377 results about "Edge maps" patented technology

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an image segmentation method, device, equipment and medium based on multi-channel edge superposition. Only the mask decoder and the expansion module are updated; obtaining a target image and prompt information, and obtaining an initial mask image; generating a multi-channel edge image set, and superposing the multi-channel edge image set with the target image and the initial mask image channel to form a multi-channel input image; and processing by a gating convolution unit, a channel compression convolution layer and a multi-stage attention decoder to generate an enhanced mask image, and outputting a target segmentation result. According to the invention, the training overhead is reduced through the frozen image encoder and the prompt encoder, and the capability of capturing boundaries and details is enhanced by using the extension module and the attention decoder, so that the definition and accuracy of a segmentation result are improved.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention provides a multi-modal alignment and cross-scale structure attention-based remote sensing image building extraction method, which comprises the following steps of: performing radiation correction and geometric correction on an original hyperspectral satellite image, and performing voxelization, normalization and registration with a hyperspectral corrected image on LiDAR point cloud data to respectively obtain the hyperspectral corrected image and LiDAR corrected data; deep features of the two types of correction data are extracted through a special encoder, and after dimension unification and channel attention screening, fusion features are obtained through fusion of a weighting and gating mechanism; multi-scale features are extracted from the fusion features through multi-receptive field convolution branch, and cross-scale enhancement features are obtained by combining cross attention and geometric modeling; a prediction edge graph is generated based on cross-scale enhancement features, high-resolution guide features are combined to compensate tiny building information, and the extraction precision is improved through edge loss optimization; and carrying out denoising, edge repairing and adhesion separation on the optimized result to obtain a final building extraction result.

The application discloses a kind of detection and defense method of counterpatch, respectively based on abnormal positioning and edge detection.The detection method of counterpatch based on abnormal positioning utilizes clean image to train the generator-adversary network of encoder-decoder structure;The output image is obtained by inputting the image to be detected into the generator-adversary network, and the absolute error is obtained by subtracting and taking absolute value;The image region whose absolute error is greater than error threshold is the region where counterpatch is located;The detection method of counterpatch based on edge detection converts the image to be detected into gray scale image and carries out edge detection, and obtains edge image;The edge lines in edge image are connected into a closed area one by one, and the closed area whose area is less than the area of counterpatch region is the region where counterpatch is located.The application can detect counterpatch based on the two schemes of abnormal positioning and edge detection respectively, and blacken the region or use image restoration algorithm to restore the region to defend counterpatch.

The invention discloses an image fusion method based on multi-dimensional complementary features and multi-scale detail enhancement, and the method comprises the steps: calculating an edge image of a source image, and splicing the edge image with the source image in a channel dimension; enhancing feature textures and edge details by a high-frequency detail enhancement module; generating three feature maps with different scales step by step through continuous down-sampling operation; from the deepest scale, the infrared and visible light features of the hierarchy are fused through a parallel three-branch fusion module; the fused features are used for reconstructing a low-resolution fusion result, and the low-resolution fusion result is transmitted to an upper layer through up-sampling operation; and in the scale of the upper layer, the steps are repeated until a final full-resolution fusion image is generated. A parallel multi-dimensional complementary fusion network PMCFusion is set, cooperative perception and efficient integration of spatial features, channel correlation and frequency domain information are achieved through a parallel three-branch fusion module PTFM, and through cross-dimensional attention interaction, the PMCFusion can selectively enhance multi-dimensional features and achieve deep complementation.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention relates to the technical field of intelligent recognition, in particular to a distributed edge calculation face recognition method and system for non-inductive passage, and the method comprises the steps: capturing a video stream through edge image collection equipment disposed at a passage entrance, and extracting a face image; and carrying out localized preprocessing, feature extraction and quick comparison with a local cache feature library by the edge computing node. If local matching succeeds, passing is controlled immediately; if the matching fails, the features are uploaded to a central cloud server, enhanced processing and global library comparison are performed by using the stronger computing capability of the central cloud server, and a result is issued and executed, so that the low delay of edge computing and the strong capability of cloud computing are effectively fused, and both the recognition efficiency and the system reliability are considered; and meanwhile, links such as encrypted transmission, dynamic updating, abnormal offline processing and detailed log recording are covered, the security, adaptability and maintainability of the system are remarkably improved while efficient non-inductive traffic is realized, and the method is suitable for intelligent traffic management in a large-scale and high-concurrency scene.

The invention discloses a diffusion model image completion method based on boundary preserving perception, and belongs to the technical field of artificial intelligence and image generation. The method comprises the following steps: firstly, preprocessing an input image and a corresponding defect mask, and extracting gradient attenuation boundary features and a structure edge graph; a boundary sensing convolution module is introduced into the completion model and is used for boundary information modeling of a coding end; meanwhile, a gating mechanism is adopted in U-Net jump connection, the weight of a gradient attenuation mask is combined, and the boundary information transmission intensity is dynamically adjusted; furthermore, in the diffusion generation process, a region self-adaptive scheduling strategy is adopted, only high-step diffusion iteration is carried out on a defect region and a boundary neighborhood, and low-step or freezing operation is carried out on a non-defect region, so that balance between boundary consistency and calculation efficiency is realized. Experimental results show that the method is superior to an existing diffusion model image completion method in the aspects of structure retentivity, edge continuity and visual quality, and has wide application prospects.

The invention discloses a high-resolution video generation method and system based on multi-modal semantic guidance, and belongs to the technical field of video generation. According to the method, firstly, a low-resolution video is generated according to a text prompt, an edge map, a depth map and optical flow features of the low-resolution video are extracted through a space-time perception module, the features are combined with the text prompt, and a saliency region mask is generated after saliency weighting. And then the edge map, the depth map, the optical flow features and the text prompt are input into a DiT model. In each layer of Transform of DiT, after input is converted into corresponding embedded representation, structural feature embedding and text embedding are aligned in a space-time dimension, global semantic association is established through a cross attention mechanism, text embedding is processed by MLP to form conditional constraints, and sparse constraints and mask guidance are applied to an attention matrix. And finally, after multi-layer Transform processing, the model outputs a high-resolution video. In the whole process, the generation from the text to the high-resolution video is realized, and the details of the saliency region are emphatically protected.

A method includes selecting a reference frame and a non-reference frame from a plurality of image frames. The method also includes generating a reference edge map identifying edges in the reference frame and a non-reference edge map identifying edges in the non-reference frame. The method further includes generating a moving edge map based on one or more movements between one or more of the edges of the reference edge map and one or more of the edges of the non-reference edge map. The method also includes generating a blend map based on the reference and non-reference frames. The method further includes modifying the blend map based on one or more indications of movement of corresponding pixels in the moving edge map to generate a modified blend map. In addition, the method includes blending the reference and non-reference frames based on the modified blend map to generate an output image.

The invention relates to the technical field of precision machining, and discloses an on-machine intelligent detection method and system for geometric parameters and abrasion states of a cutter, and the detection method comprises the following steps: irradiating the cutter through a light source, and collecting a cutter image; the cutter image collected under irradiation is machined; on the basis of the machined cutter image, obtaining a measured value of abrasion, extracting an edge image, and on the basis of the edge image, identifying the type of the cutter and extracting geometric parameters of the cutter; based on the tool type and the tool geometric parameters, determining a wear damage threshold value in tool wear detection; and comparing the measured value of the abrasion with the threshold value of the abrasion so as to judge whether the parameters of the cutter are out of tolerance or whether the abrasion exceeds the standard. The problems that during high-strength and high-hardness material machining, cutter types are mismatched, abrasion damage is difficult to recognize in time, manual missing detection exists in an existing detection technology, potential safety hazards exist, and reliability is low are solved.

The invention relates to the field of industrial internet, in particular to an industrial internet quality detection system based on edge cloud collaboration, which is characterized in that an edge acquisition module acquires image data of a production line according to a preset time period, and an edge image processing module extracts contrast features and surface texture features in the image data; a defect area contour is identified based on a standard product contour, an edge image analysis module calculates a surface anomaly index and a defect anomaly index, and a comprehensive defect score is obtained through weighting; the edge uploading module sets labels for the image data according to the comprehensive defect scores, sampling sending under high-quality labels and complete sending under low-quality labels are achieved, and the cloud analysis module conducts high-precision defect evaluation on the received image data and conducts fault early warning through time sequence analysis. The method improves the detection efficiency, reduces the network bandwidth pressure through a hierarchical uploading mechanism, and achieves the judgment of the quality degradation trend through the high-precision evaluation and time sequence analysis capabilities of the cloud.

The application relates to the field of machine vision and image recognition, and discloses a high-precision edge extraction method based on image automatic processing, which comprises the following steps: a standardized image system is constructed, and an initial standardized wood template image is acquired through the standardized image system; the initial standardized wood template image is subjected to multimodal fusion pretreatment; an Otsu algorithm is used to determine a high threshold value and a low threshold value, an initial edge image and an edge gradient image are generated after edge detection of a binary image; then, the initial edge image is input into a pre-trained lightweight adaptive U-Net model, and an edge probability image is output; the edge probability image is sequentially subjected to morphological opening operation denoising processing, B-spline curve smoothing fitting processing and coordinate system calibration processing, and wood template edge coordinates are obtained; the average error of the wood template edge coordinates and standard template edge coordinates is calculated. The application realizes accurate conversion of wood template edge from an image to actual physical coordinates.

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The invention discloses a vector vortex super lens for directional selective edge detection and an imaging system with the vector vortex super lens, and the vector vortex super lens comprises a substrate and a nano-structure unit on the substrate. The nano-structure units are arranged on the substrate according to specific phase distribution to form a nano-structure unit array; x-line polarized light carrying sample information passes through the vector vortex super lens to generate left-handed and right-handed emergent light, the two paths of emergent light are superposed to obtain an edge image carrying polarization information, and a vector edge detection function is realized. The vector vortex super lens provided by the invention has the edge extraction capability of dynamic adjustment as required, and can realize real-time directional edge detection only by rotating the polarization analyzer without other hardware modification or digital post-processing.

A molten pool image processing method in the laser powder bed melting process comprises the steps that the center of a nominal molten pool is positioned, a region of interest is extracted, an optimal gray threshold value is calculated through a self-adaptive threshold value algorithm, binary processing is conducted on a gray image, and a binary molten pool image is obtained; performing edge detection on the binary molten pool image to obtain an edge image, constructing an edge point polar coordinate sequence, and calculating a polar angle difference matrix and a polar radius difference matrix; calculating the number of contours in the edge image, and judging whether the edge image has splashing points or not; calculating a polar angle difference value of adjacent edge points, and judging whether the contour of the molten pool is closed; on the premise of ensuring that the contour of the molten pool is closed, based on the shape symmetry assumption of the molten pool, the distance between symmetric points of the molten pool is calculated, basic geometrical characteristics of the molten pool are extracted, and physical values of the basic geometrical characteristics of the molten pool are calculated. Based on the gray level and spatial distribution difference of the molten pool and the background noise, the challenge of tracking the morphology of the molten pool under any processing condition in the actual production process can be relieved.

The invention provides an underground pipe network detection positioning method and device, and the method comprises the steps: obtaining the multi-source data of an underground pipe network of a to-be-detected point and the surrounding environment, including ground image data, inspection well internal image data, adjacent inspection well spacing data, ground elevation data, pipe bottom elevation data, and position coordinates; inputting the ground image data into a semantic segmentation network, and outputting a ground semantic segmentation image; respectively extracting contours of the ground semantic segmentation image and the image data in the inspection well to obtain a first edge image and a second edge image; constructing a pavement information measurement model; pipeline diameter information, pipeline gradient and rain and sewage pipeline mixed connection conditions are determined; and integrating the pavement information measurement model, the first edge image, the second edge image, the pipeline diameter information, the pipeline gradient and the like, and drawing a planar graph and an elevation graph of the underground drainage pipe network. According to the method, high-precision detection and positioning of the underground pipe network are remarkably improved, and the method is suitable for city updating and city informatization construction.

Certain aspects of the present disclosure provide techniques and apparatus for improved three-dimensional reconstruction using machine learning. In an example method, an image depicting an object is accessed, and an edge map comprising a plurality of edges is generated based on the image. A thickness of each of the plurality of edges in the edge map is modified based on a current stage of the three-dimensional reconstruction. A rendered image depicting a set of Gaussian distributions in a three-dimensional virtual space is generated using Gaussian splatting. One or more parameters of one or more of the set of Gaussian distributions are modified based on comparing the rendered image and the edge map, and after modifying the one or more parameters, a three-dimensional wireframe model of the object is generated based on the set of Gaussian distributions.

The invention relates to the technical field of machine vision and image recognition, and discloses a high-precision edge extraction method based on automatic image processing, which comprises the following steps: constructing a standardized image system, and obtaining an initial standardized wood template image through the standardized image system; carrying out multi-modal fusion preprocessing on the initial standardized wood template image; determining a high threshold value and a low threshold value by adopting an Otsu algorithm, and generating an initial edge image and an edge gradient map after carrying out edge detection on the binarized image; inputting the initial edge image into a pre-trained lightweight adaptive U-Net model, and outputting an edge probability graph; performing morphological opening denoising processing, B-spline curve smooth fitting processing and coordinate system calibration processing on the edge probability graph in sequence to obtain wood template edge coordinates; and calculating an average error between the edge coordinates of the wood template and the edge coordinates of the standard template. According to the method, accurate conversion of the wood template edge from the image to the actual physical coordinate is realized.

The invention discloses a circle detection method based on geometric constraint and sub-pixel positioning, and the method comprises the following steps: carrying out the edge detection of a preprocessed image, calculating the gradient magnitude and gradient direction of an edge point in a binary edge image, and associating the coordinates of the edge point; performing Hough circle preliminary screening on the binarized edge image; performing grading and sorting processing on the candidate circles based on the consistency index, and performing geometric fine screening on the candidate circles according to grading and sorting priorities based on angle consistency constraint and radius stability constraint; detecting a local continuous arc section of the edge points after fine screening and removing isolated points, constructing a caliper sampling line along the radius direction of the continuous arc section, and performing sub-pixel-level positioning by detecting gradient extreme points; fitting the sub-pixel edge point set, and outputting a circle detection result; according to the invention, a multi-stage detection architecture can be adopted, and the balance between the detection precision and the efficiency is realized; and through double geometric constraints, the robustness of the algorithm is greatly improved, and the false detection and omission ratio is reduced.

The pole piece defect detection method comprises the following steps: irradiating a to-be-detected pole piece by adopting a preset light source, and carrying out image acquisition on the to-be-detected pole piece through image acquisition equipment to obtain an original image of the to-be-detected pole piece; preprocessing the original image to obtain a preprocessed image; wherein the preprocessing comprises at least one of cutting processing, denoising processing, image enhancement processing and normalization processing; and detecting the preprocessed image according to a preset defect detection model to obtain a target defect of the to-be-detected pole piece. According to the method, the high-precision image acquisition equipment is matched with the special light source lighting system to capture the pole piece edge image with extremely high definition, and the burr characteristics in the image are automatically identified through the preset defect detection model, so that efficient detection is realized, the omission ratio and the misjudgment rate are greatly reduced, and the detection efficiency is improved. And the detection stability and reliability are obviously improved.

The invention provides an industrial part image edge detection method based on an improved EdgeNAT network, and relates to the technical field of computer vision and intelligent detection, and the method specifically comprises the following steps: firstly, constructing an industrial part image data set; according to the method, multi-layer features are extracted through a DiNAT coding module, an MEE multi-scale edge enhancement module is introduced between the DiNAT coding module and an SCAF-MLA decoding module, multi-branch different convolution kernel and expansion rate structures are adopted, edge saliency guidance and fusion are carried out on the features of all layers, and multi-scale feature responses are unified through a normalized residual fusion unit (NRF); in the decoding stage, a space-channel attention fusion multi-layer aggregation module (SCAF-MLA) is adopted to generate a fine edge graph; and finally, introducing an edge saliency generation module ESG-S, extracting edge response of shallow layer features through convolution and activation mapping, and realizing edge region saliency modeling and supervision. According to the scheme, the edge detection accuracy and robustness under a complex background, a fuzzy boundary and a multi-target scene are remarkably improved.

The embodiment of the invention provides an electric precipitator polar line surface defect identification method and system, and belongs to the technical field of electric precipitation. The method comprises the following steps: acquiring an original digital color image of the polar line in a corona discharge state; generating a grayscale image based on the original digital color image, and constructing a binary image for representing a brightness area according to a preset threshold; performing median filtering and mean filtering in sequence based on the binary image to construct a de-noised binary image, and performing Canny edge detection on the de-noised binary image to generate a halo edge image of a burr tip point; and based on the halo edge image, corona discharge positions and brightness information of two sharp points of each barb are extracted, and a polar line surface defect identification result is output according to a preset normal distribution mode. According to the scheme, the corona discharge characteristics can be stably extracted in complex imaging environments such as high dust and weak light, so that the polar line surface defects can be reliably identified.

This application discloses a method, apparatus, device, and storage medium for edge detection of images, relating to the field of image edge detection technology. The method includes: acquiring an input image and performing structure-preserving filtering on the input image to obtain a smoothed image; calculating a first-order gradient magnitude map based on the smoothed image; generating an adaptive parameter mapping based on the statistical characteristics of the first-order gradient magnitude map; obtaining an edge magnitude map based on the adaptive parameter mapping; determining a global threshold based on the edge magnitude map, and setting a high threshold and a low threshold based on the global threshold; and performing double-threshold hysteresis connection processing on the edge magnitude map based on the high threshold and the low threshold to output a binary edge map. This method can improve the detection accuracy of images.

This invention belongs to the field of image processing and computer vision technology, and discloses a lightweight single-image super-resolution reconstruction method based on a directional stripe hybrid network. It captures directional structural information of the image from both the spatial and frequency domains through horizontal stripes, vertical stripes, local window attention of the directional stripe self-attention module, and direction-specific convolution of the directional high-frequency enhancement module. The directional stripe self-attention module effectively captures long-distance dependencies extending along specific directions; the directional high-frequency enhancement module extracts high-frequency components through frequency domain decomposition, combines them with direction-specific convolution, and introduces a dual-guided adaptive enhancement mechanism. It identifies texture-rich regions through local variance maps and structural boundaries through edge maps, generating adaptive weights to guide feature enhancement, accurately enhancing high-frequency edges and textures with different orientations while effectively suppressing noise. The two work together to achieve spatial-frequency domain dual-level directional modeling, significantly improving the reconstruction quality of images rich in directional textures, such as architectural scenes and cartoon images, while maintaining a lightweight design.

The application discloses a coal mine transportation belt deviation detection method based on optical flow detection and end-to-end, and belongs to the technical field of safety monitoring. In view of the problems that the prior art cannot realize real-time monitoring on the running condition of the belt, the inspection efficiency is low, and there is a certain lag, a data set is made by collecting a coal mine transportation video, a carrier roller is labeled with the aid of image labeling software, and target frame position information of the carrier roller is calculated by using a carrier roller detection model, a slope of a belt transportation direction is calculated by using an optical flow detection algorithm, a straight line is determined according to the slope and the target frame position information of the carrier roller, the target frame of the carrier roller is divided into left and right regions, an edge graph obtained through edge detection and the target frame of the carrier roller with the divided position are used to generate a mask mask, an interested region is extracted, straight line information in the edge graph is processed in combination with straight line detection, position information of the belt and the carrier roller is determined, and whether the belt deviates is judged.

The present application relates to the technical field of ultrasound image processing, and particularly relates to an image processing method, device and system based on an interventional array ultrasound transducer. The method comprises: processing a two-dimensional ultrasound image through noise evaluation to obtain a noise evaluation image; the noise evaluation image can be used to accurately partition image noise; a first filtering algorithm and a second filtering algorithm can be used to preliminarily process the partitioned noise to obtain a preliminarily denoised image; the preliminarily denoised image is then processed accurately using a U-Net model to obtain an accurately denoised image; the accurately denoised image is regionally divided to obtain an edge image and a smooth image; corresponding algorithms are executed on the edge image and the smooth image to obtain an edge denoised image and a smooth denoised image; finally, the edge denoised image and the smooth denoised image are weighted and fused to obtain a denoised ultrasound image; and the accuracy of ultrasound image denoising is improved.

The invention provides an X-ray imaging detection method and device. The method comprises the following steps: correcting original image data of X-rays to obtain a corrected original image, extracting high-frequency information of the corrected original image through Gaussian blur, and performing difference and noise suppression processing to obtain preprocessed noise reduction data; carrying out binarization processing on the preprocessed noise reduction data to obtain noise reduction data; performing edge processing on the noise reduction data through a Sober operator to obtain an edge image; and after the edge image and the corrected original image are fused, the line is identified through Hough transform, a detection part image is obtained, the device comprises a module for realizing the method, through the method and the device, the ray machine device in the DR detection system is optimized, a high-frequency constant-voltage ray machine is replaced by a conventional power frequency ray machine, and the equipment cost is reduced.

This invention provides a CIM model generation method and system based on spatial intelligence and visual knowledge fingerprints, belonging to the field of smart city digital twins; it solves the problem of low efficiency in building texture generation; specifically as follows: edge detection is performed on the top view of the original CIM model, and the plot boundaries are synthesized with the Canny edge map to generate the building layout; planning indicators are extracted from the GIS data of the target plot, and a white model is generated using a modeling engine based on the building layout; the white model is automatically UV unwrapped, and a semantic segmentation map is output based on constraint rules and construction generation logic, and then encoded based on the visual features parsed from the target plot CIM model to generate texture maps; the generated texture maps are remapped back to the surface of the CIM3 level white model to obtain a generative reconstruction model; this invention realizes the continuous regeneration of CIM data features based on spatial intelligence, and solves the contradiction between privacy and utility by resolving spatial logic reasoning and generation.