Processing part for constructing pipeline twinning model

Abstract

The invention relates to a processing part for constructing a pipeline twinborn model, and the processing part is configured to obtain point cloud data in a point cloud database, obtain a pipeline path by using an entity selection function based on the point cloud feature distribution condition in the denoised point cloud data, and perform iterative traversal on an entity database by using an entity object function; retrieving the point cloud object to obtain the spatial position coordinates of the point location; calculating the coordinates of the center point of the section of the pipeline by calculating the maximum value of the distance between any two points of the section, and calculating the center line of the pipeline according to the continuity of the coordinates of the center point of the section; after the multiple sections are executed to obtain multiple section center points about the pipeline, the section center points in the range are searched with the center point of each section as the relative original point and the distance value as the radius, retrieval is carried out, and therefore free tracking of pipeline extension simulation is achieved, and a complete pipeline model with the starting point and the ending point is formed.

Description

[0001] The original basis for this divisional application is patent application No. 202210309250.8, filed on March 25, 2022, entitled "A method and apparatus for reverse modeling of pipelines". Technical Field

[0002] This invention relates to the field of digital factories, and more particularly to a processing unit for constructing pipeline twin models. Background Technology

[0003] With the advancement of national industrialization, and considering factors such as environmental protection and low carbon emissions, the pace of new construction projects in industries like petrochemicals and power has gradually slowed down. This has made the maintenance and life extension of existing plants particularly important. How to utilize digital technologies to digitize and structure existing assets, and how to improve enterprise capacity, enhance safety, and reduce risks, have become key concerns. However, due to the complexity of information, the vast number of pipelines, and frequent maintenance and upgrades in process plants such as petroleum, chemical, and power plants, after more than a decade of operation, a large amount of online and offline data is generated, and there are often discrepancies between drawings, models, and the actual on-site conditions. Technicians cannot accurately grasp the on-site situation through data, causing significant difficulties for major overhauls and technical upgrades. Therefore, based on the actual conditions of the plant, constructing digital twins of all process equipment, pipelines, and structures has become a key and challenging task for existing plants.

[0004] The existing pipelines in the factory are quite complex and lack relevant drawings, making it very difficult for engineers to analyze them later. Furthermore, the dispersed, complex, and numerous control nodes of the old equipment make it difficult for engineers to determine the attributes of these control nodes, hindering the implementation of integrated control systems for the existing factory. This results in slow technological upgrades and stagnation in improving production efficiency.

[0005] The current method for reverse engineering existing pipeline 3D models involves: first, acquiring a large amount of point cloud information about the existing pipeline's spatial structure through 3D scanning; then, reconstructing this 3D structural information in a computer. However, due to limited scanning angles and occlusion, the resulting point cloud model still differs significantly from the actual pipeline structure. Another approach involves manually observing the point cloud model on a computer, identifying pipeline structures based on experience, and then hand-drawing a pipeline model with the same dimensions and features on the corresponding point cloud model. The point cloud model serves as a reference for manual modeling, rather than directly forming the pipeline model itself. This method remains manual and is relatively primitive, requiring significant manpower for later modeling work. This is particularly problematic for large-scale 3D pipeline modeling projects, leading to severely slow efficiency and enormous labor costs. Furthermore, the resulting 3D model of the pipeline is typically just a model, serving only as a visual representation of the structure. Manual addition of control parameters and attributes to the pipeline and its structures is necessary during the modeling process, based on an understanding and research of the existing plant's pipelines. Based on the above description, current 3D reverse engineering of pipelines, whether it's the reconstruction of the 3D structural model or the reconstruction of pipeline control parameters, is all done manually by "tracing" point cloud images. This high degree of manual involvement results in significant overall labor costs. When performing 3D reverse engineering of pipelines with numerous complex structures and / or a large number of control parameters, this high degree of manual involvement will have a very serious negative impact, severely slowing down project progress and causing substantial labor expenditures.

[0006] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a reverse modeling method for pipelines. This method is used to replicate and digitally model existing pipeline structures, acquire point cloud data of existing actual pipelines to form a point cloud database, and reverse-construct a pipeline restoration model based on the point cloud data. While restoring the pipeline model, the P&ID is indexed through the equipment port number. Based on the P&ID logical relationship, the topological relationship of the three-dimensional pipeline model is reconstructed, and the P&ID attributes are attached to the restored three-dimensional pipeline model and matched with the corresponding grade component library.

[0008] The advantages are that it firstly, it realizes the function of automatically determining the shape of pipe-like structures in point cloud data and then automatically modeling them, without human intervention. This enables the reconstruction of pipe structures, free tracking of pipes, continuous monitoring, identification and modeling of connectors, and the processing of point cloud data.

[0009] The lack of automatic recovery functions is a significant drawback. In this case, the computer can automatically generate a 3D model of the pipeline without requiring manual secondary processing of the point cloud model. After obtaining the pipeline model automatically identified and generated by the computer, personnel can further perform manual calibration, verification, modification, and division of pipeline structural units. Compared with traditional methods, this greatly reduces the proportion of manual labor in the project. The entire pipeline 3D reverse engineering is further automated and intelligent, significantly improving project efficiency and reducing labor costs.

[0010] Secondly, while automatically tracking and reconstructing the pipeline structure in three dimensions, the system also adds control parameters to the three-dimensional pipeline model based on intelligent P&ID. This allows the original pipeline control logic to be integrated into the three-dimensional pipeline model. Compared to a model that simply reflects the external structure, these control parameters can also reflect richer parameter information about the pipeline, such as pipe diameter, material, purpose, medium, flow direction, current working status, control structure model, efficiency, and so on.

[0011] By combining the P&ID (Portable & Automated Guided Access) pipeline structure with a pre-stored component library for control nodes, the existing factory's pipeline 3D reconstruction model is assisted. This allows for not only the reconstruction of the 3D model of each control node but also the generation of attribute information for that control node within the P&ID. This enables the direct overlay of control logic based on P&ID attributes onto the high-precision reconstructed 3D pipeline model. Engineers can directly observe the comprehensive 3D pipeline model to understand the routing, attributes, circuits, and functions of all pipelines in the entire factory. Furthermore, based on the added control logic, direct clicking on the control node model allows direct connection to the control circuit of the physical control node. This facilitates engineers' observation, evaluation, and control of the resulting impact while also providing the ability to directly control any equipment in the factory from the control center. This centralized control of the previously scattered and complex pipeline systems in the old factory is achieved by integrating additional pipeline logic control based on the reconstructed 3D model, resulting in centralized management and control and significantly improving the production efficiency of the old factory. Meanwhile, some pre-stored parameters in P&ID can, in turn, assist in the reverse engineering of 3D models to achieve data visualization. For example, parameters such as pipe size, length, width, diameter, and wall thickness pre-stored in P&ID are often not significant or intuitive when provided only as a list. However, when a 3D reverse engineering project based on the index to P&ID is executed to generate a 3D model of the existing structure, the attribute data in P&ID can be used to supplement the shape of the reverse 3D model reconstructed from the point cloud. This supplement includes not only the attributes that the point cloud data can "see," but also the attributes that the point cloud data cannot "see," such as wall thickness and pipe inner diameter. The model restored from the point cloud provides guidance for indexing P&ID, while P&ID provides assistance and acceleration for the construction of the reverse model of the point cloud. At the same time, it can obtain a more accurate and comprehensive reverse engineering model. The numerical attributes in P&ID can be intuitively transformed into visual attributes.The above solution automatically maps the control logic parameters in P&ID point-to-point while constructing a reverse 3D model of the existing pipeline. In effect, it organically combines the originally abstract P&ID image, which lacks spatial and structural information, into a three-dimensional, intuitive, easy-to-observe, and structurally accurate three-dimensional reverse composite model. On one hand, some retained structural parameters from the P&ID can provide missing item correction for the 3D reverse modeling process. On the other hand, the obtained 3D pipeline model can be directly used for pipeline control management in the factory, achieving all the effects of P&ID control. Furthermore, it leverages the advantages of 3D modeling to achieve superior results compared to P&ID control. Firstly, it elevates the original two-dimensional P&ID control logic to three dimensions, allowing some complex control logic that previously required hierarchical display to be directly displayed in three-dimensional space. First, it allows engineers to quickly and intuitively control parameters at multiple levels. Second, it provides a more intuitive display of structural parameters in the control logic, enabling engineers to directly confirm the structural relationships and dimensional parameters between components in the 3D structure. In particular, it allows them to intuitively view the compression and contact between components, providing a very intuitive criterion for safe production and structural optimization in the factory. Finally, it can provide a pre-simulated visual feedback function for P&ID control based on simulation technology. Based on pre-set rules, using the control links selected by engineers and the adjusted control parameters as trigger parameters, it can output an intuitive result view to engineers in the 3D pipeline model in the form of simulated overlay animation. This can help engineers understand whether the control can achieve the expected effect, whether there are any unexpected situations, etc., effectively improving the efficiency and safety of control.

[0012] Preferably, the point cloud data is obtained by scanning the actual pipeline by the scanning unit, and the point cloud data contains at least the spatial location information of one point on the actual pipeline.

[0013] Preferably, the acquired large amount of point cloud data is denoised by a combination of custom point cloud density recognition denoising and manual bounding box denoising.

[0014] Preferably, based on the point cloud feature distribution in the denoised point cloud data, an entity selection function is used to obtain the pipeline path, and an entity object function is used to iteratively traverse the entity database to retrieve point cloud objects and obtain the spatial coordinates of the points. The coordinates of the center point of the pipeline cross section are calculated by calculating the maximum distance between any two points on the cross section, and the centerline of the pipeline is calculated based on the continuity of the center point coordinates of the cross section.

[0015] Preferably, the center point of each section is taken as the relative origin, and other center points within the range are searched with the spacing value as the radius. This allows for free tracking of the pipeline extension simulation. If at least one center point is found within the search range with the spacing value as the radius, the pipeline model continues to fit and extend at that center point.

[0016] The advantages are that it can identify various complex pipe structures. By strictly controlling the spacing value, it can avoid identifying adjacent pipes with collinear centerlines but not actually connected as the same pipe. For example, if pipes A and B are both L-shaped and each has a segment with a collinear centerline, and the bends of pipes A and B are close to or even side-by-side, then under the scheme using the spacing value as the search radius, simply setting the spacing value to be less than the sum of the diameters of the side-by-side pipes A and B will prevent the pipes with the same centerline from being considered the same pipe when automatically identifying and building the pipe model. This scheme has high false positive prevention capabilities, significantly improves the accuracy of identification, and reduces the workload of subsequent manual calibration and optimization.

[0017] Preferably, connector identification is performed on the pipe model fitted by free tracking, wherein a specific distance between cross sections is set, and abrupt changes in the cross section coordinates exceeding this distance value are considered as instruments or valve groups. When the distance values ​​are the same and continuous cross sections with different radii appear, it is considered that this is a connector with different diameters.

[0018] Preferably, in the case of a reducing connector, the type of reducing connector is further determined based on the number of center points within the range, wherein,

[0019] A bend is considered a centerline where the number of center points within the range is 1 and the centerline does not belong to the same pipe as the previous center point.

[0020] When the number of center points is 2, it is considered a tee.

[0021] When the number of center points is 3, it is considered a four-way junction.

[0022] Preferably, the pipeline continuity judgment is performed on the pipeline model fitted by free tracking. If the spatial distance between the centerlines of two pipelines is less than the pipeline radius, they are considered to be the same pipeline. If the spatial distance between the two centerlines is greater than the pipeline radius, they are considered to be two pipelines.

[0023] A reverse modeling device for pipelines includes a processing unit. The processing unit obtains point cloud data about existing actual pipelines from a point cloud database and reverse-constructs a pipeline reconstruction model based on the point cloud data. While reconstructing the pipeline model, the processing unit also sends the equipment port number to the P&ID unit electrically connected to it to index the P&ID. Based on the P&ID logical relationship, the topological relationship of the three-dimensional pipeline model is reconstructed.

[0024] Preferably, the point cloud data is acquired by the scanning department by scanning the existing pipelines in the factory, and each point cloud data represents the actual spatial coordinate information of a corresponding point on the existing pipeline. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the device structure provided by the present invention;

[0026] Figure 2 This is a schematic diagram of the centerline identification of the pipeline of the present invention;

[0027] Figure 3 This is a schematic diagram of the free-tracking and identification of pipelines according to the present invention;

[0028] Figure 4 This is a schematic diagram of pipe diameter identification according to the present invention;

[0029] Figure 5 This is a schematic diagram illustrating the identification of the pipe tee connector of the present invention;

[0030] Figure 6 This is a schematic diagram illustrating the identification of the pipe four-way connector of the present invention;

[0031] Figure 7 This is a schematic diagram illustrating the identification of the pipe bend connector of the present invention;

[0032] Figure 8 This is a schematic diagram of pipeline continuity identification according to the present invention;

[0033] Figure 9 This is a schematic diagram of the pipeline structure repair of the present invention;

[0034] List of reference numerals in the attached diagram:

[0035] 100: Scanning unit; 200: Processing unit; 210: Filtering and denoising unit; 220: Centering unit; 230: Free tracking unit; 240: Connector identification unit; 250: Continuity monitoring unit; 260: Point cloud missing recovery unit; 270: P&ID unit. Detailed Implementation

[0036] The following is in conjunction with the appendix Figure 1 Please provide a detailed explanation.

[0037] This invention provides a reverse modeling device for pipelines, used to reverse model existing pipelines in an in-service plant and form a complete twin model. This solution is based on 3D laser scanning technology, employing abstract shape, intelligent judgment, and matching of a resource library (hierarchical component library) to automatically identify and create object-oriented 3D models. Simultaneously, it combines intelligent P&ID to automatically logically organize and divide the 3D pipelines, while assigning process attributes.

[0038] Specifically, the solution is as follows: A scanning unit 100 is configured to acquire point cloud data about pipelines at selected locations in the existing factory using 3D laser scanning. The selected locations are areas chosen by the scanning personnel for reverse modeling; generally, this area includes the locations of all pipelines in the factory. The scanning unit 100 can be composed of various 3D scanners, such as raster scanners (e.g., white light 3D scanners, blue light scanners), point laser scanners, line laser scanners, and area laser scanners. This embodiment uses a laser scanner as the scanning unit 100. The basic principle for acquiring the three-dimensional spatial coordinates of a point on a scanned object is as follows: the laser emitter in the scanning unit 100 emits a laser beam towards a point on the scanned object. Upon contact with the scanned object, the laser beam is reflected to the photosensitive unit of the scanning unit 100 and identified by that unit. By setting at least two photosensitive units at different angles and calculating the time difference between laser emission and reception of the reflected laser, the distance relationship between each photosensitive unit and the laser-illuminated point can be calculated. Based on the known positional relationships between the photosensitive units and the detected distance relationships, the spatial coordinates of the illuminated point can be determined using spatial vector calculation. The scanning unit 100 performs spatial coordinate detection on all points on the scanned object by rapidly emitting and receiving laser beams, thereby forming a large amount of point cloud data containing spatial coordinate information.

[0039] After performing a 3D laser scan, a large amount of point cloud data is acquired and stored in a point cloud database. This device is equipped with a processing unit 200, which acquires the point cloud data from the database and processes it to obtain a reverse simulation model of the pipeline. Specifically, the processing unit 200 first filters and denoises the point cloud data. Filtering and denoising can employ various existing techniques, such as bilateral filtering, Gaussian filtering, binning denoising, and voxel filtering. In this embodiment, filtering and denoising employs at least two methods: custom point cloud density recognition denoising and manual bounding box denoising. Custom point cloud density recognition denoising is an automatic denoising method, while manual bounding box denoising is a manually assisted denoising method. In this embodiment, the two methods are implemented in parallel to ensure that the point cloud data denoising effect meets the basic requirements of subsequent point cloud data processing and reverse engineering, improving the execution quality of subsequent engineering and reducing the difficulty of subsequent data processing. The filtering and denoising function can be implemented by a built-in calculation program in the processing unit 200, or alternatively by a filtering and denoising unit 210 located within the processing unit 200. The filtering and denoising unit 210 acquires point cloud data from the point cloud database and performs filtering and denoising on the point cloud data. Preferably, the filtering and denoising method is selected as a custom point cloud density recognition denoising method and / or a manual selection denoising method.

[0040] In this embodiment, the processing unit 200 can adapt to point cloud data of various formats obtained by scanning by various types of scanning units 100. In addition to adapting to common general point cloud data formats such as .pts and .e57, it can also read point cloud formats (.FLS, .ZFS) and native point cloud project files (.IMP, .ISPROJ) generated by mainstream 3D scanning devices on the market.

[0041] After filtering and denoising the point cloud data, the processing unit 200 processes the denoised point cloud data to obtain the shape of the pipeline. Specifically, based on the distribution of point cloud features in the denoised point cloud data, the processing unit 200 uses an entity selection function to obtain the pipeline path and iteratively traverses the entity database using entity object functions. It then retrieves the spatial coordinates of the point objects. Figure 2 As shown, the coordinates of the center point of the pipe cross-section are calculated by calculating the maximum distance between any two points on the cross-section. The centerline of the pipe is calculated based on the continuity of the center point coordinates. The calculation method is as follows: The processing unit 200 cross-sections the pipe, generating cross-section A. Cross-section A and the pipe have multiple intersection points. Any two points on the cross-section with measured spatial coordinate data are repeatedly selected, and the distance between the two points is calculated until the maximum distance is found. The midpoint of the line connecting these two points is the center point of the cross-section, which is also the center point of the pipe segment. For example, at least three points are determined in cross-section A, and the distance between the line connecting any two of these three points is exactly the maximum distance between the two points on the cross-section. Each point has at least one measured spatial coordinate data point. The selected points are set as A1(x1,y1,z1), A2(x2,y2,z2), and A3(x3,y3,z3). Let the distance between any two points be R, and calculate the maximum value of R in the current cross-section. R1 is the distance from A1 to A2, R2 is the distance from A1 to A3, and R3 is the distance from A2 to A3. Calculate and select the longest distance Rmax from the above distance calculation results. The calculation formula is as follows:

[0042]

[0043] After calculating the longest distance Rmax of the current cross section, the midpoint of the line connecting this value is the center point of the cross section. The spatial coordinates of the center point of the cross section can be obtained using the midpoint formula. Taking R1 as the longest distance Rmax as an example, let the center point of cross section A be A4(x4,y4,z4). The formula for calculating the midpoint is as follows:

[0044]

[0045] Processing unit 200 creates a cross-section B at another part of the pipeline. The coordinates of the center point of cross-section B are calculated as described above, denoted as B3(x3,y3,z3). The line containing A4 and B3 then approximates the centerline of the actual pipeline. This cross-section can be any non-parallel cross-section of the pipeline structure, meaning it can be a tangent to a line other than the pipeline's centerline. The resulting cross-section may be an obliquely tangent ellipse or a tangent perfect circle. Based on geometric principles, regardless of whether it's an ellipse or a circle, the midpoint of the line connecting any two points on that ellipse or circle, where the maximum distance is found, is the center point of that cross-section, and also the center point of the pipeline. Therefore, the center point of the pipeline within the cross-section can be calculated using the above method.

[0046] The function of finding the center point of the cross section can be executed directly by the program set in the processing unit 200, or it can be completed by the center point finding unit 220 set in the processing unit 200. The center point finding unit 220 cross-sections a pipe, selects at least two intersection points with the pipe on the cross section, calculates the distance between the lines connecting the intersection points, and continues to calculate the distance between any two intersection points to find the line connecting the maximum distance on the cross section. The midpoint of this line is the center point of the cross section.

[0047] like Figure 3 As shown, after performing multiple cross-sections to obtain multiple cross-sectional center points for the pipeline, the processing unit 200 uses each cross-sectional center point as a relative origin and a distance value as a radius to search for other center points within the range, thereby achieving free tracking of the pipeline extension simulation. Specifically, assuming the search radius is d, and using one of the established coordinate center points A1 as the relative origin, there is an established center point B1 adjacent to A1. If the distance from A1 to B1 is less than d, it means that B1 is a point on the centerline of the pipeline, and the pipeline extension is fitted towards the B1 position. Then, using the B1 position relative to the origin, the search continues to search for at least one center point whose line distance from B1 is less than d. If it exists, the pipeline is fitted based on the position of that center point; if it does not exist, no fitting is performed at that point. This free tracking and fitting of the pipeline continues. When a center point appears, when searching for other center points nearby, if no other center points are found within the unidirectional area, it is determined that the pipeline ends at that center point position. This allows for the fitting of at least one straight-through pipe model with a start and end point, and this model is equivalent to the structure of that section of the actual pipe, thus effectively reproducing and simulating the structural characteristics of that section of the actual pipe.

[0048] The free tracking pipeline fitting described above can be implemented by a program set in the processing unit 200 or by a free tracking unit 230 set in the processing unit 200. The free tracking unit 230 is electrically connected to the center point finding unit 220 to obtain information on the coordinates of several cross-section center points, and performs free tracking based on the above-mentioned method of searching and screening around the search radius to form a complete pipeline model with start and end points.

[0049] After acquiring the pipeline model, the processing unit 200 performs connector identification on the pipeline model. Specifically, as follows: Figures 4 to 7 As shown, the spacing between specific cross-sections is set. A sudden change in cross-sectional coordinates exceeding this spacing value can be considered an instrument or valve assembly. After obtaining the pipeline's centerline, a tangent can be applied to the pipeline based on the centerline to obtain a circular cross-section. The radius of this circular cross-section is calculated using the center point determined by the centerline. This tangent is then continuously applied to the pipeline as the centerline extends to continuously monitor the pipeline's radius. At this point, the spacing between cross-sections can be set, and the tangent to the pipeline and the radius can be determined using this spacing. When a sudden change occurs in the radius, or when a sudden change occurs in the cross-sectional coordinates (because the circumferential coordinates of cross-sections with equal radii usually change regularly), an instrument or valve assembly can be considered to have occurred at the point of change. When the spacing values ​​are the same, and consecutive cross-sections with different radii appear, this can be considered a reducing connector. The type is determined based on the number of points within the range. When the number of points within the range is 1, and it does not belong to the same pipeline centerline as the previous point, it is considered an elbow structure. When the number of points is 2, it is considered a tee structure. When the number of points is 3, it is considered a four-way structure. The calculation method is as follows:

[0050] Let the center point of the pipe diameter change section be the monitoring origin A. The search range is d. The number of points within the range is n.

[0051]

[0052] The aforementioned pipe connector identification can be implemented by a program built into the processing unit 200, or by a connector identification unit 240 provided in the processing unit 200, which is electrically connected to the free tracking unit 230 to obtain the pipe model and identify the connectors in the simulated pipe in the manner described above.

[0053] During the continuous point cloud reconstruction of the pipeline, continuous pipeline monitoring is also performed. For areas with severely missing point cloud data, judgments can be made based on the spatial location of the pipeline's centerline. For example... Figure 8As shown, if the spatial position distance between the centerlines of two pipes is less than the pipe radius, they can be regarded as the same pipe. If the spatial distance between the two centerlines is greater than the pipe radius, they are regarded as two pipes. The spatial position distance between the centerlines of the above pipes refers to when restoring the pipes using point clouds, two centerlines with different spatial positions appear. Based on the parallelism of these two centerlines in space, the distance between the two lines is calculated, which is the above-mentioned centerline spatial distance and can be recorded as l. At this time, the radii of the two pipes corresponding to the restoration of the two centerlines are denoted as d. Here, the radii of the two pipes may be inconsistent, such as in the case of a large pipe connecting to a small pipe, then there may be two values, d1 and d2, for the pipe radius. In the case of the same radius, if l is less than the d value, it is determined to be the same pipe; if l is greater than the d value, it is determined to be two pipes. In the case of different radii, the judgment mainly uses the minimum value of the radii. For example, if the minimum value is the d1 value, when l is less than the d1 value, it is determined to be the same pipe, and when l is greater than the d1 value, it is determined to be two pipes. Describing the above judgment method with a calculation model, it is expressed as when l < d ∧ d = dmin, it can be considered that the two pipe segments are the same pipe, where the symbol ∧ represents the intersection, that is, the AND logic; dmin represents the minimum radius value of the two pipes.

[0054] In the case of determining that the two pipes are the same pipe, the processing unit 200 moves one of the pipe segment models in a translational manner so that the centerlines of the two pipes coincide in spatial position.

[0055] In the case where the two centerlines are not parallel, the two lines can also be judged by the angle. Specifically, an error angle value can be set. When the included angle between the two lines is less than the error angle value, it is determined that the two pipes are arranged in parallel. When the included angle between the two lines is greater than the error angle value, it is determined that the two pipes are not arranged in parallel. When it is determined that the two pipes are parallel, the processing unit 200 can also perform angle adjustment on one of the pipe models so that the two pipes are in a parallel relationship on the model.

[0056] The above steps of performing continuous monitoring on the restoration of the pipes can be implemented by the relevant programs carried by the processing unit 200, or can be implemented by the pipe continuity monitoring unit 250 set in the processing unit 200. It is electrically connected to the free tracking unit 230 to obtain the pipe model and correct the misalignment of the centerline during the generation process of the pipe model in the above manner. The above pipe continuity monitoring unit 250 and the connector recognition unit 240 act on the free tracking of the free tracking unit 230 simultaneously during the process of forming the pipe model. Therefore, both the continuity monitoring unit 250 and the connector recognition unit 240 are electrically connected to the free tracking unit 230.

[0057] For cases where some point cloud data is missing from the point cloud database, a pipeline point cloud data missing recovery step is performed. This step addresses situations where, during the initial scanning and imaging of the physical pipeline using the scanning unit 100, insufficient point cloud data was collected for certain sections due to structural obstruction, image gaps, or other reasons; that is, the point cloud data for these sections is insufficient and thus missing. Figure 9 As shown, when using this location to simulate the reconstruction of the pipeline, only a portion of the observed point cloud data often exists. This point cloud data can form a roughly circular arc shape. In this case, a function is used to calculate the curvature of the existing point cloud, thereby calculating the centerline of the pipeline and generating the pipeline. The calculation method is as follows: set the line connecting four points on the same cross section as l1 and l2. Establish a planar coordinate system with this cross section, calculate the slopes of line segments l1 and l2 as k1 and k2 respectively, and then calculate the slopes of the perpendicular bisectors of the two line segments as -1 / k1 and -1 / k2. With the slopes of the perpendicular bisectors and the coordinates of the four points known, the coordinates of the center O can be obtained. Subsequently, the missing part of the pipeline in the point cloud data can be fitted based on point O. The above four points are selected from the existing point cloud data containing real spatial location data of the actual points on the physical pipeline that have been actually observed by the scanning unit 100.

[0058] The simulation recovery of point cloud data for certain parts of the pipeline can be performed by a relevant program pre-stored in the processing unit 200, or by a point cloud data missing recovery unit 260 provided in the processing unit 200. This unit is electrically connected to the free tracking unit 230 to provide point cloud data missing recovery processing function for the process of the free tracking unit 230 restoring the pipeline model.

[0059] The steps involved in "reverse engineering a pipeline restoration model based on point cloud data" include:

[0060] S11 is indexed to P&ID via the device port number;

[0061] S12 reconstructs the topology of the 3D pipeline model based on the P&ID logical relationship;

[0062] S13 attaches the P&ID attribute to the restored 3D pipe model, matching the corresponding grade component library.

[0063] The intelligent P&ID can be a separate module, called P&ID unit 270. The processing unit 200 is electrically connected to P&ID unit 270 when recovering the pipeline model based on the point cloud. After receiving the device port number, P&ID unit 270 matches it with its internally stored hierarchical component library. After obtaining the matching result, it assigns the resulting attributes to the structural device corresponding to the device port number.

[0064] P&ID is a pre-set data set or relational database that records at least P&ID logical relationships and P&ID attributes. P&ID logical relationships refer to records of hierarchical relationships and connections between pipelines and branches, pipelines and valves, and pipelines and equipment. P&ID attributes refer to pre-input attribute information for components such as pipelines, equipment, and valves, such as equipment size and pipe wall thickness. The data in P&ID is generally manually pre-input or existing P&ID data prior to the reverse engineering process of this solution.

[0065] "Indexing to P&ID by device port number" means that when reconstructing and restoring the pipeline between two selected devices pre-stored in P&ID, the data segment of P&ID is retrieved based on the port number on the assigned device, and the data segment is prepared for use in the subsequent step S12.

[0066] "Reconstructing the topology of a 3D pipeline model based on P&ID logical relationships" refers to the process of retrieving topology relationships and assigning values ​​to point cloud models in the P&ID data segment after obtaining the P&ID data of the pipeline between two assigned devices, in order of device-pipeline-branch-fittings-valve instruments when reconstructing the pipeline restoration model from point cloud data. This allows the models of each component between the two devices (including at least pipelines, branch pipes, fittings, valves, instruments, etc.) to be automatically assigned values.

[0067] "Applying P&ID attributes to the reconstructed 3D pipeline model and matching the corresponding level component library" refers to assigning the pre-stored P&ID attributes in the P&ID library to the corresponding component models during the assignment process. Some of these P&ID attribute assignments are used to build the corresponding component object models. These P&ID attributes are at least structural dimension-related attributes, such as dimensions and thickness. These parameters can be directly reflected in the model and can assist in model construction, enabling reverse model construction through bidirectional verification between point clouds and P&ID attributes.

[0068] For example, the line segment AB connects equipment A and equipment B. The valves on line AB are valves V1, V2, and V3, respectively. There is also a branch on line AB with valve V4. Therefore, the connection points of these branches on line AB form two tee structures. When the pipe connections are identified as tees, they are marked as T1 and T2, respectively. Let line T1-V2-T2 be line a, and line T1-V4-T2 be line b. Line a has three abrupt changes in pipe diameter, corresponding to valves V1, V2, and V3. Line b has one abrupt change in pipe diameter, caused by valve V4.

[0069] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A processing unit for constructing a pipeline twin model, characterized in that, The processing unit (200) is configured to: The system acquires point cloud data from the point cloud database. Based on the distribution of point cloud features in the denoised point cloud data, it uses an entity selection function to obtain the pipeline path and iterates through the entity database using entity object functions. It then retrieves the spatial coordinates of the point cloud objects. The coordinates of the center point of the pipe cross section are calculated by calculating the maximum distance between any two points on the cross section, and the centerline of the pipe is calculated by the continuity of the coordinates of the center point of the cross section. After performing multiple cross-sections to obtain multiple cross-sectional center points for the pipeline, the center points of each cross-section are used as the relative origin, and the center points of the cross-sections within the range are searched using the spacing value as the radius. This allows for free tracking of the pipeline extension simulation, forming a complete pipeline model with start and end points.

2. The processing unit according to claim 1, characterized in that, The processing unit (200) is further configured to: If at least one center point is found within the search range with the said spacing value as the radius, the pipeline model continues to fit and extend at the said center point.

3. The processing unit according to claim 1 or 2, characterized in that, The processing unit (200) is further configured to: After acquiring the pipeline model, the processing unit (200) performs connector identification on the pipeline model; Specifically, the spacing between specific cross-sections is set, and the tangent of the pipe and the radius are determined based on the spacing. Abrupt changes in cross-sectional coordinates exceeding the spacing value are considered as instrument or valve assembly. When the spacing values ​​are the same and continuous cross-sections with different radii appear, this is considered as a reducing connection.

4. The processing unit according to any one of claims 1 to 3, characterized in that, The processing unit (200) is further configured to: If the connector is considered to be a reducing connector, the type of the reducing connector is further determined based on the number of center points within the range. Among them, a centerline with a center point of 1 within the range and not belonging to the same pipe as the previous center point is considered an elbow; When the number of center points is 2, it is considered a tee. When the number of center points is 3, it is considered a four-way junction.

5. The processing unit according to any one of claims 1 to 4, characterized in that, The processing unit (200) is further configured to: Perform pipeline continuity judgment on the pipeline model fitted by free tracking. If the spatial distance between the centerlines of two pipelines is less than the pipeline radius, they are considered to be the same pipeline. If the spatial distance between the two centerlines is greater than the pipeline radius, they are considered to be two pipelines.

6. The processing unit according to any one of claims 1 to 5, characterized in that, The processing unit (200) is further configured to: Take a cross section of a pipe, select at least two intersection points with the pipe on the cross section, calculate the distance between the lines connecting the intersection points, and continue to calculate the distance between any two intersection points to find the line connecting the maximum distance on the cross section. The midpoint of the line connecting the intersection points is the center point of the cross section.

7. The processing unit according to any one of claims 1 to 6, characterized in that, The processing unit (200) is further configured to: If the two pipes are identified as the same pipe, one of the pipe segments is translated so that the centerlines of the two pipes coincide in space.

8. The processing unit according to any one of claims 1 to 7, characterized in that, The processing unit (200) is further configured to: When the two center lines are not parallel, set the error angle value; When the angle between the two lines is less than the error angle value, it is determined that the two pipes are set in parallel. When the angle between the two lines is greater than the error angle value, it is determined that the two pipes are not set parallel. When two pipes are determined to be parallel, the angle of one of the pipe models is adjusted to make the two pipes parallel in the model.

9. The processing unit according to any one of claims 1 to 8, characterized in that, The processing unit (200) is further configured to: When point cloud data is insufficient but can form an approximate arc shape, a function is used to calculate the curvature of the existing point cloud to calculate the centerline of the pipe and generate the pipe.

10. The processing unit according to any one of claims 1 to 9, characterized in that, The processing unit (200) is electrically connected to the P&ID unit (270); The processing unit (200) reverse-engineers a pipeline restoration model based on point cloud data; After receiving the device port number, the P&ID unit (270) indexes the P&ID through the device port number; Based on the P&ID logical relationship, the topology relationship of the 3D pipeline model is reconstructed. Attach the P&ID attribute to the restored 3D pipe model to match the corresponding grade component library.

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