Finite element laser scanning flatness measurement method, device and computer equipment

Abstract

The application relates to a finite element laser scanning flatness measurement method, device and computer equipment. The method comprises the following steps: dividing a scanning area of a wafer plate structure, arranging scanning stations in each scanning area, scanning the scanning stations in each construction stage, and obtaining original point cloud data; the original point cloud data of each construction stage is respectively subjected to denoising processing, and then the processed point cloud data is registered and spliced to generate a globally unified point cloud data set; a finite element model is established, data mapping and integration of each construction stage are completed, so that the globally unified point cloud data set of the construction stage is mapped to the finite element model; load distribution simulation and deformation calculation are performed on the finite element model in each construction stage to obtain simulated settlement data; simulated flatness is obtained according to the simulated settlement data of each construction stage, and a support system is optimized and adjusted according to the deviation between the simulated flatness and target flatness. The method can improve the measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of measurement and testing technology, and in particular to a finite element laser scanning method, apparatus, and computer equipment for measuring flatness. Background Technology

[0002] The field of measurement and testing technology encompasses various tools and equipment used to accurately acquire specific properties or parameters such as physical and chemical quantities. Its core content is the quantification of the properties of various objects or phenomena, covering multiple aspects including physical measurements (such as length, weight, temperature, and pressure), chemical measurements (measurements of the composition, structure, and properties of substances), biological measurements (focusing on measurements at the biological or biomolecular level), and electronic measurements (targeting electronic signals and device characteristics). In industrial production, it ensures product quality and improves production efficiency; in medical diagnosis, it provides strong support for disease diagnosis and treatment; and in environmental monitoring, it acquires environmental parameters in real time to assess environmental quality, playing a crucial role in many fields.

[0003] Flatness measurement refers to the quantitative determination of the flatness of an object's surface. However, related technologies suffer from low measurement accuracy in flatness measurement. Summary of the Invention

[0004] Therefore, it is necessary to provide a finite element laser scanning flatness measurement method, device, and computer equipment that can improve measurement accuracy in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a finite element laser scanning method for flatness measurement, including:

[0006] The scanning area of ​​the waffle plate structure is divided, and scanning stations are arranged in each scanning area. The scanning stations are scanned at each construction stage of the waffle plate structure to obtain raw point cloud data.

[0007] The original point cloud data of each construction stage are denoised, and then the processed point cloud data are registered and stitched together using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage.

[0008] Construct a three-dimensional model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage to map the globally unified point cloud dataset of the construction stage to the finite element model.

[0009] At each construction stage, load distribution simulation and deformation calculation are performed on the finite element model to obtain simulated settlement data.

[0010] The simulated flatness is obtained by analyzing the simulated settlement data of each construction stage, and the support system of the waffle slab structure is optimized and adjusted according to the deviation between the simulated flatness and the target flatness.

[0011] In one embodiment, when arranging scanning sites, the overlap rate of the scanning range between adjacent scanning sites is greater than or equal to a first threshold.

[0012] In one embodiment, the method further includes: using a black and white target plate of a first preset size as the reference point, and arranging the reference point at a preset position within a preset range of the scanning station, wherein the number of common reference points corresponding to two adjacent scanning stations is not less than a first preset number.

[0013] In one embodiment, the denoising process for the original point cloud data of each construction stage includes: filtering invalid points in the original point cloud data based on the construction coordinate system range, setting a laser intensity threshold, removing reflective anomaly points, and then using a statistical outlier filtering algorithm to calculate the distance between each point and its second preset number of nearest neighbor points, and deleting points whose distance is greater than the sum of the distance mean and multiple standard deviations.

[0014] In one embodiment, the registration and stitching includes coarse registration and fine registration; the coarse registration includes: using the coordinates of the reference point, converting the point cloud data of each scanning station to the construction coordinate system through a quaternion transformation matrix, calculating the initial rotation matrix and translation vector, and controlling the error within a first preset error range; the fine registration includes: using an iterative nearest-point algorithm to optimize the transformation parameters with a preset iterative precision, so that the error of the overlapping area of ​​the point clouds of adjacent scanning stations is controlled within a second preset error range.

[0015] In one embodiment, when establishing the finite element model, tetrahedral 10-node elements are used for mesh generation. The mesh size of the template, waffle tube, support beam, embedded parts, connectors and reinforcing ribs of the waffle plate structure is less than or equal to the second preset size. The mesh size of the concrete slab of the waffle plate structure is less than or equal to the third preset size. The total number of elements is greater than or equal to the third preset number. The mesh quality factor is greater than or equal to the second threshold. The support system of the waffle plate structure is simulated as an elastic support with a preset spring stiffness. The bottom support is fixed along the horizontal and vertical directions.

[0016] In one embodiment, the simulated flatness is obtained by performing flatness analysis based on the simulated settlement data of each construction stage, including: determining the allowable displacement threshold, predicting the settlement amount of subsequent construction stages using the exponential smoothing method based on the simulated settlement data of each construction stage, and determining the simulated flatness based on the comparison result between the settlement amount and the allowable displacement threshold; wherein, the allowable displacement threshold is calculated based on the span of the waffle plate structure, and the allowable displacement threshold is less than or equal to a third threshold.

[0017] In one embodiment, when optimizing and adjusting the support system, the following steps are taken: Adding support points: In the area where the deviation exceeds the standard, new support points are arranged along both the longitudinal and transverse directions at a spacing not greater than the fourth threshold; Replacing with high-stiffness support: After completing the step of adding support points, a retest is performed. If the requirements are still not met, the spring stiffness of the original support is replaced with a high-stiffness support with a stiffness not lower than the preset value; Adjusting the support elevation: The support elevation is adjusted using a level instrument, and the adjustment accuracy is controlled within the second preset error range.

[0018] Secondly, this application also provides a finite element laser scanning flatness measurement device, comprising:

[0019] The scanning module is used to divide the scanning area of ​​the waffle plate structure, arrange scanning stations in each scanning area, and scan the scanning stations at each construction stage of the waffle plate structure to obtain raw point cloud data.

[0020] The processing module is used to denoise the original point cloud data of each construction stage, and then register and stitch the processed point cloud data using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage.

[0021] The mapping module is used to construct a three-dimensional model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage to map the globally unified point cloud dataset of the construction stage to the finite element model.

[0022] The simulation module is used to simulate the load distribution and calculate the deformation of the finite element model at each construction stage to obtain simulated settlement data.

[0023] The adjustment module is used to perform flatness analysis based on the simulated settlement data of each construction stage to obtain the simulated flatness, and to optimize and adjust the support system of the waffle plate structure based on the deviation between the simulated flatness and the target flatness.

[0024] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the finite element laser scanning flatness measurement method provided in the first aspect of this application.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the finite element laser scanning flatness measurement method provided in the first aspect of this application.

[0026] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the finite element laser scanning flatness measurement method provided in the first aspect of this application.

[0027] The aforementioned finite element laser scanning flatness measurement method, device, computer equipment, computer-readable storage medium, and computer program product divide the scanning area of ​​the waffle plate structure, arrange scanning stations in each scanning area, and scan the scanning stations at each construction stage of the waffle plate structure to obtain raw point cloud data. The raw point cloud data of each construction stage are denoised, and then the processed point cloud data are registered and stitched together using the coordinates of reference points to generate a globally unified point cloud dataset for each construction stage. A three-dimensional model of the waffle plate structure is constructed, finite element model parameters are determined, and a finite element model is established. Data mapping and integration for each construction stage are completed to map the globally unified point cloud dataset of each construction stage to the finite element model. Load distribution simulation and deformation calculation are performed on the finite element model at each construction stage to obtain simulated settlement data. Flatness analysis is performed based on the simulated settlement data of each construction stage to obtain simulated flatness, and the support system of the waffle plate structure is optimized and adjusted based on the deviation between the simulated flatness and the target flatness. As can be seen, this embodiment maps the global point cloud dataset generated after denoising and registration at each construction stage onto the finite element model. This ensures that subsequent load simulation and deformation calculation are based on the finite element model reflecting the true geometric shape of the waffle plate, guaranteeing that the simulation analysis starts from the actual construction state. This improves the accuracy of flatness analysis and prediction, further enhancing the reliability of the optimization and adjustment of the waffle plate support system, thereby improving the flatness of the waffle plate structure. Furthermore, this embodiment divides the scanning area and arranges scanning stations and reference points to ensure overlap between adjacent station scanning ranges. It uses a uniform target plate and ensures the overlap of reference points between adjacent stations. The original point cloud data is obtained by scanning at each construction stage, ensuring the integrity and consistency of the scanning data. The original point cloud data is denoised by filtering invalid points based on the construction coordinate system range, setting a laser intensity threshold to remove reflective anomalies, and using a specific filtering algorithm to delete outliers. Coarse registration uses reference point coordinates to transform the point cloud of each station to the construction coordinate system, and fine registration uses an iterative algorithm to optimize transformation parameters, generating a globally unified point cloud dataset, effectively improving the point cloud data quality. The quality and accuracy of the waffle slab are improved; a 3D model of the waffle slab is constructed, and a specific element type is used when establishing the finite element model. The corresponding mesh size is set for different components to ensure the total number of elements and the mesh quality factor. The support system is simulated in a specific way, and data mapping and integration are completed so that the finite element model can more accurately reflect the structural characteristics. Load distribution simulation and deformation calculation are carried out. Based on the settlement data of each stage, a specific method is used to predict the settlement of the subsequent construction stage. The allowable displacement threshold is set to accurately predict the settlement. Flatness comparison analysis and deviation statistics are carried out. The support system is optimized and adjusted in a certain order and the effect is verified. The flatness of the waffle slab is effectively improved, the support system is optimized, and the quality of the industry is improved. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a diagram illustrating the application environment of the finite element laser scanning flatness measurement method in one embodiment.

[0030] Figure 2 This is a flowchart illustrating a finite element laser scanning flatness measurement method in one embodiment;

[0031] Figure 3 This is a flowchart illustrating the optimization and adjustment of the support system in one embodiment;

[0032] Figure 4 This is a flowchart illustrating the finite element laser scanning flatness measurement method in another embodiment;

[0033] Figure 5 This is a structural block diagram of a finite element laser scanning flatness measurement device in one embodiment;

[0034] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0035] To make the technical means, creative features, objectives and effects of this application easier to understand, the following describes this application in conjunction with specific implementation methods.

[0036] In related technologies, there are no clear standards for scanning area division, site layout, and reference point setting, resulting in incomplete data and low registration accuracy; point cloud denoising and registration lack specific methods and standards, resulting in poor data quality and low model construction accuracy; finite element model construction parameters and support system simulation are unclear, making it difficult for the model to reflect structural characteristics; settlement prediction lacks specific methods and error control, the optimization sequence and accuracy requirements of the support system are missing, making it difficult to improve the flatness of the waffle plate and affecting the quality of the project.

[0037] Therefore, this application proposes a finite element laser scanning method for flatness measurement, which integrates finite element simulation and laser scanning for various objects requiring flatness measurement (such as building walls, industrial parts, etc.). Using laser scanning technology, a laser beam is emitted and the reflected light signal is received to acquire the three-dimensional coordinate data of the object's surface, thereby accurately representing the actual condition of the object's surface. Simultaneously, finite element simulation is used to simulate and analyze the relationship between the measurement process, results, or the object's mechanical behavior and flatness. By establishing a finite element model, the deformation of the object under different conditions is simulated. The two methods are integrated to complete the flatness measurement. A detailed description is provided below with reference to specific application environments and data.

[0038] The finite element laser scanning flatness measurement method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0039] In one exemplary embodiment, such as Figure 2 As shown, a finite element laser scanning method for flatness measurement is provided, which is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 201 to 205. Wherein:

[0040] Step 201: Divide the scanning area of ​​the waffle plate structure, set up scanning stations in each scanning area, and scan the scanning stations at each construction stage of the waffle plate structure to obtain raw point cloud data.

[0041] For example, the waffle slab structure to be constructed is divided into scanning areas, and several scanning stations are arranged in each scanning area. At the same time, a unified reference point is established throughout the entire construction area. Then, at each construction stage of the waffle slab structure (e.g., after the formwork is erected, after the reinforcement is tied, after the concrete is poured, etc.), a laser scanner is used to scan at each scanning station to obtain raw point cloud data that can reflect the actual three-dimensional shape of the waffle slab structure at each construction stage.

[0042] Step 202: Denoise the original point cloud data for each construction stage, and then register and stitch the processed point cloud data using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage.

[0043] For example, the original point cloud data for each construction stage are first denoised, and then the original point cloud data from different scanning stations in each construction stage are registered and stitched together using the coordinate information of pre-established reference points. Optionally, by calculating the transformation relationship between the local coordinate system of each station and the pre-established construction coordinate system, the original point cloud data of all stations are transformed to the same construction coordinate system, and duplicate data and minor deviations between adjacent stations are eliminated, ultimately generating a globally unified point cloud dataset for each construction stage.

[0044] Step 203: Construct a 3D model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage to map the globally unified point cloud dataset of the construction stage to the finite element model.

[0045] For example, a finite element model of the waffle plate structure is first established, and then a globally unified point cloud dataset is mapped to the finite element model for each construction stage, so that the geometry of the finite element model is consistent with the real shape.

[0046] Step 204: Perform load distribution simulation and deformation calculation on the finite element model at each construction stage to obtain simulated settlement data.

[0047] The load distribution differs at each stage.

[0048] For example, after obtaining the finite element model after data mapping, the corresponding load distribution is applied to the finite element model to simulate the actual construction situation (such as personnel, materials, equipment, etc.) for each construction stage, and deformation calculation is performed to obtain the simulated settlement data for that construction stage. The simulated settlement data is used to reflect the settlement or deformation of the waffle plate structure at various parts during the construction stage.

[0049] Step 205: Perform flatness analysis based on the simulated settlement data of each construction stage to obtain the simulated flatness, and optimize and adjust the support system of the waffle plate structure based on the deviation between the simulated flatness and the target flatness.

[0050] Among them, simulated flatness is used as a quantitative index to characterize the surface flatness of the waffle plate structure.

[0051] For example, simulated flatness is calculated based on simulated settlement data from each construction stage, and this simulated flatness is compared with the target flatness (specified in the specifications or preset in advance) to obtain the deviation. Based on the deviation, the support system under the waffle slab is optimized and adjusted, such as adding supports in areas with large deviations. After adjustment, the effect of the optimization and adjustment can be verified until the actual flatness of the waffle slab meets the design requirements, thus controlling the flatness of the entire construction process in advance.

[0052] The aforementioned finite element laser scanning flatness measurement method divides the scanning area of ​​the waffle plate structure, arranges scanning stations within each scanning area, and scans the stations at each construction stage of the waffle plate structure to obtain raw point cloud data. The raw point cloud data for each construction stage is denoised, and then the processed point cloud data is registered and stitched together using the coordinates of reference points to generate a globally unified point cloud dataset for each construction stage. A three-dimensional model of the waffle plate structure is constructed, the finite element model parameters are determined, and a finite element model is established. Data mapping and integration for each construction stage are completed to map the globally unified point cloud dataset for each construction stage to the finite element model. Load distribution simulation and deformation calculation are performed on the finite element model at each construction stage to obtain simulated settlement data. Flatness analysis is performed based on the simulated settlement data for each construction stage to obtain simulated flatness, and the support system of the waffle plate structure is optimized and adjusted based on the deviation between the simulated flatness and the target flatness. As can be seen, the embodiments of this application map the global point cloud dataset generated by denoising and registration at each construction stage onto the finite element model, so that the subsequent load simulation and deformation calculation are all based on the finite element model that reflects the real geometric shape of the waffle plate. This ensures that the simulation analysis starts from the real construction state, thereby improving the accuracy of flatness analysis and prediction, further improving the reliability of the optimization and adjustment of the waffle plate support system, and thus improving the flatness of the waffle plate structure.

[0053] In an exemplary embodiment, when arranging scanning stations in step 201, the overlap rate of the scanning range between adjacent scanning stations is greater than or equal to a first threshold, such as ≥30%.

[0054] Step 201 also includes setting up reference points: using a black and white target plate of a first preset size (e.g., 600mm×600mm) as a reference point, and placing the reference point at a preset position within a preset range (e.g., 20m) of the scanning station, and the number of common reference points corresponding to two adjacent scanning stations is not less than the first preset number, such as 2.

[0055] For example, this embodiment is mainly used to complete the planning of the scanning area and the collection of raw point cloud data, so as to provide basic data support for subsequent data processing.

[0056] Specifically, in a semiconductor manufacturing plant waffle construction project, according to the construction drawings, the plant has a single-story building area of ​​12,000 square meters. The waffle structure has various spans, with the largest span being 8 meters. According to the construction sequence of the production line, the entire construction area is divided into 8 independent scanning areas, each with an area of ​​approximately 1,500 square meters and a size of approximately 30m × 50m. This division can ensure the coverage efficiency of the laser scanning equipment and facilitate phased management during the construction process.

[0057] Furthermore, scanning stations were set up in each scanning area, and a total station was used for station positioning. The overlap rate of the scanning range of adjacent stations was strictly controlled within 35%. Taking the first scanning area as an example, a total of 6 scanning stations were set up at its four corners and the midpoint of its long side. The distance between adjacent stations was set to 12-18m according to the actual terrain and structural characteristics. According to actual on-site measurements, the overlap rate of the scanning range of each station was between 35% and 40%, which effectively avoided scanning blind spots.

[0058] During the scanning process, reference points are set up using high-precision black and white target plates of 600mm×600mm, which are fixed to stable positions such as concrete columns or steel structure supports within 20m of the site using expansion bolts.

[0059] Taking the second scanning station as an example, four reference points are evenly arranged within a 15m radius around it, and this station shares no less than two reference points with the adjacent first and third scanning stations. This method ensures the consistency and accuracy of data from different stations during coordinate transformation.

[0060] During the key stages of waffle slab construction—namely, after the formwork installation, waffle tube laying, rebar tying, and concrete pouring—scanning operations are performed respectively.

[0061] The Trimble TX8 terrestrial laser scanning system was used. Its technical parameters are: ranging range of 0.6m to 80m, accuracy of 1mm to 5mm, 360°×282° ultra-large field of view, and single-station scanning time of 1 minute and 34 seconds. In the scanning operation after the concrete pouring was completed, 8 scanning stations were scanned in sequence, and the number of scanning points at each station reached 150 million points, which completely and accurately recorded the three-dimensional morphological data of the waffle slab structure at this stage.

[0062] Therefore, this embodiment ensures the overlap of scanning ranges of adjacent stations by dividing the scanning area and arranging scanning stations and reference points. It also uses a target plate with uniform specifications and ensures the overlap of reference points of adjacent stations. The original point cloud data is obtained by scanning at each construction stage, thus ensuring the integrity and consistency of the scanning data.

[0063] In an exemplary embodiment, the original point cloud data of each construction stage are denoised, including: filtering invalid points in the original point cloud data based on the construction coordinate system range, setting a laser intensity threshold, removing abnormal reflective points, and then using a statistical outlier filtering algorithm to calculate the average distance between each point and its second preset number (e.g., 50) nearest neighbors, and deleting points whose distance is greater than the sum of the distance mean and multiple standard deviations. For example, if the distance of a point is greater than the sum of the distance mean and twice the standard deviation, then the point is deleted.

[0064] For example, this embodiment further denoises and cleans the point cloud data to improve data quality after completing the acquisition of the original point cloud data. After acquiring the original point cloud data, spatial filtering is performed on the data based on the construction coordinate system range (e.g., X±50m, Y±50m, Z±10m, or X±60m, Y±60m, Z±12m).

[0065] Taking the raw point cloud data collected at the completion stage of rebar binding as an example, the total number of points at this stage is 8 million. After filtering by coordinate system range, about 800,000 invalid points that exceed 60m on the X-axis, 60m on the Y-axis, or 12m on the Z-axis were removed, leaving 7.2 million points as valid target area points.

[0066] Next, a laser intensity threshold (e.g., [10,250] or [12,250]) is set, and the data is filtered a second time based on the laser intensity threshold. Due to the presence of metal template reflections and strong light interference from lighting equipment at the construction site, points with laser intensity values ​​exceeding this range are usually abnormal points. After testing, approximately 500,000 abnormal reflection points with laser intensities less than 12 or greater than 250 were removed, leaving 6.7 million points.

[0067] Finally, a statistical outlier filtering algorithm is used for fine denoising. A point Q is selected, and its distance to its 60 nearest neighbors is calculated. Assuming the distance data is [2.2mm, 1.9mm, 2.4mm, ..., 9.0mm], the mean distance is calculated to be 3.5mm and the standard deviation is 1.8mm. According to the algorithm rules, points with a distance greater than the mean plus twice the standard deviation are deleted. In this embodiment, points with a distance > 3.5 + 2 × 1.8 = 7.1mm are deleted, such as points with a distance of 9.0mm. Through this algorithm, approximately 300,000 outlier noise points are removed, resulting in 6.4 million high-quality point cloud data points.

[0068] Therefore, this embodiment denoises the original point cloud data by filtering invalid points based on the construction coordinate system range, setting a laser intensity threshold to remove reflective anomalies, and using a specific filtering algorithm to delete outliers, effectively improving the quality and accuracy of the point cloud data.

[0069] In an exemplary embodiment, the registration and stitching includes coarse registration and fine registration. Coarse registration includes: using the coordinates of the reference points, converting the point cloud data of each scanning station to the construction coordinate system through a quaternion transformation matrix, calculating the initial transformation parameters: the initial rotation matrix R and the translation vector T, and controlling the error within a first preset error range (e.g., ±5mm). Fine registration includes: using an iterative nearest-point algorithm to optimize the transformation parameters with a preset iteration precision (e.g., 0.1mm), so that the error of the overlapping area of ​​the point cloud of adjacent scanning stations is controlled within a second preset error range (e.g., 0.5mm).

[0070] For example, this embodiment, based on the denoising of point cloud data, further realizes the registration and stitching of multi-site cloud data to generate a globally unified point cloud dataset.

[0071] In the coarse registration stage, using the coordinates of pre-arranged reference points, the point cloud data of each scanning station is converted to the construction coordinate system using a quaternion transformation matrix with the help of CloudCompare software. Taking the data registration of the third and fourth stations as an example, the coordinates of three reference points common to the two stations are extracted. Specifically, the coordinates of reference point C of the third station are (150, 220, 6), the coordinates of reference point D are (170, 220, 6), and the coordinates of reference point E are (160, 230, 6).

[0072] The coordinates of reference point C at the fourth station are (170, 220, 6), reference point D is (190, 220, 6), and reference point E is (180, 230, 6). Based on this, the initial transformation parameters R (rotation matrix) and T (translation vector) are calculated to control the initial registration error between the two stations within ±4mm. The actual measured error is 3mm.

[0073] In the fine registration stage, the Iterative Closest Point (ICP) algorithm is adopted. In the Geomagic ControlX software, the transformation parameters are optimized with an iterative accuracy of 0.1mm. The number of iterations for the point cloud data of the two stations after coarse registration is set to 60. In each iteration, the nearest point pair is calculated and the transformation parameters are updated. Finally, the error of the overlapping area of ​​the point cloud of adjacent stations is controlled within 0.4mm. By performing coarse and fine registration on the point cloud data of 8 stations in sequence, a global unified point cloud dataset containing 6 million points is finally generated.

[0074] Therefore, this embodiment uses coarse registration to transform the point clouds of each site into the construction coordinate system using the coordinates of the reference points, and fine registration uses an iterative algorithm to optimize the transformation parameters, generating a globally unified point cloud dataset, which further improves the quality and accuracy of the point cloud data.

[0075] In an exemplary embodiment, when establishing the finite element model, tetrahedral 10-node elements are used for mesh generation. The mesh size of the template, waffle cylinder, support beam, embedded parts, connectors, and reinforcing ribs of the waffle plate structure is less than or equal to a second preset size (e.g., 50mm). The mesh size of the concrete slab of the waffle plate structure is less than or equal to a third preset number (e.g., 100mm). The total number of elements is greater than or equal to a third preset number (e.g., 500,000). The mesh quality factor is greater than or equal to a second threshold (e.g., 0.7). The support system of the waffle plate structure is simulated as an elastic support, and the spring stiffness is a preset stiffness (e.g., 1×10⁻⁶). 6 (N / m), the bottom support is fixed along the horizontal and vertical directions, that is, the bottom support constrains the degrees of freedom in the three directions of UX, UY and UZ.

[0076] For example, this embodiment further constructs a three-dimensional finite element model of the waffle plate based on the generated global point cloud dataset, providing a calculation model for subsequent load simulation. Based on the global point cloud dataset, a three-dimensional solid model of the waffle plate is constructed using Revit software.

[0077] Taking a typical 10m×10m×1.2m waffle slab structure in this project as an example, the model includes detailed components such as templates, waffle tubes, support beams, steel bars, and embedded parts. The finite element mesh is generated using tetrahedral 10-node elements and is operated with ANSYS software, which is a commonly used finite element analysis (FEA) software in the current technology.

[0078] Among them, the grid size of the template, waffle tube, support beam, embedded parts, connectors and reinforcing bars are all controlled within 40mm, and the grid size of the concrete slab is controlled within 80mm. In specific implementation, the grid size of the template is set to 25mm, the grid size of the waffle tube is 30mm, the grid size of the support beam is 28mm, and the grid size of the concrete slab is 70mm. The total number of units is calculated to be 600,000, which meets the requirement of ≥500,000.

[0079] Mesh quality was checked using a quality factor index, requiring a mesh quality factor ≥ 0.7. ANSYS software testing showed that the average mesh quality factor for this model was 0.78, and the minimum was 0.73, both meeting the quality requirements. The support system was simulated as an elastic support, with the spring stiffness k set to 1.2 × 10⁻⁶. 6 N / m, the bottom support constrains the degrees of freedom in the UX / UY / UZ directions. In the model, the spring stiffness is set to 1.2×10 for the four corner support points and the four symmetrically distributed support points in the middle. 6 The value is N / m, and its three degrees of freedom are constrained to accurately reflect the fixed state of the bottom support during actual construction.

[0080] Therefore, in this embodiment, a three-dimensional model of the waffle plate is constructed. When establishing the finite element model, a specific element type is used, and corresponding mesh sizes are set for different components to ensure the total number of elements and mesh quality factor. The support system is simulated in a specific way to complete data mapping and integration, so that the finite element model can more accurately reflect the structural characteristics.

[0081] In an exemplary embodiment, the simulated flatness is obtained by performing flatness analysis based on the simulated settlement data of each construction stage. This includes: determining an allowable displacement threshold; predicting the settlement amount of subsequent construction stages using the exponential smoothing method based on the simulated settlement data of each construction stage; and determining the simulated flatness based on the comparison between the settlement amount and the allowable displacement threshold. The allowable displacement threshold is calculated based on the span L of the waffle slab structure, and the allowable displacement threshold is less than or equal to a third threshold. For example, the allowable displacement threshold is L / 500 and less than or equal to 5 mm, where L is the span of the waffle slab structure.

[0082] For example, this embodiment further realizes the simulation of construction load distribution and subsequent settlement prediction based on the already constructed finite element model, providing data support for construction control. When simulating load distribution, the loads of personnel, materials, equipment and other factors during the construction stage are comprehensively considered.

[0083] Taking the concrete pouring stage as an example, the construction workers are calculated as 2 people per square meter, and the load of each person is considered as 1kN, that is, the load of the construction workers is 2kN / m²; the density of C30 concrete is 2400kg / m³, and its load is calculated as 24kN / m³; the self-weight of the formwork and support system is actually weighed and measured, and calculated as 1.8kN / m². These loads are evenly applied to the corresponding parts of the finite element model through ANSYS software, and the stress distribution and deformation of each part are obtained through finite element calculation.

[0084] The allowable displacement threshold for settlement prediction design is L / 500 (L is the slab span) and ≤5mm. Taking a waffle slab with a span of 6 meters as an example, the allowable displacement threshold is 6000 / 500=12mm. However, since it is necessary to simultaneously meet the requirement of ≤5mm, the final threshold is 5mm.

[0085] Based on settlement monitoring data from each construction stage, the exponential smoothing method (α=0.3) is used to predict subsequent settlement. Assuming the measured settlements for the first five construction stages are 1.2mm, 2.0mm, 2.8mm, 3.5mm, and 4.2mm respectively, the specific prediction process is as follows:

[0086] The initial predicted value is S1 = Y1 = 1.2 mm;

[0087] Predicted value for Phase 2: S2 = 0.3 × 1.2 + (1 - 0.3) × 1.2 = 1.2 mm;

[0088] Predicted value for Phase 3: S3 = 0.3 × 2.0 + (1 - 0.3) × 1.2 = 1.44 mm;

[0089] Predicted value for Phase 4: S4 = 0.3 × 2.8 + (1 - 0.3) × 1.44 = 1.848 mm;

[0090] Predicted value for period 5: S5 = 0.3 × 3.5 + (1 - 0.3) × 1.848 = 2.3436 mm;

[0091] Predicted value for period 6: S6 = 0.3 × 4.2 + (1 - 0.3) × 2.3436 = 2.89052 mm.

[0092] The prediction error was controlled within ±1.5mm. Subsequent actual measurements showed that the actual settlement in the 6th period was 3.6mm, and the prediction error was 3.6-2.89052=0.70948mm, which is within the allowable error range.

[0093] Then, the settlement amount is compared and analyzed with the allowable displacement threshold, and the flatness is obtained by flatness analysis based on the comparison results.

[0094] Therefore, this embodiment performs load distribution simulation and deformation calculation, uses specific methods to predict the settlement amount in subsequent construction stages based on settlement data at each stage, sets allowable displacement thresholds, accurately predicts settlement, and performs flatness analysis to ensure the accuracy of simulated flatness.

[0095] In one exemplary embodiment, when optimizing and adjusting the support system, the following adjustments are made in order:

[0096] Add support points: In areas where the deviation exceeds the standard, arrange new support points along both the longitudinal and transverse directions at a spacing not greater than the fourth threshold (e.g., 1.0m).

[0097] Replace with high-stiffness support: After completing the step of adding support points, conduct a retest. If the requirements are still not met, replace the spring stiffness of the original support with one that is not lower than the preset stiffness, such as 1.5 × 10⁻⁶. 6 High-rigidity support (N / m).

[0098] Adjust the support elevation: Use a level to adjust the support elevation, and control the adjustment accuracy within the second preset error range (e.g., ±0.5mm).

[0099] For example, such as Figure 3 As shown, this embodiment, based on the settlement prediction, further realizes the waffle plate flatness comparison analysis and support system optimization and adjustment to ensure that the construction quality meets the requirements. In the flatness comparison analysis, the global point cloud data generated in step 203 is imported into PolyWorks software for comparison with the design benchmark model.

[0100] Taking the point cloud data after concrete pouring as an example, the software automatically generates a color deviation distribution map, where red areas represent areas above the reference plane and blue areas represent areas below the reference plane. The deviation value is marked in units of 0.1mm. After analysis, it was found that there was a large red area in a 15m×15m area in the middle of the factory building, with a maximum deviation of +8.5mm, which exceeds the allowable deviation requirement of ±5mm. The support system was optimized and adjusted in the following order.

[0101] Add support points: In areas where the deviation exceeds the standard, add support points at intervals of 0.8m. Specifically, in this area, arrange a new support point every 0.8m in both longitudinal and transverse directions, for a total of 8 support points, to enhance the local support stiffness.

[0102] Replace with high-stiffness support: After adding support points, retest. If the requirements are still not met, replace the support spring stiffness with 1.8×10. 6 N / m, for example, the original stiffness is 1.2×10 N / m. 6 The four supports with N / m were replaced with 1.8×10. 6 High stiffness support of N / m.

[0103] Adjusting the support elevation: Use a Leica NA728 precision level to adjust the support elevation with an accuracy of ±0.3mm. Taking a certain support point as an example, the original elevation is 5.005m and the design elevation is 5.000m. By fine-tuning the U-shaped support, the elevation is precisely adjusted to 5.000m with an error of 0.000m. The specific operation process of the Leica NA728 precision level will not be described in detail.

[0104] Finally, the optimization effect was verified: data was obtained by scanning again and compared with the point cloud data before optimization. The results showed that the maximum flatness deviation in the optimized area was reduced to +2.5mm, meeting the ±5mm requirement, proving the optimization adjustment was effective. This optimization process effectively improved the flatness of the waffle board, laying a solid foundation for subsequent equipment installation and production operation.

[0105] Therefore, this embodiment performs deviation statistics, optimizes and adjusts the support system in a certain order, and verifies the effect, effectively improving the flatness of the waffle board, optimizing the support system, and promoting the improvement of industry quality.

[0106] The measurement method of this application is described below through detailed embodiments, such as... Figure 4 As shown, the specific steps include:

[0107] S1, Divide the scanning area;

[0108] S2, Set up scanning stations and reference points;

[0109] S3 scans the scanning sites at each construction stage to obtain raw point cloud data;

[0110] S4, performs noise reduction processing on the raw point cloud data;

[0111] S5 registers and stitches the processed point cloud data to generate a globally unified point cloud dataset;

[0112] S6, construct the 3D model of the waffle board;

[0113] S7, Establish the finite element model;

[0114] S8 performs data mapping and integration to map the globally unified point cloud dataset to the finite element model;

[0115] S9 is used for load distribution simulation and deformation calculation.

[0116] S10, perform flatness analysis and deviation statistics;

[0117] S11. Optimize and adjust the support system based on the deviation, and verify the optimization effect.

[0118] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0119] Based on the same inventive concept, this application also provides a finite element laser scanning flatness measuring device for implementing the finite element laser scanning flatness measuring method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the finite element laser scanning flatness measuring device provided below can be found in the limitations of the finite element laser scanning flatness measuring method described above, and will not be repeated here.

[0120] In one exemplary embodiment, such as Figure 5 As shown, a finite element laser scanning flatness measurement device is provided, comprising: a scanning module 501, a processing module 502, a mapping module 503, a simulation module 504, and an adjustment module 505, wherein:

[0121] Scanning module 501 is used to divide the scanning area of ​​the waffle plate structure, arrange scanning stations in each scanning area, scan the scanning stations at each construction stage of the waffle plate structure, and obtain raw point cloud data.

[0122] The processing module 502 is used to denoise the original point cloud data of each construction stage, and then register and stitch the processed point cloud data using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage.

[0123] The mapping module 503 is used to construct a three-dimensional model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage, so as to map the globally unified point cloud dataset of the construction stage to the finite element model.

[0124] Simulation module 504 is used to simulate load distribution and calculate deformation of the finite element model at each construction stage to obtain simulated settlement data.

[0125] The adjustment module 505 is used to perform flatness analysis based on the simulated settlement data of each construction stage to obtain the simulated flatness, and to optimize and adjust the support system of the waffle plate structure based on the deviation between the simulated flatness and the target flatness.

[0126] In one embodiment, when the scanning module 501 is arranging scanning stations, the overlap rate of the scanning range between adjacent scanning stations is greater than or equal to a first threshold.

[0127] In one embodiment, the device further includes: a reference point arrangement module, used to use a black and white target plate of a first preset size as a reference point, and arrange the reference point at a preset position within a preset range of the scanning station, wherein the number of common reference points corresponding to two adjacent scanning stations is not less than the first preset number.

[0128] In one embodiment, the processing module 502 is specifically used to: filter invalid points in the original point cloud data based on the construction coordinate system range, set a laser intensity threshold, remove reflective abnormal points, and then use a statistical outlier filtering algorithm to calculate the distance between each point and its second preset number of nearest neighbors, and delete points whose distance is greater than the sum of the distance mean and multiple standard deviations.

[0129] In one embodiment, registration and stitching includes coarse registration and fine registration; coarse registration includes: using the coordinates of reference points, converting the point cloud data of each scanning station to the construction coordinate system through a quaternion transformation matrix, calculating the initial rotation matrix and translation vector, and controlling the error within a first preset error range; fine registration includes: using an iterative nearest point algorithm to optimize the transformation parameters with a preset iterative precision, so that the error of the overlapping area of ​​the point clouds of adjacent scanning stations is controlled within a second preset error range.

[0130] In one embodiment, when establishing the finite element model, the mapping module 503 uses tetrahedral 10-node elements for mesh generation. The mesh size of the template, waffle tube, support beam, embedded parts, connectors and reinforcing bars of the waffle plate structure is less than or equal to the second preset size. The mesh size of the concrete slab of the waffle plate structure is less than or equal to the third preset size. The total number of elements is greater than or equal to the third preset number. The mesh quality factor is greater than or equal to the second threshold. The support system of the waffle plate structure is simulated as an elastic support with a preset spring stiffness. The bottom support is fixed along the horizontal and vertical directions.

[0131] In one embodiment, the adjustment module 505 is specifically used to: perform flatness analysis based on the simulated settlement data of each construction stage to obtain simulated flatness, including: determining the allowable displacement threshold, predicting the settlement amount of subsequent construction stages using the exponential smoothing method based on the simulated settlement data of each construction stage, and determining the simulated flatness based on the comparison result between the settlement amount and the allowable displacement threshold; wherein, the allowable displacement threshold is calculated based on the span of the waffle plate structure, and the allowable displacement threshold is less than or equal to a third threshold.

[0132] In one embodiment, when the adjustment module 505 optimizes the support system, it adjusts in the following order: Add support points: In areas where the deviation exceeds the standard, new support points are arranged along both the longitudinal and transverse directions at a spacing not greater than the fourth threshold; Replace with high-stiffness support: After completing the step of adding support points, a retest is performed. If the requirements are still not met, the spring stiffness of the original support is replaced with a high-stiffness support with a stiffness not lower than the preset stiffness.

[0133] Each module in the aforementioned finite element laser scanning flatness measurement device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0134] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores finite element laser scanning flatness measurement data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a finite element laser scanning flatness measurement method.

[0135] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0136] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a finite element laser scanning flatness measurement method.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a finite element laser scanning flatness measurement method.

[0138] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a finite element laser scanning flatness measurement method.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this application.

[0141] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A finite element laser scanning method for flatness measurement, characterized in that, The method includes: The scanning area of ​​the waffle plate structure is divided, and scanning stations are arranged in each scanning area. The scanning stations are scanned at each construction stage of the waffle plate structure to obtain raw point cloud data. The original point cloud data of each construction stage are denoised, and then the processed point cloud data are registered and stitched together using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage. Construct a three-dimensional model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage to map the globally unified point cloud dataset of the construction stage to the finite element model. At each construction stage, load distribution simulation and deformation calculation are performed on the finite element model to obtain simulated settlement data. The simulated flatness is obtained by analyzing the simulated settlement data of each construction stage, and the support system of the waffle slab structure is optimized and adjusted according to the deviation between the simulated flatness and the target flatness.

2. The method according to claim 1, characterized in that, When arranging scanning stations, the overlap rate of scanning range between adjacent scanning stations is greater than or equal to the first threshold.

3. The method according to claim 1, characterized in that, The method further includes: A black and white target plate of a first preset size is used as the reference point, and the reference point is arranged at a preset position within a preset range of the scanning station, and the number of common reference points corresponding to two adjacent scanning stations is not less than the first preset number.

4. The method according to claim 1, characterized in that, The denoising process performed on the original point cloud data for each of the construction stages includes: Invalid points in the original point cloud data are filtered based on the construction coordinate system range. A laser intensity threshold is set to remove reflective anomalies. Then, a statistical outlier filtering algorithm is used to calculate the distance between each point and its second preset number of nearest neighbors. Points whose distance is greater than the sum of the mean distance and multiple standard deviations are deleted.

5. The method according to claim 1, characterized in that, The registration and stitching includes coarse registration and fine registration; The coarse registration includes: using the coordinates of the reference points, converting the point cloud data of each scanning station to the construction coordinate system through a quaternion transformation matrix, calculating the initial rotation matrix and translation vector, and controlling the error within a first preset error range; The fine registration includes: using an iterative nearest point algorithm to optimize the transformation parameters with a preset iterative precision, so that the error of the overlapping area of ​​the point cloud of adjacent scanning stations is controlled within a second preset error range.

6. The method according to claim 1, characterized in that, When establishing the finite element model, tetrahedral 10-node elements are used for mesh generation. The mesh size of the template, waffle tube, support beam, embedded parts, connectors and reinforcing bars of the waffle plate structure is less than or equal to the second preset size. The mesh size of the concrete slab of the waffle plate structure is less than or equal to the third preset size. The total number of elements is greater than or equal to the third preset number. The mesh quality factor is greater than or equal to the second threshold. The support system of the waffle plate structure is simulated as an elastic support with a preset spring stiffness. The bottom support is fixed along the horizontal and vertical directions.

7. The method according to claim 1, characterized in that, Simulated flatness is obtained by analyzing the simulated settlement data at each construction stage, including: The allowable displacement threshold is determined, and the settlement amount of subsequent construction stages is predicted by exponential smoothing method based on the simulated settlement data of each construction stage. The simulated flatness is determined according to the comparison result between the settlement amount and the allowable displacement threshold. The allowable displacement threshold is calculated based on the span of the waffle plate structure, and the allowable displacement threshold is less than or equal to the third threshold.

8. The method according to any one of claims 1 to 7, characterized in that, When optimizing and adjusting the support system, the following order should be followed: Add support points: In the area where the deviation exceeds the standard, arrange new support points in both the longitudinal and transverse directions at a spacing not greater than the fourth threshold. Replace with high-stiffness support: After completing the step of adding support points, retest. If the requirements are still not met, replace the spring stiffness of the original support with a high-stiffness support with a stiffness not lower than the preset stiffness. Adjusting the support elevation: The support elevation is adjusted using a level instrument, and the adjustment accuracy is controlled within the second preset error range.

9. A finite element laser scanning flatness measuring device, characterized in that, The device includes: The scanning module is used to divide the scanning area of ​​the waffle plate structure, arrange scanning stations in each scanning area, and scan the scanning stations at each construction stage of the waffle plate structure to obtain raw point cloud data. The processing module is used to denoise the original point cloud data of each construction stage, and then register and stitch the processed point cloud data using the coordinates of the reference points to generate a globally unified point cloud dataset for each construction stage. The mapping module is used to construct a three-dimensional model of the waffle plate structure, determine the parameters of the finite element model and establish the finite element model, and complete the data mapping and integration of each construction stage to map the globally unified point cloud dataset of the construction stage to the finite element model. The simulation module is used to simulate the load distribution and calculate the deformation of the finite element model at each construction stage to obtain simulated settlement data. The adjustment module is used to perform flatness analysis based on the simulated settlement data of each construction stage to obtain the simulated flatness, and to optimize and adjust the support system of the waffle plate structure based on the deviation between the simulated flatness and the target flatness.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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