Short-line segment prefabrication alignment intelligent control and acceptance system, method and medium

Abstract

The embodiments of this specification provide a system, method, and medium for intelligent control and acceptance of the prefabricated alignment of short-line segments. The system includes a detection module, a conversion module, a positioning module, a calibration module, an intelligent control module, and a post-processing module. The detection module is configured to scan the segment beam using a scanning device, acquire and process the scan data to generate monitoring data; the conversion module is configured to convert and process the monitoring data to generate model coordinate information; the positioning module is configured to generate model data of feature points based on engineering design data; the calibration module is configured to generate a monitoring centroid based on the monitoring data, generate a model centroid based on the model data, and generate error data based on the feature points, the monitoring centroid, and the model centroid; the intelligent control module is configured to automatically adjust the alignment of the segment beam based on the error data; and the post-processing module is configured to assess the risk level of the segment beam after the alignment adjustment, and adjust the construction progress of the segment beam based on the risk level.

Description

Technical Field

[0001] The present invention relates to the technical field of segmental prefabricated bridge construction for transportation infrastructure, and in particular to a system, method and medium for intelligent control and acceptance of the linear shape of short-line segment prefabricated bridges. Background Art

[0002] Traditional short-line segmental precast beam alignment testing primarily relies on the installation of multiple measuring towers equipped with levels and total stations. This requires multiple surveying engineers to perform repeated measurements and calibrations, making the process cumbersome and time-consuming. Furthermore, the construction of the measuring towers, the purchase and maintenance of the levels and total stations are costly. Operators may be required to work in hazardous environments, such as high altitudes and high temperatures, within complex construction sites. Furthermore, traditional segmental beam alignment formwork adjustment requires the collaboration of at least three skilled workers. These workers use total stations to locate the target center, read and record the coordinates of six measuring points on the matching beam segment, and then compare and analyze these coordinates to determine the adjustment direction. This process then guides the operator in adjusting the spatial position of the base formwork trolley. Re-measurement is required after each adjustment, a time-consuming, complex, and prone to human error. Therefore, safely and efficiently completing precast beam alignment testing is an urgent challenge in segmental bridge construction.

[0003] Building Information Modeling (BIM) technology is increasingly being used in the field of prefabrication and assembly technology. By establishing a digital model of a project structure, BIM technology integrates the physical information, geometric information, and assembly information of the building during the construction and operation phases, facilitating three-dimensional visualization briefings, construction plan simulations, and in-depth construction design during the project construction phase.

[0004] Therefore, it is necessary to propose an intelligent control and acceptance system for prefabricated alignment of short-line segments, which comprehensively applies automatic detection and BIM technology to improve construction efficiency and quality. Summary of the Invention

[0005] The purpose of this specification is to overcome the shortcomings and deficiencies of the existing technology, provide a short-line segment prefabrication alignment intelligent control and acceptance system based on drone detection, and significantly improve the prefabrication accuracy and alignment control progress of short-line segment prefabricated beams.

[0006] One or more embodiments of the present specification provide a short-line segment prefabricated linear intelligent control and acceptance system, including a detection module, a conversion module, a positioning module, a calibration module, an intelligent control module, and a post-processing module; the detection module is configured to: scan the segment beam through a scanning device to obtain scanning data, process the scanning data, and generate monitoring data, the monitoring data including positioning monitoring information and / or elevation monitoring information of the characteristic points of the segment beam; the conversion module is configured to: convert the monitoring data to generate model coordinate information; the positioning module is configured to: generate model data of the characteristic points based on engineering design data According to the present invention, the model data includes model positioning information and / or model elevation information; the calibration module is configured to: generate a monitoring centroid according to the monitoring data, generate a model centroid according to the model data, and generate error data according to the feature points, the monitoring centroid and the model centroid, and the error data includes error size and / or error change rate; the intelligent control module is configured to: automatically adjust the linear shape of the segmented beam through an adjustment device according to the error data; the post-processing module is configured to: evaluate the risk level of the segmented beam according to the segmented beam that has completed the linear adjustment, and adjust the construction progress of the segmented beam according to the risk level.

[0007] One or more embodiments of the specification provide a method for intelligent control and acceptance of the linear shape of short-line segments prefabrication, the method comprising: scanning a segment beam by a scanning device to obtain scanning data, processing the scanning data to generate monitoring data, the monitoring data including positioning monitoring information and / or elevation monitoring information of characteristic points of the segment beam; converting the monitoring data to generate model coordinate information; generating model data of the characteristic points based on engineering design data, the model data including model positioning information and / or model elevation information; generating a monitoring centroid according to the monitoring data, generating a model centroid according to the model data, and generating error data according to the characteristic points, the monitoring centroid and the model centroid, the error data including error size and / or error change rate; automatically adjusting the linear shape of the segment beam by an adjusting device according to the error data; evaluating the risk level of the segment beam based on the segment beam that has completed linear adjustment, and adjusting the construction progress of the segment beam according to the risk level.

[0008] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for intelligent control and acceptance of the prefabricated linear shape of a short line segment.

[0009] One or more embodiments of the present specification provide a short-line segment prefabrication linear intelligent control and acceptance device, including a processor, wherein the processor is used to execute the above-mentioned short-line segment prefabrication linear intelligent control and acceptance method. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0011] Figure 1 is an exemplary module diagram of a short-line segment prefabrication line shape intelligent control and acceptance system according to some embodiments of this specification;

[0012] Figure 2 is an exemplary flow chart of a method for intelligent control and acceptance of short-line segment prefabrication alignment according to some embodiments of this specification;

[0013] Figure 3 is a schematic diagram of generating a monitoring centroid according to some embodiments of this specification;

[0014] Figure 4 is a schematic diagram of determining adjustment parameters according to some embodiments of this specification;

[0015] Figure 5 This is a schematic diagram of the locations of characteristic points of segment beams and the setting of drone routes according to some embodiments of this specification;

[0016] Figure 6 is a schematic diagram of Gauss-Krüger projection coordinate conversion according to some embodiments of this specification;

[0017] Figure 7 is a schematic diagram of a segmental beam intelligent control system according to some embodiments of this specification;

[0018] Figure 8 is a schematic diagram of an intelligent positioning system for a segmental beam bottom formwork trolley according to some embodiments of this specification;

[0019] Figure 9 It is a schematic diagram of the automatic mold closing system of the side mold and inner mold of the segment beam shown in some embodiments of this specification. DETAILED DESCRIPTION

[0020] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0021] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0022] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0023] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0024] Figure 1 It is an exemplary module diagram of the short-line segment prefabrication linear intelligent control and acceptance system shown in some embodiments of this specification.

[0025] In some embodiments, as Figure 1 As shown, the short-line segment prefabrication alignment intelligent control and acceptance system 100 may include a detection module 110 , a conversion module 120 , a positioning module 130 , a calibration module 140 , an intelligent control module 150 and a post-processing module 160 .

[0026] In some embodiments, the detection module 110 is in communication with the conversion module 120 and the calibration module 140. The calibration module 140 is in communication with the positioning module 130 and the intelligent control module 150. The intelligent control module 150 is in communication with the post-processing module 160. Exemplary communication connection methods include, but are not limited to, Bluetooth, Wi-Fi, 5G, etc.

[0027] In some embodiments, the detection module 110 , the conversion module 120 , the positioning module 130 , the calibration module 140 , the intelligent control module 150 , and the post-processing module 160 may have their own processors or may share a single processor.

[0028] The processor may process data and / or information obtained from other devices or system components. The processor may execute program instructions based on this data, information, and / or processing results to perform one or more functions described herein. In some embodiments, the processor may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core processing device). By way of example only, the processor may include a central processing unit (CPU), a controller, a microprocessor, or any combination thereof.

[0029] The detection module 110 is a module for collecting and analyzing data on the segmental beam.

[0030] In some embodiments, the detection module 110 is configured to scan the segmental beam using a scanning device to obtain scanning data, process the scanning data, and generate monitoring data.

[0031] Scanning devices are devices that integrate cameras, cameras, lidar, positioning components, or other sensors and can acquire information about objects in the environment by moving and scanning. Examples include drones and self-driving cars.

[0032] In some embodiments, the detection module 110 is further configured to determine monitoring data of the feature points based on the scan data and the model data of the feature points.

[0033] In some embodiments, the detection module is further configured to adjust scanning parameters of the scanning device in response to the abnormality degree of the feature point meeting a preset condition.

[0034] In some embodiments, the detection module is further configured to generate the abnormality degree of the feature point based on the monitoring data, the estimated deformation information, and the abnormal feature model.

[0035] The conversion module 120 is a module for converting and processing monitoring data.

[0036] In some embodiments, the conversion module is configured to perform conversion processing on the monitoring data to generate model coordinate information.

[0037] The positioning module 130 is a module for determining and recording model data information of characteristic points of the segmental beam.

[0038] In some embodiments, the positioning module is configured to generate model data of the feature points based on the engineering design data, wherein the model data includes model positioning information and / or model elevation information.

[0039] In some embodiments, the positioning module may establish a three-dimensional model of the segmental beam based on the design drawings and locate characteristic points.

[0040] The calibration module 140 is a module that generates error data by comparing monitoring data with model data.

[0041] In some embodiments, the calibration module is configured to generate a monitoring centroid based on the monitoring data, generate a model centroid based on the model data, and generate error data based on the feature points, the monitoring centroid and the model centroid, the error data including the error size and / or the error change rate.

[0042] In some embodiments, the calibration module is further configured to determine abnormal feature points and / or normal feature points based on the monitoring data of the feature points; and generate monitoring centroids based on the abnormal feature points and / or normal feature points.

[0043] In some embodiments, the calibration module can calculate the coordinate error and change rate by comparing the feature points and centroid coordinates obtained by drone detection and BIM modeling.

[0044] The intelligent control module 150 is a module that automatically adjusts the linear shape of the segmental beam according to the error data to ensure that it meets the design standards and quality requirements.

[0045] In some embodiments, the intelligent control module is configured to automatically adjust the linear shape of the segmental beam through the adjustment device according to the error data.

[0046] In some embodiments, the adjustment device may include an intelligent positioning device for the bottom mold trolley, an automatic mold closing device for the side mold and inner mold, etc.

[0047] In some embodiments, the intelligent control module is further configured to determine adjustment parameters of the adjustment device based on the offset information of the feature point, the adjustment device information, and the mechanical environment characteristics.

[0048] In some embodiments, the intelligent control module is also configured to estimate the estimated deformation information of the template after the casting material is poured into the template based on the characteristics of the casting material before casting; and determine the adjustment parameters of the adjustment device based on the estimated deformation information, offset information, adjustment device information, and mechanical environment characteristics.

[0049] In some embodiments, the intelligent control module can automatically adjust the linear shape of the segmental beam by relying on the intelligent positioning device of the bottom mold trolley and the automatic clamping device of the side mold and inner mold.

[0050] The post-processing module 160 is a module for evaluating the risk level of the adjusted segmental beam and optimizing the construction progress.

[0051] In some embodiments, the post-processing module is configured to evaluate the risk level of the segmental beam based on the segmental beam after the alignment adjustment, and adjust the construction progress of the segmental beam according to the risk level.

[0052] In some embodiments, the post-processing module may use the analytic hierarchy process to perform a safety risk assessment on the segmental beam linear error and determine the segmental beam linear control construction progress according to the risk level.

[0053] For more information about the short-line segment prefabrication alignment intelligent control and acceptance system and its modules, please refer to Figure 2-Figure 9 and its related descriptions.

[0054] It should be noted that the above description of the short-line segment prefabrication line shape intelligent control and acceptance system and its modules is for convenience only and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principles of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The detection module 110, conversion module 120, positioning module 130, calibration module 140, intelligent control module 150, and post-processing module 160 disclosed herein may be different modules within a single system, or a single module may implement the functions of two or more of the aforementioned modules. For example, the modules may share a storage module, or each module may have its own storage module. Such variations are within the scope of protection of this specification.

[0055] Figure 2 This is an exemplary flow chart of the intelligent control and acceptance method for the prefabricated line shape of short line segments according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a processor.

[0056] In step 210 , the segment beam is scanned by a scanning device to obtain scanning data, and the scanning data is processed to generate monitoring data. In some embodiments, the detection module performs step 210 .

[0057] Scan data refers to data acquired using a scanning device. In some embodiments, the scan data may include at least one of images or videos of preset locations captured by a camera, as well as latitude, longitude, and elevation data collected by a lidar. The preset locations may include at least one characteristic point of a segmental beam. For example, the midpoint, corner, beam-slab junction, or pre-set hole of the segmental beam can be preset using modeling software based on a 3D model of the segmental beam.

[0058] In some embodiments, the detection module may scan the segment beam based on a drone equipped with a camera and a lidar to obtain scanning data.

[0059] Figure 5 Schematic diagram of the locations of characteristic points of segment beams and the setting of drone routes according to some embodiments of this specification.

[0060] In some embodiments, as Figure 5As shown, the segmental beam includes a matching beam segment 201 and a beam segment to be cast 202. The matching beam segment 201 refers to the portion of the segmental beam that has been cast, and the beam segment to be cast 202 refers to the portion of the segmental beam that has not been cast.

[0061] For example only, the specific steps for scanning with a drone equipped with a camera and lidar are as follows:

[0062] S101. Setting of segment beam feature points.

[0063] The top edge of the end formwork of the beam section 202 to be cast is set with an axis control point a and four elevation feature points b, c, d, e. The matching beam section 201 is set with an axis control point A and four elevation feature points B, C, D, E (where elevation feature point D is the highest point of the beam section 201) near the construction interface of the template side. Figure 5 (not shown), the beam section 202 to be cast is provided with 10 characteristic points in total, such as Figure 5 As shown in the figure, the axis control point a is located at the midpoint of the top surface of the beam section 202 to be cast in the transverse direction of the bridge; the elevation feature points b and c are located at the intersection of the top surface of the beam section 202 to be cast and the centerline of the web; the feature points d and e are located at the intersection of the bottom plate of the beam section 202 to be cast and the centerline of the web. The feature points can be marked with bolt embedded parts or identifiable colors.

[0064] S102. Drone route setting.

[0065] Control points are arranged according to the prefabrication site conditions of the beam section 202 to be cast, and a route is planned for the 10 characteristic point positions of the beam section 202 to be cast. After confirming that the take-off conditions are met, the planned route is called and the drone is imported to automatically perform flight and shooting tasks.

[0066] like Figure 5 As shown, a flight path is set at three elevations at the construction interface of each beam section 202 to be poured. The first elevation is the lower edge of the bottom plate of the beam section 202 to be poured; the second elevation is the upper edge of the top plate of the beam section 202 to be poured; and the third elevation is 1.5m above the upper edge of the top plate of the beam section 202 to be poured.

[0067] S103. Drone data collection.

[0068] Drones equipped with high-definition cameras and lidar can collect and store longitude, latitude, and elevation data for 10 feature points. After the aerial survey flight is completed, the collected data is inspected. If there is sufficient overlap, clear exposure, and accurate location data, the data is considered qualified and promptly downloaded, classified, and stored.

[0069] In some embodiments, the scanning device can also acquire scan data through other methods. For example, the drone's camera can be activated to capture images and rotated to a preset angle. During this rotation, the camera can capture video data within its field of view. The preset angle is a pre-set value and can be adjusted based on actual needs.

[0070] Monitoring data refers to data obtained by processing the scanned data, for example, data obtained by filtering, data format conversion, etc. In some embodiments, the monitoring data includes location monitoring information and / or elevation monitoring information of characteristic points of the segmental beam.

[0071] Positioning monitoring information refers to information related to the positioning of feature points, for example, the geographic coordinates of feature points, such as the latitude and longitude of feature points.

[0072] Elevation monitoring information refers to information related to the elevation of a feature point, for example, the altitude of the feature point.

[0073] Step 220 , converting the monitoring data to generate model coordinate information. In some embodiments, the conversion module executes step 220 .

[0074] The conversion process is to convert the longitude and latitude of the feature point into coordinates in the segmental beam coordinate system. For example, the longitude and latitude of the feature point are converted into coordinates in a plane rectangular coordinate system, and then the plane rectangular coordinates are converted into coordinates in the segmental beam coordinate system.

[0075] A plane rectangular coordinate system is constructed based on a preset plane. For example, it could be based on the plane above a segmental beam. The preset plane is a pre-set value and can be set as needed.

[0076] The segmental beam coordinate system is a coordinate system constructed based on a preset segmental beam, for example, based on the upper surface of the segmental beam.

[0077] Model coordinate information refers to a collection of information related to points on multiple segmental beams in a segmental beam coordinate system. For example, it may be a dataset of coordinates of multiple feature points in the segmental beam coordinate system. In some embodiments, the conversion module may generate model coordinate information based on the coordinates of the multiple feature points.

[0078] In some embodiments, the conversion module may perform conversion processing using conversion tools, such as Proj.4, GDAL (Geospatial Data Abstraction Library), Google Earth, and other tools.

[0079] In some embodiments, the steps of converting the monitoring data by the conversion module are as follows:

[0080] S201. Convert the GPS coordinates of the feature points into plane rectangular coordinates.

[0081] Figure 6 Schematic diagram of Gauss-Krüger projection coordinate transformation according to some embodiments of this specification.

[0082] The coordinates of the feature points collected by the drone are based on the GPS coordinate system. The GPS coordinates (i.e. the latitude and longitude coordinates of the feature points) on the ellipsoid can be projected into the plane rectangular coordinate system using the Gauss-Krüger projection, such as Figure 6 The Gauss-Krüger projection imagines an elliptical cylinder placed horizontally outside the reference ellipsoid and tangent to the central meridian of the ellipsoid. The ellipsoid and the elliptical cylinder center O coincide with each other and have symmetrical deviations. The center of the ellipsoid is used as the light source for projection, and the areas on both sides of the central meridian are projected onto the elliptical cylinder.

[0083] S202. Convert the plane rectangular coordinates into the coordinates in the segmental beam coordinate system.

[0084] For example, the coordinate transformation matrix is ​​used to transform the plane rectangular coordinates of the feature point into the plane global coordinates of the beam segment 202 to be cast (i.e., the coordinates in the segmental beam coordinate system). The plane global coordinates of the beam segment 202 to be cast have the axis control feature point a as the origin.

[0085] The coordinate system conversion method is to perform three types of transformations: translation, rotation, and scaling between the plane rectangular coordinate system and the overall coordinates of the beam section 202 to be cast. The coordinate conversion matrix is ​​expressed as:

[0086] = (1)

[0087] In formula (1), ( , ) is the point coordinate of the feature point in the overall coordinate system of the plane of the beam section to be cast 202; ( , ) is the coordinate of the feature point in the plane rectangular coordinate system; , is the horizontal and vertical displacement of the overall coordinates, indicating the translation of the overall coordinates of the beam segment 202 to be cast relative to the plane rectangular coordinate system in the X and Y directions; k is the scaling factor between the plane rectangular coordinate system and the overall coordinate system of the beam segment 202 to be cast, which can be preset; is the rotation matrix, is the rotation angle, which represents the rotation angle of the overall coordinate of the plane of the beam section 202 to be cast relative to the plane rectangular coordinate system.

[0088] In some embodiments, after obtaining the coordinates of the feature point in the segmental beam coordinate system, the processor may further convert the coordinates of the feature point in the segmental beam coordinate system into a three-dimensional global coordinate system, as follows:

[0089] S203. Convert the overall coordinates of the segment beam plane into a three-dimensional overall coordinate system.

[0090] For example, the difference between the elevations of the 10 characteristic points of the beam section to be cast 202 and the elevation of the axis control characteristic point a is used as the Z coordinate of the characteristic point in the three-dimensional global coordinate system.

[0091] Step 230 : Generate model data of feature points based on the engineering design data. In some embodiments, the positioning module performs step 230 .

[0092] The engineering design data refers to the design data of the segmental beam. In some embodiments, the engineering design data may include design drawings, design parameters, etc. of the segmental beam. In some embodiments, the user may input the engineering design data into the positioning module.

[0093] Model data refers to relevant data obtained when performing three-dimensional modeling of the segmental beam. In some embodiments, the model data can be obtained using three-dimensional modeling software. In some embodiments, the model data can include model positioning information and / or model elevation information.

[0094] The model positioning information refers to information related to the coordinates of the three-dimensional model of the segmental beam.

[0095] Model elevation information refers to information related to the elevation of the three-dimensional model of the segmental beam.

[0096] For example only, the steps for the positioning module to generate model data of feature points are as follows:

[0097] S301. BIM model establishment. Based on the bridge engineering design drawings, a BIM 3D model is established with the axis control feature point a of the beam segment 202 to be cast as the origin. The design parameters, structural system, materials, and construction process information of the beam segment 202 to be cast are all integrated into the model, and the coordinate positions of the 10 feature points of the beam segment 202 to be cast are marked. .

[0098] S302. Feature point positioning. According to the coordinate conversion in step S203, the three-dimensional overall coordinates of the 10 feature points of the beam section 202 to be cast are obtained. ,The coordinates of the actual feature points of the beam section 202 to be cast obtained by UAV detection are imported into the BIM model and marked.

[0099] Step 240 : Generate a monitoring centroid based on the monitoring data, generate a model centroid based on the model data, and generate error data based on the feature points, the monitoring centroid, and the model centroid. In some embodiments, a calibration module performs step 240 .

[0100] The monitoring centroid refers to the centroid of at least one surface of the segment beam determined based on monitoring data.

[0101] The model centroid refers to the centroid of at least one surface of the segmental beam determined based on model data of the three-dimensional model.

[0102] Exemplarily, the specific steps for the calibration module to determine the monitoring centroid and the model centroid are as follows:

[0103] S401. Monitoring centroid coordinate positioning of segment beams.

[0104] For example, based on step S301 , the three-dimensional overall coordinates of the ten characteristic points of the beam section 202 to be cast are obtained, and the coordinates of the monitoring centroid O of the beam section 202 to be cast are calculated.

[0105] (2)

[0106] In formula (2), , , Respectively represent the monitoring centroid O coordinate, They represent the X coordinates of the ten characteristic points of the beam segment 202 to be cast determined based on the monitoring data, They represent the Y coordinates of the ten characteristic points of the beam segment 202 to be cast determined based on the monitoring data, They respectively represent the Z coordinates of 10 characteristic points of the beam section 202 to be cast determined based on the monitoring data.

[0107] S402. Positioning the centroid coordinates of the segmental beam BIM model. Based on step S302, the three-dimensional overall coordinates of the ten characteristic points of the beam segment 202 to be cast are obtained, and the centroid O' coordinates of the model of the beam segment 202 to be cast are calculated.

[0108] (3)

[0109] In formula (3), , , Respectively represent the centroid O' of the model coordinate, They represent the X coordinates of the 10 characteristic points of the beam segment 202 to be cast in the BIM 3D model, They represent the Y coordinates of the 10 characteristic points of the beam segment 202 to be cast in the BIM 3D model, They respectively represent the Z coordinates of 10 characteristic points of the beam segment 202 to be cast in the BIM three-dimensional model.

[0110] Error data characterizes the error between the monitoring data and the model data of the 3D model. In some embodiments, the error data may include coordinate errors and angular deviations between the monitoring data and the model data in different directions (e.g., along the three coordinate axes of the segmental beam coordinate system). Coordinate errors may include axis errors, elevation errors, errors between the monitoring and model centroids, and error rates of change of at least one characteristic point of the segmental beam. Angular deviation refers to the rotational angle deviation between the monitoring and model data in different directions.

[0111] The error change rate is used to characterize the change in error data detected at the same feature point relative to the model data over time within a unit time interval. For example, the height of a support column in a segmental beam is 18m, and the model data corresponds to 18m. However, the actual height of the support column was 18.02m and 18.04m in two monitoring data sets 10 days apart. The corresponding error data were 0.02m and 0.04m, respectively. Therefore, the corresponding error change rate is 0.02m / 10d.

[0112] In some embodiments, the processor may determine the error data based on the following steps:

[0113] S403. Determine the centroid coordinate error and rate of change. Based on the coordinate error and rate of change of the monitored centroid O and the model centroid O', calculate the displacement difference in the three degrees of freedom directions of X, Y, and Z and the displacement angle difference in the three degrees of freedom directions of ∠YZ, ∠XZ, and ∠XY.

[0114] In step 250 , the segment beam is automatically adjusted linearly by the adjustment device according to the error data. In some embodiments, the intelligent control module performs step 250 .

[0115] Linear adjustment refers to adjusting the axis direction and / or elevation of at least one segment beam.

[0116] An adjustment device is a device used to adjust the linear shape of the segmental beam. In some embodiments, the adjustment device may include a formwork for pouring concrete and a pressure device that applies force to the formwork. The formwork may include a bottom form, side forms, and inner forms. The pressure device may include a bottom form trolley, an intelligent positioning device that drives the bottom form trolley, an automatic mold clamping device, a jack, and the like.

[0117] The pressure device acts on the formwork, which creates a space around the formwork to hold the concrete and maintain its shape until the concrete solidifies. By adjusting the formwork's offset or the force applied by the adjustment device, the linear shape of the segmental beam can be altered after the concrete within the formwork solidifies.

[0118] In some embodiments, the steps of automatically adjusting the linear shape of the segmental beam by the adjusting device are as follows:

[0119] S501. Adjust the coordinates based on the intelligent positioning system of the bottom mold trolley.

[0120] For example, the centroid coordinate errors and change rate values ​​of the six independent degrees of freedom of the beam section 202 to be cast are sent to the intelligent positioning system of the bottom mold trolley 206, so that the coordinate error of the monitored centroid O and the model centroid O' is within the allowable range, and the coordinate components of each degree of freedom direction of the beam section 202 to be cast are adjusted one by one, thereby adjusting the spatial posture of the current segment beam.

[0121] like Figure 7 Figure 2 shows a schematic diagram of an intelligent segmental beam control system according to some embodiments of this specification. The intelligent positioning system for the base mold trolley 206 includes a multi-point hydraulically adjustable base mold trolley, a hydraulic control module, and a hydraulic control terminal. The base mold trolley 206 is a single-layer rectangular frame with three-way adjustable hydraulic jacks installed at each corner. These three-way hydraulic jacks control the oil supply and return via hydraulic solenoid valves. The hydraulic solenoid valves are connected to the hydraulic control terminal, which transmits the coordinate error of the centroid of the beam segment 202 to the three-way adjustable hydraulic jacks.

[0122] like Figure 8 The figure shows a schematic diagram of an intelligent positioning system for a segment beam bottom formwork trolley according to some embodiments of the present invention. The three-way adjustable hydraulic jack includes a vertical jack 301, a transverse bridge jack 302, and a longitudinal bridge jack 303.

[0123] S502. Perform coordinate adjustment based on the automatic mold closing system of the side mold and the inner mold.

[0124] For example, according to the spatial posture of the beam section 202 to be cast after the bottom mold trolley 206 is adjusted in step S501, the error between the web and bottom plate of the beam section 202 to be cast and the expected size is analyzed and calculated, and then the mold closing instruction is generated, and the side mold and inner mold adjustment system is used to adjust the posture of the beam section 202 to be cast again.

[0125] like Figure 9Figure 2 shows a schematic diagram of an automatic mold-closing system for the side and inner molds of a segmental beam according to some embodiments of this specification. Upon receiving a mold-closing command, the automatic mold-closing system of the side molds pushes the side molds from the left and right sides toward the center of the beam segment 202 to be cast, while ensuring that the side molds 203 are tightly pressed against the fixed end molds 204 and the matching beam segment 201. Upon receiving a mold-closing command, the automatic mold-closing system of the inner mold 205 pushes the inner mold 205 to the designed position of the segmental beam 202 to be cast, while ensuring that the inner mold 205 is tightly pressed against the fixed end molds 204 and the matching beam segment 201.

[0126] In some embodiments, the intelligent control module may determine the reserved adjustment amount in advance based on the error change rate.

[0127] A reserve adjustment is an additional adjustment applied to a parameter when adjusting the linear shape of a segmental beam. This reserve adjustment can be used to compensate for errors. For example, when adjusting the height of a segmental beam by pouring concrete, the concrete may collapse and deform after setting, resulting in a certain error in the height of the segmental beam even after adjustment. Using the reserve adjustment, the additional concrete pour can be used to compensate for the resulting collapse of the concrete after setting.

[0128] Step 260 : Based on the segmental beams that have completed the alignment adjustment, the risk level of the segmental beams is evaluated, and the construction schedule of the segmental beams is adjusted according to the risk level. In some embodiments, step 260 is performed by a post-processing module.

[0129] The risk level is used to characterize the risk of subsequent deformation of the segmental beam. The higher the risk level, the greater the risk of deformation of the segmental beam. In some embodiments, the risk level is a preset value and can be set according to actual needs.

[0130] In some embodiments, the post-processing module may evaluate the risk level of the segmental beam based on the following manner.

[0131] S601. Re-determine the error data after linear adjustment.

[0132] For example, the coordinate errors of the three displacement degrees of freedom of the ten characteristic points of the beam section 202 to be cast after adjustment are expressed as .

[0133] S602. Determine the error standard.

[0134] In some embodiments, the post-processing module can determine the coordinate error standard corresponding to the characteristic point of the beam section 202 to be cast and the installation error standard corresponding to the template installation of the beam section 202 to be cast based on the query deviation comparison table.

[0135] The following table 1 is the deviation comparison table:

[0136]

[0137] S603. Based on the hierarchical analysis method and combined with group decision-making, error risk level assessment is performed.

[0138] For example, the post-processing module can use an expert survey method, assign expert weights, and assign risk levels for feature point coordinate errors and template safety errors. Using the analytic hierarchy process combined with group decision-making, qualitative and quantitative methods are combined to determine the risk levels of segmental beam linear errors for individual projects, unit projects, and the overall project. Risk levels are categorized from low to high as I, II, and III.

[0139] The basic steps of the hierarchical analysis method include: hierarchical structure model, pairwise judgment matrix of the target layer, calculation of relative weights of elements, consistency test, statistical weight of group decision making, and determination of risk level of the target layer.

[0140] 1. A hierarchical structural model is established based on sub-projects, unit projects, and overall projects. The overall project is the linear error of the beam segment 202 to be cast; the unit projects are the coordinate error, elevation error, surface flatness, verticality, axis deviation, and in-mold dimensions. The individual projects corresponding to the coordinate error and elevation error are the coordinates and elevations of the axis control point a and elevation feature points b, c, d, and e. The individual projects corresponding to surface flatness, verticality, axis deviation, and in-mold dimensions include the internal and external formwork measurement points of the top plate, bottom plate, and web.

[0141] 2. Target-level pairwise judgment matrix: Invite 3-5 technically and experienced linear control experts or managers to provide risk levels and risk judgment matrices for individual projects.

[0142] 1) Based on the expert's risk level assessment results for each individual project, the weight is calculated according to the following formula (4):

[0143] (4)

[0144] In formula (4), represents the weighted mean of the grade of a single project, represents the weight value corresponding to the i-th expert, represents the risk level of a single project given by the i-th expert, Indicates the number of experts.

[0145] 2) The expert division provides pairwise judgment matrices for individual projects and unit projects.

[0146] Factors at the same level are divided into several levels based on their degree of excellence or importance and assigned quantitative values. A five-level quantitative method is generally used, with the corresponding values ​​assigned being 1, 3, 5, 7, and 9, indicating the relative importance of one factor to another, with higher numbers indicating greater importance. Conversely, to indicate that one factor is less important than another, the quantitative value assigned can be the reciprocal of the above 1, 3, 5, 7, and 9. If some issues are not sufficiently described using the above five-level quantitative method, the values ​​of 2, 4, 6, and 8 can be interpolated to form a nine-level quantitative method.

[0147] The pairwise comparison judgment matrix B at the same level is shown in the following table.

[0148]

[0149] Here, the judgment matrix B should satisfy the consistency conditions, see formulas (5) and (6):

[0150] (5)

[0151] (transitive) (6)

[0152] According to the above properties of the matrix, B is a reciprocal matrix, and only the upper (lower) triangular elements need to be Give a judgment.

[0153] 3. Calculation of relative weights of elements.

[0154] For the judgment matrix B , solve the characteristic root problem according to formula (7):

[0155] (7)

[0156] In formula (7), B is the judgment matrix, is the maximum eigenvalue of the judgment matrix, is the eigenvector corresponding to the largest eigenvalue.

[0157] The resulting vector After normalization as element Sorting weight under judgment matrix B, maximum characteristic root The maximum eigenvalue and eigenvector of a matrix can be calculated using the power method, sum method, or root method.

[0158] 4.Consistency check.

[0159] If A is more important than B, B is more important than C, and C is more important than A, it goes against common sense. After that, the transitivity of the judgment matrix needs to be checked for consistency. The consistency test mainly calculates the following three indicators:

[0160] Consistency Index , calculated as follows:

[0161] (8)

[0162] In formula (8), is the order of the judgment matrix.

[0163] Average random consistency index

[0164] Average random consistency index It is obtained by taking the arithmetic mean after repeatedly calculating the eigenvalues ​​of the random judgment matrix for multiple times (more than 500 times).

[0165] Consistency ratio The calculation formula is as follows:

[0166] (9)

[0167] when When 0.1, it is generally considered that the consistency of the judgment matrix is ​​acceptable. If the value is less than 0.1, then it is generally considered that the initially established judgment matrix is ​​not satisfactory and needs to be re-analyzed and carefully revised until it passes the test.

[0168] 5. Statistical weights of group decisions.

[0169] The weights obtained by following the basic steps in the previous section are those of a single evaluator. The weights of multiple evaluators can be obtained using a group decision-making method. The group decision-making method is mainly the eigenvalue method.

[0170] Suppose there are s experts participating in the judgment, and their judgment matrices are ,in .

[0171] The weights are calculated using the weighted geometric mean comprehensive ranking vector method: for s expert judgment matrices, their ranking vectors are calculated respectively. Then calculate their weighted geometric mean comprehensive ranking vector ,in Where, represents the final weight of the j-th element, The calculation of is shown in the following formula (10):

[0172] , (10)

[0173] Where, is the calculation weight of the sth expert on the jth element, is the weight coefficient of each expert, when Sometimes .

[0174] 6. Determine the risk level of the target layer.

[0175] Based on the weight values ​​of each layer of factors and the existing expert risk levels, the risk level of the previous level is calculated according to the following formula (11):

[0176] = (11)

[0177] In formula (11), It is the weighted value of risk level of individual item / unit project; is the corresponding weight index of the risk of the i-th single item / unit project; Expert risk assessment level value of the i-th single item / unit project;

[0178] By combining the analytic hierarchy process with group decision-making, qualitative and quantitative methods are used to determine the risk level of segmental beam linear error for individual projects, unit projects, and the overall project in a step-by-step manner. The risk levels are divided into levels I, II, and III from low to high.

[0179] The score ranges corresponding to the risk levels of individual projects, unit projects and overall projects are as follows: Level I: [0, 1], Level II: (1, 2], Level III: (2, 3]

[0180] S604. Based on the assessment results, determine whether the alignment control of the beam segment 202 to be cast has passed the acceptance inspection or needs to be adjusted again. If the risk level is assessed as Level I, the precast alignment control of the beam segment 202 to be cast meets the standards and the acceptance inspection has passed. If the risk level is assessed as Level II, the precast alignment control of the beam segment 202 to be cast does not meet the standards and the acceptance inspection has failed, and steps S401-S603 need to be re-executed. If the risk level is assessed as Level III, the precast alignment control of the beam segment 202 to be cast does not meet the standards and the acceptance inspection has failed, and steps S101-S603 need to be re-executed. After repeated adjustments, when the alignment risk level of the beam segment 202 to be cast is reduced to Level I, the segment alignment inspection has passed, and the beam segment 202 to be cast can be cast on site.

[0181] S605. Store segmental beam product information, segmental beam linear intelligent control information, risk level assessment and acceptance information.

[0182] In some embodiments, the post-processing module can also determine the risk level by looking up the risk table based on the error change rate determined by on-site monitoring (such as collecting scanning data) at multiple subsequent time points for the segmental beam that has completed linear adjustment, and based on the size and development range of the error change rate.

[0183] The development amplitude is used to characterize the degree to which the error rate of change has changed over time compared to the previous test. For example, if the height of a support column in a segmental beam is 18m, and the model data corresponds to 18m, and during actual testing, the support column measured 18.02m, 18.04m, and 18.07m in three monitoring data sets separated by 10 days, respectively, the error change rate is 0.02m / 10d, 0.02m / 10d, and 0.03m / 10d. The development amplitude corresponding to the second monitoring data is 0, the development amplitude corresponding to the third monitoring data is 0, and the development amplitude corresponding to the third monitoring data is 0.01m / 10d.

[0184] In some embodiments, the risk table may include different error change rates, different error change rate development ranges and corresponding relationships with risk levels. The post-processing module may construct a risk table based on historical data. For example, the post-processing module determines the minimum error change rate and development range that lead to subsequent construction quality problems from historical data, and sets the corresponding risk level to the first level. The minimum error change rate and development range are reduced proportionally. When the error change rate and development range are 80%-99% of the minimum error change rate and development range, the corresponding risk level is set to the second level. When the error change rate and development range are 60%-79% of the minimum error change rate and development range, the corresponding risk level is set to the third level.

[0185] In some embodiments, the post-processing module can compare the risk level with the level threshold. When the risk level is higher than the level threshold, construction is suspended, the detection module re-scans the segmental beam through the scanning equipment to obtain monitoring data, the calibration module regenerates the error data, the intelligent control module re-performs linear adjustment, and the post-processing module re-evaluates the risk level until the risk level is equal to or lower than the level threshold.

[0186] The intelligent control and acceptance system and method for short-line segment prefabrication alignment provided in some embodiments of this specification can collect real-time scanning data of segmental beams during construction, determine the construction error of the segmental beams based on the scanning data and three-dimensional modeling, make linear adjustments to the segmental beams based on the construction error, and determine whether to continue construction. This system can remotely monitor and automatically control the entire construction process in real time to ensure construction quality. This can improve the control level of segmental beam alignment and prefabrication construction efficiency. Furthermore, the segmental beam alignment can be pre-adjusted based on the error change rate, and the adjustment amount can be reserved to further improve construction quality.

[0187] Figure 3 This is a schematic diagram of generating monitoring centroids according to some embodiments of this specification.

[0188] like Figure 3As shown, the detection module is further configured to determine monitoring data 320 of the feature points based on the scan data 311 and the model data 312 of the feature points. The calibration module is further configured to determine abnormal feature points 331 and / or normal feature points 332 based on the monitoring data 320 of the feature points; and generate monitoring centroids 340 based on the abnormal feature points 331 and / or the normal feature points 332.

[0189] In some embodiments, the detection module can determine the target region where the characteristic points of the segmental beam are located based on the model data 312 of the characteristic points. The target region refers to the area or location where the characteristic points are located. In some embodiments, the target region can be represented by geographic coordinates, such as longitude and latitude. The detection module can control a scanning device to scan the target region and obtain scan data 311. For more information about the model data 312, please refer to the description of step 230.

[0190] The detection module scans the target area of ​​the segment beam using a scanning device to obtain scanning data 311, and processes the scanning data 311 to obtain monitoring data 320 of the feature points. For more information about the scanning data 311 and the monitoring data 320, please refer to the description of step 210.

[0191] In some embodiments, the calibration module may determine abnormal feature points 331 and / or normal feature points 332 based on the monitoring data 320 of the feature points.

[0192] Abnormal feature points 331 are feature points with position deviations. For example, the feature points may be deformed due to thermal expansion and contraction, concrete pouring, etc., resulting in position deviations.

[0193] Normal feature points 332 refer to feature points that are not deformed.

[0194] In some embodiments, the calibration module can determine the magnitude of the change in the positional relationship between each feature point and the adjacent feature points in the monitoring data acquired at the same time point, for example, the distance, angle, etc. between the feature point and the adjacent feature point, relative to the standard distance, standard angle, etc. between the feature point and the adjacent feature point determined based on the model data.

[0195] The magnitude of change is used to characterize the degree to which the positional relationship between a feature point and an adjacent feature point has changed. In some embodiments, the calibration module may determine a first distance or a first angle between the feature point and the adjacent feature point. The calibration module may calculate a distance difference between the first distance and a standard distance, and a distance ratio between the distance difference and the standard distance, or an angle difference between the first angle and the standard angle, and an angle ratio between the angle difference and the standard angle, and determine the distance ratio or angle ratio as the magnitude of change.

[0196] The calibration module compares the amplitude of the change with the amplitude threshold. If at least two of the amplitudes of the change corresponding to a feature point exceed the amplitude threshold, the calibration module identifies the feature point as an abnormal feature point. The amplitude threshold is a preset value that can be set as needed.

[0197] In some embodiments, the calibration module may generate the monitoring centroid 340 in a variety of ways based on the abnormal feature points 331 and / or the normal feature points 332 .

[0198] For example, when the number of feature points exceeds a threshold, the calibration module may generate a monitoring centroid 340 based on the normal feature points 332. The threshold is a preset value that can be set as needed. In some embodiments, the conversion module may convert the monitoring data corresponding to the normal feature points 332 into model coordinate information and calculate the monitoring centroid 340 based on the model coordinate information corresponding to the normal feature points 332. For more information on converting the monitoring data into model coordinate information, please refer to the description of step 220.

[0199] For another example, when the calibration module calculates the monitoring centroid, it can perform weighted processing on the positioning monitoring information and elevation monitoring information of the feature point. The weight of the weighted processing is positively correlated to the distance between the feature point and the deformation area. The deformation area refers to the area / position where the abnormal feature point is located. The weight is a preset value and can be set according to actual needs. In some embodiments, the conversion module can convert the positioning monitoring information and elevation monitoring information corresponding to the feature point into model coordinate information, perform weighted processing on the model coordinate information, and calculate the monitoring centroid 340. In some embodiments, the conversion module can calculate the monitoring centroid 340 by a preset algorithm. The preset algorithm includes the following formula:

[0200] (12)

[0201] In formula (12), ) is the coordinate value of the monitoring centroid, 、… are the positioning monitoring information of feature points 1, ..., feature points n, 、… are the elevation monitoring information of feature points 1, ..., and n respectively. 、… is the weight.

[0202] In some embodiments, the calibration module generates a monitoring centroid by performing weighted calculation processing on the positioning monitoring information and elevation monitoring information of the abnormal feature points and the normal feature points, wherein the weight of the weighted calculation processing is negatively correlated with the degree of abnormality of the feature points.

[0203] In some embodiments, the calibration module obtains location monitoring information and elevation monitoring information from feature points captured by a scanning device. The detection module uses an abnormality feature model to determine the degree of abnormality of different feature points and identifies abnormal feature points based on the degree of abnormality. For example, a feature point whose degree of abnormality exceeds a threshold may be identified as an abnormal feature point.

[0204] The calibration module performs weighted calculations on the location and elevation monitoring information of multiple feature points to determine the monitoring centroid. The weights in this weighted calculation are negatively correlated to the degree of anomaly at each feature point. These weights are preset and can be adjusted as needed. For more information on anomaly levels and the anomaly feature model, see the following description.

[0205] In some embodiments, the calibration module may determine the number of feature points. For example, if the number of feature points is N, the calibration module may use 1 / N as the initial weight for each feature point. The calibration module may determine the intermediate weight of the feature points based on the initial weights. For example, the calibration module may calculate the difference between 1 and the degree of abnormality, and calculate the product of this difference and the initial weight, and determine this product as the intermediate weight. The calibration module may calculate the ratio of the intermediate weight of the feature point to the sum of the intermediate weights of all feature points, and determine this ratio as the weight to be finally applied.

[0206] When determining the monitoring centroid, weighted calculation is performed on the positioning monitoring information and elevation monitoring information of different feature points, taking into account the abnormal conditions of the feature points, ensuring that the determined centroid is more in line with reality and can accurately reflect the offset of the segmental beam.

[0207] Based on the model data of the feature points, the location or area of ​​the feature points can be estimated, so that the monitoring data of the location or area of ​​the feature points can be collected in a targeted manner, which is conducive to improving the efficiency of data collection and improving the correlation between monitoring data and feature points.

[0208] In some embodiments, the detection module is further configured to adjust scanning parameters of the scanning device in response to the abnormality degree of the feature point meeting a preset condition.

[0209] In some embodiments, the detection module can generate the abnormality degree of the feature point based on the monitoring data and the estimated deformation information through the abnormal feature model. For more information about the estimated deformation information, please refer to Figure 4 Related description.

[0210] The abnormal feature model refers to a model used to determine the degree of abnormality of a feature point. In some embodiments, the abnormal feature model is a machine learning model, such as a neural network (NN).

[0211] In some embodiments, the input of the abnormal feature model includes monitoring data, estimated deformation information, the type of feature point, and the cumulative construction time of the feature point, and the output includes the abnormality level of the feature point. For more information about the monitoring data, please refer to the relevant description of step 210.

[0212] The type of the feature point may include at least one of a midpoint, a corner, a beam-slab joint, a reserved hole, and the like.

[0213] The cumulative construction time of a feature point refers to the time interval from the time when the feature point is completed to the current time point.

[0214] In some embodiments, the detection module may train an abnormal feature model based on a sample dataset.

[0215] The sample dataset includes training samples and their corresponding labels.

[0216] In some embodiments, the training samples include sample monitoring data, sample estimated deformation information, sample type of sample feature points, and cumulative sample construction time of the sample feature points. The label is the abnormality level corresponding to the sample feature point, which can be a specific numerical value.

[0217] In some embodiments, the detection module may determine the training sample from the historical data. In some embodiments, the detection module may determine the actual deformation information of the feature point from the historical data and confirm it as the estimated deformation information of the sample.

[0218] In some embodiments, in historical data, when the error change rate of a feature point continues to increase, the detection module may determine the corresponding degree of abnormality based on the actual increase in the error change rate corresponding to the feature point during the cumulative construction time. For example, the actual increase is determined as the degree of abnormality. In some embodiments, the degree of abnormality is no greater than 1. In some embodiments, the detection module may calculate the difference between the error change rates corresponding to the end time point and the start time point of the cumulative construction time, and determine this difference as the actual increase in the error change rate.

[0219] In some embodiments, the detection module can perform multiple rounds of iterations, at least one of which includes: selecting one or more training samples from a training data set, inputting the one or more training samples into an abnormal feature model, and obtaining model prediction outputs corresponding to the one or more training samples; substituting the model prediction outputs corresponding to the one or more training samples and the labels of the one or more training samples into a predefined loss function formula to calculate the value of the loss function; and reversely updating the model parameters in the abnormal feature model based on the value of the loss function. This step can be performed using various methods. For example, the update can be based on the gradient descent method. When the iteration end condition is met, the iteration ends and a trained abnormal feature model is obtained.

[0220] Due to the different types of feature points, the magnitude of the error increase caused by their changes over time is also different. By using the abnormal feature model, the corresponding abnormality degree can be predicted according to the type of feature point, which is conducive to improving the accuracy and efficiency of determining the abnormality degree.

[0221] Scanning parameters refer to the operating parameters of the scanning device. For example, at least one of the scanning duration, scanning range, scanning frequency, and lens swing speed. The lens swing speed refers to the swing speed of the scanning lens or camera on the scanning device. For more information about scanning devices, please refer to Figure 1 Related description.

[0222] In some embodiments, the detection module may determine the abnormality level corresponding to the abnormality level based on the magnitude of the abnormality of the feature point. In some embodiments, when the magnitude of the abnormality level is within a preset numerical range, the detection module may determine the abnormality level as the abnormality level corresponding to the preset numerical range. For example, when the abnormality level is within the preset numerical range of 0.01-0.2, the abnormality level is level 1, ..., and when the abnormality level is within the preset numerical range of 0.8-0.99, the abnormality level is level 5. The preset numerical range is a preset value and can be set according to actual needs.

[0223] In some embodiments, the detection module may construct a grading table based on historical data, the grading table including abnormality grades and corresponding scanning parameters. The detection module may determine the corresponding scanning parameters according to the abnormality grade by querying the grading table.

[0224] In some embodiments, the detection module can determine the minimum value of a preset numerical range corresponding to the degree of abnormality, calculate the ratio of the degree of abnormality to the minimum value, and multiply the ratio by the scanning parameter to obtain the final adjusted scanning parameter. For example, during the last scan of the camera, the scanning parameter was a single swing of the camera within 20 seconds. The abnormality degree of a feature point corresponding to the scan data was 0.3, and the corresponding preset numerical range was 0.19-0.4. The detection module can calculate that the ratio of 0.3 and 0.19 is approximately 1.5. The detection module can calculate that the product of 20 and 1.5 is 30. Adjusting the scanning parameter to a single swing of the camera within 30 seconds can improve the accuracy of the acquired scan data, thereby subsequently improving the accuracy of the monitoring data.

[0225] Different scanning parameters are used to collect data based on different feature points. Scanning parameters can be adaptively adjusted based on abnormalities in feature points, avoiding waste of scanning device resources. This helps ensure the efficiency of the scanning device while ensuring data volume and accuracy. It can also improve the remote automation control level of the scanning device, allowing remote adjustment of the scanning device to avoid repeated manual adjustments.

[0226] Figure 4 This is a schematic diagram of determining adjustment parameters according to some embodiments of this specification.

[0227] like Figure 4 As shown, the intelligent control module is further configured to determine adjustment parameters 420 of the adjustment device based on the offset information 411 of the feature point, the adjustment device information 412 , and the mechanical environment characteristics 413 .

[0228] Offset information 411 is used to indicate the deviation between the monitoring data and the model coordinate information corresponding to the same feature point in different directions or angles. For example, in a three-dimensional coordinate system, this information can be the axial deviation of the feature point along the X, Y, and Z axes, or the angular deviation around the X, Y, and Z axes.

[0229] In some embodiments, the intelligent control module may determine the offset information based on the error data. For example, the error data may be identified as the offset information. For more information about the error data, see the description of step 240.

[0230] In some embodiments, the intelligent control module can determine the coordinate values ​​or angles of the monitoring data and model coordinate information corresponding to the same feature point in the same coordinate system, calculate the difference between the coordinate values ​​or angles corresponding to the monitoring data and the coordinate values ​​or angles corresponding to the model coordinate information, and confirm the difference as offset information 411.

[0231] The adjustment device information 412 refers to information related to the adjustment device, such as the position where the adjustment device applies force, the degree of freedom of adjustment, etc. The adjustment device information 412 is a preset value and can be set according to actual needs.

[0232] For more information about the adjustment device, please refer to the relevant description of step 250.

[0233] Mechanical environment characteristics 413 refer to characteristics related to the stress conditions or environment of the template. In some embodiments, mechanical environment characteristics 413 may include at least one of the following: template load distribution (e.g., the pressure applied by at least one jack on the template, the point of pressure application, and the weight distribution of the template); temperature field (e.g., ambient temperature, template temperature); vibration noise (e.g., the vibration conditions of the template along the X-axis, Y-axis, and Z-axis); and template material properties.

[0234] Template material properties are used to characterize the stress characteristics of the template. In some embodiments, the intelligent control module can determine template material properties based on experimental data. For example, by applying a force to the template and monitoring its displacement, the relationship between the force and displacement can be used to determine the template material properties.

[0235] Adjustment parameter 430 refers to the operating parameters of the adjustment device when adjusting the segment beam alignment, for example, at least one of the displacement, movement direction, rotation angle, and rotation direction of the output end of at least one adjustment device. When the adjustment device is not involved in the adjustment, the corresponding adjustment parameter is 0.

[0236] In some embodiments, the intelligent control module can determine from historical data the historical change data of the offset information of the segmental beam after the linear shape of the segmental beam is adjusted according to a certain historical adjustment parameter and historical adjustment device information under certain historical mechanical environment characteristics.

[0237] In some embodiments, the intelligent control module can determine the correspondence between the historical adjustment parameters and the historical change data of the offset information corresponding to each feature point, and determine the correspondence between the historical adjustment parameters and the degree of change of the offset information of different feature points through a fitting algorithm, thereby determining the reference adjustment range of at least one adjustment device.

[0238] In some embodiments, the intelligent control module can generate an adjustment parameter corresponding to the current mechanical environment characteristics and the current offset information based on a reference adjustment range of at least one adjustment device. For example, the intelligent control module can select the minimum value of the reference adjustment range as the adjustment parameter, and then fine-tune the adjustment parameter.

[0239] In some embodiments, the intelligent control module may also determine adjustment parameters based on the adjustment effectiveness of different feature points.

[0240] Adjustment effectiveness is used to characterize the effectiveness of adjustments made to different types of feature points based on adjustment parameters over a historical time period. In some embodiments, the intelligent control module can determine the adjustment effectiveness of feature points based on historical data. For example, for a particular feature point or a particular type of feature point, the intelligent control module can count the number of adjustments that satisfied the requirement with a single adjustment over multiple historical adjustments, calculate the ratio of this number of adjustments to the total number of adjustments, and determine this ratio as the adjustment effectiveness.

[0241] In some embodiments, the intelligent control module may calculate the product of the adjustment effectiveness and the interval value of the reference adjustment range, calculate the sum of the product and the minimum value of the reference adjustment range, and determine the sum as the adjustment parameter. The intelligent control module may calculate the difference between the maximum and minimum values ​​of the reference adjustment range, calculate the sum of the difference and 1, and determine the sum as the interval value of the reference adjustment range.

[0242] In some embodiments, the intelligent control module may control the regulating device to adjust the linearity of the segment beam according to the regulating device information based on the regulating parameters.

[0243] By determining the adjustment parameters of the adjustment device, the accuracy and efficiency of adjusting the linearity of the segmented beam can be improved. Furthermore, when determining the adjustment parameters, the actual working environment and the operating parameters of the adjustment device are taken into consideration, which helps to increase the success rate of the first adjustment, thereby improving construction efficiency and avoiding resource waste.

[0244] In some embodiments, the intelligent control module is further configured to, before pouring, estimate the deformation information of the template after the pouring material is poured into the template based on the characteristics of the pouring material.

[0245] Casting material characteristics refer to characteristics related to the casting material, such as at least one of the casting material type, density, mass, and volume. Casting material characteristics are preset values ​​that can be input into the intelligent control module by the user.

[0246] The estimated deformation information is used to represent the estimated deformation degree of the template.

[0247] In some embodiments, the intelligent control module may determine the deformation information based on the current feature points and the amount of casting material by querying the deformation table, and confirm it as the estimated deformation information.

[0248] The deformation table may include the correspondence between the characteristic points, the amount of pouring material and the interval time, and the deformation information of the template. The interval time is the time interval between the time point when pouring is completed and the time point when the deformation of the characteristic point is subsequently monitored. In some embodiments, the intelligent control module can construct a deformation table based on historical data. For example, for a certain characteristic point, different amounts of pouring material are poured. After the pouring is completed, the deformation of the characteristic point is monitored at an interval. The intelligent control module can confirm the amount of deformation of the characteristic point as deformation information.

[0249] In some embodiments, the intelligent control module determines the adjustment parameters of the adjustment device based on the estimated deformation information, the offset information, the adjustment device information, and the mechanical environment characteristics.

[0250] In some embodiments, the intelligent control module may calculate the product of the adjustment effectiveness and the interval value of the reference adjustment range, calculate the sum of the product, the minimum value of the reference adjustment range, and the estimated deformation information, and confirm the sum as the adjustment parameter.

[0251] When the formwork is deformed, the accommodation space formed around the formwork will become larger or smaller, thereby affecting the height change of the concrete in the vertical direction after solidification. By determining the estimated deformation information, the change of the feature point in the vertical direction can be estimated. When determining the adjustment parameters subsequently, by considering the estimated deformation information, the value corresponding to the estimated deformation information can be used as the reserved adjustment amount in the vertical direction, which is conducive to improving the accuracy of determining the adjustment parameters.

[0252] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0253] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0254] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A short-line segment prefabrication line shape intelligent control and acceptance system, characterized by: Including detection module, conversion module, positioning module, calibration module, intelligent control module, and post-processing module; The detection module is configured to: Scanning the segmental beam using a scanning device to obtain scanning data, and determining monitoring data of the feature points based on the scanning data and model data of the feature points, wherein the monitoring data includes positioning monitoring information and / or elevation monitoring information of the feature points of the segmental beam; Generate the abnormality degree of the feature point through an abnormal feature model based on the monitoring data, the estimated deformation information, the type of the feature point, and the cumulative construction time of the feature point; the abnormal feature model is a neural network model, and the estimated deformation information is determined based on the intelligent control module; In response to the abnormality degree of the feature point meeting a preset condition, adjusting scanning parameters of the scanning device, the scanning parameters including scanning duration, scanning range, scanning frequency, and lens swing speed; The conversion module is configured to: perform conversion processing on the monitoring data to generate model coordinate information; The positioning module is configured to: generate model data of the feature points based on engineering design data, wherein the model data includes model positioning information and / or model elevation information; The calibration module is configured to: determining abnormal feature points and / or normal feature points based on the monitoring data of the feature points; Performing weighted calculation processing based on the positioning monitoring information and elevation monitoring information of the abnormal feature point and / or the normal feature point to generate a monitoring centroid, wherein the weight of the weighted calculation processing is negatively correlated to the abnormality degree of the feature point; generating a model centroid based on the model data, and generating error data based on the feature point, the monitoring centroid, and the model centroid, the error data including an error magnitude and / or an error change rate, the error change rate representing how error data detected at the same feature point after a unit time period changes relative to the model data over time within the unit time period; The intelligent control module is configured as follows: Before pouring, based on the characteristics of the pouring material, the estimated deformation information of the formwork after the pouring material is poured into the formwork; Determining adjustment parameters of the adjustment device based on the estimated deformation information, offset information of the feature points, adjustment device information, and mechanical environment characteristics, wherein the adjustment device information includes a position where the adjustment device applies a force and degrees of freedom of adjustment, and the mechanical environment characteristics include load distribution, temperature field, vibration noise, and material properties of the template; Automatically adjusting the linear shape of the segmented beam by the adjustment device according to the error data, wherein the automatically adjusting the linear shape of the segmented beam by the adjustment device according to the error data comprises: determining a reserved adjustment amount in advance based on the error change rate, wherein the reserved adjustment amount refers to an additional adjustment amount added to the adjustment parameter when performing the linear adjustment on the segmented beam; The post-processing module is configured to: evaluate the risk level of the segmental beam according to the segmental beam that has completed the linear adjustment, and adjust the construction progress of the segmental beam according to the risk level.

2. A method for intelligent control and acceptance of short-line segment prefabrication line shape, characterized in that: The method comprises: Scanning the segmental beam using a scanning device to obtain scanning data, and determining monitoring data of the feature points based on the scanning data and model data of the feature points, wherein the monitoring data includes positioning monitoring information and / or elevation monitoring information of the feature points of the segmental beam; Generate the abnormality degree of the feature point through an abnormal feature model based on the monitoring data, the estimated deformation information, the type of the feature point, and the cumulative construction time of the feature point; the abnormal feature model is a neural network model, and the estimated deformation information is determined based on the intelligent control module; In response to the abnormality degree of the feature point meeting a preset condition, adjusting scanning parameters of the scanning device, the scanning parameters including scanning duration, scanning range, scanning frequency, and lens swing speed; Performing conversion processing on the monitoring data to generate model coordinate information; generating model data of the feature points based on the engineering design data, wherein the model data includes model positioning information and / or model elevation information; determining abnormal feature points and / or normal feature points based on the monitoring data of the feature points; Performing weighted calculation processing based on the positioning monitoring information and elevation monitoring information of the abnormal feature point and / or the normal feature point to generate a monitoring centroid, wherein the weight of the weighted calculation processing is negatively correlated to the abnormality degree of the feature point; generating a model centroid based on the model data, and generating error data based on the feature point, the monitoring centroid, and the model centroid, the error data including an error magnitude and / or an error change rate, the error change rate representing how error data detected at the same feature point after a unit time period changes relative to the model data over time within the unit time period; Before pouring, based on the characteristics of the pouring material, the estimated deformation information of the formwork after the pouring material is poured into the formwork; Determining adjustment parameters of the adjustment device based on the estimated deformation information, offset information of the feature points, adjustment device information, and mechanical environment characteristics, wherein the adjustment device information includes a position where the adjustment device applies a force and degrees of freedom of adjustment, and the mechanical environment characteristics include load distribution, temperature field, vibration noise, and material properties of the template; Automatically adjusting the linear shape of the segmented beam by the adjustment device according to the error data, wherein the automatically adjusting the linear shape of the segmented beam by the adjustment device according to the error data comprises: determining a reserved adjustment amount in advance based on the error change rate, wherein the reserved adjustment amount refers to an additional adjustment amount added to the adjustment parameter when performing the linear adjustment on the segmented beam; Based on the segmental beams that have completed the linear adjustment, the risk level of the segmental beams is evaluated, and the construction progress of the segmental beams is adjusted according to the risk level.

3. A short-line segment prefabricated linear intelligent control and acceptance device, characterized in that: It includes a processor, which is used to execute the short line segment prefabricated linear intelligent control and acceptance method described in claim 2.

4. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the short-line segment prefabrication linear intelligent control and acceptance method as described in claim 2.

Images