A projector control method and a projector

Abstract

This invention relates to the field of projector technology, and more particularly to a projector control method and a projector. The method includes the following steps: processing target projection image data to generate initial projection screen data; performing spatiotemporal registration of the initial projection screen data using a dual-LiDAR assembly to obtain projection scan point cloud spatial data; analyzing the geometric features of occlusion areas in the projection scan point cloud spatial data and determining the ideal projection area to generate ideal rectangular projection area data; processing the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters; and processing the intelligent projection control parameters with projection attitude control commands to obtain projection control optimization effect data. This invention achieves automatic adjustment of projection control parameters through intelligent projection surface recognition, enabling the projector to maintain stable projection effects in different environments and significantly improving the quality of the projected image.

Description

Technical Field

[0001] This invention relates to the field of projector technology, and more particularly to a projector control method and a projector. Background Technology

[0002] Projection display technology is widely used in various scenarios, such as business presentations, education, and home entertainment. Users have increasingly higher demands for projection display quality and convenience. However, the uncertainty of the projection environment has become a challenge. These environments include projection screens of different locations, shapes, and sizes, as well as obstacles and light interference. For example, the usage environment of projectors varies in different settings such as homes, educational institutions, conferences, and exhibitions, making projector setup complex. In these complex scenarios, the location and shape of the projection screen, as well as obstacles in the projection path (e.g., pillars, tables, chairs, etc. in some conference rooms or exhibition halls), can cause incomplete or distorted projected images, affecting the viewing experience. Traditional projector methods rely mainly on manually adjusting the projection angle and focus for keystone correction. This method is cumbersome, inefficient, and difficult to accurately correct, especially when the projection screen is irregularly shaped or has obstructions. Manual adjustment becomes even more difficult, easily leading to incomplete, distorted, or incorrectly corrected images, severely impacting the projection effect. Summary of the Invention

[0003] Based on this, the present invention provides a projector control method and a projector to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a projector control method includes the following steps:

[0005] Step S1: Acquire target projection image data; The image processor processes the target projection image data into an image projection signal and controls the projection lens assembly to project the image, generating initial projection image data; The dual lidar assembly performs spatiotemporal registration of the projection point cloud into the initial projection image data to obtain the projection scan point cloud spatial data.

[0006] Step S2: Extract the projection plane boundary points from the spatial data of the projected scan point cloud to generate projection plane boundary point cloud data; perform projection plane geometric feature processing based on the projection plane boundary point cloud data to generate projection plane geometric feature data.

[0007] Step S3: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data; perform occlusion area geometric feature analysis on the projection image contour data using projection surface geometric feature data to generate occlusion area geometric feature data; determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

[0008] Step S4: Process the projection posture control command for the intelligent projection control parameters, and use the image sensor to collect real-time projection images to obtain real-time projection image data; evaluate the projection control effect based on the real-time projection image data to obtain projection control optimization effect data.

[0009] This invention effectively acquires spatial data within the projection area by generating an initial projection image and precisely registering point clouds using dual lidar sensors. This ensures that the projection effect can be intelligently adjusted under different conditions of the projection screen's position, size, and shape. Further extraction of projection surface boundary points and geometric feature processing allows for the identification and adaptation to the contour features of complex projection surfaces, making the projected image more closely match the actual projection surface. Even when the projection screen is irregular or contains obstacles, it can automatically correct itself, achieving complete projection area and distortion-free image projection. Furthermore, by intelligently analyzing the geometric feature data of obstructed areas, the optimal projection area can be flexibly determined, thus avoiding image loss caused by obstacles and ensuring a consistent viewing experience. The generated projection control parameters, combined with real-time image acquisition and feedback evaluation, enable the projector to intelligently adjust its projection posture and dynamically optimize the display effect according to environmental changes. This allows the projector to maintain stable correction effects in different environments without manual adjustment, significantly improving the automatic adaptability of projection accuracy and effect. The intelligent and real-time response features of this solution are particularly suitable for complex projection needs in various scenarios such as home, education, and business presentations. It successfully solves the problems of cumbersome operation and inaccurate calibration in traditional manual adjustment methods, making projection operation more convenient and efficient, and significantly improving the application flexibility and user experience of the projector. Therefore, the projector control method of this invention uses dual lidar components to perform high-precision spatial scanning of the projection area, analyze the geometric features of the projection surface, and automatically optimize the projector's posture and image correction parameters to adapt to various complex and changing projection environments. It achieves real-time adjustment of projection parameters, ensuring clear and distortion-free projection effects on projection surfaces of different positions, shapes, and sizes.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire target projection image data; The image processor performs image projection signal processing on the target projection image data and controls the projection lens assembly to project the image, generating initial projection image data;

[0012] Step S12: Based on the preset scanning frequency data, use the dual lidar components to perform a dual-path horizontal scan of the projection area of ​​the initial projection image data to generate left and right projection horizontal scan data.

[0013] Step S13: Use a servo motor to drive the dual lidar components to perform vertical interval layer scanning of the projection area, and generate vertical layer scanning data of the projection area.

[0014] Step S14: Perform point cloud noise filtering on the horizontal scanning data of the left and right projections and the vertical layered scanning data of the projection area to obtain the horizontal scanning point cloud data and the vertical scanning point cloud data of the projection respectively.

[0015] Step S15: Perform point cloud density equalization processing based on the projected horizontal scan point cloud data and the projected vertical scan point cloud data, and perform spatiotemporal registration of the projected point cloud to obtain the projected scan point cloud spatial data.

[0016] This invention ensures a highly consistent projection foundation across various environments through precise image projection signal processing and initial projection image generation. Based on a preset scanning frequency, dual lidar horizontal scanning effectively divides the projection area into left and right scanning channels, allowing for comprehensive capture of details in the horizontal region of the projection surface. A servo motor controls the radar components to achieve vertically spaced layered scanning, further refining the point cloud distribution in the vertical direction and ensuring omnidirectional capture of the projection surface's depth information. This combination of horizontal and vertical bidirectional scanning makes spatial feature recognition of the projection area more accurate, avoiding information loss due to complex environments or obstacles. Noise filtering of the horizontal and vertical scan data eliminates unnecessary environmental interference, improving the purity of the point cloud data. Finally, point cloud density equalization ensures uniform spatial distribution of the scanned point cloud, thereby achieving high-precision spatiotemporal registration of the projected image.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Perform coarse plane fitting on the spatial data of the projected scanned point cloud to generate initial fitting projection plane parameters;

[0019] Step S22: Iteratively optimize the initial fitted projection plane parameters with weighted parameters to obtain the optimized fitted projection plane parameters;

[0020] Step S23: Use the spatial data of the projected scanning point cloud to perform point cloud mapping processing on the optimized fitted projection plane parameters, and extract the boundary points of the projection plane to generate the point cloud data of the projection plane boundary.

[0021] Step S24: Perform boundary point clustering on the projection plane boundary point cloud data and perform polygon fitting on the boundary point clusters to obtain the projection plane polygon boundary data;

[0022] Step S25: Perform projection plane corner point identification on the projection plane boundary point cloud data to obtain projection plane corner point coordinate data;

[0023] Step S26: Perform geometric feature processing on the corner coordinate data of the projection plane using the polygon boundary data of the projection plane to generate geometric feature data of the projection plane.

[0024] This invention effectively obtains the initial geometric features of the projection area by coarsely fitting the spatial data of the projection scanning point cloud. Through iterative weighted optimization of parameters, the accuracy of the projection plane fitting is further improved, allowing the projection plane parameters to more accurately conform to the actual environment, especially when facing irregular or tilted projection surfaces, ensuring the flatness and integrity of the projected image. Subsequently, point cloud mapping processing and boundary point extraction accurately identify the boundary contours of the projection surface, greatly improving the adaptability to complex boundary shapes. Boundary point clustering processing and polygon fitting further transform the boundary data into continuous and clear polygonal boundaries, enabling accurate image display on irregular projection screens and avoiding boundary distortion. Through corner point recognition, not only are the corner coordinates of the projection plane captured, but precise positioning on the polygonal boundaries is also achieved. This accurate boundary recognition ensures complete coverage of the projected image and clear edge rendering. By processing the geometric features of the polygonal boundary data and corner coordinate data, complete geometric feature data of the projection surface is generated, enhancing the adaptability of the projection system to complex-shaped projection surfaces.

[0025] Preferably, step S3 includes the following steps:

[0026] Step S31: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data;

[0027] Step S32: Analyze the geometric features of the occlusion area of ​​the projected image outline data using the geometric feature data of the projection surface, and generate the geometric feature data of the occlusion area;

[0028] Step S33: Analyze the spatial distribution of projection occlusion by analyzing the geometric feature data of the occluded area using the geometric feature data of the projection surface, and obtain the spatial distribution data of projection occlusion;

[0029] Step S34: Determine the ideal projection area based on the spatial distribution data of projection occlusion, and generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

[0030] This invention rapidly and accurately acquires the contour data of the initial projected image through projection image contour recognition. Combined with the geometric feature data of the projection surface, it performs geometric feature analysis on obstructed areas in the image, effectively identifying and distinguishing these areas. Based on this spatial distribution analysis of obstructed areas, it precisely locates the specific position and range of each obstruction, providing detailed spatial distribution data. This data provides a basis for determining the ideal projection area, enabling the control system to avoid obstructed areas and intelligently select a suitable rectangular projection area for display, thus ensuring image integrity and projection quality. Through the generation of intelligent projection control parameters, it further achieves dynamic optimization control of the projected image, allowing the projection area to be flexibly adjusted and automatically adapt to complex spatial distributions in different environments. This not only achieves intelligent avoidance of obstructed areas and adaptive selection of the projection area but also improves the visibility and overall visual experience of the projected image, solving the problems of image loss and distortion caused by obstacles.

[0031] Preferably, step S32 includes the following steps:

[0032] Step S321: Analyze the actual projection contour difference of the projection image contour data using the geometric feature data of the projection surface, and generate actual projection difference contour data.

[0033] Step S322: Based on the actual projection difference contour data, identify potential occluder point clouds in the spatial data of the projected scan point cloud to obtain potential occluder point cloud data;

[0034] Step S323: Cluster the point cloud data of potential occlusion areas to generate spatial region data of occlusion clusters;

[0035] Step S324: Extract the boundaries of occlusion objects based on the spatial region data of occlusion object clustering to generate occlusion region boundary data;

[0036] Step S325: Classify the shape of the occlusion object in the boundary data of the occlusion area to obtain the shape classification data of the occlusion area;

[0037] Step S326: Perform geometric feature analysis of the occluded area based on the shape classification data and boundary data of the occluded area to generate geometric feature data of the occluded area.

[0038] This invention accurately identifies the deviation between the actual projected image and the expected projected outline by analyzing the difference between the projected image contour data and the geometric feature data of the projection surface, generating actual projection difference contour data. Based on the difference contour data, potential occlusions are further identified in the projected scan point cloud data, thereby accurately identifying obstacles affecting the projection effect. The effective execution of this step can proactively detect and avoid any interference affecting the projection area, preventing missing or distorted projected images due to obstacles. By clustering the potential occlusion point cloud data, the specific distribution areas of these obstacles in the projection space are identified, making the influence range of each occlusion more clearly defined, ensuring that occluded areas can be accurately identified and distinguished from other areas, avoiding misjudgments and omissions. By classifying the shape of the occlusion area boundaries, the nature and location of different types of occlusions can be understood more intelligently, thereby formulating the most effective avoidance strategy. In-depth analysis of the geometric features of the occlusion area provides comprehensive geometric data support for intelligent projection control, ensuring the maximization of projection effect and image integrity.

[0039] Preferably, step S34 includes the following steps:

[0040] Step S341: Calculate the projection boundary adjustment distance based on the projection occlusion spatial distribution data to obtain the projection boundary adjustment distance data;

[0041] Step S342: Adjust the projection occlusion spatial distribution data by adjusting the projection boundary distance data to generate target projection adjustment boundary data;

[0042] Step S343: Use the target projection to adjust the boundary data to identify the unobstructed overlapping area of ​​the geometric feature data of the occluded area, and perform the visibility assessment of the projected area to obtain the area visibility assessment data.

[0043] Step S344: Based on the target projection adjustment boundary data, use the area visibility assessment data to correct the projection boundary adjustment distance data and generate the optimal projection adjustment boundary data;

[0044] Step S345: Estimate the projector position offset based on the geometric feature data of the projection surface using the optimal projection adjustment boundary data, and perform gradient descent optimization to generate projector position adjustment data;

[0045] Step S346: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data; perform projection image correction control processing on the ideal rectangular projection area data to generate projection image correction control data;

[0046] Step S347: Combine the projector position adjustment data and the projected image correction control data into intelligent projector control parameters to obtain intelligent projection control parameters.

[0047] This invention calculates the projection boundary adjustment distance using spatial distribution data of projection occlusion. This adjustment distance data allows the controller to clearly define the actual boundary of the projection area and generate target projection adjustment boundary data through boundary mapping, ensuring that the projection area conforms to the spatial distribution requirements of the actual environment. By analyzing the geometric feature data of the occlusion area, unobstructed overlapping areas can be identified, and regional visibility can be evaluated to accurately assess the projection effect and visibility of each area. Based on these evaluation results, the projection boundary adjustment distance is corrected, generating optimal projection adjustment boundary data to ensure maximum coverage and optimal display effect of the projection area. By applying the optimal projection adjustment boundary data, and by estimating the projector position offset and performing gradient descent optimization, precise adjustment of the projector position is achieved, ensuring optimal projection angle and focal length, and avoiding unnecessary image distortion or misalignment. Based on the new projector position adjustment data, the ideal projection area is redefined, and the projected image is corrected and controlled to ensure that the image remains clear and distortion-free in different environments. Finally, these adjustment data are combined into intelligent projection control parameters to ensure that the projector can automatically adapt to any complex environment and achieve accurate projection effects.

[0048] Preferably, step S346 includes the following steps:

[0049] Step S3461: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data;

[0050] Step S3462: Perform projection region triangulation on the ideal rectangular projection region data to generate ideal projection sub-region division data;

[0051] Step S3463: Construct the topological relationship of the ideal projection sub-regions based on the ideal projection sub-region division data to obtain the projection sub-region topological data;

[0052] Step S3464: Map the corner points of the projected sub-region based on the topology data of the projected sub-region, and calculate the local perspective transformation matrix to generate local perspective transformation matrix data;

[0053] Step S3465: Perform optical path projection processing on the local perspective transformation matrix data to generate an optical path projection angle matrix; perform projection image correction control processing based on the optical path projection angle matrix to generate projection image correction control data.

[0054] This invention determines an ideal rectangular projection area based on projector position adjustment data, ensuring the projection area meets actual requirements and avoiding image distortion or misalignment caused by projector position deviations. The ideal rectangular projection area is triangulated into multiple sub-regions, effectively subdividing a large projection area into smaller, more manageable parts. This operation allows for more precise projection adjustments, adapting to projection surfaces of different shapes and sizes. The topological relationships between the projection sub-regions are established based on the sub-region division, clarifying the spatial and positional relationships between each sub-region. Further processing of the sub-region topological data enables corner mapping and the generation of local perspective transformation matrices, effectively handling geometric deformations caused by angle changes in the projection surface and ensuring accurate presentation of the projected image within different sub-regions. Finally, by calculating the light path projection angle matrix, the changes in the light path during projection are successfully simulated, and based on this, projection image correction is performed, correcting image distortion caused by changes in projection angle or shape.

[0055] Preferably, step S3465 includes the following steps:

[0056] Projection distortion prediction is performed based on the projector position adjustment data to obtain projection distortion prediction data;

[0057] Based on the local perspective transformation matrix data, light path projection processing is performed to generate a light path projection angle matrix;

[0058] Based on the projection distortion prediction data, the optical path projection angle matrix is ​​used to calculate the sub-region distortion compensation amount of the topology data of the projection sub-region, and generate the sub-region deformation compensation amount data.

[0059] Based on the deformation compensation data of the sub-region, the projected pixels are remapped and the projected image is scaled to generate image projection reconstruction data.

[0060] The ideal rectangular projection area data is processed by image projection reconstruction data and then a projection area mask is generated.

[0061] Based on the projection area mask data and image projection reconstruction data, projection image correction control processing is performed to generate projection image correction control data.

[0062] This invention predicts projection distortion using projector position adjustment data, accurately estimating potential image distortion and avoiding visual inaccuracies caused by projection angle or spatial limitations. It utilizes local perspective transformation matrix data for optical path projection processing, generating an optical path projection angle matrix to accurately simulate changes in the light path across different regions. Based on the projection distortion prediction data and the optical path projection angle matrix, distortion compensation is calculated for the topological data of the projection sub-regions, effectively correcting geometric distortions in each sub-region during projection and ensuring accurate, distortion-free images in each sub-region. Pixel remapping and scaling are performed on the image based on the compensation data, ensuring distortion-free image presentation within the projection area. Projection area masking of the image projection reconstruction data effectively isolates effective and invalid projection areas, ensuring the image is presented only within the actual visible area. Projection image correction control processing using projection area mask data and image projection reconstruction data ensures the projector automatically adjusts, eliminating any image distortion caused by projection angle or occlusion, thus maintaining image clarity and accuracy even in complex environments.

[0063] Preferably, step S4 includes the following steps:

[0064] Step S41: Process the intelligent projection control parameters with projection attitude control commands to obtain optimized projection control command data;

[0065] Step S42: Execute motor control commands according to the optimized projection control command data, and use an image sensor to collect real-time projection images to obtain real-time projection image data;

[0066] Step S43: Identify the projection reference mark on the real-time projection screen data using the preset projection reference mark data, extract key feature points of the projection screen, and generate optimized projection screen feature point data.

[0067] Step S44: Calculate the geometric distortion of the feature point data of the optimized projection screen to generate optimized geometric distortion data; evaluate the edge sharpness of the real-time projection screen data to obtain edge sharpness data; evaluate the brightness uniformity of the real-time projection screen data to obtain brightness uniformity data; evaluate the image projection integrity of the real-time projection screen data to obtain image projection integrity data.

[0068] Step S45: Based on the optimized feature point data of the projected image, evaluate the color reproduction of the real-time projected image data and the initial projected image data to obtain color reproduction data;

[0069] Step S46: Use preset projection control optimization weight data to perform weighted evaluation of the control effect on the optimized geometric distortion data, edge sharpness data, brightness uniformity data, image projection integrity data, and color reproduction data, and obtain projection control optimization effect data.

[0070] This invention optimizes projection posture control through intelligent projection control parameters, enabling the projection system to adapt to changes in angle and position in real time, ensuring consistently stable projection effects. By controlling the motor execution of optimized control commands and combining this with real-time image data collected by an image sensor, the system continuously monitors and acquires the latest projection status information during projection. In real-time image data acquisition, based on preset projection reference mark recognition and key feature point extraction, key features of the projected image are accurately obtained, resulting in more refined control of geometric distortion. Multi-dimensional evaluation of indicators such as edge sharpness, brightness uniformity, and image integrity ensures that the overall image quality meets ideal visual effects. Color reproduction evaluation of the real-time image and the initial projected image further guarantees the color performance of the projected image, maintaining color consistency and naturalness under different ambient light conditions. Weighted processing of multiple evaluation data, including geometric distortion, edge sharpness, brightness uniformity, projection integrity, and color reproduction, using preset optimization weights, effectively evaluates the overall performance of the projection effect, significantly improving the intelligence and automation level of the projection system.

[0071] Preferably, the present invention also provides a projector, which includes a projector host and a dual lidar assembly. The projector host internally houses a projection lens assembly, an image processor, and a main controller. The dual lidar assembly includes a first lidar, a second lidar, and two servo motors. The first and second lidars are respectively fixed on both sides of the projector host and are driven by the servo motors to perform vertical scanning. The dual lidar assembly, the projection lens assembly, and the image processor are electrically connected to the main controller via a data bus. The main controller is used to execute the projector control method described above, and the main controller includes the following modules:

[0072] The projection point cloud processing module is used to acquire target projection image data; the image processor processes the target projection image data for image projection signal processing and controls the projection lens assembly to project the image, generating initial projection image data; the dual lidar assembly performs spatiotemporal registration of the initial projection image data to obtain projection scan point cloud spatial data.

[0073] The projection surface feature extraction module is used to extract projection surface boundary points from the spatial data of the projection scan point cloud and generate projection plane boundary point cloud data; and to perform projection surface geometric feature processing based on the projection plane boundary point cloud data to generate projection surface geometric feature data.

[0074] The projection control parameter optimization module is used to identify the projection image contour from the initial projection image data to obtain projection image contour data; to analyze the geometric features of the occlusion area from the projection image contour data using the geometric feature data of the projection surface to generate occlusion area geometric feature data; to determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; and to process the ideal rectangular projection area data using intelligent projection control parameters to obtain intelligent projection control parameters.

[0075] The projection attitude control module is used to process projection attitude control commands for intelligent projection control parameters, and to acquire real-time projection images using an image sensor to obtain real-time projection image data; based on the real-time projection image data, the projection control effect is evaluated to obtain projection control optimization effect data. Attached Figure Description

[0076] Figure 1 This is a flowchart illustrating the steps of a projector control method according to the present invention;

[0077] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S1.

[0078] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4.

[0079] Figure 4 A schematic diagram of the projector's square frame;

[0080] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0081] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0082] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0083] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0084] To achieve the above objectives, please refer to Figures 1 to 4 This invention provides a projector control method, comprising the following steps:

[0085] Step S1: Acquire target projection image data; The image processor processes the target projection image data into an image projection signal and controls the projection lens assembly to project the image, generating initial projection image data; The dual lidar assembly performs spatiotemporal registration of the projection point cloud into the initial projection image data to obtain the projection scan point cloud spatial data.

[0086] Step S2: Extract the projection plane boundary points from the spatial data of the projected scan point cloud to generate projection plane boundary point cloud data; perform projection plane geometric feature processing based on the projection plane boundary point cloud data to generate projection plane geometric feature data.

[0087] Step S3: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data; perform occlusion area geometric feature analysis on the projection image contour data using projection surface geometric feature data to generate occlusion area geometric feature data; determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

[0088] Step S4: Process the projection posture control command for the intelligent projection control parameters, and use the image sensor to collect real-time projection images to obtain real-time projection image data; evaluate the projection control effect based on the real-time projection image data to obtain projection control optimization effect data.

[0089] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of the projector control method of the present invention. In this embodiment, the projector control method includes the following steps:

[0090] Step S1: Acquire target projection image data; The image processor processes the target projection image data into an image projection signal and controls the projection lens assembly to project the image, generating initial projection image data; The dual lidar assembly performs spatiotemporal registration of the projection point cloud into the initial projection image data to obtain the projection scan point cloud spatial data.

[0091] In this embodiment of the invention, target projection image data is acquired, for example, by reading an image or video file to be projected from a computer, mobile device, or other data source. An image processor receives this data and performs image projection signal processing, such as adjusting image resolution, color space, brightness, and contrast to adapt to the characteristics of the projector and the projection environment. The processed image data is sent to the projection lens assembly, which controls the projection to generate an initial projection image. Simultaneously, a dual-laser radar assembly begins operation, scanning the projection area at a preset scanning frequency, emitting laser beams and receiving reflected signals. By analyzing the flight time and intensity of the laser beams, three-dimensional point cloud data of the projection area can be obtained. This point cloud data represents the positional information of the projected image in three-dimensional space. To ensure the point cloud data corresponds to the projected image data, spatiotemporal registration of the point cloud is required. This can be achieved by embedding known patterns or markers in the projected image, or by using image processing technology to identify feature points of the projected image, thereby establishing a spatial correspondence between the point cloud data and the image data, ultimately obtaining the projected scan point cloud spatial data.

[0092] Step S2: Extract the projection plane boundary points from the spatial data of the projected scan point cloud to generate projection plane boundary point cloud data; perform projection plane geometric feature processing based on the projection plane boundary point cloud data to generate projection plane geometric feature data.

[0093] In this embodiment of the invention, the projected scanned point cloud spatial data obtained in step S1 is used to perform coarse plane fitting of the point cloud using the RANSAC (Random Sample Consensus) algorithm to obtain initial fitted projection plane parameters, such as the plane normal vector and the distance between the plane and the origin. To improve the fitting accuracy, iterative weighted least squares optimization is performed on the initial fitted projection plane parameters to better adapt the fitted plane to the distribution of the projected point cloud data. The optimized projection plane parameters are then used to perform point cloud mapping processing on the projected scanned point cloud spatial data, projecting the point cloud data onto the fitted plane. Based on the distribution of the point cloud on the plane, edge detection algorithms are used to extract the boundary points of the projection plane, generating projection plane boundary point cloud data. Next, DBSCAN (Density-Based Spatial Clustering for Noise Applications) clustering processing is performed on the boundary point cloud data to divide the boundary points into several clusters. Polygon fitting is performed on each point cluster, for example, using least squares to fit polygons, to obtain the polygon boundary data of the projection plane. Corner detection algorithms, such as the Harris corner detection algorithm, are used to identify the corners of the projection plane, obtaining the corner coordinate data of the projection plane. Based on the polygon boundary data and corner coordinate data of the projection plane, calculate the geometric features of the projection plane, such as area, perimeter, aspect ratio, and angle, and generate geometric feature data of the projection plane.

[0094] Step S3: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data; perform occlusion area geometric feature analysis on the projection image contour data using projection surface geometric feature data to generate occlusion area geometric feature data; determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

[0095] In this embodiment of the invention, edge detection and contour extraction are performed on the initial projected image data generated in step S1. For example, edge detection is performed using the Canny operator, and then morphological processing methods are used to extract the projection image contour data. Using the geometric feature data of the projection surface generated in step S2, combined with the projection image contour data, occlusion region analysis is performed on the projected image. By comparing the projection image contour with the projection surface boundary, occluded areas in the projected image are identified, and the spatial position and shape of the occluder are determined using point cloud data. Point cloud clustering is performed on the identified occluded areas to extract the boundaries of the occluded areas and classify their shapes (e.g., rectangles, circles, irregular shapes, etc.), ultimately generating geometric feature data of the occluded areas. Based on the geometric feature data of the occluded areas and the geometric feature data of the projection surface, the ideal projection area is determined. The ideal projection area is typically a rectangular area whose size and position maximize the use of the projection surface and avoid occlusion. Finally, based on the data of the ideal rectangular projection area, the corresponding projection parameters, such as projection angle, scaling ratio, and offset, are calculated to obtain intelligent projection control parameters.

[0096] Step S4: Process the projection posture control command for the intelligent projection control parameters, and use the image sensor to collect real-time projection images to obtain real-time projection image data; evaluate the projection control effect based on the real-time projection image data to obtain projection control optimization effect data.

[0097] In this embodiment of the invention, projection posture control commands are generated based on the intelligent projection control parameters generated in step S3, such as controlling the horizontal and vertical rotation angles of the projector and the lens focal length. These commands are sent to the projector's motor control system to control the projector to adjust its posture. Simultaneously, an image sensor acquires real-time projection image data. Using preset projection reference marker data, projection reference markers are identified in the real-time projection image data, and key feature points of the projection image, such as the four corner points, are extracted. The deviation of these feature points from their ideal positions is calculated to obtain the geometric distortion. Simultaneously, image quality is evaluated on the real-time projection image data, including assessments of edge sharpness, brightness uniformity, and image integrity. The real-time projection image data is compared with the initial projection image data to evaluate color reproduction. Based on preset projection control optimization weights, a weighted evaluation is performed on indicators such as geometric distortion, edge sharpness, brightness uniformity, image integrity, and color reproduction to obtain projection control optimization effect data. Based on the optimization effect data, the projection control parameters can be further adjusted to achieve the best projection effect. For example, if edge sharpness is low, the lens focal length or projection angle can be adjusted; if brightness uniformity is poor, the light source intensity distribution of the projector can be adjusted; if geometric distortion exists, the projector's orientation or image correction can be performed based on the distortion. Through iterative optimization, the optimal projection effect can ultimately be achieved.

[0098] As an example of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart illustrating the implementation steps of step S1 is provided in this example. Step S1 includes:

[0099] Step S11: Acquire target projection image data; The image processor performs image projection signal processing on the target projection image data and controls the projection lens assembly to project the image, generating initial projection image data;

[0100] In this embodiment of the invention, target projection image data is obtained from a preset data source. The data source can be a computer, tablet, mobile phone, or other device, or a network server or storage device. The data transmission protocol can be a common protocol, such as HDMI, DP, or network protocols. The image data can be in various formats, such as JPEG, PNG, BMP image formats, or MP4, AVI, or other video formats. After receiving the image data, the image processor performs a series of preprocessing operations. These operations include: image format conversion, resolution adjustment, color space conversion (e.g., RGB to YUV), gamma correction, brightness and contrast adjustment, etc. The purpose of preprocessing is to convert the image data into a format suitable for projector display and optimize image quality. After preprocessing, the image data is sent to the projection lens assembly. By controlling the driving circuit of the projection lens assembly, the focal length, aperture, and other parameters of the projection lens are precisely controlled, ultimately generating an initial projection image on the projection surface.

[0101] Step S12: Based on the preset scanning frequency data, use the dual lidar components to perform a dual-path horizontal scan of the projection area of ​​the initial projection image data to generate left and right projection horizontal scan data.

[0102] In this embodiment of the invention, based on a preset scanning frequency (e.g., 10Hz), the dual-LiDAR assembly begins a dual-path horizontal scan of the projection area. Each LiDAR is responsible for scanning half of the projection area, thereby improving scanning efficiency and accuracy. The LiDAR calculates the distance to the target point by emitting a laser beam and measuring the time it takes for the laser beam to return. Simultaneously, the LiDAR also records the intensity information of the laser beam, which helps distinguish the target point from background noise. Precise synchronization of the two LiDARs is required, which can be achieved using a high-precision clock signal or a dedicated synchronization interface. The left and right LiDARs work synchronously and collaboratively, each generating horizontal scan data. This data contains a large number of three-dimensional points, each containing its three-dimensional coordinates (X, Y, Z) and intensity information. The dual-path scan data provides more comprehensive information about the projection area, reducing blind spots and errors caused by single-path scanning. After data acquisition, the data is transmitted to a data processing unit for further processing.

[0103] Step S13: Use a servo motor to drive the dual lidar components to perform vertical interval layer scanning of the projection area, and generate vertical layer scanning data of the projection area.

[0104] In this embodiment of the invention, a servo motor is used to precisely control the vertical movement of a dual LiDAR assembly, achieving vertical layer-by-layer scanning of the projection area. The servo motor drives the LiDAR assembly to scan layer by layer in the vertical direction according to a preset vertical interval (e.g., 5cm). After each layer is scanned, the servo motor drives the LiDAR assembly to move to the next layer and perform the next layer scan. This process continues until the vertical scanning of the entire projection area is completed. Vertical layer-by-layer scanning can acquire the height information of the projection area.

[0105] Step S14: Perform point cloud noise filtering on the horizontal scanning data of the left and right projections and the vertical layered scanning data of the projection area to obtain the horizontal scanning point cloud data and the vertical scanning point cloud data of the projection respectively.

[0106] In this embodiment of the invention, point cloud noise filtering is performed on the horizontal projection scanning data and the vertical layered scanning data of the projection area obtained in steps S12 and S13. Common noise filtering methods include statistical filtering, spatial filtering, and morphological filtering. Statistical filtering methods, such as median filtering or mean filtering, can effectively remove outliers. Spatial filtering methods, such as neighborhood search-based filtering methods, can remove spatially isolated points. Morphological filtering methods, such as opening or closing operations, can remove some fine noise. The choice of a suitable filtering method depends on the characteristics of the point cloud data and the type of noise. When filtering, the filtering parameters need to be carefully adjusted to avoid filtering out valid data. After filtering, the horizontal projection scanning point cloud data and the vertical projection scanning point cloud data are obtained respectively.

[0107] Step S15: Perform point cloud density equalization processing based on the projected horizontal scan point cloud data and the projected vertical scan point cloud data, and perform spatiotemporal registration of the projected point cloud to obtain the projected scan point cloud spatial data.

[0108] In this embodiment of the invention, the projected horizontal and vertical scan point cloud data obtained in step S14 are subjected to point cloud density equalization processing. Since the point cloud density varies in different areas during lidar scanning, density equalization is necessary to ensure the uniformity of the point cloud data and improve the accuracy of subsequent processing. Common point cloud density equalization methods include grid-based interpolation methods and Poisson distribution-based sampling methods. Then, the processed point cloud data is spatiotemporally registered, fusing the horizontal and vertical scan data into a complete projected scan point cloud spatial data. Spatiotemporal registration needs to consider both the temporal and spatial information of the point cloud data to ensure spatial consistency. Common spatiotemporal registration methods include the ICP (Iterative Closest Point) algorithm and feature-matching-based registration methods. After registration, the final projected scan point cloud spatial data is obtained.

[0109] Preferably, step S2 includes the following steps:

[0110] Step S21: Perform coarse plane fitting on the spatial data of the projected scanned point cloud to generate initial fitting projection plane parameters;

[0111] Step S22: Iteratively optimize the initial fitted projection plane parameters with weighted parameters to obtain the optimized fitted projection plane parameters;

[0112] Step S23: Use the spatial data of the projected scanning point cloud to perform point cloud mapping processing on the optimized fitted projection plane parameters, and extract the boundary points of the projection plane to generate the point cloud data of the projection plane boundary.

[0113] Step S24: Perform boundary point clustering on the projection plane boundary point cloud data and perform polygon fitting on the boundary point clusters to obtain the projection plane polygon boundary data;

[0114] Step S25: Perform projection plane corner point identification on the projection plane boundary point cloud data to obtain projection plane corner point coordinate data;

[0115] Step S26: Perform geometric feature processing on the corner coordinate data of the projection plane using the polygon boundary data of the projection plane to generate geometric feature data of the projection plane.

[0116] In this embodiment of the invention, the Random Sample Consensus Algorithm (RANSAC) is used to perform coarse plane fitting on the projected scan point cloud spatial data obtained in step S15. RANSAC is a robust plane fitting method that can effectively handle datasets containing outliers. The algorithm flow is as follows: First, three points are randomly selected from the point cloud data, and the plane equation determined by these three points is calculated. Then, the distance from all points in the point cloud data to the plane is calculated, and points with a distance less than a set threshold are designated as inliers, and the remaining points as outliers. If the number of inliers exceeds the set threshold, the plane fitting result is considered valid, and the parameters of the plane (e.g., the plane normal vector and the distance from the plane to the origin) are recorded. This process is repeated multiple times, and the plane with the most inliers is selected as the final fitting result. The initial fitted projection plane parameters obtained in step S21 are optimized using iterative weighted least squares. Least squares is a commonly used parameter estimation method that minimizes the sum of squared errors between the fitted plane and the point cloud data. Iterative weighted least squares introduces weighting factors based on least squares, assigning different weights to different points, thereby improving fitting accuracy. The algorithm flow is as follows: First, calculate the distance from each point in the point cloud data to the plane based on the initial fitting plane parameters. Then, assign different weights to each point according to the distance; the closer the distance, the greater the weight. Calculate the new plane parameters using the weighted least squares method. Repeat the above process multiple times until the plane parameters converge or the preset number of iterations is reached. Using the optimized fitting projection plane parameters obtained in step S22, project the projected scan point cloud spatial data obtained in step S15 onto the plane. The projection method involves projecting each 3D point onto the plane to obtain the 2D coordinates of that point on the plane. This can be achieved using vector projection. After projection, the point cloud data projected onto the fitting plane is obtained. Next, the boundary points of the projection plane need to be extracted. Edge detection algorithms, such as the Canny operator, can be used to process the projected 2D point cloud data and identify the boundaries of the plane. Cluster the projection plane boundary point cloud data generated in step S23 to divide the boundary points into different clusters. After clustering, perform polygon fitting on each boundary point cluster. Commonly used polygon fitting methods include least squares and α-shape algorithms. Least squares can fit simple polygons, while the α-shape algorithm can fit more complex shapes. After fitting, the polygon boundary data of the projection plane is obtained. Corner point identification is performed on the projection plane boundary point cloud data obtained in step S24. Corner points are points on the boundary with large curvature changes, usually representing the corners of the projection plane. Commonly used corner point identification algorithms include the Harris corner detection algorithm and the FAST corner detection algorithm. These algorithms identify corner points by calculating local features of the image or point cloud data. The selection of a suitable corner point identification algorithm needs to be adjusted according to the distribution of boundary points and the noise level. The identified corner point coordinates constitute the projection plane corner point coordinate data.Based on polygon boundary data, geometric features such as the area, perimeter, and aspect ratio of the projection surface can be calculated. Based on corner coordinate data, geometric features such as the angles and directions of the projection surface can be calculated. These geometric features collectively describe the shape and size of the projection surface. For example, the area, perimeter, and degree measures of each angle of the polygon can be calculated. The centroid coordinates, major and minor axis lengths, etc., of the polygon can also be calculated. These calculations can be implemented using geometric algorithms or mathematical library functions. Finally, the calculated geometric feature data are integrated to generate the geometric feature data of the projection surface.

[0117] Preferably, step S3 includes the following steps:

[0118] Step S31: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data;

[0119] Step S32: Analyze the geometric features of the occlusion area of ​​the projected image outline data using the geometric feature data of the projection surface, and generate the geometric feature data of the occlusion area;

[0120] Step S33: Analyze the spatial distribution of projection occlusion by analyzing the geometric feature data of the occluded area using the geometric feature data of the projection surface, and obtain the spatial distribution data of projection occlusion;

[0121] Step S34: Determine the ideal projection area based on the spatial distribution data of projection occlusion, and generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

[0122] In this embodiment of the invention, contour recognition is performed on the initial projected image data generated in step S11 to obtain the boundary information of the projected image. Image preprocessing, such as noise reduction and grayscale conversion, is required to improve the accuracy of contour recognition. Then, image edge detection algorithms, such as the Canny operator or the Sobel operator, are used to detect edge information in the image. These algorithms identify edges by calculating the gradient or second derivative of image pixels. The detected edge information is typically a series of discrete pixels. To obtain a complete contour line, these discrete pixels need to be connected and fitted. Commonly used methods include curve fitting algorithms, such as least squares or spline interpolation. Furthermore, morphological processing methods, such as dilation and erosion operations, can be used to optimize the shape and continuity of the contour line. Finally, geometric data representing the contour of the projected image is obtained, such as a series of coordinate points or polygons, i.e., the projected image contour data. The projected image contour data is matched and aligned with the geometric data of the projection surface. This can be achieved through geometric transformations, such as affine transformations or perspective transformations. After alignment, occlusion areas can be identified by comparing the projected image contour and the projection surface boundary. If the outline of the projected image does not coincide with the boundary of the projection surface in certain areas, these areas are considered to be occluded. Next, geometric feature analysis of the occluded areas is required, such as calculating the area, perimeter, centroid coordinates, and shape of the occluded areas. These geometric features can be used to describe the size, shape, and location of the occluded areas. Image segmentation techniques can be used to further refine the occluded areas and extract more precise geometric features. Ultimately, all these geometric feature data constitute the geometric feature data of the occluded areas. Combining the geometric feature data of the occluded areas with the geometric data of the projection surface, the location and extent of the occluded areas in three-dimensional space are determined. This requires combining the three-dimensional point cloud data obtained in step S1 to project the two-dimensional occluded area information into three-dimensional space. By analyzing the point cloud data, the type, size, and location of the occluding object can be determined. Then, based on the distribution of the occluded areas in three-dimensional space, the spatial distribution pattern of projection occlusion can be analyzed. For example, the density, distribution range, and degree of occlusion of the occluded areas can be calculated. All these spatial distribution data constitute the spatial distribution data of projection occlusion. Based on the spatial distribution of projection occlusion, a rectangular area that maximizes the utilization of the projection surface and avoids occlusion is determined as the ideal projection area. This can be achieved through optimization algorithms, such as genetic algorithms or simulated annealing algorithms, to find the rectangle with the largest area while ensuring no occlusion. After determining the ideal projection area, the corresponding projection control parameters need to be calculated, such as the projector's horizontal and vertical rotation angles, lens focal length, image scaling ratio, and offset. These parameters need to be adjusted according to the position and size of the ideal projection area to ensure that the projected image is accurately projected onto the ideal area. These parameters can be calculated based on the projection model and geometric transformation relationships.Ultimately, all these parameters constitute the intelligent projection control parameters, which are used to control the projector to project images.

[0123] Preferably, step S32 includes the following steps:

[0124] Step S321: Analyze the actual projection contour difference of the projection image contour data using the geometric feature data of the projection surface, and generate actual projection difference contour data.

[0125] Step S322: Based on the actual projection difference contour data, identify potential occluder point clouds in the spatial data of the projected scan point cloud to obtain potential occluder point cloud data;

[0126] Step S323: Cluster the point cloud data of potential occlusion areas to generate spatial region data of occlusion clusters;

[0127] Step S324: Extract the boundaries of occlusion objects based on the spatial region data of occlusion object clustering to generate occlusion region boundary data;

[0128] Step S325: Classify the shape of the occlusion object in the boundary data of the occlusion area to obtain the shape classification data of the occlusion area;

[0129] Step S326: Perform geometric feature analysis of the occluded area based on the shape classification data and boundary data of the occluded area to generate geometric feature data of the occluded area.

[0130] In this embodiment of the invention, the geometric feature data of the projection surface obtained in step S26 and the contour data of the projected image obtained in step S31 are used to perform actual projection contour difference analysis to identify potential occlusion areas. First, the projection image contour data needs to be projected onto the projection plane determined in step S2. This can be achieved through geometric transformations, such as affine transformations or perspective transformations, converting image coordinates to projection plane coordinates. Then, the projected contour data is compared with the projection surface boundary data extracted in step S2. By calculating the distance difference between the projection image contour and the projection surface boundary, areas where the contour and boundary do not coincide can be identified; these areas are potential occlusion areas. The distance difference can be represented as a difference contour, which reflects the degree of deviation between the projection image contour and the projection surface boundary. The actual projection difference contour data is mapped into a three-dimensional point cloud space. This can be achieved by matching the points on the difference contour with the corresponding point cloud data. The matching method can be selected based on the density and distribution of the point cloud data, such as nearest neighbor matching or feature-based matching. After matching, the three-dimensional point cloud data corresponding to the difference contour can be identified as potential occlusion point cloud data. Choosing a suitable clustering algorithm requires adjustment based on the distribution and noise level of the point cloud data. After clustering, each cluster represents a potential occlusion region, and the point cloud data it contains constitutes the point cloud information of that region. Convex hull-based boundary extraction methods can be used, such as calculating the convex hull of each cluster to obtain its boundary. Alternatively, neighborhood search-based boundary extraction methods can be used, such as calculating the neighborhood of each point cloud and then identifying boundary points based on the distribution of points within that neighborhood. The extracted boundary points constitute the boundary data of the occlusion region. For example, geometric features such as perimeter, area, and aspect ratio of the boundary data can be calculated, and shapes can be classified into rectangles, circles, triangles, etc., based on these features. Machine learning-based shape classification methods, such as using convolutional neural networks (CNNs) to train and classify the boundary data, can identify more complex shapes. Finally, shape classification data of the occlusion region is obtained. Based on the shape classification results of the occlusion, an appropriate geometric feature calculation method is selected. For example, for a rectangular occlusion, its geometric features such as length, width, and area can be calculated; for a circular occlusion, its geometric features such as radius and area can be calculated; and for an irregularly shaped occlusion, its geometric features such as perimeter, area, and centroid can be calculated. This geometric feature data can be used to describe the size, shape, and position of the occlusion, ultimately generating geometric feature data of the occlusion area.

[0131] Preferably, step S34 includes the following steps:

[0132] Step S341: Calculate the projection boundary adjustment distance based on the projection occlusion spatial distribution data to obtain the projection boundary adjustment distance data;

[0133] Step S342: Adjust the projection occlusion spatial distribution data by adjusting the projection boundary distance data to generate target projection adjustment boundary data;

[0134] Step S343: Use the target projection to adjust the boundary data to identify the unobstructed overlapping area of ​​the geometric feature data of the occluded area, and perform the visibility assessment of the projected area to obtain the area visibility assessment data.

[0135] Step S344: Based on the target projection adjustment boundary data, use the area visibility assessment data to correct the projection boundary adjustment distance data and generate the optimal projection adjustment boundary data;

[0136] Step S345: Estimate the projector position offset based on the geometric feature data of the projection surface using the optimal projection adjustment boundary data, and perform gradient descent optimization to generate projector position adjustment data;

[0137] Step S346: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data; perform projection image correction control processing on the ideal rectangular projection area data to generate projection image correction control data;

[0138] Step S347: Combine the projector position adjustment data and the projected image correction control data into intelligent projector control parameters to obtain intelligent projection control parameters.

[0139] In this embodiment of the invention, the spatial position and shape of the obstructing object are analyzed to determine the influence range of each obstruction on the projection area. The influence range can be estimated based on the size of the obstructing object and its distance from the projection surface. Then, based on a preset projection safety margin (e.g., setting a minimum safety distance to ensure that the projected image does not overlap with the obstruction), the distance that needs to be adjusted for the projection boundary is calculated. This distance needs to consider the shape and position of the obstructing object, as well as factors such as the projection angle of the projector. The calculation method can employ a geometric model; for example, for a simple rectangular obstruction, the distance that the projection boundary needs to move can be directly calculated; for obstructions with complex shapes, more complex geometric calculation methods or numerical simulation methods can be used. Finally, the projection boundary adjustment distance data is obtained. The projection area boundary is expanded or contracted outward by the projection boundary adjustment distance. This mapping process can be understood as performing a translation transformation on the projection area boundary. The magnitude of the transformation is the distance calculated in step S341. After mapping, a new projection area boundary is generated, i.e., the target projection adjustment boundary data. This new boundary excludes the influence area of ​​the obstruction. The adjusted projection boundary is compared with the geometric feature data of the occlusion area to identify non-overlapping regions, i.e., unobstructed overlapping regions. Then, the visibility of these unobstructed overlapping regions is evaluated. Visibility evaluation can consider multiple factors, such as light intensity, ambient brightness, and projector brightness. Visibility scores can be calculated by simulating projection effects or using model-based evaluation methods. If the visibility of certain areas is low, the projection boundary distance needs to be further adjusted to improve the visibility of these areas. The correction method can be determined based on the visibility evaluation data and a preset threshold. For example, if the visibility score is below the threshold, the projection boundary adjustment distance needs to be increased; conversely, it can be decreased. After correction, optimal projection adjustment boundary data is generated. By comparing the optimal projection adjustment boundary data with the geometric feature data of the projection surface, the distance and angle the projector needs to move can be calculated to ensure the projected image is accurately projected onto the optimal projection area. This calculation process can employ geometric transformation methods, such as affine transformations or perspective transformations. To further optimize the projector's position, optimization algorithms such as gradient descent can be used to iteratively adjust the projector's position until the preset accuracy requirements are met, obtaining the projector position adjustment data. Based on the adjusted projector position, the boundary of the projection area is recalculated to determine the ideal rectangular projection area. Then, projection image correction control processing is performed on the ideal rectangular projection area data. Image correction can correct image distortions caused by projector position adjustments and irregular projection surface shapes, such as trapezoidal distortion or perspective distortion. Image correction methods can employ geometric transformations, such as affine transformations or perspective transformations, to transform the image and eliminate image distortions.The projector position adjustment data generated in step S345 and the projection image correction control data generated in step S346 are combined to generate the final intelligent projection control parameters. These parameters include the projector's horizontal and vertical rotation angles, lens focal length, image scaling ratio, offset, and image correction parameters. These parameters will be sent to the projector's control system to control the projector to project the image.

[0140] Preferably, step S346 includes the following steps:

[0141] Step S3461: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data;

[0142] Step S3462: Perform projection region triangulation on the ideal rectangular projection region data to generate ideal projection sub-region division data;

[0143] Step S3463: Construct the topological relationship of the ideal projection sub-regions based on the ideal projection sub-region division data to obtain the projection sub-region topological data;

[0144] Step S3464: Map the corner points of the projected sub-region based on the topology data of the projected sub-region, and calculate the local perspective transformation matrix to generate local perspective transformation matrix data;

[0145] Step S3465: Perform optical path projection processing on the local perspective transformation matrix data to generate an optical path projection angle matrix; perform projection image correction control processing based on the optical path projection angle matrix to generate projection image correction control data.

[0146] In this embodiment of the invention, the projection range of the projection area on the projection surface is calculated based on the adjusted position and orientation of the projector. This calculation process needs to consider factors such as the projector's lens parameters, projection distance, and the shape of the projection surface. Geometric projection models or 3D modeling software can be used for the calculation. To avoid occlusion, the boundary of the projection area needs to avoid the occlusion areas identified in step S32. The final determined projection area is a rectangle whose position and size maximize the use of the projection surface and avoid occlusion. The coordinates of the four corner points of this rectangular area, along with its length and width information, constitute the ideal rectangular projection area data. Triangulation is performed on the ideal rectangular projection area data generated in step S3461, dividing the rectangular area into multiple triangular sub-regions. Triangulation is a commonly used geometric processing method that can divide any polygon into multiple triangles. The result of triangulation is multiple triangles, each defined by the coordinates of three vertices. These triangles and their vertex coordinate information constitute the ideal projection sub-region division data. Topological relationships are constructed on the ideal projection sub-region division data generated in step S3462 to describe the connection relationships between the various triangular sub-regions. Topological relationships represent the adjacency relationships between triangular sub-regions, as well as their boundary information. These relationships can be represented using adjacency matrices or adjacency lists. Elements in an adjacency matrix indicate whether two triangular sub-regions are adjacent, while an adjacency list stores a list of adjacent sub-regions for each triangular sub-region. Determining the corner coordinates of each triangular sub-region requires mapping these coordinates to the image coordinate system. This mapping process considers the projector's lens parameters and projection transformation relationships. After mapping, for each triangular sub-region, its corresponding local perspective transformation matrix can be calculated. The perspective transformation matrix transforms the point coordinates within the triangular sub-region to the image coordinate system. Based on the local perspective transformation matrix, the light path projection angle for each triangular sub-region is calculated. The light path projection angle represents the direction of light from the projector to the projection surface. Calculating the light path projection angle requires considering the projector's lens parameters and projection transformation relationships. Then, this angle information is integrated to generate a light path projection angle matrix. Finally, the projected image is corrected based on the light path projection angle matrix. Correction methods can employ inverse perspective transformation to map the image coordinates to the projection plane coordinate system, thereby eliminating image distortion. Finally, projection image correction control data is generated.

[0147] Preferably, step S3465 includes the following steps:

[0148] Projection distortion prediction is performed based on the projector position adjustment data to obtain projection distortion prediction data;

[0149] Based on the local perspective transformation matrix data, light path projection processing is performed to generate a light path projection angle matrix;

[0150] Based on the projection distortion prediction data, the optical path projection angle matrix is ​​used to calculate the sub-region distortion compensation amount of the topology data of the projection sub-region, and generate the sub-region deformation compensation amount data.

[0151] Based on the deformation compensation data of the sub-region, the projected pixels are remapped and the projected image is scaled to generate image projection reconstruction data.

[0152] The ideal rectangular projection area data is processed by image projection reconstruction data and then a projection area mask is generated.

[0153] Based on the projection area mask data and image projection reconstruction data, projection image correction control processing is performed to generate projection image correction control data.

[0154] In this embodiment of the invention, a model-based method is employed, such as using a polynomial model or a radial distortion model to fit the lens distortion characteristics of the projector, and calculating geometric distortion based on the projector's positional offset. Alternatively, a data-driven method can be used, such as training a machine learning model using existing projection distortion data and predicting projection distortion based on the projector's positional adjustment data. The prediction result is a dataset describing the degree of projection distortion, containing distortion information for each pixel, constituting the projection distortion prediction data. For each triangular sub-region, the ray direction vector from the projector to each pixel in that sub-region can be calculated using its corresponding local perspective transformation matrix. The ray direction vector can be represented as a three-dimensional vector, with its direction representing the direction of light propagation. By calculating the angle between the ray direction vector and the projection plane, the ray projection angle can be obtained. The ray projection angle information of all triangular sub-regions is integrated to generate a matrix, where each row corresponds to a sub-region, each column corresponds to a pixel, and the element value is the ray projection angle corresponding to that pixel. Based on the projection distortion prediction data, the distortion degree of each pixel is determined. Then, based on the ray projection angle matrix, the direction of the ray corresponding to each pixel is determined. By combining projection distortion prediction data and ray direction information, the distortion compensation amount required for each pixel can be calculated. The compensation amount can be represented as a two-dimensional vector, where its direction and magnitude represent the displacement required for the pixel. The distortion compensation amounts of all pixels within each sub-region are integrated to generate a matrix. Each row of this matrix corresponds to a sub-region, and each column corresponds to a pixel, with each element representing the distortion compensation amount for that pixel. Based on the sub-region distortion compensation data, a displacement transformation is performed on each pixel to compensate for projection distortion. This displacement transformation can be implemented using bilinear interpolation or other interpolation methods. Then, based on the size and shape of the ideal projection region, the projected image is scaled to fit the size of the ideal projection region. This scaling process can also be implemented using bilinear interpolation or other interpolation methods, generating image projection reconstruction data. Based on the ideal rectangular projection region data, a binary mask image is generated. In the mask image, pixels within the ideal projection region have a value of 1, while pixels in other regions have a value of 0. Then, this binary mask image is used to mask the image projection reconstruction data. The pixel values ​​of regions with a pixel value of 0 in the mask image are set to 0, while the pixel values ​​of regions with a pixel value of 1 are retained, generating projection area mask data. The image data after masking is used as the final projected image data. The generated projection image correction control data contains the final projected image data and related parameter information, such as image size and pixel position, and is used to control the projector to display the final corrected image.

[0155] As an example of the present invention, reference is made to... Figure 3 As shown, Figure 1A detailed flowchart illustrating the implementation steps of step S4 is provided in this example. Step S4 includes:

[0156] Step S41: Process the intelligent projection control parameters with projection attitude control commands to obtain optimized projection control command data;

[0157] In this embodiment of the invention, the intelligent projection control parameters generated in step S347 are processed to generate instruction data for controlling the projector's attitude. The intelligent projection control parameters include information such as the projector's horizontal and vertical rotation angles, lens focal length, image scaling ratio, and image correction parameters. These parameters need to be converted into specific motor control instructions to control the various motors of the projector to perform corresponding actions. The conversion process needs to consider the driving characteristics and mechanical structure parameters of each motor. For example, the horizontal rotation angle needs to be converted into the rotation angle or pulse count of the horizontal motor, the vertical rotation angle needs to be converted into the rotation angle or pulse count of the vertical motor, and the lens focal length needs to be converted into control signals for the lens drive motor, etc. After conversion, optimized projection control instruction data is obtained, which contains control instructions for each motor and is used to control the projector's attitude and lens parameters.

[0158] Step S42: Execute motor control commands according to the optimized projection control command data, and use an image sensor to collect real-time projection images to obtain real-time projection image data;

[0159] In this embodiment of the invention, based on the optimized projection control command data generated in step S41, the projector's motors are controlled to perform corresponding actions, and real-time projection image data is acquired using an image sensor. The motor control command data is sent to the projector's motor control system, driving each motor to rotate or move according to the commands. Simultaneously, the image sensor acquires real-time projection image data. The image sensor can be a CMOS or CCD sensor, and its output data is image data, typically a digital image in RGB format.

[0160] Step S43: Identify the projection reference mark on the real-time projection screen data using the preset projection reference mark data, extract key feature points of the projection screen, and generate optimized projection screen feature point data.

[0161] In this embodiment of the invention, the real-time projection image data acquired in step S42 is processed using preset projection reference mark data to identify the projection reference marks and extract key feature points of the projection image. The preset projection reference mark data can be marks with specific patterns or shapes, such as checkerboard patterns or circular marks. Image processing algorithms, such as template matching algorithms or corner detection algorithms, are used to identify the positions of these marks in the real-time image. After identifying the reference marks, key feature points of the projection image, such as the four corners of the image or other feature points, can be extracted based on the position and geometric relationships of the marks, generating optimized projection image feature point data.

[0162] Step S44: Calculate the geometric distortion of the feature point data of the optimized projection screen to generate optimized geometric distortion data; evaluate the edge sharpness of the real-time projection screen data to obtain edge sharpness data; evaluate the brightness uniformity of the real-time projection screen data to obtain brightness uniformity data; evaluate the image projection integrity of the real-time projection screen data to obtain image projection integrity data.

[0163] In this embodiment of the invention, the geometric distortion of the projected image is calculated based on optimized feature point data. Geometric distortion reflects the degree of deviation between the projected image and the ideal image. The calculation method can be based on the location of the feature points and a preset ideal location. Then, edge sharpness, brightness uniformity, and image projection integrity are evaluated on the real-time projected image data. Edge sharpness evaluation can employ edge detection algorithms, such as the Sobel or Canny operators, to calculate the sharpness of image edges. Brightness uniformity evaluation calculates the brightness differences in different areas of the image. Image projection integrity evaluation determines whether the projected image is complete and whether there are any missing or distorted parts. Finally, optimized geometric distortion data, edge sharpness data, brightness uniformity data, and image projection integrity data are obtained.

[0164] Step S45: Based on the optimized feature point data of the projected image, evaluate the color reproduction of the real-time projected image data and the initial projected image data to obtain color reproduction data;

[0165] In this embodiment of the invention, the real-time projected image data is aligned with the initial projected image data. This can be achieved by extracting key feature points in step S43. After alignment, the color information of the two images can be compared to calculate the color fidelity. Color fidelity reflects the degree of color consistency between the projected image and the original image. The calculation method can use a color space distance metric, such as CIE76 color space distance or CIE94 color space distance, to obtain color fidelity data.

[0166] Step S46: Use preset projection control optimization weight data to perform weighted evaluation of the control effect on the optimized geometric distortion data, edge sharpness data, brightness uniformity data, image projection integrity data, and color reproduction data, and obtain projection control optimization effect data.

[0167] In this embodiment of the invention, preset projection control optimization weight data are used to perform a weighted evaluation of the optimized geometric distortion data, edge sharpness data, brightness uniformity data, image projection integrity data, and color reproduction data obtained in steps S44 and S45, resulting in projection control optimization effect data. The preset projection control optimization weight data is set according to actual needs; for example, the weights of different indicators can be adjusted based on application scenarios and user preferences. The weighted evaluation method can employ linear weighting or non-linear weighting. Linear weighting multiplies the scores of each indicator by their corresponding weights and then sums them; non-linear weighting uses different weight calculation methods depending on different combinations of indicators. The final projection control optimization effect data reflects the overall performance of the projection system. This data can serve as feedback information to adjust projector parameters to achieve the best projection effect.

[0168] Preferably, the present invention also provides a projector, for reference Figure 4 As shown, the projector includes a projector main unit and a dual-LiDAR assembly. The projector main unit internally houses a projection lens assembly, an image processor, and a main controller. The dual-LiDAR assembly includes a first LiDAR, a second LiDAR, and two servo motors. The first and second LiDARs are respectively fixed on both sides of the projector main unit and are driven by the servo motors for vertical scanning. The dual-LiDAR assembly, the projection lens assembly, and the image processor are electrically connected to the main controller via a data bus. The main controller is used to execute the projector control method described above and includes the following modules:

[0169] The projection point cloud processing module is used to acquire target projection image data; the image processor processes the target projection image data for image projection signal processing and controls the projection lens assembly to project the image, generating initial projection image data; the dual lidar assembly performs spatiotemporal registration of the initial projection image data to obtain projection scan point cloud spatial data.

[0170] The projection surface feature extraction module is used to extract projection surface boundary points from the spatial data of the projection scan point cloud and generate projection plane boundary point cloud data; and to perform projection surface geometric feature processing based on the projection plane boundary point cloud data to generate projection surface geometric feature data.

[0171] The projection control parameter optimization module is used to identify the projection image contour from the initial projection image data to obtain projection image contour data; to analyze the geometric features of the occlusion area from the projection image contour data using the geometric feature data of the projection surface to generate occlusion area geometric feature data; to determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; and to process the ideal rectangular projection area data using intelligent projection control parameters to obtain intelligent projection control parameters.

[0172] The projection attitude control module is used to process projection attitude control commands for intelligent projection control parameters, and to acquire real-time projection images using an image sensor to obtain real-time projection image data; based on the real-time projection image data, the projection control effect is evaluated to obtain projection control optimization effect data.

[0173] This application addresses the challenge of employing dual lidar components for precise spatial scanning and point cloud registration of the projection area. This allows the invention to intelligently identify and adapt to different positions, shapes, and sizes of the projection surface, automatically adjusting and optimizing the projection effect to ensure optimal performance in various environments. Precise image projection signal processing and initial projection image generation ensure a highly consistent projection foundation across different environments. The dual lidar components, using a preset scanning frequency, effectively divide the projection area into left and right scanning channels, allowing for comprehensive capture of details in the horizontal region of the projection surface. A servo motor controls the lidar components to perform vertically spaced layered scanning, further refining the point cloud distribution in the vertical direction and ensuring comprehensive capture of the projection surface's depth information. This combination of horizontal and vertical bidirectional scanning makes spatial feature identification of the projection area more accurate, avoiding information loss due to complex environments or obstacles. Coarse plane fitting of the projection scan point cloud spatial data effectively obtains the initial geometric features of the projection area. By iteratively weighted and optimizing parameters, the accuracy of the projection plane fitting is further improved, allowing the projection plane parameters to more precisely match the actual environment. This is especially important when facing irregular or tilted projection surfaces, ensuring the flatness and integrity of the projected image. Subsequently, point cloud mapping processing and boundary point extraction accurately identify the boundary contours of the projection surface, greatly enhancing the adaptability to complex boundary shapes. By intelligently analyzing the geometric feature data of occlusion areas, the optimal projection area can be flexibly determined, thus avoiding image loss caused by obstacles and ensuring a consistent viewing experience. The generated projection control parameters, combined with real-time image acquisition and feedback evaluation, enable the projector to intelligently adjust its projection posture and dynamically optimize the display effect according to environmental changes. This allows the projector to maintain stable correction effects in different environments without manual adjustment, significantly improving the automatic adaptability of projection accuracy and effect.

[0174] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0175] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method of controlling a projector, characterized by, The method is applied to a projector, which includes a projector host and a dual LiDAR assembly. The projector host includes a projection lens assembly, an image processor, and a main controller. The dual LiDAR assembly, the projection lens assembly, and the image processor are electrically connected to the main controller via a data bus. The projector control method includes the following steps: Step S1: Acquire target projection image data; The image processor processes the target projection image data into an image projection signal and controls the projection lens assembly to project the image, generating initial projection image data; The dual lidar assembly performs spatiotemporal registration of the projection point cloud into the initial projection image data to obtain the projection scan point cloud spatial data. Step S2: Extract the projection plane boundary points from the spatial data of the projected scan point cloud to generate projection plane boundary point cloud data; perform projection plane geometric feature processing based on the projection plane boundary point cloud data to generate projection plane geometric feature data. Step S3: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data; perform occlusion area geometric feature analysis on the projection image contour data using projection surface geometric feature data to generate occlusion area geometric feature data; determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters. Step S4: Process the projection posture control command for the intelligent projection control parameters, and use the image sensor to collect real-time projection images to obtain real-time projection image data; evaluate the projection control effect based on the real-time projection image data to obtain projection control optimization effect data.

2. The projector control method according to claim 1, characterized by, Step S1 includes the following steps: Step S11: Acquire target projection image data; The image processor performs image projection signal processing on the target projection image data and controls the projection lens assembly to project the image, generating initial projection image data; Step S12: Based on the preset scanning frequency data, use the dual lidar components to perform a dual-path horizontal scan of the projection area of ​​the initial projection image data to generate left and right projection horizontal scan data. Step S13: Use a servo motor to drive the dual lidar components to perform vertical interval layer scanning of the projection area, and generate vertical layer scanning data of the projection area. Step S14: Perform point cloud noise filtering on the horizontal scanning data of the left and right projections and the vertical layered scanning data of the projection area to obtain the horizontal scanning point cloud data and the vertical scanning point cloud data of the projection respectively. Step S15: Perform point cloud density equalization processing based on the projected horizontal scan point cloud data and the projected vertical scan point cloud data, and perform spatiotemporal registration of the projected point cloud to obtain the projected scan point cloud spatial data.

3. The projector control method according to claim 1, characterized by, Step S2 includes the following steps: Step S21: Perform coarse plane fitting on the spatial data of the projected scanned point cloud to generate initial fitting projection plane parameters; Step S22: Iteratively optimize the initial fitted projection plane parameters with weighted parameters to obtain the optimized fitted projection plane parameters; Step S23: Use the spatial data of the projected scanning point cloud to perform point cloud mapping processing on the optimized fitted projection plane parameters, and extract the boundary points of the projection plane to generate the point cloud data of the projection plane boundary. Step S24: Perform boundary point clustering on the projection plane boundary point cloud data and perform polygon fitting on the boundary point clusters to obtain the projection plane polygon boundary data; Step S25: Perform projection plane corner point identification on the projection plane boundary point cloud data to obtain projection plane corner point coordinate data; Step S26: Perform geometric feature processing on the corner coordinate data of the projection plane using the polygon boundary data of the projection plane to generate geometric feature data of the projection plane.

4. The projector control method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform projection image contour recognition on the initial projection image data to obtain projection image contour data; Step S32: Analyze the geometric features of the occlusion area of ​​the projected image outline data using the geometric feature data of the projection surface, and generate the geometric feature data of the occlusion area; Step S33: Analyze the spatial distribution of projection occlusion by analyzing the geometric feature data of the occluded area using the geometric feature data of the projection surface, and obtain the spatial distribution data of projection occlusion; Step S34: Determine the ideal projection area based on the spatial distribution data of projection occlusion, and generate ideal rectangular projection area data; process the ideal rectangular projection area data with intelligent projection control parameters to obtain intelligent projection control parameters.

5. The projector control method according to claim 4, characterized in that, Step S32 includes the following steps: Step S321: Analyze the actual projection contour difference of the projection image contour data using the geometric feature data of the projection surface, and generate actual projection difference contour data. Step S322: Based on the actual projection difference contour data, identify potential occluder point clouds in the spatial data of the projected scan point cloud to obtain potential occluder point cloud data; Step S323: Cluster the point cloud data of potential occlusion areas to generate spatial region data of occlusion clusters; Step S324: Extract the boundaries of occlusion objects based on the spatial region data of occlusion object clustering to generate occlusion region boundary data; Step S325: Classify the shape of the occlusion object in the boundary data of the occlusion area to obtain the shape classification data of the occlusion area; Step S326: Perform geometric feature analysis of the occluded area based on the shape classification data and boundary data of the occluded area to generate geometric feature data of the occluded area.

6. The projector control method according to claim 4, characterized in that, Step S34 includes the following steps: Step S341: Calculate the projection boundary adjustment distance based on the projection occlusion spatial distribution data to obtain the projection boundary adjustment distance data; Step S342: Adjust the projection occlusion spatial distribution data by adjusting the projection boundary distance data to generate target projection adjustment boundary data; Step S343: Use the target projection to adjust the boundary data to identify the unobstructed overlapping area of ​​the geometric feature data of the occluded area, and perform the visibility assessment of the projected area to obtain the area visibility assessment data. Step S344: Based on the target projection adjustment boundary data, use the area visibility assessment data to correct the projection boundary adjustment distance data and generate the optimal projection adjustment boundary data; Step S345: Estimate the projector position offset based on the geometric feature data of the projection surface using the optimal projection adjustment boundary data, and perform gradient descent optimization to generate projector position adjustment data; Step S346: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data; perform projection image correction control processing on the ideal rectangular projection area data to generate projection image correction control data; Step S347: Combine the projector position adjustment data and the projected image correction control data into intelligent projector control parameters to obtain intelligent projection control parameters.

7. The projector control method according to claim 6, characterized in that, Step S346 includes the following steps: Step S3461: Determine the ideal projection area based on the projector position adjustment data, and generate ideal rectangular projection area data; Step S3462: Perform projection region triangulation on the ideal rectangular projection region data to generate ideal projection sub-region division data; Step S3463: Construct the topological relationship of the ideal projection sub-regions based on the ideal projection sub-region division data to obtain the projection sub-region topological data; Step S3464: Map the corner points of the projected sub-region based on the topology data of the projected sub-region, and calculate the local perspective transformation matrix to generate local perspective transformation matrix data; Step S3465: Perform optical path projection processing on the local perspective transformation matrix data to generate an optical path projection angle matrix; perform projection image correction control processing based on the optical path projection angle matrix to generate projection image correction control data.

8. The projector control method according to claim 7, characterized in that, Step S3465 includes the following steps: Projection distortion prediction is performed based on the projector position adjustment data to obtain projection distortion prediction data; Based on the local perspective transformation matrix data, light path projection processing is performed to generate a light path projection angle matrix; Based on the projection distortion prediction data, the optical path projection angle matrix is ​​used to calculate the sub-region distortion compensation amount of the topology data of the projection sub-region, and generate the sub-region deformation compensation amount data. Based on the deformation compensation data of the sub-region, the projected pixels are remapped and the projected image is scaled to generate image projection reconstruction data. The ideal rectangular projection area data is processed by image projection reconstruction data and then a projection area mask is generated. Based on the projection area mask data and image projection reconstruction data, projection image correction control processing is performed to generate projection image correction control data.

9. The projector control method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Process the intelligent projection control parameters with projection attitude control commands to obtain optimized projection control command data; Step S42: Execute motor control commands according to the optimized projection control command data, and use an image sensor to collect real-time projection images to obtain real-time projection image data; Step S43: Identify the projection reference mark on the real-time projection screen data using the preset projection reference mark data, extract key feature points of the projection screen, and generate optimized projection screen feature point data. Step S44: Calculate the geometric distortion of the feature point data of the optimized projection screen to generate optimized geometric distortion data; evaluate the edge sharpness of the real-time projection screen data to obtain edge sharpness data; evaluate the brightness uniformity of the real-time projection screen data to obtain brightness uniformity data; evaluate the image projection integrity of the real-time projection screen data to obtain image projection integrity data. Step S45: Based on the optimized feature point data of the projected image, evaluate the color reproduction of the real-time projected image data and the initial projected image data to obtain color reproduction data; Step S46: Use preset projection control optimization weight data to perform weighted evaluation of the control effect on the optimized geometric distortion data, edge sharpness data, brightness uniformity data, image projection integrity data, and color reproduction data, and obtain projection control optimization effect data.

10. A projector, characterized in that, The projector includes a projector main unit and a dual-laser radar assembly. The projector main unit internally houses a projection lens assembly, an image processor, and a main controller. The dual-laser radar assembly includes a first laser radar, a second laser radar, and two servo motors. The first and second laser radars are fixed on opposite sides of the projector main unit, performing horizontal scanning of the projection area based on a preset scanning frequency, and vertical scanning driven by the servo motors. The dual-laser radar assembly, projection lens assembly, and image processor are electrically connected to the main controller via a data bus. The main controller is used to execute the projector control method as described in claim 1, and includes the following modules: The projection point cloud processing module is used to acquire target projection image data; the image processor processes the target projection image data for image projection signal processing and controls the projection lens assembly to project the image, generating initial projection image data; the dual lidar assembly performs spatiotemporal registration of the initial projection image data to obtain projection scan point cloud spatial data. The projection surface feature extraction module is used to extract projection surface boundary points from the spatial data of the projection scan point cloud and generate projection plane boundary point cloud data; and to perform projection surface geometric feature processing based on the projection plane boundary point cloud data to generate projection surface geometric feature data. The projection control parameter optimization module is used to identify the projection image contour from the initial projection image data to obtain projection image contour data; to analyze the geometric features of the occlusion area from the projection image contour data using the geometric feature data of the projection surface to generate occlusion area geometric feature data; to determine the ideal projection area based on the occlusion area geometric feature data to generate ideal rectangular projection area data; and to process the ideal rectangular projection area data using intelligent projection control parameters to obtain intelligent projection control parameters. The projection attitude control module is used to process projection attitude control commands for intelligent projection control parameters, and to acquire real-time projection images using an image sensor to obtain real-time projection image data; based on the real-time projection image data, the projection control effect is evaluated to obtain projection control optimization effect data.

Images