Electromechanical pipeline real mold consistent measurement method, system, equipment, medium and product

Abstract

The invention discloses an electromechanical pipeline real-model consistent measurement method, system and device, a medium and a product, and relates to the field of electromechanical installation engineering, and the method comprises the steps: obtaining the three-dimensional point cloud data of an electromechanical pipeline; constructing a three-dimensional point cloud model based on the three-dimensional point cloud data after data processing; the three-dimensional point cloud model is compared with a BIM model, a model comparison result of the three-dimensional point cloud model and the BIM model is obtained, and the BIM model is created based on the initial position data; and generating a model analysis report based on the model comparison result, the model analysis report being used for correcting the construction difference to ensure the real model consistency of the electromechanical pipeline. According to the invention, the accuracy of the real mold consistency measurement method is improved.

Description

Technical Field

[0001] The present application relates to the field of mechanical and electrical installation engineering, and particularly to a method, system, device, medium and product for measuring the actual model consistency of mechanical and electrical pipelines. Background Art

[0002] In recent years, the construction industry has gradually developed towards informatization and intelligentization. With the continuous progress of technology and the in-depth application, ensuring that the installation positions of mechanical and electrical pipelines at the construction site are actually consistent with the design positions of mechanical and electrical pipelines in the BIM model has become an indispensable core issue in the intelligent construction process. The rise of the intelligent construction concept not only brings new development opportunities to the construction industry but also puts forward higher requirements. The common method for measuring the actual model consistency of mechanical and electrical pipelines is to conduct on-site manual measurement and then compare and analyze it with the BIM mechanical and electrical pipeline model.

[0003] However, the method of on-site manual measurement has a large workload and low accuracy. When facing complex mechanical and electrical pipeline system projects, the manual on-site measurement method is inefficient, which greatly restricts the progress of the project and the control of quality, resulting in large errors in the actual application of the actual model consistency measurement method based on BIM technology. Summary of the Invention

[0004] The purpose of the present application is to provide a method, system, device, medium and product for measuring the actual model consistency of mechanical and electrical pipelines, which can improve the accuracy of the actual model consistency measurement.

[0005] To achieve the above purpose, the present application provides the following solutions: In the first aspect, the present application provides a method for measuring the actual model consistency of mechanical and electrical pipelines, including: Obtaining three-dimensional point cloud data of mechanical and electrical pipelines; Constructing a three-dimensional point cloud model based on the processed three-dimensional point cloud data, where the data processing includes preprocessing; Comparing the three-dimensional point cloud model with the BIM model to obtain a model comparison result between the three-dimensional point cloud model and the BIM model, where the BIM model is created based on initial position data; Generating a model analysis report based on the model comparison result, where the model analysis report is used to correct construction differences and ensure the actual model consistency of mechanical and electrical pipelines.

[0006] In the second aspect, the present application provides a system for measuring the actual model consistency of mechanical and electrical pipelines, including: An obtaining module for obtaining three-dimensional point cloud data of mechanical and electrical pipelines; A constructing module for constructing a three-dimensional point cloud model based on the processed three-dimensional point cloud data; A comparison module for comparing the 3D point cloud model with the BIM model to obtain a model comparison result between the 3D point cloud model and the BIM model, wherein the BIM model is created based on initial position data; A generation module for generating a model analysis report based on the model comparison result, the model analysis report being used to correct construction differences and ensure the physical model consistency of the mechanical and electrical pipelines.

[0007] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the steps of the method for measuring the physical model consistency of mechanical and electrical pipelines described in any one of the above.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for measuring the physical model consistency of mechanical and electrical pipelines described in any one of the above are implemented.

[0009] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method for measuring the physical model consistency of mechanical and electrical pipelines described in any one of the above are implemented.

[0010] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a method, system, device, medium and product for measuring the physical model consistency of mechanical and electrical pipelines. By acquiring the 3D point cloud data of the mechanical and electrical pipelines, a 3D point cloud model is constructed based on the processed 3D point cloud data, and the 3D point cloud model is compared with the BIM model to obtain a model comparison result between the 3D point cloud model and the BIM model, wherein the BIM model is created based on initial position data. The time cycle of mechanical and electrical pipeline measurement and comparison is shortened through a digital workflow, improving construction efficiency. A model analysis report is generated based on the model comparison result, and the model analysis report is used to correct construction differences and ensure the physical model consistency of the mechanical and electrical pipelines. The present application reduces the cost consumption during construction, improves the accuracy of physical model consistency measurement, and thus improves the overall quality of construction projects. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1This is an application environment diagram of a method for measuring the consistency between the physical model and the virtual model of mechanical and electrical pipelines in an embodiment of the present application; Figure 2 This is a schematic flowchart of a method for measuring the consistency between the physical model and the virtual model of mechanical and electrical pipelines provided in an embodiment of the present application; Figure 3 This is a schematic diagram of the functional modules of a system for measuring the consistency between the physical model and the virtual model of mechanical and electrical pipelines provided in an embodiment of the present application; Figure 4 This is a schematic structural diagram of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0014] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0015] The method for measuring the consistency between the physical model and the virtual model of mechanical and electrical pipelines provided in the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed in the cloud or on other servers. The terminal 102 can send the three-dimensional point cloud data of the mechanical and electrical pipelines to be processed to the server 104. After receiving the three-dimensional point cloud data of the mechanical and electrical pipelines to be processed, for the three-dimensional point cloud data to be processed, the server 104 constructs a three-dimensional point cloud model based on the processed three-dimensional point cloud data, compares the three-dimensional point cloud model with the BIM model, obtains the model comparison result between the three-dimensional point cloud model and the BIM model, and generates a model analysis report based on the model comparison result. The server 104 can feedback the obtained model analysis report to the terminal 102. In addition, in some embodiments, the method for measuring the consistency between the physical model and the virtual model of mechanical and electrical pipelines can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly process the three-dimensional point cloud data to be processed, or the server 104 can obtain the three-dimensional point cloud data to be processed from the data storage system and process the three-dimensional point cloud data to be processed.

[0016] Among them, the terminal 102 can be but is not limited to various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0017] In an exemplary embodiment, as Figure 2 shown, a method for measuring the actual model consistency of mechanical and electrical pipelines is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiment of the present application, taking this method applied to Figure 1 the server 104 therein as an example for illustration, it includes the following steps 201 to step 204. Among them: Step 201, obtain the three-dimensional point cloud data of the mechanical and electrical pipelines.

[0018] Specifically, during the construction process, it is necessary to use three-dimensional equipment to scan the mechanical and electrical pipelines on site, so as to obtain the three-dimensional point cloud data of the mechanical and electrical pipelines. The three-dimensional point cloud data is the actual position data.

[0019] During the construction process, in order to ensure the construction quality and improve the construction efficiency, it is necessary to accurately position and measure the mechanical and electrical pipelines on site. An advanced three-dimensional scanning device is used to comprehensively scan the mechanical and electrical pipelines on site. Through three-dimensional scanning technology, the three-dimensional point cloud data of the mechanical and electrical pipelines in the three-dimensional space can be obtained, including the length, orientation, height of the pipelines, and the relative position relationship with other structures. The three-dimensional scanning device can capture the detailed information on site and generate a high-precision three-dimensional point cloud model, thereby helping the construction personnel intuitively understand the layout of the pipelines and better carry out the installation, maintenance, and management of the pipelines.

[0020] It can be understood that before performing 3D scanning, it is first necessary to select a suitable 3D scanning device, which includes but is not limited to laser scanners, structured light scanners, and photogrammetry devices. In this embodiment, a laser scanner with more advantages in terms of accuracy and speed is selected. To ensure the quality of the scanning results, a reasonable scanning strategy needs to be formulated. Scanning from multiple angles ensures obtaining complete data from different perspectives; scanning overlapping areas facilitates better stitching and fusion of data in subsequent processing; and detailed scanning is performed to capture specific areas that require high precision. Before starting the scanning, the selected 3D scanning device needs to be calibrated to ensure its accuracy, including internal parameter calibration and external parameter calibration. Internal parameter calibration mainly adjusts the internal parameters of the device to ensure its measurement accuracy; external parameter calibration aligns the device with the external coordinate system to ensure that the scanned data can accurately reflect the position and shape of the actual object.

[0021] According to the formulated scanning strategy, use a 3D scanning device to perform a detailed scan of the mechanical and electrical pipelines at the construction site. During the scanning process, by continuously monitoring the scanned data, scanning parameters and device positions can be promptly discovered and adjusted to avoid data omission and errors, thus ensuring the coherence and integrity of the scanned data. After the scanning is completed, the original scanned data is stored. During the storage process, it is necessary to ensure the integrity and security of the data so that these 3D point cloud data can be used in subsequent processing and analysis.

[0022] Step 202: Construct a 3D point cloud model based on the processed 3D point cloud data, where data processing includes preprocessing.

[0023] Specifically, to construct a 3D point cloud model, it is necessary to import the 3D point cloud data of the mechanical and electrical pipelines obtained in step 201 into the model data processing module. After data processing, a 3D point cloud model of the mechanical and electrical pipelines is generated. Data processing includes data preprocessing and data optimization processing. Among them, the preprocessing of the 3D point cloud data includes the following steps A1 - A2: A1: Denoise the 3D point cloud data.

[0024] Specifically, let the set of 3D point cloud data points be , where the th point of the cloud data contains its 3D coordinates . For each point , calculate the average distance of its nearest neighbors: ; Then calculate the average distance of all points and the standard deviation of all points: ; ; If the average distance exceeds several times the standard deviation of a point , then that point is considered a noise point. The above-mentioned several times is usually set to 1.5 to 2 times.

[0025] That is is a noise point if and only if ; It is usually set to 1.5 to 2.

[0026] A2. Downsample the denoised three-dimensional point cloud data.

[0027] When processing three-dimensional point cloud data, denoising is usually performed first to improve data quality. The denoised point cloud data can be further downsampled to reduce the data volume and extract key information. The sampling method usually uses voxel grid filtering. The basic principle of this method is to divide the three-dimensional point cloud data into many small voxels, and each voxel represents a small cubic region in three-dimensional space. Then, by selecting a representative point within each voxel to replace all the points within that voxel, data simplification is achieved.

[0028] Specifically, let the voxel size be , for each point in the point cloud data, its corresponding voxel index can be calculated. The calculation method of the voxel index is as follows: First, determine the th point of the three-dimensional cloud data containing its three-dimensional coordinates . Divide these coordinates by the voxel size V respectively to obtain the three-dimensional coordinates of the corresponding voxel index: ; For all the points within each voxel, take its centroid as the representative point of that voxel: For all the points within each voxel, calculate their centroid as the representative point of that voxel.

[0029] The calculation method of the centroid is as follows: Assume that the set of points within the voxel is {P 1 , P 2 ,..., P n}, where n represents the number of points within the voxel, and each point P m (m = 1, 2,..., n) corresponds to a three-dimensional coordinate (xm , y m , z m ).

[0030] The coordinates of the centroid C (x c , y c , z c ) can be calculated by the following formula: x c =(Σx m ) / n; y c =(Σy m ) / n; z c =(Σz m ) / n; where Σ represents the sum of the coordinates of all voxel interior points. Finally, the obtained centroid C is used as the representative point to replace all points within the original voxel, thereby realizing the downsampling of the point cloud data. Through the above method, the scale of the point cloud data can be effectively reduced while retaining its main features.

[0031] The data optimization process for the preprocessed three-dimensional point cloud data includes the following steps B1~B2: B1. Smooth the three-dimensional point cloud data after downsampling through Gaussian filtering. By this signal processing method, the noise and irregularities in the data are reduced, making the data smoother and more continuous.

[0032] Let the point cloud data be , and the data after Gaussian filtering be . The Gaussian filtering process can be expressed by the following formula: ; where is the Gaussian kernel function, and the Gaussian function is defined as: .

[0033] B2. Perform data fusion on the three-dimensional point cloud data after data smoothing.

[0034] Data fusion involves integrating the set of points to be interpolated with the set of known points to fill in the blank areas in the data, improve the integrity and usability of the data, and further optimize the data quality.

[0035] Let the set of points to be interpolated be , and the set of known points be . For each point to be interpolated , find the nearest known point , and assign the value of to , the interpolation point to be obtained is calculated by the following formula and all known points distance: ; where is the three-dimensional coordinate of the interpolation point to be obtained, is the three-dimensional coordinate of the known point; the interpolation point to be obtained and all known points the minimum distance point is calculated by the following formula: ; Assign the value of the known minimum distance point to the interpolation point : .

[0036] B3. Perform data compression processing on the three-dimensional point cloud data after data fusion processing to obtain the three-dimensional point cloud data after data processing, thereby reducing the data volume.

[0037] Specifically, we adopt a compression algorithm based on voxel grid to effectively compress the data. First, divide the entire cloud space into many small cube units, and each such small cube is called a voxel. Inside each voxel, a representative point is selected to replace all points inside the voxel. Usually, this representative point can be the center point or centroid of the voxel. Regarding the detailed formula and steps for specifically calculating the voxel representative point, reference can be made to step A2 mentioned above, which will not be elaborated here.

[0038] In one embodiment, the three-dimensional point cloud data after being processed in step B3 is used to construct a three-dimensional point cloud model through a triangular mesh.

[0039] Specifically, constructing a three-dimensional point cloud model through a triangular network can convert the three-dimensional point cloud data into a mesh model composed of triangles. A triangular mesh can be generated through a triangulation method. Triangulation is a process of connecting the points in the point cloud data into triangles to form a continuous surface. The specific process of constructing a triangular mesh is as follows: Let the three-dimensional point cloud data be , through triangulating the point cloud data, a complete triangular mesh is generated, where the triangulation is represented by the following formula: Let the triangular mesh be , where each triangle is composed of three vertices , and the goal of constructing the triangular mesh is to minimize the total area of all triangles. The process of minimizing the total area of all triangles is represented by the following formula: ; The area formula of the above triangle is: ; ; Among them, , and are the two-dimensional coordinates of the three vertices of the triangle respectively.

[0040] Step 203: Compare the 3D point cloud model with the BIM model to obtain the model comparison result between the 3D point cloud model and the BIM model, where the BIM model is created based on the initial position data.

[0041] The above step 203 includes the following steps 2031 to 2032: Step 2031: Set the 3D point cloud model and the BIM model in the same coordinate system through the ICP algorithm. Step 2031 includes the following sub-steps: Step C1: Based on the initial rotation matrix and the initial translation vector, determine the BIM data point closest to each 3D point cloud data point.

[0042] Let the point cloud model data be , and the BIM model data be . For each point cloud data point , find the closest point in the BIM model. The distance between the point cloud model data point and the closest point j in the BIM model data points is calculated by the following formula: ; Among them, is the initial rotation matrix, is the initial translation vector, is the point cloud model data point, is the BIM model data point.

[0043] Step C2: Obtain the matching point data set of the 3D point cloud model and the BIM model based on the rotation matrix and the translation vector, where the matching point data set contains multiple groups of matching points, and each group of matching points contains the closest 3D point cloud data point and the BIM data point.

[0044] Let the current transformation matrix be , where is the rotation matrix, is the translation vector. For each pair of matching points , calculate the rotation matrix and the translation vector , so that the point cloud model coincides with the BIM model to the greatest extent after transformation.

[0045] Solve the optimal and through the singular value decomposition (SVD) method as follows: Calculate the mean value of the point cloud model data and the BIM model data : ; ; Decentralize the point cloud model data and the BIM model data : ; ; Among them, is the point after decentralizing the cloud model data , is the point after decentralizing the BIM model data ; Construct the covariance matrix : ; Perform singular value decomposition (SVD) on : ; Calculate the rotation matrix and the translation vector : ; ; Among them, and are orthogonal matrices, is a diagonal matrix, is the transpose matrix; Based on the rotation matrix and the translation vector , obtain the optimal matching point dataset by optimizing the objective function, where the objective function is: .

[0046] Step 2032, calculate the deviation between the actual installation position of the computer electrical pipeline and the design model position. Step 2032 includes the following sub-steps: Step D1, calculate the inter-point distance between the three-dimensional point cloud data points and the BIM data points in each group of matching points. ​

[0047] Let the 3D point cloud model data be , and the BIM model data be ; The Euclidean distance formula is as follows: ; Among them, is the three-dimensional coordinate of the point cloud model data point, is the three-dimensional coordinate of the BIM model data point.

[0048] Step D2: Mark the 3D point cloud data points with the distance between all points greater than the preset value of the point spacing as deviation points, and generate a deviation point set based on all the deviation points.

[0049] Set the threshold distance , and count all the points with deviations exceeding the threshold, and mark them as deviation points: Deviation point ; Step D3: Calculate the average deviation and the maximum deviation of the deviation points.

[0050] ; ; Among them, is the set threshold distance, is the point cloud model data point, is the Euclidean distance between each pair of optimal matching points.

[0051] Step 204: Generate a model analysis report based on the model comparison results. The model analysis report is used to correct the construction differences and ensure the physical model consistency of the mechanical and electrical pipelines.

[0052] The above step 204 includes the following sub-steps: Step E1: Generate a difference analysis report based on the deviation point set, the average deviation, and the maximum deviation.

[0053] Specifically, based on a set of selected deviation point sets, combined with the data of average deviation and maximum deviation, a difference analysis report is generated. This report will comprehensively display the statistical data of the overall deviation, describe in detail the distribution of deviation points, and analyze the trend of deviation. Through the comprehensive evaluation of these key indicators, the degree of difference and its variation law in the data set can be better understood. First, the overall deviation statistics section will summarize the deviation values of all deviation points, calculate the specific values of the average deviation and the maximum deviation, which are used to provide a reference for the overall deviation level and judge the overall consistency of the data set. The deviation point distribution section will display the specific positions and deviation values of each deviation point, which are visually presented in the form of charts to identify abnormal points or special areas in the data set. The deviation trend analysis section will explore the change trend of deviation over time or other variables. By comparing the deviation data in different time periods or under different conditions, the fluctuation law of deviation and potential influencing factors are found to predict the future deviation trend.

[0054] Step E2: Obtain the correction suggestions for each deviation point through the difference analysis report.

[0055] Step E3: Make on-site corrections to the mechanical and electrical pipelines based on the correction suggestions, and / or correct the initial position data in the BIM model based on the correction suggestions.

[0056] Specifically, it is necessary to statistically analyze and sort out the data point deviations, including collecting the deviation values of each data point and classifying and sorting these deviation values. During the classification and sorting process, the priority of each deviation is determined based on the severity of the deviation and the urgency of correction. To ensure that the deviations can be effectively corrected, specific correction suggestions are provided for each deviation point. These suggestions will detail the direction of adjustment and the specific magnitude of adjustment. After completing the deviation statistics and the formulation of correction suggestions, a model comparison analysis report is generated. This report is used to record the differences between the model and the actual construction situation, provide corresponding correction suggestions, and submit them to the construction management personnel for review. The construction management personnel will carefully check the accuracy and completeness of the report content to ensure that no important deviation information and correction suggestions are omitted. During the review process, the construction management personnel can put forward their own modification opinions to further improve the report content.

[0057] The model comparison analysis report after passing the review will contain the confirmed deviation information and correction suggestions. This report will be handed over to the construction team for specific on-site correction work. The construction team will adjust each deviation point according to the instructions in the report to ensure that all deviations are corrected in a timely and accurate manner. Through this series of steps, the deviations during the construction process can be effectively controlled, thereby improving the quality and efficiency of the entire project.

[0058] In one embodiment, the mechanical and electrical pipelines are corrected on-site based on the correction suggestions, or the initial position data in the BIM model is corrected based on the correction suggestions, or the mechanical and electrical pipelines on-site and the initial position data in the BIM model are corrected simultaneously based on the correction suggestions. After correcting the mechanical and electrical pipelines on-site, or correcting the initial position data in the BIM model, or correcting both of the above, a three-dimensional device is used to re-scan the mechanical and electrical pipelines on-site to obtain the three-dimensional point cloud data of the new mechanical and electrical pipelines, and steps 201 to 204 are repeated to ensure the consistency between the actual model and the virtual model of the mechanical and electrical pipelines. In this embodiment, the newly constructed three-dimensional point cloud model is compared with the BIM model after the initial position data is corrected.

[0059] In one embodiment, the comparison result between the three-dimensional point cloud model generated in step 203 and the BIM model can also be visually displayed to the construction team through the AR (augmented reality) module, facilitating an intuitive understanding of the problems existing in the mechanical and electrical pipelines. The specific steps are as follows: Step F1: Select an AR device suitable for the construction environment. The AR device includes, but is not limited to, AR glasses, tablet devices, etc. The selected device can scan through the reference landmark points arranged on-site and adjust the coordinate system for precise calibration. The calibration process should ensure that the coordinate system of the AR device is completely aligned with the coordinate system of the construction site. Only when the coordinate systems are aligned can the virtual model be accurately superimposed on the actual scene, thus providing intuitive and accurate reference information for the construction personnel.

[0060] Step F2: Convert the difference analysis result between the three-dimensional point cloud model data and the BIM model data so that it can be recognized and read by the AR device. After completing the conversion process, import the processed three-dimensional model data into the AR device so that it can be displayed in the augmented reality environment. This is used to visually see the differences between the three-dimensional point cloud model data and the BIM model data through the AR device.

[0061] Step F3: Overlay the point cloud model and the BIM model at the actual construction site, and display the comparison results between the two through visualization technology to more intuitively identify and analyze deviations. For the detected deviation points, color marking and symbol marking methods are used for differentiation. For example, normal points will be marked green, indicating no deviation or the deviation is within the acceptable range; slightly deviated points will be marked yellow to prompt relevant personnel to pay attention; while severely deviated points will be marked red to attract high attention. In addition, different symbols will also be used to represent different types of deviations, such as position deviation, angle deviation, etc., to facilitate quick identification of the problem type. To provide more detailed information, information labels will be added next to each deviation point, which contain specific deviation values, deviation directions, and recommended correction measures, etc. In this way, on-site management personnel and technical personnel can quickly obtain key information and formulate corresponding correction plans to ensure construction quality and accuracy.

[0062] The present application also provides an application scenario, which applies the above-mentioned method for consistent measurement of the actual model of mechanical and electrical pipelines, and is applied to the scenario of the T3 terminal project of the third-phase expansion project of an airport. The project is located on the southeast side of Terminals T1 and T2. The project consists of a main building, a southeast finger corridor, a southwest finger corridor, a northeast finger corridor, and a northwest finger corridor. It has 5 floors above ground, a building height of 42.95 m, and a building area of 487867 m 2 .

[0063] Specifically, the three-dimensional laser scanning technology adopted in this project uses a laser generator with a wavelength of 635 nm to emit a red laser line, and cooperates with two industrial cameras and a flash to quickly scan the surface of the object. After the laser rays irradiate on the surface of the object, refraction and reflection occur, and the data at this moment is captured by the industrial cameras. Finally, a three-dimensional model of the object is generated through a computer and professional software. This technology has the advantages of high precision, high efficiency, and non-contact measurement, and can accurately capture the shape, position, and size information of the pipeline.

[0064] This project adopts three-dimensional laser scanning technology, and a laser generator with a wavelength of 635 nanometers is used to emit a red laser line. Through the coordinated work of two high-precision industrial cameras and an efficient flash, the surface of the object can be scanned quickly and accurately. When these red laser lines irradiate on the surface of the object, refraction and reflection phenomena will occur. At this time, the two industrial cameras quickly capture the data information at this moment. The captured data is then transmitted to the computer system and processed and analyzed through professional three-dimensional modeling software. These software can convert the captured data into a detailed three-dimensional model, so as to achieve an accurate description of the shape, position, and size of the object. This three-dimensional laser scanning technology has high precision, high efficiency, and uses a non-contact measurement method, enabling the technology to capture the shape, position, and size information of complex pipelines.

[0065] In the initial stage of the project, the scanning team went to the construction site for detailed exploration work. They deeply understood the specific environment of the project to accurately determine the scanning scope and the specific locations of the scanning stations. After fully understanding the site conditions, the team formulated a scanning implementation plan based on the exploration results. This plan covered multiple aspects, from the planning of the scanning route, the layout of the stations, to the setting of the scanning parameters. Before officially starting the pipeline scanning work, the team members preheated and calibrated the 3D laser scanner. This process was to ensure that the equipment could operate in the best condition, thereby improving the accuracy and reliability of the scanning data. During the scanning process, the team members adjusted the scanning parameters according to the actual site conditions, such as the resolution and the scanning quality, to ensure that the obtained data could meet the expected quality standards. In addition, for the convenience of later data processing, special target balls were placed between each scanning station. These target balls served as reference points, helping to improve the accuracy and efficiency of the data scanning.

[0066] After the on-site pipeline scanning work, the first step is to import the collected original scanning data into the point cloud processing software. This process involves stitching the data obtained from each scanning station to ensure the integrity and accuracy of the data. Next, the stitched data needs to be further processed, including removing the noise and excluding the point cloud data that does not belong to the completed delivery items. These non-delivery items may include scaffolding, construction workers, and other temporary facilities. Through these steps, a clean and accurate point cloud data model can be finally generated.

[0067] After the generation of the point cloud data model, it is necessary to register the point cloud model with the BIM model (Building Information Model). The registration process involves precisely aligning the spatial positions of the two models for subsequent data analysis. Through this registration, the differences between the point cloud model and the BIM model can be compared in detail to check their consistency. Finally, a detailed model comparison and analysis report will be generated based on the above analysis results. This report will record the differences between the two models in detail, providing an important reference for subsequent construction and design adjustments as well as model data adjustments.

[0068] After analyzing the model comparison and analysis report, the construction team formulates a specific on-site rectification plan or makes necessary modifications to the BIM model. For the deviations pointed out in the report, the team members carry out corresponding adjustment work to ensure that the BIM model is completely consistent with the actual site conditions. Through this series of corrective measures, a completed BIM model that meets the on-site installation requirements is finally formed. This model can not only be used for information management but also provide strong support for later operation and maintenance, ensuring the smooth progress and efficient management of the entire project.

[0069] Since the project was launched and implemented, the construction accuracy and efficiency during the project construction process have been significantly improved, mainly reflected in the following aspects: (1) The measurement accuracy has been significantly improved: By adopting the three-dimensional laser scanning technology, detailed information of the pipeline is obtained with millimeter-level or even higher accuracy, effectively avoiding the errors that may occur in traditional measurement methods, thus ensuring the accuracy and reliability of the data.

[0070] (2) The construction period has been greatly shortened: Compared with the traditional manual measurement method, applying the three-dimensional laser scanning technology to the measurement method of mechanical and electrical pipelines saves time in measurement and data processing, thus shortening the completion time of the entire project.

[0071] (3) The cost has been effectively controlled: The introduction of the automated measurement technology reduces the dependence on manpower, thus reducing the cost during the measurement process. In addition, by improving the construction efficiency, the overall cost of the project is indirectly reduced. This includes not only the direct measurement cost, but also the time and resource costs saved due to the efficiency improvement.

[0072] (4) The management level has been significantly improved: The BIM model after completion is helpful for realizing the pipeline management throughout the life cycle, making the project management more scientific and efficient, conducive to better monitoring and managing the project progress, promptly discovering and solving problems, thus ensuring the smooth progress of the project.

[0073] Based on the same inventive concept, the embodiment of the present application also provides a mechanical and electrical pipeline actual model consistent measurement system for implementing the mechanical and electrical pipeline actual model consistent measurement method involved above. The implementation solutions for solving problems provided by this system are similar to the implementation solutions recorded in the above method. Therefore, the specific limitations in one or more embodiments of the mechanical and electrical pipeline actual model consistent measurement system provided below can refer to the limitations on the mechanical and electrical pipeline actual model consistent measurement method in the above text, and will not be elaborated here.

[0074] In an exemplary embodiment, as Figure 3 shown, a mechanical and electrical pipeline actual model consistent measurement system is provided, including: An acquisition module 310, configured to acquire three-dimensional point cloud data of the mechanical and electrical pipeline; A construction module 320, configured to construct a three-dimensional point cloud model based on the three-dimensional point cloud data after data processing, wherein the data processing includes preprocessing; A comparison module 330, configured to compare the three-dimensional point cloud model with the BIM model to obtain a model comparison result between the three-dimensional point cloud model and the BIM model, wherein the BIM model is created based on the initial position data; A generation module 340 is configured to generate a model analysis report based on the model comparison result. The model analysis report is used to correct construction differences and ensure the consistency of the actual model of the mechanical and electrical pipelines.

[0075] As an alternative implementation, the data processing further includes data optimization processing.

[0076] As an alternative implementation, the preprocessing module is specifically configured to: Denoise the three-dimensional point cloud data; Perform downsampling on the denoised three-dimensional point cloud data; As an alternative implementation, in terms of preprocessing, the construction module 320 is specifically configured to: Denoise the three-dimensional point cloud data; Perform downsampling on the three-dimensional point cloud data after the denoising process; As an alternative implementation, in terms of optimization processing, the construction module 320 is specifically configured to: Perform data smoothing on the three-dimensional point cloud data after the downsampling process; Perform data fusion on the three-dimensional point cloud data after the data smoothing process; Perform data compression on the three-dimensional point cloud data after the data fusion process to obtain the processed three-dimensional point cloud data.

[0077] As an alternative implementation, the comparison module 330 is specifically configured to: Set the three-dimensional point cloud model and the BIM model in the same coordinate system through the ICP algorithm; Calculate the deviation between the actual installation position and the design model position of the mechanical and electrical pipelines.

[0078] As an alternative implementation, in terms of setting the three-dimensional point cloud model and the BIM model in the same coordinate system through the ICP algorithm, the comparison module 330 is specifically configured to: Align the three-dimensional point cloud model and the BIM model in the same coordinate system based on an initial transformation matrix. The three-dimensional point cloud model includes a number of three-dimensional point cloud data points, and the BIM model includes a number of BIM data points; Determine the BIM data point closest to each three-dimensional point cloud data point based on an initial rotation matrix and an initial translation vector; Obtain a matching point data set of the three-dimensional point cloud model and the BIM model based on a rotation matrix and a translation vector. The matching point data set includes multiple groups of matching points, and each group of matching points includes the closest three-dimensional point cloud data point and BIM data point; Calculating the deviation between the actual installation position and the design model position of the electromechanical pipeline specifically includes: Calculating the distance between the three-dimensional point cloud data points and the BIM data points in each group of the matching points; Marking the three-dimensional point cloud data points with the inter-point distance greater than the preset value of the point spacing as deviation points, and generating a deviation point set based on all the deviation points; Calculating the average deviation and the maximum deviation of the deviation points.

[0079] As an alternative implementation manner, the calculation module is specifically configured to: Calculate the distance between the three-dimensional point cloud data points and the BIM data points in each group of the matching points; Mark the three-dimensional point cloud data points with the inter-point distance greater than the preset value of the point spacing as deviation points, and generate a deviation point set based on all the deviation points; Calculate the average deviation and the maximum deviation of the deviation points.

[0080] Generating a difference analysis report based on the deviation point set, the average deviation, and the maximum deviation.

[0081] As an alternative implementation manner, the generation module 340 is specifically configured to: Generate a difference analysis report based on the deviation point set, the average deviation, and the maximum deviation; Obtaining a correction suggestion for each deviation point through the difference analysis report; Performing on-site correction on the electromechanical pipeline based on the correction suggestion, and / or correcting the initial position data in the BIM model based on the correction suggestion.

[0082] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store three-dimensional point cloud data of electromechanical pipelines. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for measuring the consistency of the actual model of electromechanical pipelines.

[0083] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0084] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0085] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0086] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0089] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0090] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0091] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for measuring the consistency of a real model of an electromechanical pipeline, characterized in that: The electromechanical pipeline real model consistent measurement method comprises: Obtain 3D point cloud data of electromechanical pipelines; Constructing a three-dimensional point cloud model based on the three-dimensional point cloud data after data processing, wherein the data processing includes preprocessing; Comparing the three-dimensional point cloud model with the BIM model to obtain a model comparison result between the three-dimensional point cloud model and the BIM model, wherein the BIM model is created based on the initial position data; A model analysis report is generated based on the model comparison result, and the model analysis report is used to correct construction differences and ensure the consistency of the actual models of the electromechanical pipelines.

2. The method for measuring the consistency of the electromechanical pipeline model according to claim 1 is characterized in that: The data processing also includes data optimization processing.

3. The method for measuring the consistency of the electromechanical pipeline model according to claim 2 is characterized in that: The pre-processing comprises: Performing denoising processing on the three-dimensional point cloud data; Performing downsampling processing on the three-dimensional point cloud data that has undergone the denoising processing; The data optimization process includes: Performing data smoothing processing on the three-dimensional point cloud data after the downsampling processing; Performing data fusion processing on the three-dimensional point cloud data after the data smoothing processing; The three-dimensional point cloud data after the data fusion processing is subjected to data compression processing to obtain the three-dimensional point cloud data after data processing.

4. The method for measuring the consistency of electromechanical pipeline real models according to claim 1 is characterized in that: The comparing the three-dimensional point cloud model with the BIM model to obtain a comparison result between the three-dimensional point cloud model and the BIM model includes: The three-dimensional point cloud model and the BIM model are set in the same coordinate system by using an ICP algorithm; The deviation between the actual installation position of the electromechanical pipeline and the design model position is calculated.

5. The method for measuring the consistency of electromechanical pipeline real models according to claim 4 is characterized in that: The step of setting the three-dimensional point cloud model and the BIM model in the same coordinate system by using the ICP algorithm specifically includes: Aligning the three-dimensional point cloud model and the BIM model in the same coordinate system based on an initial transformation matrix, the three-dimensional point cloud model comprising a plurality of three-dimensional point cloud data points, and the BIM model comprising a plurality of BIM data points; Based on the initial rotation matrix and the initial translation vector, determining the BIM data point closest to each of the three-dimensional point cloud data points; Acquire a matching point data set between the three-dimensional point cloud model and the BIM model based on a rotation matrix and a translation vector, wherein the matching point data set includes multiple groups of matching points, and each group of matching points includes the closest three-dimensional point cloud data point and the BIM data point; The calculating of the deviation between the actual installation position of the electromechanical pipeline and the design model position specifically includes: Calculating the distance between the three-dimensional point cloud data points and the BIM data points in each group of matching points; Mark all the three-dimensional point cloud data points whose inter-point distances are greater than a preset point spacing value as deviation points, and generate a deviation point set based on all the deviation points; The average deviation and the maximum deviation of the deviation points are calculated.

6. The method for measuring the consistency of electromechanical pipeline real models according to claim 5 is characterized in that: The model analysis report is generated based on the model comparison result, and the model analysis report is used to correct the construction differences and ensure the consistency of the actual model of the electromechanical pipeline, specifically including: Generate a difference analysis report based on the deviation point set, the average deviation and the maximum deviation; Obtain correction suggestions for each of the deviation points through the difference analysis report; The electromechanical pipelines are corrected on site based on the correction suggestion, and / or the initial position data in the BIM model is corrected based on the correction suggestion.

7. A system for measuring the real model of an electromechanical pipeline, characterized in that: The electromechanical pipeline real model consistent measurement system comprises: An acquisition module is used to acquire three-dimensional point cloud data of electromechanical pipelines; A construction module, used for constructing a three-dimensional point cloud model based on the three-dimensional point cloud data after data processing; A comparison module, used for comparing the three-dimensional point cloud model with the BIM model to obtain a model comparison result between the three-dimensional point cloud model and the BIM model, wherein the BIM model is created based on the initial position data; A generation module is used to generate a model analysis report based on the model comparison result, and the model analysis report is used to correct the construction differences and ensure the consistency of the actual model of the electromechanical pipeline.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electromechanical pipeline real model consistent measurement method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electromechanical pipeline real model consistent measurement method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the electromechanical pipeline real model consistent measurement method described in any one of claims 1 to 6 are implemented.

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