A prefabricated component intelligent production and transportation method, system, device and medium suitable for building industrialized construction mode

Abstract

This invention discloses an intelligent production and transportation method, system, equipment, and medium for prefabricated components adapted to industrialized building construction, belonging to the field of industrialized building technology. The method includes: acquiring the geometric parameters, material parameters, production completion time, and hoisting time of the prefabricated components; generating control data by performing coordinate transformation on the geometric parameters through a data platform; driving the marking and formwork equipment based on the control data, and driving the material placement equipment based on the material parameters; collecting measured geometric data of the produced components through 3D scanning, extracting features, and comparing them with the geometric parameters to calculate deviation values; calculating the transportation departure time while meeting construction time constraints; processing the produced components to conform to the geometric parameters based on the deviation value when it exceeds a preset threshold; and adjusting the calculation method of the transportation departure time based on transportation location data. This invention achieves automatic driving of production equipment by design data and full-process data collaboration.

Description

Technical Field

[0001] This invention relates to the field of building industrialization technology, specifically to a method, system, equipment, and medium for the intelligent production and transportation of prefabricated components adapted to building industrialization construction methods. Background Technology

[0002] The production and transportation of precast concrete components are crucial links in the industrialization of construction. Existing precast component production methods suffer from the following technical problems: In terms of production operations, processes such as component marking and positioning, and mold arrangement are carried out manually. Workers need to manually measure and mark on the production mold according to the design drawings, and the positioning accuracy is affected by human factors.

[0003] In terms of quality control, existing technologies mainly use manual sampling for quality inspection, which cannot cover all components and poses a risk that components with potential quality defects may enter the construction site.

[0004] In terms of data management, there is a lack of automatic conversion mechanisms between BIM data in the design phase and process parameters in the production phase, requiring manual data conversion and entry. Delays in quality data collection during the production process affect the traceability and feedback of quality issues.

[0005] In terms of transportation scheduling, the transportation plan was not linked to production progress and construction hoisting plans. The dispatching of transport vehicles relied on manual coordination, making it impossible to dynamically adjust departure times based on production completion and construction needs, resulting in increased vehicle waiting times. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: how to achieve automated control and full-scale quality inspection of the precast component production process by establishing an automatic conversion mechanism between BIM data in the design stage and process parameters in the production stage, and at the same time achieve full-process collaborative management through the linkage of production data, quality data and transportation scheduling.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, comprising, Obtain the geometric parameters, material parameters, production completion time, and hoisting time of the precast components; The geometric parameters are transformed using a data platform to generate control data for driving production equipment. The control data is used to drive the marking equipment to perform contour marking and drive the mold placement equipment to place the mold; the material parameters are used to drive the concrete placement equipment to complete the concrete pouring, and the produced components and the pouring completion time are obtained. The measured geometric data of the manufactured components are acquired by three-dimensional scanning. The edge features and embedded features of the components are extracted from the measured geometric data. The edge features and embedded features are compared with the geometric parameters, the deviation value is calculated and a quality judgment result is generated. In response to the deviation value exceeding a preset threshold, the manufactured components are processed according to the deviation value to make them conform to the geometric parameters. Based on the quality assessment results, qualified components are selected, and the transportation departure time is calculated based on the hoisting time and the production completion time, while meeting the construction time constraints. The transportation equipment is controlled to transport the qualified components to the construction site according to the transportation departure time, and transportation location data is collected. The deviation value and transportation location data are fed back to the data platform, which then adjusts the calculation method for the transportation departure time based on the transportation location data.

[0009] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to the industrialized construction method of the present invention, wherein: the coordinate transformation of the geometric parameters through the data platform includes: Obtain the coordinate data of the geometric parameters in the design coordinate system; Establish the transformation matrix between the design coordinate system and the production equipment platform coordinate system; The coordinate data is converted into target coordinates in the pedestal coordinate system based on the transformation matrix.

[0010] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in this invention, wherein: the generation of control data for driving the production equipment includes: Path planning is performed on the coordinate points of the corresponding contour in the target coordinates to generate line drawing path data for driving the line drawing device; The pose calculation is performed on the coordinate points corresponding to the pre-embedded positions in the target coordinates to generate mold positioning data for driving the mold-making equipment.

[0011] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in this invention, wherein: the acquisition of measured geometric data of the produced components through three-dimensional scanning includes: The scanning device is controlled to scan the manufactured component by multi-angle scanning to obtain spatial point data on the surface of the manufactured component; The spatial point data is registered and stitched together; The measured geometric data is generated based on the stitched data.

[0012] The beneficial effects of this preferred technical solution are as follows: By controlling the scanning device to scan the manufactured components through multi-angle scanning, spatial point data of the component surface can be obtained from different perspectives, avoiding data blind spots caused by single-angle scanning. By registering and stitching the spatial point data, the local data collected from different angles are integrated into a complete three-dimensional geometric model, thus obtaining the full measured geometric data of the component surface.

[0013] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to the industrialized construction method of the present invention, wherein: the extraction of component edge features and embedded features from the measured geometric data includes: The boundary points in the measured geometric data are identified using an edge detection algorithm, and the edge features of the component are extracted. The location of the embedded parts in the measured geometric data is located by a region recognition algorithm, and the embedded features are extracted.

[0014] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in this invention, the calculated deviation value includes: The component edge features are matched and compared with the contour information in the geometric parameters to calculate the contour deviation; The embedded features are matched and compared with the embedded position information in the geometric parameters to calculate the embedded position deviation; The deviation value is obtained by combining the contour deviation and the pre-embedded position deviation.

[0015] The beneficial effects of this preferred technical solution are as follows: by matching and comparing the component edge features with the contour information to calculate the contour deviation, and by matching and comparing the embedded features with the embedded position information to calculate the embedded position deviation, a classified and quantitative assessment of different types of quality problems is achieved. The deviation value obtained by combining the contour deviation and the embedded position deviation provides a clear data basis for adjusting the parameters of the data platform.

[0016] As a preferred embodiment of the intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in this invention, the calculation of transportation departure time under the condition of meeting construction time constraints includes: The time window for the components to arrive at the construction site is determined based on the hoisting time. The latest departure time is calculated based on the production completion time and transportation time. Determine whether the latest departure time is within the time window; if so, determine the transportation departure time as the latest departure time.

[0017] This invention provides an intelligent production and transportation system for prefabricated components that is adapted to industrialized building construction methods.

[0018] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent production and transportation system for prefabricated components adapted to industrialized building construction methods, comprising: The data acquisition module is used to acquire the geometric parameters, material parameters, production completion time, and hoisting time of the prefabricated components. The data platform is used to perform coordinate transformation on the geometric parameters and generate control data for driving production equipment. The production execution module includes a marking device, a mold placement device, and a material placement device. The marking device marks the outline according to the control data, the mold placement device places the mold according to the control data, and the material placement device completes the concrete pouring according to the material parameters. The quality inspection module is used to collect measured geometric data of manufactured components through three-dimensional scanning, extract component edge features and embedded features from the measured geometric data, compare the component edge features and embedded features with the geometric parameters, calculate the deviation value and generate a quality judgment result; The transportation scheduling module is used to screen qualified components based on the quality judgment results and calculate the transportation departure time based on the hoisting time and production completion time, while meeting the construction time constraints. The transportation execution module is used to control the transportation equipment to transport the qualified components to the construction site according to the transportation departure time, and to collect transportation location data; The data platform is also used to receive the deviation value and transportation location data, and adjust the calculation method of the transportation departure time based on the transportation location data.

[0019] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction.

[0020] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction.

[0021] The beneficial effects of this invention are as follows: This invention generates control data to drive production equipment by transforming geometric parameters into coordinates, achieving automatic conversion from design data to equipment control parameters, eliminating manual measurement and marking, and ensuring positioning accuracy is no longer affected by human factors. By collecting measured geometric data of produced components through 3D scanning and comparing it with design data, quality inspection of each component is achieved, avoiding the risk of substandard components entering the construction site due to manual sampling. By processing unqualified components based on deviation values, the quality of each component is ensured to meet design requirements. By feeding back transportation location data to the data platform and dynamically adjusting the calculation method for transportation departure times, transportation plans are optimized based on actual road conditions. By calculating transportation departure times under the condition of meeting construction time constraints, precise matching of transportation and construction needs is achieved, reducing vehicle waiting time. The data platform enables real-time interaction of data from design, production, quality inspection, and transportation, forming a full-process data linkage mechanism. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a general flowchart of a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, provided as an embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, including: Step S1: Obtain the geometric parameters, material parameters, production completion time, and hoisting time of the prefabricated components; Step S2: Perform coordinate transformation on the geometric parameters through the data platform to generate control data for driving the production equipment; Step S3: Drive the marking device to perform contour marking and drive the mold placement device to place the mold according to the control data; drive the concrete placement device to complete the concrete pouring according to the material parameters, and obtain the produced components and the pouring completion time; Step S4: Collect measured geometric data of the manufactured component by three-dimensional scanning, extract the component edge features and pre-embedded features from the measured geometric data, compare the edge features and pre-embedded features with the geometric parameters, calculate the deviation value and generate a quality judgment result. In response to the deviation value exceeding a preset threshold, process the manufactured component according to the deviation value to make it conform to the geometric parameters. Step S5: Select qualified components based on the quality judgment results, and calculate the transportation departure time based on the hoisting time and the production completion time, while meeting the construction time constraints. Step S6: Control the transportation equipment to transport the qualified components to the construction site according to the transportation departure time, and collect transportation location data; Step S7: Feed back the deviation value and transportation location data to the data platform, and the data platform adjusts the calculation method of the transportation departure time according to the transportation location data.

[0026] The following technical problems exist in the current production of precast components: In terms of production operations, BIM data from the design phase must be manually converted into production process parameters by workers. Workers measure the outline dimensions and embedded positions of components according to the design drawings and mark them on the production mold. Positioning accuracy is affected by human factors, easily leading to measurement errors and misinterpretation of drawings. Regarding quality control, quality inspection relies on manual sampling, which can only inspect a portion of components, failing to cover all components and posing a risk of potentially defective components entering the construction site. In terms of transportation scheduling, transportation plans are not linked to production progress and construction needs. When production is delayed or construction plans change, transportation departure times cannot be adjusted promptly, increasing vehicle waiting times. Regarding data management, quality data during production and location data during transportation cannot be promptly fed back to the production and scheduling stages, lacking a data-driven continuous optimization mechanism.

[0027] This embodiment obtains the geometric parameters, material parameters, and hoisting time of the components in step S1, providing basic data for subsequent production and transportation. Step S2 uses a data platform to perform coordinate transformation on the geometric parameters to generate control data, achieving automatic conversion from design data to equipment control parameters and solving the accuracy problem of manual conversion. Step S3 drives the production equipment to perform marking, mold laying, and material placement operations based on the control data, achieving automated control of the production process. Step S4 collects the measured geometric data of the produced components through 3D scanning and compares it with the design data, achieving quality inspection of each component and solving the problem of insufficient coverage of manual sampling inspection. Step S5 calculates the transportation departure time under the constraint of construction time, achieving precise matching between transportation and construction needs. Step S6 collects transportation location data to provide a basis for dynamic adjustment. Step S7 feeds back the deviation value and transportation location data to the data platform. The deviation value data is used for quality traceability and production process analysis. The transportation location data is used to identify delayed road sections and dynamically adjust the calculation method of transportation departure time, forming a closed-loop mechanism for transportation optimization and solving the problem that transportation plans cannot be dynamically adjusted according to actual road conditions.

[0028] Example 2, an embodiment of the present invention, provides a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, based on the previous embodiment, including: In this embodiment, in step S2, the coordinate transformation is performed by: reading the global coordinate point set of the geometric parameters in the BIM model; calculating the rotation matrix and translation vector based on the positional and angular relationships between the origin of the production platform and the origin of the BIM model; and combining the rotation matrix and translation vector into a 4×4 homogeneous transformation matrix. The target coordinates in the platform coordinate system are obtained by performing matrix multiplication on the global coordinate point set using this homogeneous transformation matrix. If the global coordinate point set of the component's outer contour in the BIM is... ,in The three-dimensional coordinates of the coordinate point in the design coordinate system are calculated. Obtain the set of coordinate points in the platform coordinate system pedestal, among which, This is a 4×4 homogeneous transformation matrix. The transformation operation is performed on all coordinate points in the point set in batches to obtain the transformed target coordinates.

[0029] In an optional implementation, in step S2, the coordinate transformation can be performed by: reading the global coordinate point set of the geometric parameters in the BIM model, and measuring the rotation angle and translation of the platform coordinate system relative to the design coordinate system. A coordinate transformation matrix is ​​constructed based on the rotation angle and translation. This transformation matrix is ​​then applied to each coordinate point in the global coordinate point set to perform coordinate transformation, obtaining the target coordinates in the platform coordinate system. Finally, transformation operations are performed sequentially on all coordinate points in the point set to complete the coordinate system transformation.

[0030] In another optional implementation, step S2 can further involve: selecting at least four non-coplanar control points on the pedestal, measuring the coordinates of these control points in the pedestal coordinate system, and simultaneously reading the coordinates of the corresponding control points in the design coordinate system from the BIM model. Based on the corresponding coordinates of the control points in the two coordinate systems, the parameters of the coordinate transformation matrix are calculated using the least squares method. The calculated transformation matrix is ​​then used to transform the global coordinate point set in the geometric parameters to obtain the target coordinates in the pedestal coordinate system.

[0031] Step S2: The geometric parameters are transformed using a data platform to generate control data for driving the production equipment, including the following steps A1-A5: A1: Obtain the coordinate data of the geometric parameters in the design coordinate system; A2: Establish the transformation matrix between the design coordinate system and the production equipment platform coordinate system; A3: Based on the transformation matrix, convert the coordinate data into target coordinates in the pedestal coordinate system.

[0032] A4: Perform path planning on the coordinate points of the corresponding contour in the target coordinates to generate line drawing path data for driving the line drawing device; A5: Perform pose calculation on the coordinate points corresponding to the pre-embedded positions in the target coordinates to generate mold positioning data for driving the mold-laying equipment.

[0033] In this embodiment, step A2 involves: calculating a rotation matrix and a translation vector based on the positional and angular relationship between the origin of the production mold and the origin of the BIM model, and then combining the rotation matrix and translation vector into a 4×4 homogeneous transformation matrix. This homogeneous transformation matrix contains the rotation matrix... Translation vector A 4×4 matrix, where the rotation matrix is... For a 3×3 matrix, the translation vector is... It is a 3×1 vector. The coordinate transformation relationship between the design coordinate system and the platform coordinate system is realized through this homogeneous transformation matrix.

[0034] In an optional implementation, in step A2, the transformation matrix can be constructed by: measuring the rotation angle and translation of the platform coordinate system relative to the design coordinate system; constructing a rotation matrix based on the measured rotation angle; constructing a translation vector based on the measured translation; and combining the rotation matrix and translation vector into a coordinate transformation matrix. The rotation angle and translation are calculated by setting a measurement reference point on the platform, measuring the coordinate difference between the reference point and the two coordinate systems.

[0035] In another optional implementation, in step A2, the transformation matrix can also be calculated by: selecting at least four non-coplanar control points on the pedestal, measuring the coordinates of the control points in the pedestal coordinate system, reading the coordinates of the corresponding control points in the design coordinate system from the BIM model, and calculating the parameters of the transformation matrix using the least squares method based on the corresponding coordinate relationship of the control points in the two coordinate systems. The transformation matrix is ​​automatically solved through the coordinate correspondence of the control points, eliminating the need for pre-measuring rotation angles and translations.

[0036] In this embodiment, step S4 involves the following three-dimensional scanning method: A 3D camera is used to scan the manufactured component to obtain point cloud data of the component's surface. The 3D camera is controlled to move along the length or width of the component, scanning and acquiring point cloud data of different areas of the component at different positions. The point cloud data acquired from multiple scans are stitched together to form a point cloud dataset covering the entire surface of the component, which serves as the measured geometric data.

[0037] In an optional implementation, in step S4, the three-dimensional scanning method can be achieved by: using a laser scanner to perform a three-dimensional scan of the manufactured component. The laser scanner emits a laser beam to scan the surface of the component point by point, receives the reflected laser signals, calculates the distance from the scanned point to the scanner based on the laser flight time or phase difference, and obtains the three-dimensional coordinate data of each point on the component surface. By controlling the scanning angle and position of the laser scanner, a comprehensive scan of the component surface can be achieved.

[0038] In another optional implementation, step S4 can also involve: scanning the manufactured component using a structured light scanning device, whereby the structured light scanning device projects an coded grating or stripe pattern onto the component surface, and a camera captures images of the grating or stripe deformation on the component surface. The three-dimensional coordinates of each point on the component surface are then calculated based on the deformation of the grating or stripes. By projecting different grating patterns and capturing multiple images, dense three-dimensional point cloud data of the component surface is obtained.

[0039] Further, in step S4: The measured geometric data of the manufactured component is acquired through 3D scanning. Edge features and embedded features of the component are extracted from the measured geometric data. The edge features and embedded features are compared with the geometric parameters to calculate the deviation value and generate a quality judgment result. In response to the deviation value exceeding a preset threshold, the manufactured component is processed according to the deviation value to conform to the geometric parameters, including the following steps B1-B8: B1: The scanning device is controlled to scan the manufactured component by means of multi-angle scanning to obtain spatial point data on the surface of the manufactured component; B2: Register and stitch the spatial point data; B3: Generate the measured geometric data based on the spliced ​​data.

[0040] B4: Identify the boundary points in the measured geometric data using an edge detection algorithm, and extract the edge features of the component; B5: Locate the embedded parts in the measured geometric data using a region recognition algorithm, and extract the embedded features.

[0041] B6: Match and compare the edge features of the component with the contour information in the geometric parameters, and calculate the contour deviation; B7: Match and compare the pre-embedded features with the pre-embedded position information in the geometric parameters, and calculate the pre-embedded position deviation; B8: The deviation value is obtained by combining the contour deviation and the pre-embedded position deviation.

[0042] In this embodiment, step B2 involves registration and stitching: registering and aligning spatial point data acquired from different scanning positions to unify multiple sets of spatial point data into the same coordinate system. By identifying overlapping areas in different sets of point cloud data, calculating the coordinate transformation relationship between different sets of point cloud data, performing coordinate transformation on each set of point cloud data, and merging all point cloud data into a complete point cloud dataset. The point cloud data from each scanning position are then stitched sequentially along the length or width direction of the component to form point cloud data covering the entire surface of the component.

[0043] In an optional implementation, in step B2, the registration and stitching can be performed by: using an iterative nearest-point algorithm to register and stitch the spatial point data. One set of point cloud data is selected as a reference point cloud, and the other sets of point cloud data are registered sequentially. For the point cloud to be registered, the nearest corresponding point is searched in the reference point cloud, and the rotation matrix and translation vector are calculated based on the corresponding point pairs to perform coordinate transformation on the point cloud to be registered. The process of searching for corresponding points and calculating transformation parameters is iteratively executed until the distance between point clouds converges, completing the point cloud registration. All registered point cloud data are then merged.

[0044] In another optional implementation, step B2, registration and stitching can also be achieved by: extracting feature points from each set of spatial point data, including points with high curvature or corner points in the point cloud data; matching the feature points extracted from different sets of point cloud data to identify corresponding feature point pairs in different sets of point clouds; calculating the coordinate transformation relationship between different sets of point clouds based on the coordinate relationship of the corresponding feature point pairs; and merging the point cloud data after performing coordinate transformation on each set. Rapid registration and stitching of point clouds is achieved through feature point matching.

[0045] In step S4, the process of processing the manufactured components based on the deviation value includes: in response to a non-conforming quality judgment result, the data platform generates a deviation processing instruction. The deviation processing instruction includes the deviation type, deviation location, and deviation value. If the deviation value is within the adjustable range, the deviation processing instruction guides the processing of the component to ensure that the actual geometric dimensions meet the geometric parameter requirements. If the deviation value exceeds the adjustable range, the component is marked as a non-conforming product. By processing non-conforming components specifically based on their deviation values, the quality of each component meets the design requirements, achieving full-volume quality inspection and quality assurance.

[0046] Furthermore, in step S5: selecting qualified components based on the quality judgment results, and calculating the transportation departure time based on the hoisting time and the production completion time, while meeting the construction time constraints, the following steps C1-C3 are included: C1: Determine the time window for the components to arrive at the construction site based on the hoisting time; C2: Calculate the latest departure time based on the production completion time and transportation time; C3: Determine whether the latest departure time is within the time window; if so, determine the transportation departure time as the latest departure time.

[0047] Example 3, an embodiment of the present invention, provides a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, based on the previous embodiment, including: In step S3, during the process of driving the scribing device to perform contour marking and driving the mold-laying device to place the mold according to the control data, the production processes are executed in a coordinated manner in the following order: Based on the scribing path data generated in step A4, the scribing equipment marks the outer contour line and embedded position marks of the component on the surface of the production mold. After completing the contour marking, the scribing equipment sends a scribing completion signal to the data platform.

[0048] After receiving the marking completion signal, the data platform initiates the mold placement process. Based on the mold positioning data generated in step A5, the mold placement equipment first places the side and end molds of the component, aligning them with the outline of the marking marks for mold positioning. After the side and end molds are placed, the mold placement equipment places the embedded molds according to the pre-embedded position marks. Once all molds have been placed, the mold placement equipment sends a mold placement completion signal to the data platform.

[0049] After receiving the mold-laying completion signal, the data platform initiates the oiling process. The oiling equipment sprays release agent onto the mold surface and sends an oiling completion signal upon completion.

[0050] After receiving the oiling completion signal, the data platform initiates the concrete placement process. The concrete placement equipment determines the concrete mix proportions and placement quantity based on the material parameters, and pours the concrete into the mold according to the predetermined placement path. After completing the concrete pouring, the equipment records the pouring completion time and sends a production completion signal to the data platform.

[0051] In step A4, the process of generating line path data by planning the path for the coordinate points of the corresponding contour in the target coordinates includes: Extract the corner point coordinate sequence of the component's outer contour from the target coordinates obtained in step A3. For a rectangular wall panel component, the outer contour includes four corner points, and the coordinate sequence is denoted as follows: ,in, Indicates the first The coordinates of each corner point in the platform coordinate system The corner points are numbered, with values ​​ranging from 1 to... These are the X and Y coordinate components of the corner point, respectively.

[0052] The line path is generated according to the connection order of the corner points. The starting point of the line path is set to the first corner point. The line marking equipment starts from the starting point. Move along a straight line to the second corner point Generate sequentially from to from to from return The straight line segments form a closed rectangular outline path.

[0053] For components containing circular arc boundaries, the starting coordinates of the arc are extracted from the target coordinates. End point coordinates Center coordinates and radius ,in, Indicates the coordinates of the starting point of the arc. Indicates the coordinates of the endpoint of the arc. Represents the coordinates of the center of the arc. Indicates the radius of the arc. The drawn path uses circular interpolation, starting from the starting point. Begin with the center of the circle Center, radius Perform circular motion to the endpoint .

[0054] The line marking path data includes a path type identifier, a key point coordinate sequence, and motion parameters. The path type identifier distinguishes between straight paths and circular paths. The key point coordinate sequence consists of the coordinates of each corner point on the path. The motion parameters include the line marking speed. The line marking path data is converted into G-code format and sent to the controller of the line marking device.

[0055] In step A5, the process of generating mold positioning data by calculating the pose of the coordinate points corresponding to the pre-embedded positions in the target coordinate system includes: Extract the position coordinates of the embedded parts from the target coordinates obtained in step A3. ,in, This indicates the coordinates of the center of the embedded part in the platform coordinate system. These are the coordinates. Components. The type and orientation information of the embedded part are read from the geometric parameters.

[0056] For circular pipeline embedded parts, the orientation of the embedded part is determined by the axial direction of the pipeline. The pipeline axis is determined by the direction vector. It means that, among them, It is a unit direction vector. The vector is respectively in The components along the axis. The pose of the pipeline clamping mold in the molding equipment includes position and orientation components. The position component is the coordinate of the center of the embedded part. Attitude components are expressed through axial vectors. Convert to rotation angle representation.

[0057] Given that the pipeline is placed horizontally and its axial vector lies in the XY plane, calculate the angle between the axial vector and the X-axis. Determine the rotation angle about the Z-axis, where, This represents the rotation angle about the Z-axis, with zero rotation angles about the X and Y axes. In response to the pipeline being placed vertically, the axial vector is along the Z-axis, and the rotation angle about the X-axis is 90 degrees.

[0058] For rectangular connector embedded parts, the orientation of the embedded parts is determined by the normal vector of the mounting surface and the in-plane direction. The normal vector of the mounting surface determines the rotation angles about the X and Y axes, and the in-plane direction angle determines the rotation angle about the Z axis.

[0059] The mold positioning data includes position coordinates. and posture angle ,in, Let be the rotation angle about the Y-axis. This represents the rotation angle around the Z-axis. The mold positioning data is converted into six-degree-of-freedom pose commands for the robotic arm and sent to the controller of the mold-laying equipment.

[0060] In step B4, the process of identifying boundary points in the measured geometric data using an edge detection algorithm includes: In the point cloud of the measured geometric data generated in step B3, for each point Calculate its normal vector ,in, Represents the first point in the point cloud One point, subscript Number the points. Point The normal vector. The normal vector is calculated by taking a point... The neighborhood point set is obtained by plane fitting. The neighborhood point set is a set of points. Surrounding radius Points within the range, where, This represents the neighborhood radius. The best-fit plane for fitting the neighborhood point set using the least squares method is the normal vector of the fitted plane. normal vector .

[0061] It should be noted that sampling points are randomly selected from the point cloud of the measured geometric data generated in step B3, and the distance between each sampling point and its nearest neighbor is calculated. The average of these distances is used as the average spacing of the point cloud. The neighborhood radius is set to a number that is several times the average spacing of the point cloud, so that the neighborhood contains a sufficient number of points for plane fitting.

[0062] Calculate the angle between the normal vectors of adjacent points in a point cloud. For a point... and its nearest neighbor Calculate the angle between the normal vectors ,in, Indicates the first The angle between a point and the normal vector of its neighboring points. Responding to the angle... Greater than the threshold Judgment point Let be the candidate boundary points, where This represents the included angle threshold for edge detection.

[0063] It should be noted that the edge type information of the component is read from the geometric parameters, and the edge angle threshold corresponding to that edge type is queried from the edge type-threshold correspondence table. Optionally, by manually marking the actual edge positions of some components, the distribution characteristics of the angle between the normal vectors of adjacent points at the edge are statistically analyzed, and the angle value that can distinguish edge points from non-edge points is selected as the threshold.

[0064] Because of the reinforcing mesh on the component surface, the point cloud contains both reinforcing bar points and concrete surface points. The reinforcing bar points are linearly distributed along the reinforcing bar axis. Candidate boundary points are screened, and points conforming to the linear distribution characteristics are removed. A straight line is fitted to the sequence of candidate boundary points; if the fitting error of the fitted line is less than a threshold, the sequence is determined to be a reinforcing bar point and removed.

[0065] It should be noted that the nominal diameter of the reinforcing bar is read from the material parameters. The fitting error threshold is determined based on the reinforcing bar radius. After performing a straight line fitting on the candidate boundary point sequence, the perpendicular distance from each point in the sequence to the fitted line is calculated, and the root mean square error of the perpendicular distance is taken as the fitting error. If the fitting error is less than the reinforcing bar radius, the sequence is determined to be a reinforcing bar point and is discarded.

[0066] The selected boundary candidate points are clustered according to spatial proximity. Those points with a distance less than the cluster radius are grouped together. The points are aggregated into one class, where, This represents the cluster radius. Each cluster of points forms a boundary line segment. The coordinates of the two endpoints of each boundary line segment are extracted and denoted as the component edge features.

[0067] It should be noted that the cluster radius is determined based on the average spacing of the point cloud, and the cluster radius is made larger than the average spacing of the point cloud to connect adjacent edge points.

[0068] In this embodiment of the application, when identifying boundary points, the corner coordinates of the component design profile are read from the geometric parameters, and the design corner coordinates are converted into predicted corner positions in the pedestal coordinate system according to the transformation matrix established in step A2. ,in, Indicates the first The predicted positions of each corner point. Establish a radius of centered on The spherical search area, in which, The search radius is determined based on the component dimensions. A subset of the point cloud is extracted within the spherical search area, and edge detection is performed on this subset. By using design data to predict the approximate location of the edges, the edge detection range is limited to the vicinity of the predicted location, reducing the computational load of the global search.

[0069] In step B5, the process of locating the embedded part in the measured geometric data using a region identification algorithm includes: The type information of the embedded part is read from the geometric parameters. The type information includes the geometric shape of the embedded part. The geometric shape includes circle, rectangle and annulus.

[0070] In the point cloud of the measured geometric data generated in step B3, for each point Calculate the distance from it to the fitted plane formed by its neighboring point set, where, Represents the first point in the point cloud One point, subscript This is a point number; the distance reflects the local curvature. The response is when the distance exceeds a curvature threshold. The point is determined to be located in a region of high curvature, where... This represents the curvature threshold. Points with larger curvatures are marked as the feature point set. ,in, The set of feature points representing the area of ​​the embedded part.

[0071] It should be noted that the depth information or protrusion height of the embedded part is read from the geometric parameters. The curvature threshold is determined based on the depth or height of the embedded part. For embedded parts whose depth or height information is not explicitly marked, the geometric dimensions of the embedded part are read from the geometric parameters, and the curvature threshold is determined based on the geometric dimensions.

[0072] Based on the geometry of the embedded parts, the feature point set Geometric fitting is performed. For a circular pipe hole, the feature point set is distributed along the boundary of the circular depression. A circular fitting is performed on the feature point set using least-squares circle fitting. The center coordinates of the fitted circle are obtained. and radius ,in, Represents the coordinates of the center of the fitted circle. These are the X and Y components of the center coordinates, respectively. This represents the radius of the fitted circle.

[0073] For the rectangular connector embedded part, the feature point set is distributed on the boundary of the rectangular recess. Line segment detection is performed on the feature point set, and four mutually perpendicular line segments are extracted as the four sides of the rectangle. The coordinates of the four corner points and the center coordinates of the rectangle are calculated.

[0074] The confirmed center coordinates and geometric parameters of the embedded part are extracted as embedded features. In an optional embodiment, when locating the embedded part, the design position coordinates of the embedded part are read from the geometric parameters, and the design position coordinates are converted into the predicted position in the pedestal coordinate system according to the transformation matrix established in step A2. ,in, This indicates the predicted location of the embedded part. (Based on the predicted location) Establish a radius of centered on The spherical search area, in which, This represents the search radius for the embedded part, determined based on its size and prediction accuracy. A subset of the point cloud is extracted within the search area, and region identification is performed on this subset. By using design data to predict the approximate location of the embedded part, the region identification range is limited to the vicinity of the predicted location, reducing the computational load of the global search.

[0075] In step B6, the process of matching and comparing the component edge features with the contour information in the geometric parameters to calculate the contour deviation includes: Read the corner coordinate sequence of the component design profile from the geometric parameters, and convert the design corner coordinates into reference corner coordinates in the pedestal coordinate system according to the transformation matrix established in step A2. ,in, Indicates the first The base coordinates of each corner point, subscript Standard for representation.

[0076] Obtain the measured corner coordinate sequence from the component edge features extracted in step B4. ,in, Indicates the first The measured coordinates of the corner points are given by the subscript "mes".

[0077] Perform point-to-point matching between the reference corner point and the measured corner point. For the first... Reference corner points Search for the nearest point in the measured corner point sequence as the corresponding point. Calculate the Euclidean distance between corresponding pairs of points: in, Indicates the first Contour deviation at each corner point These are the measured corner coordinates. The X and Y components, , These are the coordinates of the reference corner point. of Quantity.

[0078] The deviation sequence is obtained by calculating the profile deviation at each of the four corner points. The maximum value in the deviation sequence is taken as the component's profile deviation. : in, This indicates the deviation in the component's profile.

[0079] In step B7, the process of matching and comparing the pre-embedded features with the pre-embedded position information in the geometric parameters to calculate the pre-embedded position deviation includes: Read the design position coordinates of the embedded part from the geometric parameters, and convert the design position coordinates into reference position coordinates in the pedestal coordinate system according to the transformation matrix established in step A2. ,in, Indicates the reference position coordinates of the embedded parts. These are the coordinates. Quantity.

[0080] Obtain the measured center coordinates of the embedded parts from the embedded features extracted in step B5. ,in, Indicates the measured center coordinates of the embedded part. These are the coordinates. Quantity.

[0081] Calculate the spatial distance between the reference position and the measured position: in, This indicates the deviation of the embedded position of the embedded part. The embedded position deviation is calculated for all embedded parts in the component, and the maximum value is taken as the embedded position deviation of the component.

[0082] In step B8, after obtaining the deviation value by combining the contour deviation and the pre-embedded position deviation, a quality judgment result is generated according to the quality judgment standard: The component type identifier is read from the geometric parameters. The component type identifier includes exterior wall panels, interior wall panels, and floor slabs.

[0083] The quality assessment thresholds for different component types are retrieved from the quality standard database. These thresholds are determined based on the functional requirements and construction standards of the component type. For exterior wall panels, because they are located on the building facade, contour deviations directly affect the appearance's flatness, resulting in relatively strict quality assessment thresholds. For interior wall panels, because they are located inside the building and are subsequently covered by a decorative layer, the quality assessment thresholds are relatively lenient. For floor slabs, due to the high requirements for floor levelness, the quality assessment thresholds are relatively strict.

[0084] Obtain the contour deviation threshold for this component type from the quality standard database. and the threshold for deviation of the pre-embedded position ,in, This indicates the profile deviation threshold for this component type. This indicates the threshold for the pre-embedded position deviation of this component type.

[0085] After obtaining the appropriate threshold based on the component type, quality assessment is performed. This is in response to contour deviation. Less than or equal to the contour deviation threshold And the pre-embedded position deviation Less than or equal to the pre-embedded position deviation threshold The quality assessment result was qualified.

[0086] Response to profile deviation Greater than the contour deviation threshold Or deviation of the pre-embedded position Greater than the pre-embedded position deviation threshold The quality assessment result is "unqualified". The specific values ​​and locations of the exceeding standards are recorded, and the information on the unqualified components is fed back to the data platform for parameter adjustment.

[0087] In response to a quality assessment result indicating non-compliance, the data platform generates a deviation handling instruction.

[0088] Deviation handling instructions include the deviation type, deviation location, and deviation value. For profile deviations, the deviation handling instructions indicate the corner point number and deviation vector. The corner point deviation vector is calculated as follows: ,in Indicates the first The deviation vector at each corner point. For pre-embedded position deviations, the deviation processing instruction marks the pre-embedded part number and position offset vector. The position offset vector is calculated as follows: ,in This represents the position offset vector of the embedded part.

[0089] The adjustable range threshold for this component type is read from the quality standard database. The adjustable range threshold is determined based on the component type and deviation type. For profile deviations, the profile adjustable threshold is read from the quality standard database. ,in Indicates the upper limit of the adjustable range of the profile. Responds to profile deviations. Smaller than the contour trimmable threshold The contour deviation is determined to be within the adjustable range. For pre-embedded position deviation, the adjustable threshold for the pre-embedded position is read from the quality standard database. ,in This indicates the upper limit of the adjustable range for the pre-embedded location. It responds to deviations in the pre-embedded location. The threshold for adjustment can be set if the location is smaller than the pre-embedded position. The deviation of the pre-embedded position is determined to be within the range that can be corrected.

[0090] It should be noted that the adjustable range threshold is determined based on the functional requirements of the component type and the feasibility of subsequent adjustments. The contour deviation threshold for this component type is retrieved from the quality standard database. and the threshold for deviation of the pre-embedded position The contour adjustment threshold is determined based on the contour deviation threshold, and the pre-embedded position adjustment threshold is determined based on the pre-embedded position deviation threshold.

[0091] In response to the deviation value being within the adjustable range, a deviation processing instruction is sent to the production management terminal to guide the processing of the component. The processing method is determined based on the deviation type and location. For contour deviations, the processing method is based on the deviation vector. Determine the location and direction of the corner points requiring adjustment. For pre-embedded position deviations, determine the location offset vector. Determine the direction and distance that need to be adjusted for the embedded parts.

[0092] After processing, the component undergoes a re-inspection. The measured geometric data of the processed component is re-acquired using 3D scanning, and the quality is re-evaluated following steps B1 to B8. If the re-inspection quality evaluation result is satisfactory, the quality evaluation result for the component is updated.

[0093] When a deviation value exceeds the correctable range, the component is marked as non-conforming and recorded in the quality database for quality traceability. Non-conforming item information includes component number, deviation type, deviation value, and inspection time.

[0094] In step C1, the process of determining the time window for the component to arrive at the construction site based on the hoisting time includes: Read the planned hoisting time of the components from the construction plan database. ,in, This indicates the start time of the hoisting operation of the component at the construction site.

[0095] According to the construction site's operational procedures, components need to be unloaded and inspected upon arrival at the site. The standard time required for unloading and inspection is as follows. Set to 1 hour, of which, This indicates the time required for unloading and acceptance. The latest arrival time for components is set one hour before the hoisting time. in, Indicates the latest arrival time of the component.

[0096] Due to the limited storage capacity at the construction site, components should not arrive too early. The earliest arrival time for components is 24 hours prior to the hoisting start time. in, Indicates the earliest arrival time of the component.

[0097] The time window for components to arrive at the construction site is defined as follows: This time window serves as a constraint for transportation scheduling, ensuring that vehicles arrive within this time window when calculating departure times in steps C2 and C3.

[0098] In step C2, the process of calculating the latest departure time based on the production completion time and transportation time includes: Obtain the component's production completion time from step S1. .

[0099] Based on the distance from the factory to the construction site and the average speed of transport vehicles Calculate transportation time ,in, Indicates the total length of the transportation route. This indicates the average speed of the vehicle. This indicates the transportation time. Transportation time includes travel time, loading time, and allowance.

[0100] Calculate the latest departure time : in, This indicates the latest departure time.

[0101] In step C3, the process of determining whether the latest departure time is within the time window includes: comparing the latest departure times. With production completion time The relationship. In response to The latest departure time for transportation is determined. .

[0102] In response to The information that the component would arrive late was relayed to the construction site.

[0103] In step S7, when the data platform adjusts the coordinate transformation parameters based on the deviation value, it first performs a systematic determination of the deviation data: In an optional implementation, in step S7, the data platform can systematically analyze the deviation data to optimize subsequent production processes.

[0104] The data platform reads the most recently produced continuous data from the quality database. Deviation data for several similar components at the same measurement location, among which... Indicates the number of sampled components. For the component's... Read the continuous corner points. The contour deviation data sequence of each component at this location ,in Indicates the first in the sequence The profile deviation of a component at this location, subscript Number the components in the sequence, with values ​​from 1 to... .

[0105] Calculate the average value of the biased data series ： in, This represents the average of all deviation values ​​in the sequence.

[0106] Calculate the standard deviation of the biased data series : in, This represents the standard deviation of the biased sequence.

[0107] Determine the type of deviation based on statistical characteristics. (Response to...) and This was determined to be a systematic bias, among which, Indicates the average threshold. This represents the standard deviation threshold.

[0108] It should be noted that the profile deviation threshold for this component type is read from the quality standard database. Average threshold Based on contour deviation threshold Determined. Standard deviation threshold. Based on the average threshold Determined. Optionally, the distribution range of the mean and standard deviation can be statistically analyzed by examining the deviation sequence characteristics of known systematic deviation cases in historical production data.

[0109] In response to the determination of a systematic deviation, the parameter adjustment process is initiated. (Regarding the first...) The systematic deviation of each corner point is compensated by adding the corresponding coordinate data of the corner point when establishing the transformation matrix in step A2.

[0110] Read the first from the geometric parameters The coordinates of each corner point in the design coordinate system. Before performing the coordinate transformation, a compensation vector is added to these coordinates. ,in, This represents the compensation vector. The compensation vector is calculated based on the average value. The direction of the compensation is increased in the opposite direction. .

[0111] Calculate the first The offset direction vector of the measured position of each corner point relative to the reference position ,in, This represents the offset direction vector. Normalizing the offset direction vector yields the unit direction vector. ,in, This represents the unit offset direction vector. The compensation vector is: After obtaining the coordinate data in step A1, the first... The coordinates of each corner point are increased with a compensation vector, and subsequent coordinate transformations are based on the compensated coordinates, thus achieving pre-compensation for the deviation.

[0112] If the criteria for systematic deviation are not met, the deviation is classified as random. Random deviations are only recorded in the quality database for quality monitoring and do not trigger parameter adjustments.

[0113] It should be noted that this systematic deviation analysis and parameter optimization is an optional implementation method used to continuously optimize process parameters during production. This optimization does not change the geometric parameters themselves in the design stage, but rather compensates for systematic deviations in advance in the production control stage by adjusting the compensation parameters in the coordinate transformation process, thereby reducing the probability of subsequent components producing the same deviations.

[0114] In step S7, when the data platform adjusts the calculation method of the transportation departure time based on the transportation location data, a road segment delay identification mechanism is established: The data platform receives the transportation location data fed back from step S6. The transportation location data includes vehicle GPS coordinates and timestamps. ,in, This indicates the time of GPS data collection. Based on the vehicle's transport route, the entire transport route is divided into... Several road sections, among which... This indicates the total number of road segments. Each road segment... Defined by the starting position and the ending position, where, Indicates the first Each road segment, subscript This is the road segment number.

[0115] Determine the time when the vehicle passes through the road segment. Enter the road segment in response to the vehicle's position. Record the entry time near the starting point. ,in, Indicates that the vehicle has entered the road section. The moment. Response to the vehicle's location arriving at the road segment. Record the departure time near the finish line location. ,in, Indicates that the vehicle has left the road section. At that moment.

[0116] Calculate the actual travel time of the road segment ,in, Indicates road segment The actual driving time.

[0117] Read road segments from the route database length and design driving speed ,in, Indicates the length of the road segment. This indicates the design speed for the road segment. The theoretical travel time for the road segment is calculated as follows: in, Indicates road segment The theoretical travel time.

[0118] Calculate the delay time of the road segment: in, Indicates road segment The delay time.

[0119] In response to Determine the road section There are significant delays, among which, This indicates the delay threshold. The number of the delayed road segment is used. Delay time and detection time Recorded in the transportation delay database.

[0120] It should be noted that the time window width is calculated from the time window determined in step C1. The delay determination threshold is determined based on the time window width. Optionally, the road type attribute of the road segment is read from the path database, and the corresponding delay determination threshold for that road type is queried from the road type-delay threshold correspondence table based on the road type attribute.

[0121] For components awaiting subsequent transport, the transport delay database is queried when planning the transport route. This is in response to planned routes containing delayed segments. In step C2, the delay time of the delayed route will be calculated. This is added to the total transportation time. When calculating the latest departure time in step C3, the departure time is advanced accordingly to compensate for the expected delay.

[0122] In the above optional implementation, after adjusting the coordinate transformation compensation parameters based on systematic deviation analysis, the impact on subsequent production and transportation is simultaneously assessed: After the parameter adjustment process is completed, the data platform records the parameter adjustment time. ,in, This indicates the moment when the parameter adjustment is complete. Assess the impact of the parameter adjustment on the production equipment. Adjusting the coordinate transformation parameters requires updating the production equipment's control program, which necessitates pausing the production line, leading to production delays.

[0123] Standard time for reading equipment control program updates from the production equipment database ,in, This indicates the time required for equipment upgrades. The estimated start time for production of components awaiting subsequent production is postponed. .

[0124] Retrieve the list of components awaiting production from the production planning database. For each component in the list, update its production completion time. ,in, This indicates the updated production completion time.

[0125] Delayed production completion times impact transportation schedules. For each component, the latest departure time is recalculated in step C2. This is based on the updated production completion time. and the latest arrival time of the time window Calculate the new latest departure time ,in, This indicates the latest departure time after the update.

[0126] In response to The transportation plan can still meet the time window constraints. In response to The system sends an early warning to the construction management system, notifying the component that its arrival will be delayed. The warning includes the component number and the originally scheduled hoisting time. Estimated actual arrival time and delay duration ,in, Indicates the estimated actual arrival time. Indicates the delay duration.

[0127] Example 4 is an embodiment of the present invention, which provides an intelligent production and transportation system for prefabricated components adapted to industrialized building construction methods, comprising: The data acquisition module is used to acquire the geometric parameters, material parameters, production completion time, and hoisting time of the prefabricated components. The data platform is used to perform coordinate transformation on the geometric parameters and generate control data for driving production equipment. The production execution module includes a marking device, a mold placement device, and a material placement device. The marking device marks the outline according to the control data, the mold placement device places the mold according to the control data, and the material placement device completes the concrete pouring according to the material parameters. The quality inspection module is used to collect measured geometric data of manufactured components through three-dimensional scanning, extract component edge features and embedded features from the measured geometric data, compare the component edge features and embedded features with the geometric parameters, calculate the deviation value and generate a quality judgment result; The transportation scheduling module is used to screen qualified components based on the quality judgment results and calculate the transportation departure time based on the hoisting time and production completion time, while meeting the construction time constraints. The transportation execution module is used to control the transportation equipment to transport the qualified components to the construction site according to the transportation departure time, and to collect transportation location data; The data platform is also used to receive the deviation value and transportation location data, and adjust the calculation method of the transportation departure time based on the transportation location data.

[0128] This embodiment also provides an electronic device applicable to a method for intelligent production and transportation of prefabricated components adapted to industrialized building construction, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for intelligent production and transportation of prefabricated components adapted to industrialized building construction as proposed in the above embodiment.

[0129] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for intelligent production and transportation of prefabricated components adapted to the industrialized construction method of buildings, as proposed in the above embodiments.

[0130] The storage medium proposed in this embodiment and the method for intelligent production and transportation of prefabricated components adapted to the industrialized construction method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0131] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent production and transportation of prefabricated components adapted to the industrialized construction of buildings, characterized in that, include: Obtain the geometric parameters, material parameters, production completion time, and hoisting time of the precast components; The geometric parameters are transformed using a data platform to generate control data for driving production equipment. Based on the control data, drive the marking equipment to perform contour marking and drive the mold placement equipment to place the mold. Based on the material parameters, drive the material placement equipment to complete the concrete pouring, and obtain the produced components and the pouring completion time. The measured geometric data of the manufactured components are acquired by three-dimensional scanning. The edge features and embedded features of the components are extracted from the measured geometric data. The edge features and embedded features are compared with the geometric parameters, the deviation value is calculated and a quality judgment result is generated. In response to the deviation value exceeding a preset threshold, the manufactured components are processed according to the deviation value to make them conform to the geometric parameters. Based on the quality assessment results, qualified components are selected, and the transportation departure time is calculated based on the hoisting time and the production completion time, while meeting the construction time constraints. The transportation equipment is controlled to transport the qualified components to the construction site according to the transportation departure time, and transportation location data is collected. The deviation value and transportation location data are fed back to the data platform, which then adjusts the calculation method for the transportation departure time based on the transportation location data.

2. A method for intelligent production and transportation of prefabricated components adapted to the industrialized construction of buildings according to claim 1, characterized in that, The coordinate transformation of the geometric parameters through the data platform includes: Obtain the coordinate data of the geometric parameters in the design coordinate system; Establish the transformation matrix between the design coordinate system and the production equipment platform coordinate system; The coordinate data is converted into target coordinates in the pedestal coordinate system based on the transformation matrix.

3. The intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in claim 2, characterized in that, The generation of control data for driving production equipment includes: Path planning is performed on the coordinate points of the corresponding contour in the target coordinates to generate line drawing path data for driving the line drawing device; The pose calculation is performed on the coordinate points corresponding to the pre-embedded positions in the target coordinates to generate mold positioning data for driving the mold-making equipment.

4. The intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in claim 3, characterized in that, The acquisition of measured geometric data of the manufactured components via three-dimensional scanning includes: The scanning device is controlled to scan the manufactured component by multi-angle scanning to obtain spatial point data on the surface of the manufactured component; The spatial point data is registered and stitched together; The measured geometric data is generated based on the stitched data.

5. The intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in claim 4, characterized in that, The extraction of component edge features and embedded features from the measured geometric data includes: The boundary points in the measured geometric data are identified using an edge detection algorithm, and the edge features of the component are extracted. The location of the embedded parts in the measured geometric data is located by a region recognition algorithm, and the embedded features are extracted.

6. The intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in claim 5, characterized in that, The calculated deviation value includes: The component edge features are matched and compared with the contour information in the geometric parameters to calculate the contour deviation; The embedded features are matched and compared with the embedded position information in the geometric parameters to calculate the embedded position deviation; The deviation value is obtained by combining the contour deviation and the pre-embedded position deviation.

7. The intelligent production and transportation method for prefabricated components adapted to industrialized building construction as described in claim 6, characterized in that, The calculation of transport departure time under the condition of meeting construction time constraints includes: The time window for the components to arrive at the construction site is determined based on the hoisting time. The latest departure time is calculated based on the production completion time and transportation time. Determine whether the latest departure time is within the time window; if so, determine the transportation departure time as the latest departure time.

8. A prefabricated component intelligent production and transportation system adapted to industrialized building construction, employing the prefabricated component intelligent production and transportation method adapted to industrialized building construction as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire the geometric parameters, material parameters, production completion time, and hoisting time of the prefabricated components. The data platform is used to perform coordinate transformation on the geometric parameters and generate control data for driving production equipment. The production execution module includes a marking device, a mold placement device, and a material placement device. The marking device marks the outline according to the control data, the mold placement device places the mold according to the control data, and the material placement device completes the concrete pouring according to the material parameters. The quality inspection module is used to collect measured geometric data of manufactured components through three-dimensional scanning, extract component edge features and embedded features from the measured geometric data, compare the component edge features and embedded features with the geometric parameters, calculate the deviation value and generate a quality judgment result; The transportation scheduling module is used to screen qualified components based on the quality judgment results and calculate the transportation departure time based on the hoisting time and production completion time, while meeting the construction time constraints. The transportation execution module is used to control the transportation equipment to transport the qualified components to the construction site according to the transportation departure time, and to collect transportation location data; The data platform is also used to receive the deviation value and transportation location data, and adjust the calculation method of the transportation departure time based on the transportation location data.

9. 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 intelligent production and transportation method of prefabricated components adapted to the industrialized construction method of buildings, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent production and transportation method of prefabricated components adapted to the industrialized construction method of buildings, as described in any one of claims 1 to 7.

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