Digital twin scene dynamic mapping generation method and device based on standard band correction

Abstract

The invention provides a digital twinning scene dynamic mapping generation method and device based on standard band correction, and relates to the technical field of data processing and digital twinning scene construction. According to the method, the dynamic image data set of the construction site is collected and processed, so that frame selection and category labeling are carried out on each target object in the image; a standard band of the image is set and adjusted to correct the visual angle of the image, and all target objects in the image are framed in the standard band for target recognition; converting the two-dimensional pixel coordinates of the recognized target object into three-dimensional coordinates in an actual scene through perspective projection calculation; then, the calculated three-dimensional coordinates are subjected to deviation rectification through an adjacent point position deviation rectification algorithm based on Procrustes analysis; and inputting the rectified three-dimensional coordinates into a pre-established BIM model to realize real-time mapping updating of the dynamic object of the digital twin scene. According to the method, the coordinate calculation precision during dynamic mapping of the digital twinborn scene can be improved, and the requirements of real-time performance and complexity of the dynamic scene are met.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and digital twin scene construction, and in particular to a method and device for dynamic mapping generation of a digital twin scene based on standard band correction. Background Art

[0002] Digital twin technology is a technology that uses digital means to construct dynamic mapping models of physical entities (such as buildings, equipment, construction sites, etc.) in virtual space. It is widely used in construction, manufacturing, transportation and other fields.

[0003] The construction site in the construction field is a complex and dynamically changing environment, involving a large number of dynamic objects, such as building materials, machinery and equipment, construction personnel, etc. In order to realize the digital management of the construction site, it is necessary to collect the location information of these dynamic objects in real time and map it to the digital twin model. In the prior art, surveillance cameras are usually used as the main data acquisition equipment, and the two-dimensional pixel coordinates in the image are converted into three-dimensional coordinates in the actual scene through the principle of perspective projection. However, this process involves complex mathematical calculations, poor real-time performance in dynamic scenes, and is easily affected by factors such as camera distortion, viewing angle limitations, lighting changes, and multi-target interference, resulting in insufficient accuracy and real-time performance of coordinate calculation.

[0004] In view of this, the applicant filed this application after studying the existing technology. Summary of the invention

[0005] The present invention aims to provide a method and device for dynamic mapping generation of digital twin scenes based on standard band correction, so as to solve the shortcomings of the existing methods such as insufficient accuracy and real-time performance of coordinate calculation.

[0006] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: A method for generating dynamic mapping of a digital twin scene based on standard band correction, comprising: S1, collect dynamic image datasets of the construction site, and process each image in the image dataset to select and label each target object in the image; S2, setting and adjusting the standard band of the image to correct the image viewing angle, framing all the targets in the image within the standard band for target recognition; S3, converting the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation; S4, based on the adjacent point correction algorithm of Procrustes analysis, corrects the calculated three-dimensional coordinates; S5, input the corrected three-dimensional coordinates into the pre-built BIM model to achieve real-time mapping and updating of dynamic objects in the digital twin scene.

[0007] Preferably, the target objects are safety factors existing at the construction site, including personnel, piled materials, construction tools, construction items, ladders and construction machinery with a risk of falling.

[0008] Preferably, the operation of processing each image includes: Filtering the image data set to remove images that do not contain the target object in the scene or that do not fully capture the target object, images that cause the target object to be blurred due to environmental influences, and duplicate images; Use deep learning technology to select and label each target object in the image data.

[0009] Preferably, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on image distortion; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated, specifically: Assume that the height of the camera is h, that is, the two-dimensional coordinate is (0, h); the reference plane is the plane where the target object is located, that is, the X-axis; the angle between the camera and the projection range of the standard belt is γ, and the angle γ is calculated according to the distance between the camera height h and the standard belt. The formula is: ; in, , They are the lower and upper limits of the standard band on the X-axis respectively; If the camera optical axis is used as the basis for the movement of the standard belt, when the standard belt is moved, the actual distance of each target object on the reference plane is calculated according to the fixed angle γ of each camera, so as to obtain the two-dimensional position of the target object. The expression is: ; in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target object projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves; θ is the pitch angle of the camera and the horizontal direction.

[0010] Preferably, S3 is specifically: The angle between the camera and the two edges of the object passing through the maximum range of the lens is called the field of view FOV. If the object exceeds the range of the field of view FOV, it will not be captured by the camera. The angle of the field of view is , i.e. the diagonal field of view; According to the diameter of the imaged object on the diagonal of the rectangular photosensitive surface, the diagonal field of view is used. Calculate the object distance s. Since the field of view of the camera is fixed and known, and the range that can be captured is also related to the focal length of the lens, the formula for the object distance s that the camera can capture is: ; in, is the diameter of the imaged object within the visible range of the lens, c is the point of the target image, and is also the intersection of the optical axis and the reference plane; The two-dimensional image captured by the camera is a projection of the three-dimensional space on a plane. Based on the camera posture and the coordinates of the target object in the image, i.e., the target frame, the position of the target object in the real world is obtained. Assume that the three-dimensional coordinates of the camera are point H (0, h, 0), and the pitch angle between H and the horizontal direction is θ; the reference plane is the plane where the target object is located, and is set on the same plane as the center of the target object. The straight line s is the object distance that the camera can capture, that is, the optical axis. Assume that point c ( , 0, 0) is the point of the target object, which is also the intersection of the optical axis and the reference plane. The coordinates of point c and the trigonometric function relationship of angle θ are obtained as follows: ; ; According to the perspective projection principle, the image captured by the camera is perpendicular to the optical axis, and the projection on the reference plane is a trapezoid, so the midpoint of the target image is set to point c; then, for any two-dimensional coordinate point Q in the target image ( , ), whose value is obtained by measuring transformation, and the corresponding three-dimensional coordinates of Q in the three-dimensional coordinate system are calculated as follows: , the formula is: ; So as to obtain the passing point The equation of the straight line l between and H is: ; Then the coordinates of the intersection point P of the straight line l and the reference plane are ( , , ), the three-dimensional coordinate formula of point P is: ; Thus, the two-dimensional coordinate point Q is mapped to the corresponding world coordinate point P in the digital twin scene; The two-dimensional coordinates of each target in the image are converted into three-dimensional coordinates; then the three-dimensional coordinates are transformed by the perspective projection of the camera to obtain the world coordinates of all targets mapped to the digital twin scene.

[0011] Preferably, the adjacent point correction algorithm based on Procrustes analysis is to find the optimal rotation matrix through singular value decomposition SVD to perform correction, specifically: Take the calculated three-dimensional coordinates of the target object as the set of points to be corrected, and select a point to be corrected from the set of points to be corrected , and select the corresponding three-dimensional coordinates of the actual point of a target as a reference point; calculate and center of gravity, and The center of gravity is shifted to , so that the centers of mass of the two coincide and eliminate the translation difference. The formula is: ; ; Where N is the total number of points on the target object; The calculated point The centroid of For actual point The centroid of Calculate the covariance matrix of the centered reference point and the point to be corrected , the formula is: ; in, is the centroid The transposed matrix of the coordinate point matrix calculated after centering; for The actual point matrix after centralization; is the covariance matrix; T represents the transposed matrix; Covariance matrix Perform SVD decomposition, the formula is: ; Among them, U and V are orthogonal matrices decomposed by SVD respectively; Σ is a diagonal matrix, that is, a singular value matrix; The rotation matrix R is calculated through U and V, and the translation vector t is calculated by combining the center of mass of the two points. The formula is: ; ; Where R is the rotation matrix; t is the translation vector; Apply the rotation matrix R and translation vector t to each target point in the set of points to be corrected to obtain the new coordinates after correction and alignment. The formula is: ; in, for Deskew the points after alignment.

[0012] Preferably, when the corrected three-dimensional coordinates are input into a pre-built BIM model, the corrected three-dimensional coordinates are imported into an Excel file, and the Excel file is imported into the BIM model using the Dynamo plug-in to complete the mapping of the digital twin scene.

[0013] Preferably, it also includes: adjusting the coordinates of the target object in the BIM model by a grid drawing auxiliary calibration method to achieve accurate mapping between the actual scene and the digital scene; specifically: In the BIM model, a three-dimensional coordinate system is constructed that is consistent with the physical coordinate system of the actual scene; Use a square grid to calibrate each point in the scene. The nodes of the grid should correspond one-to-one to the reference points in the actual scene. Each grid point in the BIM model represents a reference coordinate in the actual scene; According to the accuracy requirements, the grid is subdivided into size; Based on the data of the target objects in the actual scene, the targets in the grid are adjusted.

[0014] The present invention also provides a digital twin scene dynamic mapping generation device based on standard band correction, comprising: The image acquisition unit is used to collect dynamic image data sets of the construction site and process each image in the image data set to select and classify each target object in the image; The target recognition unit is used to set and adjust the standard band of the image to correct the image viewing angle and frame all the targets in the image within the standard band for target recognition; wherein, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on the distortion of the image; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated, specifically: let the height of the camera be h, that is, the two-dimensional coordinates be (0, h); the reference plane is the plane where the target is located, that is, the X-axis; the angle between the camera and the projection range of the standard band is γ, and the angle γ is calculated according to the distance between the camera height h and the standard band, and the formula is: ; in, , They are the lower and upper limits of the standard band on the X-axis respectively; If the camera optical axis is used as the basis for the movement of the standard belt, when the standard belt is moved, the actual distance of each target object on the reference plane is calculated according to the fixed angle γ of each camera, so as to obtain the two-dimensional position of the target object. The expression is: ; in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves; θ is the pitch angle of the camera and the horizontal direction; A three-dimensional conversion unit, used to convert the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation; The three-dimensional coordinate correction unit is used to correct the calculated three-dimensional coordinates based on the adjacent point correction algorithm of Procrustes analysis; The real-time mapping unit is used to input the corrected three-dimensional coordinates into the pre-built BIM model to achieve real-time mapping updates of dynamic objects in the digital twin scene.

[0015] The present invention also provides a digital twin scene dynamic mapping generation device based on standard band correction, including a processor and a memory, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a digital twin scene dynamic mapping generation method based on standard band correction as described above.

[0016] The present invention also provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, a method for generating dynamic mapping of a digital twin scene based on standard band correction as described above is implemented.

[0017] In summary, compared with the prior art, the present invention has the following beneficial effects: (1) The present invention enhances scene adaptability in complex environments. The present invention improves the robustness of target detection and coordinate calculation in complex environments through sensors (such as cameras) and deep learning technology, ensuring the stability and accuracy of the technology under complex conditions such as target occlusion and lighting changes.

[0018] (2) The present invention solves the problem of insufficient coordinate calculation accuracy caused by camera distortion. The present invention introduces a "standard belt" with minimal camera distortion effect obtained from relevant experimental tests, thereby weakening and effectively avoiding the influence of camera distortion on the target object coordinate calculation, and improving the coordinate calculation accuracy of the target object in the actual scene. In particular, in the calculation of targets within the distance range of the "standard belt", the accuracy of coordinate calculation can be ensured, and by combining the change of calculation angle with the projection principle, the actual position of the target object can be inferred, and the dynamic tracking function of the image device can be fed back.

[0019] (3) The present invention realizes the real-time mapping and updating of dynamic objects, meeting the real-time requirements of dynamic scenes. The present invention deeply integrates the BIM model with dynamic data collection through the Dynamo plug-in in the BIM software, and updates the dynamic object information in the digital twin model in real time, ensuring that the digital twin model can accurately reflect the dynamic changes of the construction site. The coordinate position is corrected by the adjacent point correction algorithm based on Procrustes analysis, and then the corrected coordinate data is input into the script instructions written by Dynamo through an Excel file, so as to realize the automatic adjustment of the target object in the BIM model, improve the accuracy of coordinate calculation, and meet the real-time and accuracy requirements of dynamic scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of a method for generating dynamic mapping of a digital twin scene based on standard band correction provided in Example 1.

[0022] Figure 2 A flowchart of a method for generating dynamic mapping of a digital twin scene based on standard band correction is provided in Example 1.

[0023] Figure 3 A schematic diagram of a method for generating dynamic mapping of a digital twin scene based on standard band correction provided in Example 1.

[0024] Figure 4 This is a schematic diagram of the standard tape provided in Example 1.

[0025] Figure 5 This is a schematic diagram of the standard band sight range provided in Example 1.

[0026] Figure 6 This is a schematic diagram of the field of view (FOV) model provided in Example 1.

[0027] Figure 7 This is a schematic diagram of the diagonal field of view model provided in Example 1.

[0028] Figure 8 A perspective projection principle model diagram provided in Example 1.

[0029] Fig. 9 A schematic diagram of a digital twin scene dynamic mapping generation device based on standard band correction provided in Example 2.

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] Embodiment 1 Embodiment 1 of the present invention provides a method for dynamic mapping generation of a digital twin scene based on standard band correction, which can be implemented by a dynamic mapping generation device for a digital twin scene based on standard band correction (hereinafter referred to as a dynamic mapping generation device), and in particular, executed by one or more processors in the dynamic mapping generation device.

[0033] In this embodiment, the dynamic mapping generation device may be an electronic device equipped with a processor, which has a computer program for generating dynamic mapping of digital twin scenes based on standard band correction and can be executed, such as a computer, a smart phone, a smart tablet, a workstation, etc., which is not limited here.

[0034] In this embodiment, the core of digital twin technology is to collect data from the physical world in real time through sensors, cameras and other devices, and build a dynamic mapping model in the virtual space to achieve real-time interaction and synchronization between the physical world and the digital world. In the field of construction, BIM technology provides digital twins with rich static building information, including geometric information, attribute information and management information of buildings. However, in dynamic environments such as construction sites, how to generate dynamic object mapping in real time and accurately still faces many challenges.

[0035] The existing technologies have the following main defects in the generation of dynamic object mapping in digital twin scenes: (1) Poor adaptability to scenes in complex environments. Existing technologies have poor adaptability to complex construction sites and are difficult to handle problems such as target occlusion, illumination changes, and multi-target interference. For example, at a construction site, the target may be obscured by other objects, or the image quality may degrade due to illumination changes, affecting the accuracy of target detection and coordinate calculation. In these situations, the on-site surveillance cameras cannot accurately identify and filter out valid images, so it is necessary to filter the on-site surveillance images saved by the camera to improve the quality of the image data.

[0036] (2) Camera distortion leads to large coordinate errors in the calculated objects. In the prior art, the coordinate calculation based on the perspective projection of the camera does not fully consider the influence of camera distortion. Camera distortion is a geometric deviation caused by the optical characteristics of the lens, including radial distortion and tangential distortion. Radial distortion includes barrel distortion or pincushion distortion. This distortion will cause the image to bend and deform, resulting in the constructed position of the target object in the image being inconsistent with the position in the actual scene. In addition, distortion correction involves many factors, and it cannot fully guarantee that the image can meet the requirements of coordinate calculation after distortion correction.

[0037] (3) Poor real-time performance in dynamic scenes. In dynamic environments such as construction sites, the position and state of the target objects are constantly changing, and the digital twin model needs to be updated in real time. Existing technologies are usually static modeling based on BIM and lack real-time mapping capabilities. For example, at a construction site, the position and state of target objects such as building materials, machinery and equipment, and construction personnel are constantly changing, and existing technologies make it difficult to update the information of these dynamic objects in real time. In addition, the efficiency of dynamic data collection and processing is insufficient, resulting in poor real-time performance of dynamic object mapping. However, using the Dynamo plug-in that comes with the BIM software, combined with perspective projection to calculate the coordinates of dynamic objects, can meet the real-time performance of dynamic scenes to a certain extent.

[0038] like Figure 1-Figure 3 As shown, a method for generating dynamic mapping of a digital twin scene based on standard band correction includes steps S1 to S5.

[0039] S1, collects dynamic image datasets of the construction site and processes each image in the image dataset to select and label each target object in the image.

[0040] In this embodiment, according to the actual situation of the construction site, a surveillance camera or a drone is used to capture images of the site to form an image data set. The camera automatically saves the on-site surveillance images every second and automatically uploads them to the cloud disk for storage.

[0041] The target objects in this embodiment are safety factors existing at the construction site, including personnel, piled materials, construction tools, construction objects, ladders and construction machinery with a risk of falling. Therefore, the on-site images captured by the camera must contain one or more of the above factors to form an image data set.

[0042] Then, each image is processed, including: filtering the image data set to remove images that do not contain the target object in the scene or the target object is not fully captured, images in which the target object is blurred due to environmental influences, and repeated images; Preprocess the collected images, including denoising, contrast enhancement, brightness adjustment, etc., to improve image quality and facilitate subsequent target recognition and category labeling; Label the objects in the filtered image according to the labeling scheme. This embodiment uses deep learning technology to select and label each target object in the image data. The labeling information should be accurate and detailed so that the subsequent target recognition model can accurately identify the target object. For example, the labeling scheme of the PASCAL VOC dataset is used to select and label each target object in the image data, and the label information is the category of the target object.

[0043] In this step, through target recognition and category labeling, the key information in the image can be extracted in a structured form, allowing the computer to "understand" the image content; it is also convenient for subsequent import into the BIM model, so that the entity objects in the physical world can be accurately mapped to the digital world, ensuring the authenticity and accuracy of the digital twin scene.

[0044] In the process of target recognition, an existing deep learning target recognition model, such as the YOLO series target detection model, can be used for target recognition and labeling of the present invention after training. The identified target object needs to be framed, that is, a bounding box is drawn around it to clarify its position and range. At the same time, each target object also needs to be labeled with its category (such as workers, machinery, building materials, etc.) for subsequent classification and analysis.

[0045] S2, set the standard band of the image and adjust it to correct the image viewing angle, and frame all targets in the image within the standard band for target recognition; wherein, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on image distortion; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the targets in the image, ensuring that the coordinates of the targets at all points can be accurately calculated.

[0046] This step aims to improve the accuracy and efficiency of target recognition by correcting the viewing angle and framing the target range with a standard band, thereby reducing unnecessary calculations.

[0047] Due to the distortion of the lens, the coordinate data calculated by the perspective projection algorithm will be biased. Lens distortion is caused by the deviation of the lens manufacturing accuracy and assembly process, which can usually be described by a mathematical model. Commonly used distortion models include radial distortion and tangential distortion. Radial distortion includes barrel distortion or pincushion distortion.

[0048] The image improved after distortion correction using the commonly used distortion model, but still did not meet the expected assumptions. This may be because the distortion correction formula implicitly changes the position ratio of the pixels, and the relationship between this change and the focal length is not linear. For example, the scaling introduced during the correction process is related to the focal length, resulting in different degrees of coordinate magnification or reduction; the corrected coordinates are calculated based on the ideal projection of the camera's internal parameters and may not be aligned with the actual real physical measurement coordinates. Image distortion correction requires interpolation operations (such as bilinear interpolation or resampling), and numerical errors may be introduced during the interpolation process, resulting in complex reasons such as coordinates not matching the actual ones.

[0049] Therefore, the general distortion correction method cannot meet the actual needs. The embodiment of the present invention proposes the idea of ​​"standard belt". The purpose of setting the standard belt is to correct the image viewing angle and ensure that the target object in the image can be identified and analyzed at a relatively uniform and accurate viewing angle. By adjusting the standard belt, the image distortion caused by the change of the camera viewing angle can be corrected, so that the target object in the image can be framed and identified more accurately.

[0050] The standard belt is: imitating the imaging principle of the camera, and setting the position area within a specific range from the camera (the specific range can be obtained through experimental testing) as the standard belt. In this area, the camera has little effect on the distortion of the image, and the area can be moved by simulating the adjustment of the pitch angle θ of the camera with the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated.

[0051] Without considering distortion correction, in order to determine the scope of influence of lens distortion, the camera model used in this embodiment is DH-IPC-HFW1230M-A-I1, and the focal length is 6mm. The Dahua camera captured the picture and obtained that when the target object is within 300-420cm from the camera, the camera has little effect on the distortion of the image, and the calculated coordinates of the target object are also more accurate. In addition, due to the limitations of the camera's shooting angle, there may be incomplete targets at the borders of the captured objects, and due to the imaging principle of the camera, the calculated X-axis coordinates of the target object will show an opposite trend to the actual scene coordinates, and the Z-axis will not be affected. These problems are all caused by the inherent parameters inside the camera, and it is difficult to correct them by a third party such as an algorithm or manual adjustment.

[0052] like Figure 4 As shown in FIG. 1 , for this purpose, the position area between 300-420 cm from the camera is defined as the “standard zone” of this camera. Figure 5 As shown, by adjusting the pitch angle θ of the camera with respect to the horizontal direction, the “standard belt” is moved up or down in the scene, so that targets that are farther or closer or targets that are not fully photographed can be completely framed in the “standard belt” for dynamic capture, thereby ensuring that the coordinates of targets at all points in the scene can be accurately calculated.

[0053] By dynamically adjusting the pitch angle θ of the standard tape and the simulated camera, the target in the image can be dynamically captured and framed. During the adjustment, the position of the standard tape should be gradually optimized in combination with real-time image feedback. This ensures that no matter where the target is in the image, its coordinates can be accurately calculated, ensuring the accuracy and stability of the recognition results, and providing a reliable data basis for subsequent target tracking, behavior analysis, etc.

[0054] Assume that the height of the camera H is h, that is, the two-dimensional coordinate is (0, h); the reference plane is the plane where the target object is located, that is, the X-axis; the angle between the camera and the projection range of the standard belt is γ, and the angle γ is calculated according to the distance between the camera height h and the standard belt. The formula is: ; in, , They are the lower and upper limits of the standard belt on the X-axis respectively.

[0055] Using the above formula, it can be deduced that the angle γ of this camera is 7.93°.

[0056] like Figure 5As shown, if the optical axis of the camera is used as the basis for the movement of the standard belt, when the optical axis is at the original position, the pitch angle θ of the camera and the horizontal direction is -27.51°, that is, the angle θ between the optical axis and the horizontal direction is -27.51°, and the vertical field angle of the camera selected in the experiment is 27°, which means that the calculation of the standard belt is accurate and useful. When the standard belt is moved (for example, calculated by moving 60cm each time), when the camera model remains unchanged, the angle γ of each camera remains unchanged. If the distance factor of the target object in the actual scene is uncertain, γ is calculated according to the formula, and then the actual distance coordinate of the target object is calculated from γ, and then the actual position of the target object is inferred. In this way, the actual distance of each target object on the reference plane is calculated, and the two-dimensional coordinates of the target object are obtained, and the expression is: ; in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target object projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves.

[0057] like Figure 4 The position coordinates of point D can be calculated by the following formula: .

[0058] S3, converting the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation.

[0059] Specifically, Figure 6 As shown in the figure, the angle formed by the camera H and the two edges of the target object through the maximum range of the lens is called the field of view FOV; if the target object exceeds the range of the field of view FOV, it will not be captured by the camera. The angle of the field of view is , that is, the diagonal field of view.

[0060] The field of view angle includes the horizontal field of view angle (HFOV, ), vertical field of view (VFOV, ) and diagonal field of view (DFOV, ),in, , , are the angles corresponding to the field of view.

[0061] Usually, the default FOV is the horizontal field of view when there is no special description. However, for optical devices such as cameras and video cameras, since their photosensitive surfaces are rectangular, the method of the present invention calculates the field of view using the diameter of the imaged object on the diagonal of the rectangular photosensitive surface, that is, using the diagonal field of view for calculation.

[0062] like Figure 7As shown, according to the diameter of the imaged object on the diagonal of the rectangular photosensitive surface, the diagonal field of view is used. Calculate the object distance s. Since the field of view of the camera is fixed and known, and the range that can be captured is also related to the focal length of the lens, the formula for the object distance s that the camera can capture is: ; in, is the diameter of the imaged object within the visible range of the lens, a and b are the two end points of the diameter, and c is the center point of the target image, which is also the intersection of the optical axis and the reference plane. is the diagonal field of view, and s is the object distance that the camera can capture.

[0063] The two-dimensional image captured by the camera is a projection of the three-dimensional space on a plane. Based on the camera posture and the coordinates of the target object in the image, that is, the target frame, the position of the target object in the real world is obtained.

[0064] like Figure 8 As shown, let the three-dimensional coordinates of the camera be point H (0, h, 0), and the pitch angle between H and the horizontal direction be θ; the reference plane is the plane where the target object is located, and is set on the same plane as the center of the target object. Figure 7 It can be seen that the straight line s is the object distance that the camera can capture, that is, the optical axis, and point c ( , 0, 0) is the point of the target object, which is also the intersection of the optical axis and the reference plane. The coordinates of point c and the trigonometric function relationship of angle θ are obtained as follows: ; ; According to the perspective projection principle, the image captured by the camera is perpendicular to the optical axis, and the projection on the reference plane is a trapezoid. Therefore, in order to simplify the calculation, the midpoint of the target image is set to point c. Then, for any two-dimensional coordinate point Q in the target image ( , ), whose value is obtained by measuring transformation, and the corresponding three-dimensional coordinates of Q in the three-dimensional coordinate system are calculated as follows: , the formula is: ; So as to obtain the passing point The equation of the straight line l between and H is: ; Then the coordinates of the intersection point P of the straight line l and the reference plane are ( , , ), the three-dimensional coordinate formula of point P is: ; Thus, the two-dimensional coordinate point Q is mapped to the corresponding world coordinate point P in the digital twin scene; Convert the two-dimensional coordinates of each target in the image into three-dimensional coordinates; then multiply the three-dimensional coordinates by the transformation matrix M, and perform the perspective projection transformation of the camera to obtain the world coordinates of all target objects mapped to the digital twin scene.

[0065] The camera transformation matrix M is a combination of the internal parameter matrix K and the external parameter matrix [R|t], and the formula is: M = K[R | t]; The form of the camera internal parameter matrix K is usually as shown in the following formula: ; in, and are the focal lengths of the camera in the x and y directions, respectively, in pixels; and are the principal point coordinates of the image (i.e. the pixel coordinates at the center of the image).

[0066] The external parameter matrix [R|t] describes the position and direction of the camera. The coordinate system selected by the model proposed in the embodiment of the present invention is aligned with the origin of the world coordinate system and is not rotated. In this case, the transformation matrix M is the internal parameter matrix K. That is: ; t= ; Where R is the rotation matrix, is the unit matrix, indicating no rotation; t is the translation vector, indicating that the camera is located at the origin of the world coordinate system.

[0067] S4, based on the adjacent point correction algorithm of Procrustes analysis, corrects the calculated three-dimensional coordinates.

[0068] In this embodiment, Procrustes analysis is a method for comparing the consistency of two sets of data by analyzing shape distribution. Mathematically speaking, it is to continuously iterate and find a standard shape, and use the least squares method to find the affine transformation of each object shape to this standard shape. This process is also called least squares orthogonal mapping.

[0069] The adjacent point correction algorithm based on Procrustes analysis is to find the optimal rotation matrix through singular value decomposition SVD to correct the deviation, specifically: Take the calculated three-dimensional coordinates of the target object as the set of points to be corrected, and select a point to be corrected from the set of points to be corrected , and select the corresponding three-dimensional coordinates of the actual point of a target In this embodiment, the center point of the target object that is within the standard band of the camera and is not blocked is preferably selected as the reference point.

[0070] calculate and center of gravity, and The center of gravity is shifted to , so that the centers of mass of the two coincide and eliminate the translation difference. The formula is: ; ; Where N is the total number of points on the target object; The calculated point The centroid of For actual point The centroid of Calculate the covariance matrix of the centered reference point and the point to be corrected , the formula is: ; in, is the centroid The transposed matrix of the coordinate point matrix calculated after centering; for The actual point matrix after centralization; is the covariance matrix; T represents the transposed matrix; Covariance matrix Perform SVD decomposition, the formula is: ; Among them, U and V are orthogonal matrices decomposed by SVD respectively; Σ is a diagonal matrix, that is, a singular value matrix; The rotation matrix R is calculated through U and V, and the translation vector t is calculated by combining the center of mass of the two points. The formula is: ; ; Where R is the rotation matrix; t is the translation vector; Apply the rotation matrix R and translation vector t to each target point in the set of points to be corrected to obtain the new coordinates after correction and alignment. The formula is: ; in, for Deskew the points after alignment.

[0071] S5, input the corrected 3D coordinates into the pre-built BIM model to achieve real-time mapping of dynamic objects in the digital twin scene.

[0072] When inputting the corrected three-dimensional coordinates into a pre-built BIM model, first import the corrected three-dimensional coordinates into an Excel file, and then use the Dynamo plug-in to import the Excel file into the BIM model to complete the mapping of the digital twin scene.

[0073] The calculated three-dimensional coordinates of the target object are imported into the BIM model using the Dynamo plug-in, and aligned with the target object in the actual scene. Although the coordinates imported into the BIM are corrected, due to factors such as calculation and lens distortion, there may still be inaccurate matching between the target object points in the model and the points in the actual scene (such as a certain distance between the points in the BIM and the points in the actual scene, or the target object that should be at point A is matched to point C, etc.), so further adjustments can be made in the BIM model to achieve accurate mapping between the actual scene and the digital scene.

[0074] This can be adjusted by: (1) Calibration aided by grid drawing In the BIM model, a three-dimensional coordinate system is constructed that is consistent with the physical coordinate system of the actual scene, and then each point in the scene is calibrated by drawing a square grid. The nodes of the grid should correspond one-to-one to the reference points in the actual scene. Each grid point can represent a reference coordinate in the actual scene in the BIM model. Then, according to the accuracy requirements, the grid is subdivided into (such as 6cm×6cm) size.

[0075] Set the relative position of the target on the grid and perform preliminary placement according to the coordinate data obtained in steps S3 to S4. Combined with the data in the actual scene, adjust the position of the target in the grid through camera calibration or manual adjustment to align it with the point in the actual scene as much as possible.

[0076] (2) Optimized alignment method based on three-dimensional coordinate system First, select some clear and easy-to-calibrate control points (such as corner points, reference surfaces, etc.) in the actual scene. The 3D coordinates of these points need to be accurately matched in the BIM model. Through the coordinate data obtained from steps S3 to S4, these points can be converted from the camera coordinate system to the world coordinate system, and then further adjustments can be made using node commands in Dynamo based on the differences between the existing points in the BIM model and the actual coordinate points.

[0077] (3) Automated calibration and matching Based on the point errors between the existing BIM points and the actual scene, a script is written in Dynamo to import the three-dimensional coordinates obtained through steps S3 to S4 into the BIM model, automatically calculate and update the points, and adjust the position of the target object in BIM to minimize the difference with the actual point. And use dynamic updates in BIM to gradually correct the errors. A dynamic feedback mechanism is implemented in the BIM model. When new actual scene data is input, the system can automatically adjust and output the adjusted coordinates to ensure that the position of the target object in the scene is accurately aligned.

[0078] In summary, compared with the prior art, the present invention has the following beneficial effects: (1) The present invention enhances scene adaptability in complex environments. In view of the poor adaptability of existing technologies to complex construction sites and the difficulty in dealing with problems such as target occlusion, illumination changes, and multi-target interference, the robustness of target detection and coordinate calculation in complex environments is improved through sensors (such as cameras) and deep learning technology, ensuring the stability and accuracy of the technology under complex conditions such as target occlusion and illumination changes.

[0079] (2) The present invention solves the problem of insufficient coordinate calculation accuracy caused by camera distortion. The prior art does not fully consider the impact of camera distortion, resulting in large errors in the coordinate calculation of the target object. By introducing a "standard belt" with minimal camera distortion influence obtained from relevant experimental tests, the influence of camera distortion on the target object coordinate calculation is weakened and effectively avoided, thereby improving the coordinate calculation accuracy of the target object. In particular, in the calculation of targets within the distance range of the "standard belt", the accuracy of coordinate calculation can be ensured, and by combining the change in calculation angle with the projection principle, the actual position of the target object can be inferred, and the dynamic tracking function of the image device can be fed back.

[0080] (3) The present invention realizes the real-time mapping and updating of dynamic objects, meeting the real-time requirements of dynamic scenes. The existing technology is usually based on BIM static modeling, lacks the ability to map dynamic objects in real time, has low coordinate calculation accuracy, and is difficult to meet the real-time update requirements of dynamic scenes such as construction sites. Through the Dynamo plug-in built into the BIM software, the BIM model is deeply integrated with dynamic data collection, and the dynamic object information (such as building materials, machinery and equipment, construction personnel, etc.) in the digital twin model is updated in real time to ensure that the digital twin model can accurately reflect the dynamic changes of the construction site. The coordinate position is corrected by the adjacent point correction algorithm based on Procrustes analysis, and then the corrected coordinate data is input into the script instruction written by Dynamo through an Excel file to realize the automatic adjustment of the target object in the BIM model, improve the accuracy of coordinate calculation, and meet the real-time and accuracy requirements of dynamic scenes.

[0081] Embodiment 2 like Fig. 9 As shown, the second embodiment of the present invention further provides a digital twin scene dynamic mapping generation device based on standard band correction, comprising: The image acquisition unit is used to collect dynamic image data sets of the construction site and process each image in the image data set to select and classify each target object in the image; The target recognition unit is used to set and adjust the standard band of the image to correct the image viewing angle and frame all the targets in the image within the standard band for target recognition; wherein, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on the distortion of the image; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated, specifically: let the height of the camera be h, that is, the two-dimensional coordinates be (0, h); the reference plane is the plane where the target is located, that is, the X-axis; the angle between the camera and the projection range of the standard band is γ, and the angle γ is calculated according to the distance between the camera height h and the standard band, and the formula is: ; in, , They are the lower and upper limits of the standard band on the X-axis respectively; If the camera optical axis is used as the basis for the movement of the standard belt, when the standard belt is moved, the actual distance of each target object on the reference plane is calculated according to the fixed angle γ of each camera, so as to obtain the two-dimensional position of the target object. The expression is: ; in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves; θ is the pitch angle of the camera and the horizontal direction; A three-dimensional conversion unit, used to convert the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation; The three-dimensional coordinate correction unit is used to correct the calculated three-dimensional coordinates based on the adjacent point correction algorithm of Procrustes analysis; The real-time mapping unit is used to input the corrected three-dimensional coordinates into the pre-built BIM model to achieve real-time mapping updates of dynamic objects in the digital twin scene.

[0082] Embodiment 3 The third embodiment of the present invention also provides a digital twin scene dynamic mapping generation device based on standard band correction, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the digital twin scene dynamic mapping generation method based on standard band correction as described above.

[0083] Embodiment 4 The fourth embodiment of the present invention also provides a computer-readable storage medium, on which computer-readable storage medium is stored computer-readable instructions. When the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, the method for generating dynamic mapping of a digital twin scene based on standard band correction as described above is implemented.

[0084] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0086] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0087] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0088] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0089] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0090] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for generating dynamic mapping of digital twin scenes based on standard band correction, characterized in that: include: S1, collects dynamic image datasets of the construction site, and processes each image in the image dataset to select and label each target object in the image; S2, set the standard band of the image and adjust it to correct the image viewing angle, frame all the targets in the image within the standard band for target recognition; wherein, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on the distortion of the image; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated, specifically: let the height of the camera be h, that is, the two-dimensional coordinate is (0, h); the reference plane is the plane where the target is located, that is, the X-axis; the angle between the camera and the projection range of the standard band is γ, and the angle γ is calculated according to the distance between the camera height h and the standard band, and the formula is: ； in, , They are the lower and upper limits of the standard band on the X-axis respectively; If the camera optical axis is used as the basis for the movement of the standard belt, when the standard belt is moved, the actual distance of each target object on the reference plane is calculated according to the fixed angle γ of each camera, so as to obtain the two-dimensional position of the target object. The expression is: ； in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves; θ is the pitch angle of the camera and the horizontal direction; S3, converting the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation; S4, based on the adjacent point correction algorithm of Procrustes analysis, corrects the calculated three-dimensional coordinates; S5, input the corrected three-dimensional coordinates into the pre-built BIM model to achieve real-time mapping and updating of dynamic objects in the digital twin scene.

2. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that ,The target objects are the safety factors existing at the construction site, including personnel, ,pile materials, construction tools, construction items, ladders and construction machinery with ,falling risks.

3. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that ,The operations for processing each image include: Filtering the image data set to remove images that do not contain the target object in the scene or that do not fully capture the target object, images that cause the target object to be blurred due to environmental influences, and duplicate images; Use deep learning technology to select and label each target object in the image data.

4. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that , the S3 is specifically: The angle between the camera and the two edges of the object passing through the maximum range of the lens is called the field of view FOV. If the object exceeds the range of the field of view FOV, it will not be captured by the camera. The angle of the field of view is , i.e. the diagonal field of view; According to the diameter of the imaged object on the diagonal of the rectangular photosensitive surface, the diagonal field of view is used. Calculate the object distance s. Since the field of view of the camera is fixed and known, and the range that can be captured is also related to the focal length of the lens, the formula for the object distance s that the camera can capture is: ； in, is the diameter of the imaged object within the visible range of the lens, c is the point of the target image, and is also the intersection of the optical axis and the reference plane; The two-dimensional image captured by the camera is a projection of the three-dimensional space on a plane. Based on the camera posture and the coordinates of the target object in the image, i.e., the target frame, the position of the target object in the real world is obtained. Assume that the three-dimensional coordinates of the camera are point H (0, h, 0), and the pitch angle between H and the horizontal direction is θ; the reference plane is the plane where the target object is located, and is set on the same plane as the center of the target object. The straight line s is the object distance that the camera can capture, that is, the optical axis. Assume that point c ( , 0, 0) is the point of the target object, which is also the intersection of the optical axis and the reference plane. The coordinates of point c and the trigonometric function relationship of angle θ are obtained as follows: ； ； According to the perspective projection principle, the image captured by the camera is perpendicular to the optical axis, and the projection on the reference plane is a trapezoid, so the midpoint of the target image is set to point c; then, for any two-dimensional coordinate point Q in the target image ( , ), whose value is obtained by measuring transformation, and the corresponding three-dimensional coordinates of Q in the three-dimensional coordinate system are calculated as follows: , the formula is: ； So as to obtain the passing point The equation of the straight line l between and H is: ； Then the coordinates of the intersection point P of the straight line l and the reference plane are ( , , ), the three-dimensional coordinate formula of point P is: ； Thus, the two-dimensional coordinate point Q is mapped to the corresponding world coordinate point P in the digital twin scene; The two-dimensional coordinates of each target in the image are converted into three-dimensional coordinates; then the three-dimensional coordinates are transformed by the perspective projection of the camera to obtain the world coordinates of all targets mapped to the digital twin scene.

5. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that ,The adjacent point correction algorithm based on Procrustes analysis is to find the optimal rotation matrix through singular value decomposition SVD to correct the deviation, specifically: Take the calculated three-dimensional coordinates of the target object as the set of points to be corrected, and select a point to be corrected from the set of points to be corrected , and select the corresponding three-dimensional coordinates of the actual point of a target as a reference point; calculate and center of gravity, and The center of gravity is shifted to , so that the centers of mass of the two coincide and eliminate the translation difference. The formula is: ； ； Where N is the total number of points on the target object; The calculated point The centroid of For actual point The centroid of Calculate the covariance matrix of the centered reference point and the point to be corrected , the formula is: ； in, is the centroid The transposed matrix of the coordinate point matrix calculated after centering; for The actual point matrix after centralization; is the covariance matrix; T represents the transposed matrix; Covariance matrix Perform SVD decomposition, the formula is: ； Among them, U and V are orthogonal matrices decomposed by SVD respectively; Σ is a diagonal matrix, that is, a singular value matrix; The rotation matrix R is calculated through U and V, and the translation vector t is calculated by combining the center of mass of the two points. The formula is: ； ； Where R is the rotation matrix; t is the translation vector; Apply the rotation matrix R and translation vector t to each target point in the set of points to be corrected to obtain the new coordinates after correction and alignment. The formula is: ； in, for Deskew the points after alignment.

6. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that ,When the corrected three-dimensional coordinates are input into the pre-built BIM model, the corrected three-dimensional coordinates are imported into the Excel file, and the Dynamo plug-in is used to import the Excel file into the BIM model to complete the mapping of the digital twin ,scene.

7. According to claim 1, a method for generating dynamic mapping of digital twin scenes based on standard band correction is characterized in that , also includes: adjusting the coordinates of the target objects in the BIM model through the grid drawing auxiliary calibration method to achieve accurate mapping between the actual scene and the digital scene; specifically: In the BIM model, a three-dimensional coordinate system is constructed that is consistent with the physical coordinate system of the actual scene; Use a square grid to calibrate each point in the scene. The nodes of the grid should correspond one-to-one to the reference points in the actual scene. Each grid point in the BIM model represents a reference coordinate in the actual scene; According to the accuracy requirements, the grid is subdivided into size; Based on the data of the target objects in the actual scene, the targets in the grid are adjusted.

8. A digital twin scene dynamic mapping generation device based on standard band correction, characterized in that: The image acquisition unit is used to collect dynamic image data sets of the construction site and process each image in the image data set to select and classify each target object in the image; The target recognition unit is used to set and adjust the standard band of the image to correct the image viewing angle and frame all the targets in the image within the standard band for target recognition; wherein, the standard band is: based on the camera imaging principle, a position area that is within a specific range from the camera and has a weak effect on the distortion of the image; and the area can be moved by simulating the adjustment of the pitch angle of the camera with respect to the horizontal direction to dynamically capture and frame the target in the image, ensuring that the coordinates of the target at all points can be accurately calculated, specifically: let the height of the camera be h, that is, the two-dimensional coordinates be (0, h); the reference plane is the plane where the target is located, that is, the X-axis; the angle between the camera and the projection range of the standard band is γ, and the angle γ is calculated according to the distance between the camera height h and the standard band, and the formula is: ； in, , They are the lower and upper limits of the standard band on the X-axis respectively; If the camera optical axis is used as the basis for the movement of the standard belt, when the standard belt is moved, the actual distance of each target object on the reference plane is calculated according to the fixed angle γ of each camera, so as to obtain the two-dimensional position of the target object. The expression is: ； in, is the position of the target point i on the X-axis, that is, the distance between the camera and the target projected on the reference plane; i is the target point number; n is the number of times the optical axis point moves; θ is the pitch angle of the camera and the horizontal direction; A three-dimensional conversion unit, used to convert the two-dimensional pixel coordinates of the identified target object into three-dimensional coordinates in the actual scene through perspective projection calculation; The three-dimensional coordinate correction unit is used to correct the calculated three-dimensional coordinates based on the adjacent point correction algorithm of Procrustes analysis; The real-time mapping unit is used to input the corrected three-dimensional coordinates into the pre-built BIM model to achieve real-time mapping updates of dynamic objects in the digital twin scene.

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