness adjustment result is compensated to determine the brightness adjustment strategy.

[0040] In one feasible implementation, based on an environmentally sensitive dataset, brightness feedback data from multiple previously adjusted areas is continuously validated. This process aims to ensure that the LED display's brightness remains within its optimal range despite environmental changes. Validation feedback data is generated by continuously collecting the difference between actual and expected brightness, reflecting the performance and errors of the initial brightness adjustment results in the actual environment. After obtaining the validation feedback data, the expected brightness data of the LED display is extracted, representing the ideal brightness value under current environmental conditions. By performing deviation analysis on these expected brightness data and the previously obtained initial brightness adjustment results, a brightness deviation distribution map is generated, depicting the error distribution of brightness adjustment under different block and environmental conditions. To account for the device aging effect of the LED display, weighted calculations are performed using device aging parameters. These parameters record the aging degree and historical performance data of the device, reflecting potential brightness decay or color temperature changes during long-term use. By traversing the brightness deviation distribution map and weighting the deviations of different areas according to the device aging parameters, the source of deviation in each area and its impact on brightness adjustment are determined. After weighted calculation, the deviation source weight coefficient is obtained. The larger the deviation source weight coefficient, the greater the impact of equipment aging on brightness deviation. Finally, the calculated deviation source weight coefficient is multiplied by the difference between the actual brightness and the desired brightness to obtain the weighted corrected brightness compensation value. Then, the actual brightness is subtracted from the weighted corrected brightness compensation value to obtain the compensated brightness value. By adding these compensated brightness values ​​to the brightness adjustment strategy, the brightness of the display screen is adjusted to ensure that the LED display screen provides a clear and balanced display effect under various conditions.

[0041] Furthermore, the brightness compensation unit, during continuous verification, includes:

[0042] Based on the effective light intensity data, the pedestrian trajectory density data, and the meteorological environment data, a three-dimensional environmental perception tensor is constructed through data mapping. Multi-dimensional feedback feature vectors are extracted from the three-dimensional environmental perception tensor, and these vectors include the light gradient change rate, the coordinates of pedestrian gathering areas, and meteorological change labels. The brightness feedback data of multiple areas is continuously verified according to the light gradient change rate to generate light verification feedback data; the brightness feedback data of multiple areas is continuously verified according to the coordinates of pedestrian gathering areas to generate pedestrian verification feedback data; the brightness feedback data of multiple areas is continuously verified according to the meteorological change labels to generate meteorological verification feedback data; the light verification feedback data, the pedestrian verification feedback data, and the meteorological verification feedback data are then correlated and integrated to obtain verification feedback data.

[0043] In one feasible implementation, firstly, based on the collected effective light intensity data, pedestrian flow density data, and meteorological environment data, data mapping is performed to align the three types of data (light intensity, pedestrian flow, and meteorological conditions) in spatial and temporal dimensions, forming a complete environmental perception data set. Then, these data are fused to construct a three-dimensional environmental perception tensor. This tensor contains information from the three dimensions of light intensity, pedestrian flow density, and meteorological conditions, providing a basic data structure for subsequent analysis and feedback verification. Subsequently, based on the constructed three-dimensional environmental perception tensor, multi-dimensional feedback feature vectors are obtained through multi-scale extraction. These feature vectors summarize the key changing factors in the environmental data, specifically including the light gradient change rate, pedestrian flow gathering area coordinates, and meteorological abrupt change labels. The light gradient change rate describes the rate of change of light intensity in time and space, reflecting the magnitude of changes in light conditions; the pedestrian flow gathering area coordinates identify densely populated areas within the target area of ​​the display screen, reflecting hotspots of crowd activity; and the meteorological abrupt change labels mark abrupt changes in meteorological conditions (such as sudden drops in temperature, rapid changes in wind speed, etc.), helping to identify changes in display requirements caused by meteorological abrupt changes. Subsequently, based on the rate of change of illumination gradient, brightness feedback data from multiple areas were continuously verified. This verification aimed to detect whether the display's brightness adjustment responded promptly to environmental changes under sudden illumination shifts. By comparing actual feedback with expected results, illumination verification feedback data was generated, indicating whether the display's brightness adjustment met requirements under specific illumination conditions. Based on the coordinates of areas with high pedestrian traffic, brightness feedback data from multiple areas was continuously verified. This verification aimed to ensure that the display's brightness was appropriately increased in densely populated areas to improve visibility. By continuously monitoring pedestrian density data at these coordinates, it was determined whether the actual feedback met the needs of changes in pedestrian traffic, i.e., whether it met the brightness range corresponding to pedestrian density, and pedestrian flow verification feedback data was generated. Based on meteorological change labels, brightness feedback data from multiple areas was continuously verified to ensure that the display could intelligently adjust its brightness under meteorological changes (such as strong winds or heavy rain). For example, during rainfall or intense sunlight, did the display reduce its brightness or color temperature to the required level? This verification process generated meteorological verification feedback data, reflecting the display's response to changes in weather conditions. Finally, the feedback data from illumination, pedestrian flow, and weather conditions are correlated and integrated. The purpose of this integration is to combine the feedback results from different environmental factors (illumination, pedestrian flow, and weather) to form a unified set of verification feedback data. This data comprehensively reflects the performance of the display brightness adjustment under various environmental changes. In this way, brightness can be adjusted more precisely, ensuring that the display effect remains optimal in different environments, making it more intelligent and adaptable to constantly changing environmental conditions.

[0044] Furthermore, when the brightness compensation unit extracts the multidimensional feedback feature vector, it includes:

[0045] Multi-scale extraction is performed based on the three-dimensional environment perception tensor to obtain multi-scale features; light intensity feedback is performed according to the multi-scale features to construct a light intensity matrix, and gradient change calculation is performed based on the light intensity matrix to obtain the light gradient change rate; pedestrian density feedback is performed according to the multi-scale features to construct a pedestrian density heatmap, and the pedestrian density heatmap is traversed to perform clustering and division to construct the coordinates of the pedestrian clustering area; meteorological change feedback is performed according to the multi-scale features to construct a meteorological time series, and meteorological abrupt change identification is performed according to the meteorological time series to construct the meteorological abrupt change label.

[0046] In one feasible implementation, based on the constructed 3D environment perception tensor, analysis can generally be performed at both a local scale (smaller area or short time period) and a global scale (larger area or long time period). The local scale is used to capture detailed information, while the global scale is used to capture macroscopic trends. Then, sliding windows are used to operate on the 3D environment perception tensor, and features are extracted from each window. The size of each window can be determined according to the selected scale; for example, at a smaller scale, the window size is smaller to capture local details, while at a larger scale, the window size is larger to capture global trends. At different scales, methods such as Gaussian filtering and median filtering are used to process light intensity data, meteorological data, and pedestrian density data to balance local details and global information, thereby extracting effective information at each scale, such as the changing trend and fluctuation of light intensity, the density fluctuation and rate of change of pedestrian density, the rate of change of temperature, and the changing trend of humidity. After obtaining features at multiple scales, these features are aggregated using methods such as weighted fusion and splicing, fusing features from all scales into a single multi-scale feature. This multi-scale feature integrates information from different scales in the environmental perception tensor, reflecting both microscopic environmental changes and macroscopic environmental trends. For light intensity, light intensity feedback is generated based on the obtained multi-scale features. This process analyzes light intensity data at different scales to obtain the light intensity variations in each region, and then constructs a light intensity matrix based on this data. This matrix records the light intensity information for each region in detail, allowing analysis of the brightness requirements and adjustment levels for each region. Subsequently, gradient change calculation is performed based on the light intensity matrix, i.e., the rate of change of light intensity in each region is evaluated. By calculating the gradient of light intensity changes over time or space, the light gradient change rate is obtained. This metric measures the abruptness of light changes, reflecting sudden changes in light intensity, such as rapid increases or decreases in ambient light. The system can adjust the smoothness of brightness changes accordingly to avoid visual discomfort caused by abrupt changes. For pedestrian density, multi-scale features are used to provide feedback on pedestrian density, allowing us to understand the distribution of people within the target area of ​​the display screen. This feedback data is then converted into a heatmap to visualize the population distribution. The heatmap shows density changes in different areas, with different colors representing different density levels; warmer colors indicate higher density, while cooler colors indicate lower density. The heatmap is then iterated through, and densely populated areas are identified through threshold comparison. Adjacent high-density areas are merged into a group, defined as pedestrian clusters, thus helping the system transform pedestrian distribution data into multiple clearly defined regions. After clustering, the center position of each cluster is used as its coordinates, which are then used as the basis for brightness adjustment.For meteorological changes, the system uses multi-scale features to provide feedback, acquires real-time meteorological data, and constructs a meteorological time series. This series records changes in meteorological data at different points in time, reflecting the changing trends of meteorological conditions (such as temperature, humidity, and wind speed). Based on the meteorological time series, abrupt changes in meteorological conditions (rates of change exceeding limits), such as a sudden drop in temperature or a sharp change in wind speed, are detected, and the moments of these abrupt changes are identified, generating meteorological abrupt change labels. These labels help identify changes in meteorological conditions in real time and adjust the brightness of the display screen accordingly, avoiding discomfort under extreme weather conditions. In summary, through this series of processes, the system can fully understand and respond to environmental changes, including dynamic changes in lighting, pedestrian flow, and weather, ensuring that the LED display screen achieves precise brightness adjustment and color temperature control under various conditions, providing consistently optimized display effects.

[0047] In summary, the LED display screen brightness intelligent adjustment system combined with environmental perception provided in this application embodiment has the following technical effects:

[0048] The brightness analysis unit is used to perceive the target area of ​​the LED display screen in real time through the environmental perception module, obtain an environmental perception dataset, perform multi-dimensional brightness analysis on the environmental perception dataset, and generate a dynamic brightness adjustment parameter set. The brightness adjustment unit is used to retrieve the device parameter set of the LED display screen, correct the dynamic brightness adjustment parameter set, generate a brightness control command set, execute the brightness control command set to adjust the brightness of the LED display screen, and generate an initial brightness adjustment result. The brightness compensation unit is used to continuously verify the initial brightness adjustment result based on the environmental perception dataset, compensate the initial brightness adjustment result according to the verification feedback data, determine a brightness adjustment strategy, and intelligently adjust the brightness of the LED display screen according to the brightness adjustment strategy. Through the above steps, the technical problems of traditional LED display screens—such as a single brightness adjustment method, inability to dynamically adapt to the display characteristics of small-pitch LEDs according to ambient light, resulting in poor display effects and excessive energy consumption—are solved. This achieves the technical effect of adaptive brightness optimization of small-pitch LED displays through environmental perception and dynamic compensation strategies, significantly improving display consistency, reducing energy consumption, and extending device lifespan.

[0049] Example 2, based on the same inventive concept as the LED display screen brightness intelligent adjustment system combined with environmental perception in the previous examples, such as... Figure 2 As shown in the embodiments of this application, an intelligent brightness adjustment method for an LED display screen incorporating environmental perception is provided. This method includes:

[0050] The target area of ​​the LED display screen is sensed in real time by the environmental sensing module to obtain an environmental sensing dataset. Multi-dimensional brightness analysis is performed on the environmental sensing dataset to generate a dynamic brightness adjustment parameter set. The device parameter set of the LED display screen is retrieved, and the dynamic brightness adjustment parameter set is corrected to generate a brightness control command set. The brightness control command set is executed to adjust the brightness of the LED display screen, generating an initial brightness adjustment result. Based on the initial brightness adjustment result and the environmental sensing dataset, continuous verification is performed. The initial brightness adjustment result is compensated based on the verification feedback data to determine a brightness adjustment strategy. The brightness of the LED display screen is then intelligently adjusted using this strategy.

[0051] Furthermore, the methods include:

[0052] The environmental sensing module senses the light intensity of the target area of ​​the LED display screen to obtain spectral distribution data. Based on this spectral distribution data, multi-channel filtering is performed to generate effective light intensity data. The environmental sensing module also captures pedestrian movement trajectories within the target area of ​​the LED display screen, calculating pedestrian trajectory density data. A meteorological monitoring terminal is connected to the environmental sensing module to construct meteorological environmental data. The effective light intensity data, pedestrian trajectory density data, and meteorological environmental data are aligned according to spatiotemporal characteristics to obtain an environmental sensing feature vector group. This environmental sensing feature vector group is then added to the environmental sensing dataset.

[0053] Furthermore, the methods include:

[0054] Based on the effective light intensity data, brightness constraints are set, and environmental brightness correlation analysis is performed on the pedestrian trajectory density data and the meteorological environment data according to the brightness constraints to construct an environment-brightness mapping network. Brightness prediction is performed based on the environmental perception dataset to obtain an initial brightness value matrix. Environmental change response is performed on the initial brightness value matrix according to the environment-brightness mapping network to generate brightness switching data. Distributed brightness control of the LED display screen is performed through the brightness switching data to generate the dynamic brightness adjustment parameter set.

[0055] Furthermore, the methods include:

[0056] Based on the set of device parameters of the LED display screen, device aging analysis is performed to obtain device aging parameters, which include a historical brightness dataset. Light decay analysis is then performed by traversing the historical brightness dataset to generate light decay compensation data. The dynamic brightness adjustment parameter set is corrected based on the light decay compensation data to determine the brightness light decay correction result. Color temperature correction is performed based on the brightness light decay correction result to obtain the full-screen color temperature difference value of the LED display screen. A color temperature-brightness lookup table is constructed based on the brightness light decay correction result and the full-screen color temperature difference value. The LED display screen is then divided into regions according to the color temperature-brightness lookup table to generate multiple pixel blocks. Each pixel block contains multiple control instruction packets, and these control instruction packets correspond to the pixel blocks. The multiple control instruction packets are then encapsulated to generate the brightness control instruction set.

[0057] Furthermore, the methods include:

[0058] The LED display screen is pixel-scanned to obtain the initial brightness of the RGB three-color LEDs; based on the brightness decay correction result, the initial brightness of the RGB three-color LEDs is compensated for gain to generate RGB gain coefficients; target color temperature data is set, and the target color temperature data is mapped to the color space according to the RGB gain coefficients to construct a dynamic mixing matrix; based on the dynamic mixing matrix, a color temperature correction command is initiated to perform color temperature gradient correction to obtain the full-screen color temperature difference of the LED display screen.

[0059] Furthermore, the methods include:

[0060] Based on the environmental perception dataset, a distribution analysis is performed on the multiple pixel blocks to generate spatial distribution features; a block priority weight matrix is ​​constructed for the multiple pixel blocks according to the spatial distribution features; the multiple control instruction packets are sorted according to the block priority weight matrix to generate an instruction execution sequence; the multiple pixel blocks are independently controlled according to the instruction execution sequence to obtain multiple area brightness feedback data; and the multiple area brightness feedback data are added to the initial brightness adjustment result.

[0061] Furthermore, the methods include:

[0062] Based on the environmental perception dataset, the brightness feedback data of the multiple regions is continuously verified to generate verification feedback data; based on the verification feedback data, the expected brightness data of the LED display screen is extracted, and the expected brightness data is compared with the initial brightness adjustment result to construct a brightness deviation distribution map; combined with the device aging parameters, the brightness deviation distribution map is traversed and weighted to determine the deviation source weight coefficient; based on the deviation source weight coefficient, the initial brightness adjustment result is compensated to determine the brightness adjustment strategy.

[0063] Furthermore, the methods include:

[0064] Based on the effective light intensity data, the pedestrian trajectory density data, and the meteorological environment data, a three-dimensional environmental perception tensor is constructed through data mapping. Multi-dimensional feedback feature vectors are extracted from the three-dimensional environmental perception tensor, and these vectors include the light gradient change rate, the coordinates of pedestrian gathering areas, and meteorological change labels. The brightness feedback data of multiple areas is continuously verified according to the light gradient change rate to generate light verification feedback data; the brightness feedback data of multiple areas is continuously verified according to the coordinates of pedestrian gathering areas to generate pedestrian verification feedback data; the brightness feedback data of multiple areas is continuously verified according to the meteorological change labels to generate meteorological verification feedback data; the light verification feedback data, the pedestrian verification feedback data, and the meteorological verification feedback data are then correlated and integrated to obtain verification feedback data.

[0065] Furthermore, the methods include:

[0066] Multi-scale extraction is performed based on the three-dimensional environment perception tensor to obtain multi-scale features; light intensity feedback is performed according to the multi-scale features to construct a light intensity matrix, and gradient change calculation is performed based on the light intensity matrix to obtain the light gradient change rate; pedestrian density feedback is performed according to the multi-scale features to construct a pedestrian density heatmap, and the pedestrian density heatmap is traversed to perform clustering and division to construct the coordinates of the pedestrian clustering area; meteorological change feedback is performed according to the multi-scale features to construct a meteorological time series, and meteorological abrupt change identification is performed according to the meteorological time series to construct the meteorological abrupt change label.

[0067] In summary, any step of the method described above can be stored as a computer instruction or program in an unrestricted computer memory, and can be called and identified by an unrestricted computer processor to implement any method in the embodiments of this application, without any additional restrictions.

[0068] Furthermore, the "first" or "second" mentioned above may not only represent a sequential relationship, but may also represent a specific concept, and / or refer to the individual or collective selection of multiple elements. Clearly, those skilled in the art can make various modifications and variations to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A LED display screen brightness intelligent adjusting system combined with environmental perception, characterized in that, The system comprises: a brightness analysis unit for real-time sensing of a target area of an LED display screen by an environment sensing module, obtaining an environment sensing data set, performing multi-dimensional brightness analysis on the environment sensing data set, and generating a dynamic brightness adjustment parameter set; a brightness adjustment unit for calling a device parameter set of the LED display screen, correcting the dynamic brightness adjustment parameter set, generating a brightness control instruction set, performing brightness adjustment on the LED display screen according to the brightness control instruction set, and generating an initial brightness adjustment result; a brightness compensation unit for continuously verifying the initial brightness adjustment result based on the initial brightness adjustment result and the environment sensing data set, compensating the initial brightness adjustment result according to verification feedback data, determining a brightness adjustment strategy, and intelligently adjusting the brightness of the LED display screen through the brightness adjustment strategy; the brightness analysis unit comprises: performing light sensing on the target area of the LED display screen by the environment sensing module, and obtaining spectral distribution data of the target area; performing multi-channel filtering processing based on the spectral distribution data to generate effective light intensity data; capturing pedestrian movement trajectories of the target area of the LED display screen by the environment sensing module, and calculating pedestrian trajectory density data; connecting a meteorological monitoring terminal by the environment sensing module to construct meteorological environment data; aligning the effective light intensity data, the pedestrian trajectory density data, and the meteorological environment data according to space-time characteristics to obtain an environment sensing feature vector group; adding the environment sensing feature vector group to the environment sensing data set; the brightness analysis unit further comprises: setting a brightness constraint condition based on the effective light intensity data, performing environment brightness correlation analysis on the pedestrian trajectory density data and the meteorological environment data according to the brightness constraint condition, and constructing an environment-brightness mapping network; performing brightness prediction based on the environment sensing data set to obtain an initial brightness value matrix; performing environment mutation response on the initial brightness value matrix according to the environment-brightness mapping network to generate brightness switching data; performing distributed brightness control on the LED display screen through the brightness switching data to generate the dynamic brightness adjustment parameter set.

2. The system for intelligent adjustment of brightness of LED display screen in combination with environment perception according to claim 1, characterized in that, the brightness adjustment unit comprises: performing device aging analysis based on the device parameter set of the LED display screen to obtain a device aging parameter, the device aging parameter including a device historical brightness data set; performing light decay analysis by traversing the device historical brightness data set to generate light decay compensation data; correcting the dynamic brightness adjustment parameter set based on the light decay compensation data to determine a brightness light decay correction result; performing color temperature correction according to the brightness light decay correction result to obtain a full-screen color temperature difference value of the LED display screen; constructing a color temperature-brightness lookup table based on the brightness light decay correction result and the full-screen color temperature difference value; performing area fragmentation on the LED display screen according to the color temperature-brightness lookup table to generate a plurality of pixel blocks, the plurality of pixel blocks including a plurality of control instruction packets, wherein the plurality of control instruction packets and the plurality of pixel blocks have a corresponding relationship; The plurality of control instruction packets are packaged to generate the brightness control instruction set.

3. The system for intelligent adjustment of brightness of LED display screen in combination with environment perception according to claim 2, characterized in that, The brightness adjustment unit includes: Pixel scanning is performed on the LED display screen to obtain initial brightness of RGB three-color LEDs; Based on the brightness light decay correction result, initial brightness of the RGB three-color LEDs is compensated for gain to generate RGB gain coefficients; Target color temperature data is set, and the target color temperature data is mapped in a chromaticity space according to the RGB gain coefficients to construct a dynamic mixing matrix; Based on the dynamic mixing matrix, a color temperature correction instruction is started to correct a color temperature gradient to obtain the full-screen color temperature difference value of the LED display screen.

4. The system for intelligent adjustment of brightness of LED display screen in combination with environment perception according to claim 2, characterized in that, The brightness adjustment unit includes: Based on the environmental perception data set, spatial distribution characteristics are generated according to the plurality of pixel blocks; A block priority weight matrix of the plurality of pixel blocks is constructed according to the spatial distribution characteristics; The plurality of control instruction packets are sorted according to the block priority weight matrix to generate an instruction execution sequence; The plurality of pixel blocks are independently controlled according to the instruction execution sequence to obtain a plurality of regional brightness feedback data; The plurality of regional brightness feedback data is added to the initial brightness adjustment result.

5. The system for intelligent adjustment of brightness of LED display screen in combination with environment perception according to claim 4, characterized in that, The brightness compensation unit includes: Based on the environmental perception data set, the plurality of regional brightness feedback data is continuously verified to generate verification feedback data; Expected brightness data of the LED display screen is extracted based on the verification feedback data, and deviation analysis is performed on the expected brightness data and the initial brightness adjustment result to construct a brightness deviation distribution map; The brightness deviation distribution map is iterated and weighted calculated in combination with the device aging parameters to determine a deviation source weight coefficient; Based on the deviation source weight coefficient, the initial brightness adjustment result is compensated for brightness to determine the brightness adjustment strategy.

6. The system for intelligent adjustment of brightness of LED display screen in conjunction with environment perception as claimed in claim 5 wherein, The brightness compensation unit includes: Based on the effective light intensity data, the people flow trajectory density data, and the meteorological environment data, a three-dimensional environmental perception tensor is constructed through data mapping; A multi-dimensional feedback feature vector is extracted through the three-dimensional environmental perception tensor, and the multi-dimensional feedback feature vector includes a light gradient change rate, a people flow aggregation area coordinate, and a meteorological mutation label; The plurality of regional brightness feedback data is continuously verified according to the light gradient change rate to generate light verification feedback data; The plurality of regional brightness feedback data is continuously verified according to the people flow aggregation area coordinate to generate people flow verification feedback data; The plurality of regional brightness feedback data is continuously verified according to the meteorological mutation label to generate meteorological verification feedback data; The light verification feedback data, the people flow verification feedback data, and the meteorological verification feedback data are associated and integrated to obtain verification feedback data.

7. The system for intelligent adjustment of brightness of LED display screen in combination with environment perception according to claim 6, characterized in that, The brightness compensation unit includes: Multi-scale features are obtained through multi-scale extraction based on the three-dimensional environmental perception tensor; Light intensity feedback is performed according to the multi-scale features to construct a light intensity matrix, and a gradient change calculation is performed based on the light intensity matrix to obtain the light gradient change rate; According to the multi-scale feature, the crowd density feedback is performed, the crowd density heat map is constructed, the crowd density heat map is traversed for aggregation division, and the crowd aggregation region coordinate is constructed; According to the multi-scale feature, the weather change feedback is performed, the weather time sequence is constructed, the weather mutation identification is performed according to the weather time sequence, and the weather mutation label is constructed.

8. The LED display screen brightness intelligent adjusting method combined with environmental perception, characterized in that, The method is executed by the LED display screen brightness intelligent adjustment system combined with environmental perception according to any one of claims 1 to 7, and the method comprises: The target area of the LED display screen is sensed in real time by the environmental perception module to obtain an environmental perception data set, the environmental perception data set is analyzed in multiple dimensions for brightness to generate a dynamic brightness adjustment parameter set; The device parameter set of the LED display screen is called to correct the dynamic brightness adjustment parameter set to generate a brightness control instruction set, the brightness control instruction set is executed to adjust the brightness of the LED display screen to generate an initial brightness adjustment result; Based on the initial brightness adjustment result combined with the environmental perception data set, continuous verification is performed, the initial brightness adjustment result is compensated according to the verification feedback data, a brightness adjustment strategy is determined, and the brightness of the LED display screen is intelligently adjusted through the brightness adjustment strategy.

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