Projection lamp shadow effect intelligent adjustment method based on artificial intelligence

Abstract

The invention provides a projection lamp shadow effect intelligent adjustment method based on artificial intelligence, and the method comprises the steps: obtaining the material type and reflection coefficient of the wall surface of each region of an exhibition space through a pre-established wall surface material database, carrying out the classification processing according to the reflection characteristics of different materials, and obtaining a material reflection distribution diagram; acquiring light intensity distribution in the space based on stray light distribution characteristics, performing gridding decomposition on the light intensity distribution, and obtaining light intensity and light field distribution uniformity of each grid area through numerical integration; according to the adjusted projection angle and light source position, generating a control parameter for the projection equipment, adjusting the output intensity and direction of the projection light source, and obtaining the light distribution intensity after the control parameter of the projection equipment is adjusted; and if the contrast ratio and the color deviation value of the detail area do not meet the preset standard, optimizing the light source control parameters through an ambient light compensation mechanism in combination with the accumulated intensity data of the space corner to obtain a light compensation result.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent adjustment method for projection lighting effects based on artificial intelligence. Background Technology

[0002] The design of lighting environments in exhibition spaces is a crucial research area, directly impacting the visual effects of exhibits and the immersive experience for visitors. Especially with the widespread use of projection technology, the interaction between light propagation and spatial materials has become a key factor influencing exhibition quality. Proper lighting control not only enhances the clarity of exhibits but also creates an atmosphere that aligns with the theme. However, current solutions for lighting management in exhibition spaces often overlook the complex impact of diverse wall materials on light propagation. They focus more on the hardware performance of projection equipment or light control in a single environment, lacking in-depth consideration of the interactions between different wall materials within the space, particularly the complex light field distribution formed after multiple reflections. Typical exhibition spaces often contain a variety of surface materials, including concrete walls, metal decorative panels, fabric upholstery, and glass partitions. These materials exhibit significant inconsistencies in spectral absorption, reflectivity, and scattering behavior. For example, when a high-brightness projection beam shines on a dark-colored fabric wall, its high light absorption rate significantly reduces the ambient light intensity in that area. Conversely, when the same intensity of light shines on a light-colored metal surface, strong specular reflection creates localized bright areas, resulting in extremely uneven light intensity distribution. This inconsistency in light absorption caused by material differences leads to areas of excessively strong or weak light within the exhibition space, particularly in corners and material boundaries, where the cumulative effect of stray light produces complex light field distributions with drastic intensity variations. This non-uniform distribution of stray light further exacerbates the interference of the projected light, making it difficult to accurately reproduce the details and colors of the exhibits, thus creating a pressing technical challenge. Therefore, in exhibition spaces composed of mixed-material walls, a key issue is how to dynamically adjust the ambient light compensation mechanism to address differences in light absorption and stray light intensity variations, ensuring the clarity and stability of the projection display. Summary of the Invention

[0003] This invention provides an intelligent adjustment method for projection light and shadow effects based on artificial intelligence, mainly including: By using a pre-established wall material database, the material types and reflectance coefficients of the walls in each area of ​​the exhibition space are obtained. Different materials are categorized based on their reflectance characteristics to obtain a material reflectance distribution map. Ambient lighting data at various locations in the exhibition space is collected in real time using light sensors. The rate of change of light intensity and the direction vector of the main light source are analyzed to determine the distribution of the ambient light interference coefficient. Based on the ambient light interference coefficient distribution and the material reflectance distribution map, the reflection path of light on the wall is traced to determine the stray light distribution characteristics. Based on the stray light distribution characteristics, the light intensity distribution data within the space is obtained, decomposed into grid cells, and the total light intensity value and light field distribution uniformity mark of each grid area are calculated. The light field distribution uniformity mark indicates areas with uneven distribution. The region is an area of ​​uneven illumination. Based on the ambient light interference coefficient distribution, the light compensation amount is calculated, and the light source position and projection angle of the projection device are adjusted. According to the adjusted light source position and projection angle, control parameters for the projection device are generated, and the output intensity and direction of the light source are adjusted to obtain the adjusted light distribution intensity data. Based on the light distribution intensity data, detailed areas are identified, and contrast and color deviation values ​​are calculated. When the contrast and color deviation values ​​do not meet preset standards, the light source control parameters are optimized based on stray light distribution characteristics. Based on the optimized light source control parameters, the ambient light interference coefficient distribution is continuously monitored, the light compensation parameters are dynamically adjusted, the light field distribution data is updated, and the real-time light uniformity value is calculated.

[0004] Furthermore, by using a pre-established wall material database, the material type and reflectance coefficient of the walls in each area of ​​the exhibition space are obtained. The materials are then categorized based on their reflectance characteristics to obtain a material reflectance distribution map, including: Material identifiers and spatial coordinates of each area in the exhibition space are extracted from the wall material database. The material type and reflectance coefficient value corresponding to the material identifier are queried. Based on the reflectance coefficient value, the area is classified into low reflectance, medium reflectance, and high reflectance categories, and different grayscale values ​​are assigned to each category. Based on the spatial coordinates and the grayscale values, the outline of each area is drawn on a two-dimensional plane and filled with the corresponding grayscale values ​​to form a preliminary reflectance distribution image. Edge detection is performed on the preliminary reflectance distribution image to mark the positions where grayscale values ​​change, and the material reflectance distribution map containing boundary information is generated.

[0005] Furthermore, the process involves real-time acquisition of ambient lighting data at various locations within the exhibition space using a light sensor, analysis of the rate of change of light intensity and the direction vector of the main light source, determination of the ambient light interference coefficient distribution, and, based on the ambient light interference coefficient distribution and the material reflection distribution map, tracing the reflection path of light on the wall surface to determine stray light distribution characteristics, including: The illumination sensor collects illumination intensity values ​​and timestamps at various locations, calculates the difference in illumination intensity values ​​between adjacent timestamps to obtain the rate of change of illumination intensity; based on the ratio of the difference in illumination intensity between adjacent locations to the distance, the direction vector of the main light source is determined; based on the rate of change of illumination intensity and the direction vector of the main light source, an ambient light interference coefficient is assigned to generate the ambient light interference coefficient distribution; based on the ambient light interference coefficient distribution and the material reflection distribution map, a virtual light beam is emitted to trace the propagation path of the light on walls with different reflectivity, and the intensity attenuation after each reflection is calculated; the cumulative intensity value of the reflected light at spatial grid points is statistically analyzed to generate the stray light distribution characteristics containing the intensity distribution data.

[0006] Furthermore, the step of acquiring spatial light intensity distribution data based on the stray light distribution characteristics, decomposing it into grid cells, and calculating the total light intensity value and light field distribution uniformity marker for each grid region includes: Light intensity values ​​are extracted from the stray light distribution characteristics to construct light intensity distribution data covering the exhibition space; the light intensity distribution data is divided into cubic grid cells to obtain the light intensity value set within each grid cell; for the light intensity value set, the light intensity integral value between adjacent points is calculated to obtain the total light intensity value of each grid area; based on the total light intensity value, the sum of squares of the light intensity difference with adjacent grids is calculated to determine the uniformity mark of the light field distribution.

[0007] Furthermore, the area marked as uneven in light field distribution is an area of ​​uneven illumination. Based on the ambient light interference coefficient distribution, the light compensation amount is calculated, and the position of the light source and the projection angle of the projection device are adjusted, including: Identify the non-uniform grid regions marked by the uniformity of the light field distribution and extract the total light intensity value; calculate the average light intensity value of the uniform grid region and determine the difference between it and the total light intensity value; calculate the light compensation amount based on the difference and the distribution of the ambient light interference coefficient; and determine the light source position and projection angle of the compensation light source based on the light compensation amount and the grid center coordinates.

[0008] Furthermore, the step of generating control parameters for the projection device based on the adjusted light source position and projection angle, adjusting the light source output intensity and direction, and obtaining adjusted light distribution intensity data includes: Based on the light source position and the projection angle, calculate the distance between the projection device and the center of the target grid to determine the target brightness parameter value; set the color temperature parameter and projection angle parameter according to the target brightness parameter value; send the target brightness parameter value, the color temperature parameter, and the projection angle parameter to the projection device to adjust the light source output; calculate the light intensity contribution value according to the adjusted light source output and update the light distribution intensity data.

[0009] Furthermore, the step of identifying detailed areas and calculating contrast and color deviation values ​​based on the light distribution intensity data includes: Based on the light distribution intensity data, calculate the light intensity difference between adjacent grids, mark grid boundaries exceeding a threshold, and define the region containing the grid boundaries as a detail region; for the light distribution intensity data of the detail region, calculate the frequency components of the light intensity difference sequence to determine the sharpness marker value; based on the light distribution intensity data, calculate the maximum and minimum light intensity differences to obtain the contrast ratio; based on the color temperature parameter, calculate the color temperature difference to obtain the color deviation value.

[0010] Furthermore, when the contrast ratio and color deviation values ​​do not meet the preset standards, the light source control parameters are optimized based on the stray light distribution characteristics, including: When the contrast ratio and color deviation values ​​do not meet the preset standards, the average light intensity value of the detail area and the light intensity data of the stray light distribution characteristics are obtained, and the light intensity imbalance is calculated. Based on the light intensity imbalance and the current light intensity value, the compensation coefficient is determined, the light source power value and the illumination range radius are adjusted, and the optimized light source control parameters are generated.

[0011] Furthermore, based on the optimized light source control parameters, the continuous monitoring of the ambient light interference coefficient distribution, dynamic adjustment of the light compensation parameters, updating of the light field distribution data, and calculation of real-time light uniformity values ​​include: Based on the optimized light source control parameters, ambient light data is periodically collected, the light intensity change rate and the angle between the main light source direction vectors are calculated, and the ambient light interference coefficient distribution is updated. Based on the ambient light interference coefficient distribution, the light source power parameters are adjusted. Based on the adjusted light source power parameters, new light intensity data is collected, and the light field distribution data is updated. Based on the light field distribution data, the light intensity variation coefficient is calculated to obtain the light uniformity value.

[0012] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an intelligent adjustment method for projection lighting effects based on artificial intelligence, solving the business problems of uneven light field and degraded projection quality caused by differences in the reflective properties of wall materials, ambient light interference, and stray light accumulation in exhibition environments. The method obtains the reflective characteristics of materials through a pre-built wall material database, combines it with real-time collected ambient light data, analyzes light intensity changes and light source direction, constructs a material reflectance distribution map, and performs ray tracing to accurately model stray light distribution and corner accumulation intensity. Based on this, it calculates light field uniformity through grid decomposition and numerical integration, and uses an adaptive light compensation algorithm for uneven areas to dynamically adjust the brightness, color temperature, and projection angle of the projection device, generating optimized control parameters. Simultaneously, it analyzes the contrast and color deviation in detailed areas through image quality assessment, and further optimizes the light source parameters using an ambient light compensation mechanism to ensure projection clarity and color accuracy. Finally, this invention continuously monitors ambient light interference and dynamically updates light compensation parameters to achieve real-time light field uniformity optimization, significantly improving the visual effect and immersive experience of the exhibition space. Attached Figure Description

[0013] Figure 1 This is a flowchart of an intelligent adjustment method for projection light and shadow effects based on artificial intelligence, according to the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0015] like Figure 1 This embodiment of an artificial intelligence-based intelligent adjustment method for projection light and shadow effects may specifically include: Step S101: Using a pre-established wall material database, obtain the material type and reflectance coefficient of the walls in each area of ​​the exhibition space, classify the reflectance characteristics of different materials, and obtain a material reflectance distribution map.

[0016] The spatial codes and corresponding wall material identifiers for each area of ​​the exhibition space are retrieved from a pre-established wall material database. Based on the material identifiers, the material type and reflectance coefficient of each area's walls are obtained, along with the wall area and spatial coordinates. For the obtained reflectance coefficients, materials are classified according to preset reflectance coefficient thresholds: reflectance coefficients less than 0.3 are classified as low-reflectance materials with a grayscale value of 80; reflectance coefficients between 0.3 and 0.7 are classified as medium-reflectance materials with a grayscale value of 160; and reflectance coefficients greater than 0.7 are classified as high-reflectance materials with a grayscale value of 240. Based on the spatial coordinates and corresponding grayscale values ​​of each area, the outlines of each area are drawn on a two-dimensional plane of the exhibition space, and the corresponding areas are filled with the assigned grayscale values, forming a preliminary reflectance characteristic distribution image. Edge detection processing is performed on the preliminary reflectance characteristic distribution image to identify the locations of grayscale value changes as boundaries between areas with different reflectance characteristics. Boundary markers are superimposed at these boundaries to obtain a material reflectance distribution map containing area reflectance characteristics and boundary information.

[0017] Specifically, establishing a wall material database requires systematic collection and management of material information for the exhibition space.

[0018] In one possible implementation, the database contains multi-dimensional attributes such as material identification code, material type name, reflection coefficient, sound absorption coefficient, and surface roughness.

[0019] For example, the material identifier for marble walls is MT001, with a reflectance of 0.85, indicating its strong light reflectivity; while the material identifier for fabric-covered walls is MT015, with a reflectance of only 0.15, primarily serving to absorb light. Spatial coding uses a combination of exhibition hall number and area sequence number, such as EX01-A03 representing the third wall in area A of exhibition hall 1. The classification threshold for reflectance is set based on optical reflection characteristics and exhibition lighting requirements.

[0020] Specifically, the grayscale value of 80 for low-reflectivity materials represents dark gray. These materials, such as matte black paint and dark wood veneer, can absorb most of the incident light and reduce glare. The grayscale value of 160 for medium-reflectivity materials represents medium gray, including ordinary latex paint walls and light-colored wood, which have moderate light reflection capabilities. The grayscale value of 240 for high-reflectivity materials represents light gray, with typical examples including glass curtain walls, stainless steel decorative surfaces, and white glossy paint. These materials can reflect most of the light back into the environment.

[0021] In one embodiment, spatial coordinates can be obtained by importing CAD drawings from the exhibition hall. Each wall area is represented as a polygonal outline on the two-dimensional plan, and the coordinates of its vertices constitute the spatial location information of that area. During the drawing process, the position and shape of each area on the plan are determined based on its coordinate information, and then filled with the corresponding grayscale values.

[0022] For example, if the coordinates of the four vertices of a rectangular display wall area are (10,20), (50,20), (50,40), and (10,40), and the material of this area is highly reflective white painted glass, then this rectangular area is filled with a grayscale value of 240. Edge detection is performed using the gradient operator method in image processing, identifying boundaries by calculating the grayscale difference between adjacent pixels.

[0023] It should be noted that when the difference in grayscale values ​​between two adjacent regions exceeds a preset threshold, that location is determined to be the boundary of the change in reflectivity.

[0024] For example, the difference between the gray value of 80 in the low-reflection area and the gray value of 240 in the high-reflection area is 160, which far exceeds the boundary recognition threshold of 50. Therefore, a clear boundary line mark will be generated at the junction of the two areas.

[0025] Step S102: Real-time acquisition of ambient light data at various locations; analysis of light intensity changes and identification of light source direction to obtain the degree of ambient light interference; ray tracing of material reflection distribution map; analysis of light reflection behavior on different wall surfaces; determination of stray light distribution characteristics.

[0026] Ambient lighting data is collected in real time by light sensors placed at various locations in the exhibition space. The light intensity value and collection timestamp of each collection point are recorded. The light intensity change rate is obtained by calculating the difference between the light intensity values ​​of adjacent timestamps. At the same time, the ratio of the difference in light intensity between adjacent collection points to the distance is calculated as the light gradient. The direction vector of the main light source is determined from the direction of the largest light gradient. Based on the light intensity change rate and the main light source direction vector, when the light intensity change rate exceeds a preset change threshold and the angle between the main light source direction vector and the exhibit lighting design direction is greater than a preset angle threshold, an ambient light interference coefficient of 1.0 is assigned; otherwise, an ambient light interference coefficient of 0.3 is assigned, resulting in the distribution of ambient light interference coefficients for each collection point. Based on the distribution of ambient light interference coefficients and the pre-obtained material reflection distribution map, virtual light beams are emitted from the direction pointed to by the main light source direction vector. The initial intensity of each light beam is multiplied by the ambient light interference coefficient of the corresponding location. The propagation path of the light beam is tracked on walls with different reflectivity according to the geometric optics law that the angle of incidence equals the angle of reflection. The light intensity after each reflection is attenuated according to the wall reflectivity. The cumulative light intensity values ​​of all reflected rays at spatial grid points are statistically analyzed to form three-dimensional light intensity distribution data. The cumulative light intensity values ​​of the corner regions are extracted as corner cumulative intensity, and the stray light distribution characteristics containing three-dimensional light intensity distribution data and corner cumulative intensity are output.

[0027] Specifically, the density of the light sensors directly affects the accuracy of ambient light data acquisition.

[0028] In one possible implementation, sensors are evenly distributed within the exhibition space at a grid spacing of 2 meters × 2 meters, with each sensor capable of measuring light intensity in the range of 0 to 100,000 lux. The specific process of differential calculation involves subtracting the light intensity value from the previous moment from the current light intensity value, and then dividing by the time interval to obtain the rate of change of light intensity.

[0029] For example, if the light intensity at a sampling point changes from 500 lux to 800 lux within 10 seconds, then the rate of change of light intensity is 30 lux per second. The calculation of the light gradient involves the analysis of light intensity differences at spatial locations.

[0030] Specifically, if two adjacent sampling points A and B have light intensities of 1000 lux and 1200 lux respectively, and the distance between them is 2 meters, then the light gradient in that direction is 100 lux per meter. By comparing the light gradient values ​​of all adjacent point pairs, the direction with the largest gradient points to the main light source. This method can accurately identify the directional characteristics of different light sources such as window light, skylights, or side spotlights. The setting of the ambient light interference coefficient reflects considerations for the protection of exhibit lighting.

[0031] It should be noted that when the external light source forms a large angle with the designed lighting direction, unwanted shadows or reflections will be generated on the surface of the exhibit. An interference coefficient of 1.0 indicates that the stray light effect in this area needs to be given special attention, while 0.3 indicates that the area is less affected by ambient light interference.

[0032] For example, the exhibit's front lighting is designed with a 45-degree incident angle, while sunlight from the side windows enters at an angle close to 90 degrees. The angle between the two exceeds the preset threshold of 45 degrees, and the rate of change in light intensity exceeds the threshold of 20 lux per second, thus assigning a high interference coefficient. The ray tracing process follows the basic laws of geometrical optics.

[0033] In one embodiment, the virtual light beam emitted from the main light source comprises multiple rays, each carrying initial light intensity information. When the light ray encounters a marble wall with a reflectivity of 0.8, the reflected light intensity becomes 0.8 times the original; when it encounters a dark wood veneer with a reflectivity of 0.2, the reflected light intensity is only 0.2 times the original. The light intensity gradually decreases after multiple reflections until it falls below a preset minimum threshold, at which point tracking stops. The spatial grid points allow for a quantifiable representation of the light intensity distribution.

[0034] For example, the exhibition space can be divided into a 0.5m x 0.5m x 0.5m cubic grid, with each grid point recording the sum of the intensities of all reflected light passing through that point. Space corners are often where multiple walls intersect, easily leading to multiple reflections and light accumulation. By extracting the cumulative light intensity values ​​from the grid points in the corner areas, it's possible to assess whether these locations experience excessive stray light accumulation, thus affecting the visual appeal of the exhibits or causing glare and discomfort for visitors.

[0035] Step S103: Based on the stray light distribution characteristics, obtain the light intensity distribution in space, decompose the light intensity distribution into a grid, and obtain the light intensity and light field distribution uniformity of each grid region through numerical integration.

[0036] Based on the three-dimensional light intensity data and corner cumulative intensity values ​​in the stray light distribution characteristics, light intensity values ​​at various locations within the space are extracted to construct a light intensity distribution dataset covering the entire exhibition space. The light intensity distribution dataset is then decomposed into a grid, dividing the space into multiple cubic grid cells according to a preset grid size. Each grid cell contains light intensity values ​​for several locations, forming a set of light intensity values ​​for each grid cell. For each grid cell's set of light intensity values, the distance between adjacent locations is used as the integration interval length, and the light intensity value of each location is used as the function value. The trapezoidal rule is used to calculate the integral: the light intensity values ​​of two adjacent points are added, divided by 2, and then multiplied by the distance. The integral values ​​of all intervals are accumulated to obtain the total light intensity value for that grid area. Based on the total light intensity value of each grid area, the square root of the sum of the squares of the light intensity differences between each grid and all its adjacent grids is calculated and divided by the number of adjacent grids to obtain the light intensity variation metric for that grid. If this metric is less than a preset uniformity threshold, a uniformity flag of 1 is assigned; otherwise, it is 0. The light field distribution uniformity data, consisting of the total light intensity value and the uniformity flag for each grid, is then output.

[0037] Specifically, the stray light distribution characteristics, as the output of the preprocessing, contain the cumulative light intensity information at each location in space.

[0038] In one possible implementation, three-dimensional light intensity data is stored in the form of spatial coordinates and corresponding light intensity values. For example, the light intensity value at location (5,3,2) is 350 lux. These discrete data points collectively constitute a description of the lighting environment of the entire exhibition space. Corner cumulative intensity values ​​pay particular attention to areas where multiple reflective surfaces converge, such as corners and the junction of the ceiling and walls, where abnormal light concentration often occurs. The core of mesh decomposition lies in discretizing continuous space.

[0039] Specifically, a 10m × 8m × 4m exhibition space, divided into 20×16×8 cubic grid cells using a 0.5m grid size, will generate 2560 such cells. Each grid cell acts as a small light intensity collection container, potentially containing multiple initial measurement points. For example, a grid cell might contain eight measurement points with light intensity values ​​of 320, 335, 328, 342, 330, 338, 325, and 340 lux. These eight values ​​collectively reflect the illumination conditions of that grid area. The application of the trapezoidal rule embodies the fundamental idea of ​​numerical integration.

[0040] When calculating the total light intensity within a grid cell, simply adding the intensity values ​​at each point is insufficient; the continuity of the spatial distribution must be considered. The region between two adjacent measurement points is considered a trapezoid, with its area equal to half the sum of the intensity values ​​at the two points multiplied by the distance between them. For example, two measurement points 0.2 meters apart with intensities of 300 and 320 lux respectively would have an integral contribution of (300 + 320) / 2 × 0.2 = 62 lux·m. Summing the integral contributions of all adjacent point pairs within the grid yields the total light intensity value for that grid. Assessing the uniformity of the light field distribution involves analyzing the degree of variation in spatial light intensity.

[0041] In one embodiment, each grid needs to be compared with its six neighboring grids. If the total light intensity of the central grid is 1500 lux·m³, and the values ​​of its six neighboring grids are 1480, 1520, 1490, 1510, 1495, and 1505 lux·m³, then the differences are calculated as: 20, 20, 10, 10, 5, and 5. The sum of the squares of these differences is (20² + 20² + 10² + 10² + 5² + 5²) = 1050, which, after taking the square root and dividing by 6, yields approximately 13.2. When the preset uniformity threshold is 30, this value is less than the threshold, therefore the grid area is marked as uniformly distributed.

[0042] Step S104: If the uniformity of the light field distribution is lower than the preset light uniformity threshold, then an adaptive light compensation algorithm is used for the uneven lighting area. The required light compensation amount for each grid area is calculated in combination with the degree of ambient light interference, and the projection angle and the position of the light source are adjusted.

[0043] If the uniformity of the light field distribution is marked as 0, which is lower than the preset light uniformity threshold, the corresponding grid area is identified as an area of ​​uneven illumination. The total light intensity value of this area is extracted, and the average of the total light intensity values ​​of all grid areas marked as 1 is calculated. The difference between the two is used as the initial compensation requirement value. The compensation amount is calculated based on the initial compensation requirement value and the ambient light interference coefficient corresponding to the area: when the ambient light interference coefficient is 1.0, the actual light compensation amount is equal to the initial compensation requirement value multiplied by 1.5; when the ambient light interference coefficient is 0.3, the actual light compensation amount is equal to the initial compensation requirement value multiplied by 1.0. Based on the actual light compensation amount and the grid center coordinates of the uneven area, the initial position of the compensation light source is set at a preset height above the grid. The angle between the line connecting the compensation light source to the grid center and the vertical direction is calculated as the projection angle. When the compensation amount exceeds the preset threshold, the position of the compensation light source is shifted horizontally until the projection angle is less than the preset maximum angle, thus obtaining the adjusted projection angle value and the three-dimensional coordinate position of the light source.

[0044] Specifically, the uniformity of the light field distribution serves as a criterion for judgment, directly determining whether light compensation processing is required.

[0045] In one possible implementation, when the uniformity of a certain grid region is marked as 0, it indicates that the light intensity variation in that region exceeds the acceptable range.

[0046] For example, a grid area located in a corner of the exhibition hall might have a total light intensity of only 800 lux·m³, while the surrounding uniform area marked 1 has an average of 1500 lux·m³, a difference of 700 lux·m³. This significant difference would result in exhibits appearing poorly in that location. The initial compensation requirement is calculated based on statistical analysis of actual measurement data.

[0047] Specifically, assuming the exhibition hall contains 100 grids, with 85 grids having a uniformity rating of 1, the total light intensity values ​​of these uniform grids range from 1400 to 1600 lux·m³, with a calculated average of 1500 lux·m³. For a non-uniform grid with an intensity value of 900 lux·m³, the initial compensation requirement is 600 lux·m³. This difference reflects the increased light intensity needed for that area. The ambient light interference coefficient plays a moderating role in the compensation calculation.

[0048] It should be noted that the 1.5 multiplier corresponding to the high interference factor of 1.0 takes into account the instability of ambient light. When strong external light sources such as direct sunlight cause interference, simple compensation is often insufficient to offset its effect, thus requiring a stronger compensation light source. Conversely, a low interference factor of 0.3 indicates a relatively stable environment, in which case a standard compensation of 1.0 is sufficient.

[0049] For example, for an initial compensation requirement of 600 lux·m³, the actual compensation amount in high-interference areas is 900 lux·m³, while low-interference areas only require 600 lux·m³. The position and angle adjustment of the compensation light source follow the principles of optical projection.

[0050] In one embodiment, the compensation light source is initially positioned 2 meters directly above the center of the uneven grid. The angle formed by the line connecting this position to the grid center and the vertical line is the initial projection angle, typically 0 degrees. When the compensation amount is large, vertical projection may cause localized overexposure or produce strong shadows. In this case, by horizontally shifting the position of the light source, increasing the projection angle to 30 or 45 degrees, a softer supplementary lighting effect can be obtained.

[0051] For example, when the actual compensation reaches 900 lux·m³, the light source is shifted to the side by 1.5 meters, forming a projection angle of about 37 degrees, which satisfies the supplementary lighting needs while avoiding the discomfort caused by direct sunlight.

[0052] Step S105: Based on the adjusted projection angle and light source position, generate control parameters for the projection device, adjust the output intensity and direction of the projection light source, and obtain the light distribution intensity after the control parameters of the projection device are adjusted.

[0053] Based on the adjusted projection angle and the three-dimensional coordinates of the light source, the straight-line distance between the projection device and the center of the target grid is calculated. The actual light compensation is multiplied by the distance and then divided by the preset standard projection distance to obtain the target brightness parameter value of the projection device. Based on the target brightness parameter value, if the value is higher than the preset high brightness threshold, the color temperature parameter of the projection device is set to 6500K; if the value is lower than the preset low brightness threshold, the color temperature parameter is set to 3000K; otherwise, it is set to 4000K. Simultaneously, the projection angle value is used as the projection angle control parameter. The target brightness parameter value, color temperature parameter, and projection angle control parameter are sent to the projection device. The projection device adjusts the light source output power and rotates the projection lens to the specified angle according to the received parameters, completing the physical adjustment of the light source output intensity and projection direction. Based on the adjusted light source output power and projection angle, the light intensity contribution value of the projected light at each grid position is calculated according to the law of light intensity attenuation with the square of the distance. The calculated light intensity contribution value is added to the light intensity distribution data before adjustment to obtain the light distribution intensity after the adjustment of the projection device control parameters.

[0054] Specifically, the calculation of target brightness parameters needs to take into account both projection distance and compensation requirements.

[0055] In one possible implementation, when the projection device is 5 meters from the center of the target grid, and the standard projection distance is set to 3 meters, the actual required light source power needs to be increased accordingly. Assuming a grid requires 600 lux·cubic meters of light compensation, the calculation process is: 600 multiplied by 5 and then divided by 3, yielding a target brightness parameter value of 1000 lux·cubic meters. This adjustment ensures consistent lighting compensation effects at different distances. The choice of color temperature parameters directly affects the visual atmosphere of the exhibition space.

[0056] Specifically, a high color temperature of 6500K produces cool white light characteristics, which is suitable for areas that require strong lighting and can enhance the audience's attention and alertness; a low color temperature of 3000K produces warm yellow light effects, creating a warm and comfortable exhibition environment; and neutral white light of 4000K strikes a balance between the two.

[0057] For example, when the target brightness parameter value reaches 1200 lux·m³, exceeding the preset high brightness threshold of 1000 lux·m³, the system automatically selects a 6500K color temperature. This cool white light can enhance the visual clarity of high-brightness areas. Adjusting the parameters of a projection device involves the coordinated work of multiple hardware components.

[0058] It should be noted that the output power of the light source is adjusted by controlling the supply current. When a brightness of 1000 lux·m³ is required, the internal power control module will adjust the current accordingly. The angle adjustment of the projection lens is precisely controlled by a stepper motor, with each pulse signal corresponding to a 0.1-degree rotation angle, ensuring the accuracy of the projection direction. Color temperature adjustment is achieved through a built-in color temperature filter wheel, with different filter positions corresponding to different color temperature values. The light intensity attenuation calculation follows the basic laws of physical optics.

[0059] In one embodiment, the light intensity decaying with the square of the distance means that doubling the distance reduces the light intensity to one-quarter of its original value. The light beam emitted from the projection device experiences varying degrees of attenuation as it reaches different grid positions. The light intensity received by a grid 3 meters from the projection device is four times that of a grid 6 meters away. This attenuation pattern allows for the accurate calculation of the actual light intensity contribution received at each grid position. These contributions are then superimposed onto the original light intensity distribution data to form the complete compensated light field distribution.

[0060] Step S106: Determine the detail area based on the light intensity of each grid area, perform image quality assessment processing on the adjusted light distribution intensity, analyze and obtain the projection sharpness and detail fidelity, and obtain the contrast and color deviation values ​​of the detail area.

[0061] Based on the light intensity values ​​of each grid region, the difference in light intensity between adjacent grids is calculated. When the difference exceeds a preset gradient threshold, the corresponding grid boundary is marked as an intensity change boundary, and the continuous grid set containing the boundary is defined as a detail region. For the adjusted light distribution intensity data within the detail region, the light intensity difference sequence between each grid and its adjacent grids is calculated. The difference sequence is subjected to a Discrete Fourier Transform to obtain frequency components. When the sum of the amplitudes of components with frequencies greater than a preset frequency threshold exceeds a preset proportion of the total amplitude, a sharpness flag value of 1 is assigned; otherwise, it is 0. Simultaneously, the ratio of the standard deviation of light intensity to the mean light intensity within the region is calculated as the detail fidelity value. Based on the detail fidelity value and the light intensity distribution of the detail region, the maximum and minimum light intensity values ​​within the region are extracted. The difference between the two is calculated and divided by the average value to obtain the contrast ratio value. At the same time, the color temperature value corresponding to each grid is obtained from the color temperature parameter setting record of the projection device. The sum of the absolute values ​​of the differences between the color temperature value of each grid and the mean color temperature within the detail region is calculated to obtain the color deviation value.

[0062] Specifically, the identification of detailed areas is based on the spatial variation characteristics of light intensity.

[0063] In one possible implementation, when the light intensity difference between adjacent grids reaches 200 lux, it exceeds the preset gradient threshold of 150 lux, indicating that there is a significant lighting transition at that location.

[0064] For example, at the edges of exhibits, due to the boundary effect of the projected beam, there is often a sharp change from high to low light intensity. These intensity change boundaries are usually continuously distributed, forming halo areas surrounding the exhibit, which have a crucial impact on the projection display quality. The Discrete Fourier Transform plays an important role in analyzing light intensity change patterns.

[0065] Specifically, the sequence of light intensity differences within the detailed region is treated as a discrete signal and decomposed into sinusoidal components of different frequencies using Fourier transform. High-frequency components reflect rapid changes in light intensity, corresponding to edges and detailed features in the image; low-frequency components represent slow changes, corresponding to large areas of uniform illumination.

[0066] For example, a difference sequence containing 8 sampling points, after transformation, yields 8 frequency components. The high-frequency components with frequencies greater than 4 account for 70% of the total amplitude, exceeding the preset proportion of 60%. Therefore, the sharpness flag is set to 1, indicating that the area possesses rich detail information. The calculation of the detail fidelity value reflects the dispersion of light intensity distribution.

[0067] It's important to note that the standard deviation reflects the dispersion of light intensity values ​​around the mean; a larger standard deviation indicates more drastic changes in light intensity. When the standard deviation of light intensity in a certain detail area is 150 lux and the mean is 1000 lux, the ratio is 0.15. This ratio is directly used as the detail fidelity value; a smaller value indicates a more uniform light intensity distribution and higher detail fidelity. This quantification method can objectively assess the stability of the projected light field. The contrast ratio calculation uses a relative difference method to eliminate the influence of absolute brightness.

[0068] In one embodiment, the maximum luminous intensity within the detail area is 1800 lux, the minimum luminous intensity is 600 lux, and the average is 1200 lux. The contrast ratio is calculated by dividing the difference of 1200 by the average of 1200, resulting in a contrast ratio of 1.0. This normalized contrast ratio allows for fair comparisons across different brightness levels. Obtaining the color deviation value involves assessing color temperature consistency. The color temperature setting corresponding to each grid can be traced from the control logs of the projection device. For example, a detail area may contain 9 grids, with 5 grids set to 4000K, 3 grids to 3000K, and 1 grid to 6500K, resulting in an average color temperature of approximately 3944K. The absolute values ​​of the differences between each grid and the average are 56K, 56K, 56K, 56K, 56K, 944K, 944K, 944K, and 2556K, respectively, summing to 5678K, which is the color deviation value. A large deviation value indicates a significant color temperature inconsistency in that area, affecting the harmony of the visual effect.

[0069] Step S107: If the contrast and color deviation values ​​of the detail area do not meet the preset standards, the ambient light compensation mechanism is used to optimize the light source control parameters by combining the cumulative intensity data of the corner of the space to obtain the light compensation result.

[0070] If the contrast value of the detailed area is less than the preset contrast threshold or the color deviation value is greater than the preset deviation threshold, the current average light intensity value and the cumulative intensity data of the spatial corners of that detailed area are obtained. The absolute value of the difference between the two is calculated as the light intensity imbalance. The light intensity imbalance is multiplied by a preset compensation ratio coefficient to obtain an additional compensation light intensity value. A compensation coefficient is calculated based on the additional compensation light intensity value. The compensation coefficient is equal to the current average light intensity value plus the additional compensation light intensity value and then divided by the current average light intensity value. The existing light source power is multiplied by the compensation coefficient to obtain a new light source power value. At the same time, the illumination range radius is adjusted according to the ratio of the number of grids occupied by the detailed area to the number of preset standard grids, resulting in optimized control parameters that include the new light source power value and the illumination range radius. The optimized control parameters are applied to the projection device, and the light intensity distribution data of the detailed area is re-acquired. The new maximum and minimum light intensity values ​​are calculated to obtain the optimized contrast ratio. The optimized contrast ratio is subtracted from the unoptimized contrast ratio and then divided by the unoptimized contrast ratio to obtain the contrast improvement rate. At the same time, the unoptimized color deviation value is subtracted from the optimized color deviation value and then divided by the unoptimized color deviation value to obtain the color accuracy index. The contrast improvement rate and color accuracy index are output as the light compensation results.

[0071] Specifically, the calculation of light intensity imbalance reveals the degree of unevenness in the distribution of light in the exhibition space.

[0072] In one possible implementation, when the average light intensity in the detailed area is 800 lux, and the cumulative intensity in the corner reaches 1500 lux, the absolute difference between the two is 700 lux. This value directly reflects the lighting difference between this area and its surrounding environment. The preset compensation ratio is typically set between 0.6 and 0.8; assuming a value of 0.7, the additional compensation light intensity is 490 lux. This compensation method based on actual differences avoids new unevenness caused by overcompensation. The calculation of the compensation coefficient embodies the idea of ​​relative adjustment.

[0073] Specifically, the current average light intensity of 800 lux is added to an additional compensation light intensity of 490 lux, resulting in a target light intensity of 1290 lux. This target light intensity is then divided by the current light intensity of 800 lux, yielding a compensation coefficient of 1.61. If the existing light source power is 50 watts, multiplying by the compensation coefficient results in a new light source power of 80.5 watts. This proportional adjustment method ensures a linear relationship in the increase of light intensity, avoiding visual discomfort caused by abrupt changes. The dynamic adjustment of the illumination range is based on the actual coverage area of ​​the detailed region.

[0074] It should be noted that the preset standard grid number represents the ideal coverage capability of a single projection light source, for example, set to 25 grids. When the detail area contains 40 grids, the ratio is 1.6, indicating that the illumination range needs to be expanded. If the original illumination radius is 3 meters, the adjusted illumination range radius is 4.8 meters. This adjustment ensures that the entire detail area receives sufficient illumination coverage. The application of secondary optimization control parameters needs to consider the device's response time and stability.

[0075] In one embodiment, after the new 80.5-watt power value and 4.8-meter illumination radius parameters are sent to the projection device, the device needs a certain amount of time to complete internal adjustments. Power adjustment is achieved by changing the supply current, while illumination range adjustment is achieved by adjusting the focal length or position of the projection lens. After the adjustments are complete, re-acquiring light intensity distribution data in detailed areas becomes crucial for evaluating the optimization effect. The calculation of contrast improvement rate provides a quantitative indicator of improvement.

[0076] For example, if the contrast ratio before optimization is 0.8, and after optimization, the maximum light intensity of 1600 lux and the minimum light intensity of 800 lux are remeasured, the new contrast ratio is calculated to be 1.0. The contrast ratio improvement rate is the difference between the old and new contrast ratios (0.2) divided by the original contrast ratio of 0.8, resulting in 0.25, or a 25% improvement rate. The calculation of the color accuracy index is similar. If the original color deviation value is 5000K, and after optimization it is reduced to 2000K, then the color accuracy index is 0.6, indicating a 60% reduction in color deviation. These quantitative indicators provide an objective evaluation standard for improving the exhibition effect.

[0077] Step S108: Based on the light compensation results, continuously monitor the degree of ambient light interference, dynamically adjust the light compensation parameters, update the light field distribution data of the exhibition space, determine the stray light distribution characteristics, and obtain the real-time light uniformity.

[0078] Based on the light compensation results composed of contrast enhancement rate and color accuracy indicators, ambient light data is collected by a light sensor according to a preset monitoring cycle. The rate of change of light intensity and the angle between the main light source direction vector and the current time are calculated. If the rate of change or the angle exceeds a preset threshold, the ambient light interference coefficient is updated to a high value; otherwise, it is updated to a low value. Based on the updated ambient light interference coefficient and the current light source power value, when the ambient light interference coefficient is high, the light source power value is multiplied by a preset enhancement factor to obtain a new power parameter; when the ambient light interference coefficient is low, the current power parameter remains unchanged, and the new power parameter is sent to the projection device. After the projection device adjusts its output according to the new power parameter, it re-collects the light intensity data of each grid in the exhibition space, replaces the old values ​​at the corresponding positions in the light field distribution data with the collected new light intensity values, and obtains the real-time updated light field distribution data. Based on the real-time updated light field distribution data, the standard deviation and average value of the light intensity values ​​of all grids are calculated. The standard deviation is divided by the average value to obtain the coefficient of variation, which is the real-time light uniformity value.

[0079] Specifically, continuous monitoring of changes in ambient light is the foundation for achieving dynamic compensation.

[0080] In one possible implementation, the monitoring cycle is set to collect data every 5 minutes. This cycle captures the gradual change in ambient light without causing visual interference due to overly frequent adjustments. When the windows of the exhibition space are directly exposed to sunlight, the rate of change in light intensity may reach 50 lux per second within a short period, far exceeding the preset threshold of 20 lux. Simultaneously, the angle between the direction of sunlight incidence and the original lighting direction may reach 60 degrees, exceeding the preset threshold of 45 degrees. If either of these conditions is met, the ambient light interference coefficient is updated to a high value of 1.0, indicating that enhanced compensation is needed. The binarization design of the ambient light interference coefficient simplifies the control logic.

[0081] Specifically, the settings of a high value of 1.0 and a low value of 0.3 are based on extensive practical experience. The high value corresponds to scenarios with strong external light source interference, such as direct midday sunlight or temporary auxiliary lighting; the low value corresponds to relatively stable indoor environments, such as cloudy days or nighttime exhibitions. This simplification avoids complex continuous adjustments and improves the stability and reliability of the system. The power parameter adjustment uses a multiple relationship to achieve rapid response.

[0082] It should be noted that the preset enhancement factor is typically set between 1.3 and 1.5. Assuming the current light source power is 60 watts, when the ambient light interference coefficient is updated to a high value, the new power parameter is 60 multiplied by 1.4, equaling 84 watts. This adjustment range effectively compensates for the influence of external light sources without causing over-illumination. When the interference coefficient is low, the original power remains unchanged, avoiding unnecessary energy consumption and equipment damage. The real-time data update mechanism ensures the accuracy of the light field distribution.

[0083] In one embodiment, the exhibition space is divided into 200 grids, and each re-collection requires acquiring 200 light intensity data points. The newly acquired data directly overwrites the original data. For example, if the original light intensity value of a grid is 1000 lux, and after power adjustment, the new measured value is 1300 lux, then the data at that location is updated immediately. This real-time update method ensures that subsequent calculations are based on the latest actual conditions. The coefficient of variation, as a quantitative indicator of light uniformity, has good normalization properties.

[0084] For example, when the standard deviation of all grid light intensity values ​​is 180 lux and the average is 1200 lux, the coefficient of variation is 0.15. This dimensionless value allows for fair comparisons at different brightness levels. The smaller the coefficient of variation, the more uniform the light intensity distribution. By continuously monitoring the trend of this indicator, the effect of dynamic compensation can be evaluated. When the coefficient of variation stabilizes below 0.2, it indicates that the exhibition space has achieved good light uniformity.

[0085] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for intelligent adjustment of projection light and shadow effects based on artificial intelligence, characterized in that, The method includes: By using a pre-established wall material database, the material types and reflectance coefficients of the walls in each area of ​​the exhibition space are obtained. The reflectance characteristics of different materials are then classified to obtain a material reflectance distribution map. Real-time acquisition of ambient lighting data at various locations in the exhibition space; analysis of light intensity change rate and main light source direction vector; determination of ambient light interference coefficient distribution; based on the ambient light interference coefficient distribution and the material reflection distribution map, tracking of light reflection path on the wall surface; determination of stray light distribution characteristics. Based on the stray light distribution characteristics, spatial light intensity distribution data is obtained, decomposed into grid cells, and the total light intensity value and light field distribution uniformity mark of each grid area are calculated. The uneven areas indicated by the light field distribution uniformity markers are areas of uneven illumination. The position of the light source and the projection angle of the projection device are adjusted in conjunction with the distribution of the ambient light interference coefficient. Based on the adjusted light source position and projection angle, control parameters for the projection device are generated, the output intensity and direction of the light source are adjusted, and the adjusted light distribution intensity data is obtained. Based on the light distribution intensity data, detailed areas are identified, and contrast and color deviation values ​​are calculated; the light source control parameters are then optimized based on the contrast and color deviation values. Based on the optimized light source control parameters, the distribution of the ambient light interference coefficient is continuously monitored, the light compensation parameters are dynamically adjusted, the light field distribution data is updated, and the real-time light uniformity value is calculated.

2. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The process involves using a pre-established wall material database to obtain the material types and reflectivity of the walls in each area of ​​the exhibition space. Different materials are then categorized based on their reflectivity characteristics to obtain a material reflectivity distribution map, including: Material identifiers and spatial coordinates of each area in the exhibition space are extracted from the wall material database. The material type and reflectance coefficient value corresponding to the material identifier are queried. Based on the reflectance coefficient value, the area is classified into low reflectance, medium reflectance, and high reflectance categories, and corresponding grayscale values ​​are assigned to each category. Based on the spatial coordinates and the grayscale values, the outlines of each area are drawn on a two-dimensional plane and filled with the corresponding grayscale values ​​to form a preliminary reflectance distribution image. Edge detection is performed on the preliminary reflectance distribution image to mark the locations of grayscale value changes, generating the material reflectance distribution map containing boundary information.

3. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The system collects ambient lighting data at various locations within the exhibition space in real time, analyzes the rate of change of light intensity and the direction vector of the main light source, determines the distribution of the ambient light interference coefficient, and, based on the distribution of the ambient light interference coefficient and the material reflection distribution map, tracks the reflection path of light on the wall surface to determine the stray light distribution characteristics, including: The illumination sensor collects illumination intensity values ​​and timestamps at various locations, calculates the difference in illumination intensity values ​​between adjacent timestamps to obtain the rate of change of illumination intensity; based on the ratio of the difference in illumination intensity between adjacent locations to the distance, the direction vector of the main light source is determined; based on the rate of change of illumination intensity and the direction vector of the main light source, an ambient light interference coefficient is assigned to generate the ambient light interference coefficient distribution; based on the ambient light interference coefficient distribution and the material reflection distribution map, a virtual light beam is emitted to trace the propagation path of the light on walls with different reflectivity, and the intensity attenuation after each reflection is calculated; the cumulative intensity value of the reflected light at spatial grid points is statistically analyzed to generate the stray light distribution characteristics containing the intensity distribution data.

4. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The process of acquiring spatial light intensity distribution data based on the stray light distribution characteristics, decomposing it into grid cells, and calculating the total light intensity value and light field distribution uniformity marker for each grid region includes: Light intensity values ​​are extracted from the stray light distribution characteristics to construct light intensity distribution data covering the exhibition space; the light intensity distribution data is divided into cubic grid cells to obtain the light intensity value set within each grid cell; for the light intensity value set, the light intensity integral value between adjacent points is calculated to obtain the total light intensity value of each grid area; based on the total light intensity value, the sum of squares of the light intensity difference with adjacent grids is calculated to determine the uniformity mark of the light field distribution.

5. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The marking of uneven light field distribution indicates areas of uneven illumination. Adjusting the light source position and projection angle of the projection device based on the ambient light interference coefficient distribution includes: Identify the non-uniform grid regions marked by the uniformity of the light field distribution and extract the total light intensity value; calculate the average light intensity value of the uniform grid region and determine the difference between it and the total light intensity value; calculate the light compensation amount based on the difference and the distribution of the ambient light interference coefficient; and determine the light source position and projection angle of the compensation light source based on the light compensation amount and the grid center coordinates.

6. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The process of generating control parameters for the projection device based on the adjusted light source position and projection angle, adjusting the light source output intensity and direction, and obtaining adjusted light distribution intensity data includes: Based on the light source position and the projection angle, calculate the distance between the projection device and the center of the target grid to determine the target brightness parameter value; set the color temperature parameter and projection angle parameter according to the target brightness parameter value; send the target brightness parameter value, the color temperature parameter, and the projection angle parameter to the projection device to adjust the light source output; update the light distribution intensity data according to the adjusted light source output.

7. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The process of identifying detailed areas and calculating contrast and color deviation values ​​based on the light distribution intensity data includes: Based on the light distribution intensity data, calculate the light intensity difference between adjacent grids, mark the grid boundaries exceeding the threshold, and define the region containing the grid boundaries as the detail region; for the light distribution intensity data of the detail region, calculate the frequency components of the light intensity difference sequence to determine the sharpness marker value; based on the light distribution intensity data, calculate the maximum and minimum light intensity differences to obtain the contrast ratio; based on the color temperature parameter, calculate the color temperature difference to obtain the color deviation value.

8. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The optimization of light source control parameters based on contrast and color deviation values ​​includes: When the contrast ratio and color deviation values ​​do not meet the preset standards, the average light intensity value of the detail area and the light intensity data of the stray light distribution characteristics are obtained, and the light intensity imbalance is calculated. Based on the light intensity imbalance and the current light intensity value, the compensation coefficient is determined, the light source power value and the illumination range radius are adjusted, and the optimized light source control parameters are generated.

9. The intelligent adjustment method for projection light and shadow effects based on artificial intelligence according to claim 1, characterized in that, The process of continuously monitoring the ambient light interference coefficient distribution based on optimized light source control parameters, dynamically adjusting light compensation parameters, updating light field distribution data, and calculating real-time light uniformity values ​​includes: Based on the optimized light source control parameters, ambient light data is periodically collected, the light intensity change rate and the angle between the main light source direction vectors are calculated, and the ambient light interference coefficient distribution is updated. Based on the ambient light interference coefficient distribution, the light source power parameters are adjusted. Based on the adjusted light source power parameters, new light intensity data is collected, and the light field distribution data is updated. Based on the light field distribution data, the light intensity variation coefficient is calculated to obtain the light uniformity value.

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