A method for detecting the accuracy of the whole construction process of a steel part

Abstract

The application provides a steel bar part construction whole-process precision detection method, steel bar unit detection, steel bar piece detection, steel bar part manufacturing process detection, steel bar part yard storage detection, steel bar part hoisting process detection and concrete pouring completion detection, solves the problem that steel bar part detection is not systematic and deviation sources cannot be effectively traced back, and effectively improves the construction precision control of steel bar parts during construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel bar component construction, and particularly relates to a steel bar component construction whole-process precision detection method. BACKGROUND

[0002] With the vigorous promotion of the state to the "fabricated building", the proportion of the domestic new construction fabricated building area in the new building area is continuously improved, the steel bar component construction technology is more and more widely used in the field of construction, and the steel bar component is assembled in the prefabricated jig in advance, then the whole is hoisted to the to-be-poured segment to complete the installation, and then the mold pouring is completed, which not only greatly saves the steel bar construction time, but also reduces the high-altitude operation personnel, and realizes the win-win of benefit and safety.

[0003] One of the key control points of the steel bar component construction technology is the forming precision of the steel bar component, and the forming precision determines whether the butt joint can be successfully completed and whether the steel bar protection layer thickness meets the specification requirements. The manufacturing method of the steel bar component is to first arrange the building concrete outer contour corner points in the prefabricated jig, and then position the steel bar component through the corner points. After forming, the finished product cannot be effectively detected due to the influence of the jig. In addition, only the finished product of the steel bar component is detected in the steel bar component construction process, and the whole process is not detected. When the precision of the steel bar component deviates, it cannot be effectively traced, so that a vacuum period appears in the precision control of the steel bar component.

[0004] Therefore, a steel bar component whole-process precision control and detection method is needed to detect the whole process of the steel bar component construction. SUMMARY

[0005] The main purpose of the present application is to provide a steel bar component construction whole-process precision detection method to solve the problems in the background art.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0007] S1, steel bar unit detection: a plurality of first high-definition cameras are installed on the side of the steel bar bending equipment, image information of the bending area of the steel bar unit is collected, and the processing system is used for analysis and comparison, so as to judge whether the bending formed steel bar unit meets the precision requirement;

[0008] S2, steel bar piece detection: a plurality of second high-definition cameras are installed on the cloth gantry of the piece processing equipment, and the image information of the piece positioning jig on the piece processing equipment is collected in different zones, and the processing system is used for analysis and comparison, so as to judge whether the steel bar piece skeleton meets the precision requirement;

[0009] S3, steel bar part manufacturing process detection: steel bar part manufacturing is located in the part jig, a plurality of target balls are installed on each surface of the steel bar part, a plurality of observation stations are erected on the side of the part jig, the steel bar part is scanned by a three-dimensional laser scanner, the scanning result is three-dimensionally modeled by measurement calculation software, and the measurement data of the steel bar part is obtained;

[0010] S4, steel bar part field storage detection: the same as step 3, when the steel bar part is stored in the field, the steel bar part is scanned by a three-dimensional laser scanner, the scanning result is three-dimensionally modeled by measurement calculation software, and the measurement data of the steel bar part is obtained;

[0011] S5, steel bar part hoisting process detection: according to the steel bar part formation and the position to be installed, the position where each surface intersects is determined, a group of short-distance planar array laser sensors are installed at each surface intersection, and a plurality of groups of short-distance planar array laser sensors form a dynamic measurement system, so as to dynamically monitor the steel bar part during hoisting;

[0012] S6, detection after concrete pouring is completed: a plurality of target balls are installed on the concrete, and the concrete is scanned by a three-dimensional laser scanner in the observation station, and the scanning result is processed by measurement calculation software, so as to obtain the measurement data of the concrete.

[0013] Preferably, the specific steps of step S1 are as follows:

[0014] A1, according to the processing site environment and the size of the formed steel bar unit element, a plurality of first high-definition cameras are arranged on the side of the bending equipment, so that the plurality of first high-definition cameras can completely shoot the image of the steel bar unit element after bending forming;

[0015] A2, after the bending processing of the steel bar unit element is completed, the image information of the bending formed steel bar unit element is collected by the first high-definition camera;

[0016] A3, the collected image information is automatically transmitted to the matching processing system for algorithm processing, so as to calculate the steel bar unit element detection data;

[0017] The algorithm processing includes automatic splicing of a plurality of first high-definition camera images, steel bar contour detection based on semantic segmentation, identification and marking of the bending area based on target detection, straight line detection based on connected domain, bending angle and length calculation, and the algorithm is based on deep learning and image processing, so as to calculate the bending angle and the length of the steel bar;

[0018] A4, the actual data calculated is compared with the design data, if the error exceeds the specified allowable value, an alarm is sent to notify the operator to adjust the bending equipment processing parameters according to the deviation, otherwise, the next bending processing of the steel bar unit element is carried out.

[0019] Preferably, the specific steps of step S2 are as follows:

[0020] B1. Selecting a plurality of second high-definition cameras according to the size of the reinforcing sheet body and mounting them on the cloth gantry of the sheet body processing equipment, so that the plurality of second high-definition cameras can completely capture the image of the reinforcing sheet body skeleton on the positioning jig frame;

[0021] B2. The sheet body processing equipment starts to process the reinforcing sheet body. After the cloth gantry and cloth robot complete the cloth work on the positioning jig frame, the second high-definition camera collects the image information of the reinforcing sheet body skeleton;

[0022] B3. The collected image information is automatically transmitted to the matching processing system for algorithm processing, so as to calculate the skeleton line length between each node;

[0023] The algorithm processing includes automatic splicing of a plurality of second high-definition camera images, and analyzing the connected domain features through image processing methods;

[0024] For the angle of the bending position, a straight line of the bending area is fitted;

[0025] For reinforcing length calculation, the skeleton line is directly extracted from the connected domain;

[0026] For each bending position, nodes are extracted, and the skeleton line length between each node is calculated, i.e. the length of each segment of the reinforcing bar;

[0027] B4. Compare the calculated actual data with the design data. If the error exceeds the specified allowable value, an alarm is issued to notify the operator to adjust the processing parameters of the sheet body processing equipment according to the deviation. After the adjustment is completed, the collection and measurement are performed again, and after the review is correct, the welding gantry is started for welding operation.

[0028] Preferably, the automatic splicing method of the image is: input image; feature extraction; image registration; calculating homography matrix H using RANSAC; deformation and fusion; output image.

[0029] Preferably, the specific detection method of step S3 is as follows:

[0030] C1. After the reinforcing parts are assembled in the part jig frame, a plurality of target balls are installed on each surface of the reinforcing parts, and a plurality of observation stations are arranged on the side of the part jig frame;

[0031] C2. The three-dimensional scanner in the observation station sequentially scans each target ball to obtain the target ball data on the reinforcing parts, and the measurement software identifies the coordinates of each target ball in the scanning order and converts them to the same coordinate system;

[0032] C3, using data processing software to calculate the three-dimensional coordinates on the outer contour edge of the transferred steel part according to the coordinates of the target ball, and to calculate the edges and faces of the steel part, visualize the drawing data, generate a three-dimensional model of the steel part, and thus obtain the measurement data of the steel part.

[0033] Preferably, the specific detection method of step S4 is as follows:

[0034] D1, installing multiple target balls on each face of the steel part, and erecting multiple observation stations around the part jig;

[0035] D2, scanning the steel part through the three-dimensional laser scanner in the observation station to obtain point cloud data of the steel part;

[0036] D3, performing preliminary optimization processing on the point cloud data through software, and then performing data processing through data processing software, fitting the main steel center line and radius of the steel part according to the scanning data of the main steel of the top and bottom openings;

[0037] D4, splicing each target ball to a coordinate system through data processing software, calculating the face and edge data of the steel part, and building a three-dimensional model, and thus obtaining the measurement data of the steel part.

[0038] Preferably, in step S5, before the steel part is hoisted, the positions of each short-range area laser sensor are calibrated through a total station, and when the steel part shakes in the air, the coordinates after the control angle is static are calculated through discrete sampling of the shaking period, so as to dynamically monitor the steel part during hoisting.

[0039] Preferably, the specific detection method of step S6 is as follows:

[0040] E1, installing multiple target balls on the concrete, and erecting multiple observation stations around the concrete;

[0041] E2, scanning the top face of the concrete through the three-dimensional laser scanner in the observation point, and collecting the steel part data completely through collection in different directions on both sides;

[0042] E4, after the measurement is completed, importing the data into the data processing software for data processing, and calculating the three-dimensional model of the top opening of the steel part and the required measurement data.

[0043] Preferably, the processing method of the data processing software is: manually selecting a landmark point or nearby, the program automatically finds the point cloud data of the landmark point and calculates the center coordinates of the landmark point; extracting the landmark points in the adjacent scanning model, and corresponding the same landmark points when extracting; performing rough splicing on the model, and then performing secondary automatic fine matching on the layout overlapping model, and completing the precise splicing of the model.

[0044] Preferably, the target balls are installed on each face of the steel reinforcement part or concrete, and at least 3 groups of target balls are installed vertically, and at least 2 target balls in each group, and the target balls in each group are installed fixedly on the same horizontal line;

[0045] The erection of the observation station should ensure that the two adjacent three-dimensional laser scanners can repeatedly measure at least 3 same target balls in the scanning process, and the repeatedly measured target balls are different lines.

[0046] The present application provides a steel reinforcement part construction whole process precision detection method, solves the problem that the steel reinforcement part detection is not systematic, and the deviation source cannot be effectively traced back, and effectively improves the construction precision control of the steel reinforcement part during construction. BRIEF DESCRIPTION OF DRAWINGS

[0047] The present application will be further described below in combination with the drawings and embodiments:

[0048] Figure 1 is a steel reinforcement unit detection schematic diagram of the present application;

[0049] Figure 2 is a structure top view of the sheet processing equipment of the present application;

[0050] Figure 3 is a cloth gantry and second high-definition camera installation schematic diagram of the present application;

[0051] Figure 4 is a steel reinforcement part manufacturing process detection top view of the present application;

[0052] Figure 5 is a target ball installation steel reinforcement part schematic diagram of the present application;

[0053] Figure 6 is a steel reinforcement part hoisting process detection schematic diagram of the present application;

[0054] Figure 7 is a steel reinforcement part hoisting process detection schematic diagram of the present application; Figure 6 top view;

[0055] In the figure: bending equipment 1; first high-definition camera 2; sheet processing equipment 3; cloth gantry 4; second high-definition camera 5; positioning jig frame 6; part jig frame 7; target ball 8; three-dimensional laser scanner 9; short distance area array laser sensor 10; cloth robot 11; welding gantry 12. DETAILED DESCRIPTION

[0056] Embodiment 1

[0057] As shown in the figure, a steel reinforcement part construction whole process precision detection method, Figures 1-7

[0058] Step one: steel reinforcement unit detection ​

[0059] The required measurement data for the steel bar unit detection is the bending angle and the steel bar length. As shown in Figure 1 A first high-definition camera 2 is placed on the side of the steel bar bending equipment 1 to capture and collect videos by aligning the bending area. Then, an algorithm processing is performed by using a processing system, which includes automatic splicing of multiple camera images, steel bar contour detection based on semantic segmentation, identification and marking of the bending area based on target detection, straight line detection based on connected domains, bending angle and length calculation. The algorithm part is mainly based on deep learning and image processing, and can realize real-time processing and display of the bending angle and the steel bar length.

[0060] Step two: steel bar piece detection

[0061] The required measurement data for the steel bar piece detection is the bending angle and the length of each steel bar. The structure of the piece processing equipment 3 is as shown in Figures 2-3 A second high-definition camera 5 is installed on the cloth gantry 4 of the piece processing equipment 3 to capture and collect videos by dividing the area. The images of multiple cameras are spliced to obtain a panoramic view of the positioning jig 6. The steel bar length and the bending position angle on the platform are calculated by using deep learning and image processing methods. The algorithm processing includes automatic splicing of multiple camera images, steel bar contour detection based on semantic segmentation, identification and marking of the bending area based on target detection, steel bar length calculation and angle calculation based on connected domains.

[0062] Step three: steel bar part manufacturing process detection

[0063] The required measurement data for the steel bar part manufacturing process detection is the coordinate data of the top and bottom openings of the steel bar at each corner point. During the manufacturing of the steel bar part, it is in the part jig 7, which causes difficulty in measurement. Therefore, it is considered to transfer the measurement by leading out the steel bar at the corner point. As shown in Figure 4 Because only three sides can be observed at one observation station, four observation stations need to be set up.

[0064] The measurement method is as follows: after the part assembly is completed, the measurement target balls 8 are laid out before welding, as shown in Figure 5 Then, a three-dimensional laser scanner 9 Leica 3D Disto is used to measure each target ball 8 in sequence; the overlapping parts of adjacent observation points should ensure that the three target balls 8 with the same name can be measured again, which is beneficial to subsequent data processing. The measurement results are converted by measurement calculation software. Then, the point coordinates of the outer contour line of the steel bar part to which the target ball 8 is transferred are calculated according to the target point coordinate values, and the three-dimensional coordinates of the outer contour line and the surface of the steel bar part are calculated and modeled, so as to obtain the measurement data.

[0065] Step four: steel bar part in-field storage detection

[0066] The required measurement data for storing steel reinforcement components in the yard are: the coordinate data of the top and bottom openings of the steel reinforcement at each corner point and the spacing of the steel reinforcement. Therefore, the geometric feature dimensions of the steel reinforcement components can be quickly obtained by measuring them with a 3D laser scanner 9 Leica P40.

[0067] The measurement method is as follows: First, target spheres 8 are set up. Target spheres 8 can be directly placed on the component. It is important to ensure that the target spheres 8 are arranged such that two adjacent observation stations can simultaneously see all three target spheres 8, and that the three target spheres 8 are not collinear. After the target spheres 8 are set up, observation stations are erected to scan the reinforcing steel component using a 3D laser scanner 9. Furthermore, when setting up observation stations, it is advisable to select stations that can observe as many surfaces of the component as possible, thereby reducing the number of station setups. Three sets of non-collinear target spheres 8 should be scanned within the overlapping area of ​​two stations. After the measurement is completed, the point cloud data of the reinforcing steel component is imported into the measurement software for data processing and image stitching. Finally, a 3D model of the reinforcing steel component and the required measurement data are obtained.

[0068] Step 5: Inspection of the steel reinforcement component hoisting process

[0069] When hoisting reinforcing steel components, the required measurement data includes the coordinates of the corner and bottom openings of the reinforcing steel. Figures 6-7 As shown. Based on the shape of the reinforcing steel components and their installation location, the intersection points of each surface are determined. At each intersection point, a set of short-range area array laser sensors 10 with their own IMU system are arranged to form a dynamic measurement system with other sets. Before measurement, the positions of each sensor need to be calibrated using a total station. For the reinforcing steel components swaying in the air, the coordinates of the control corner point after it comes to rest can be calculated through discrete sampling of its sway period. This enables dynamic monitoring of the reinforcing steel components during the hoisting and installation process.

[0070] Step Six: Inspection after concrete pouring is completed

[0071] After the concrete is poured, the required data for measurement are: the coordinate data of the top of the steel bars at each corner point, as well as the spacing and length of the steel bars. Therefore, the geometric dimensions of the steel bar components can be quickly obtained by measuring with a 3D laser scanner 9 Leica P40.

[0072] The measurement method is as follows: First, target spheres 8 are set up in the same manner as in step four. Then, observation stations are set up to scan the top surface of the reinforcing steel component. Stations can be set up on the surface of concrete that has just been poured, thus reducing the number of target sphere placements and station setups. During scanning, three non-collinear target spheres 8 should be observed from the same station. Data on the reinforcing steel component is collected completely through two acquisitions from different directions. After the measurement is completed, the data is imported into the measurement software for data processing, finally obtaining a three-dimensional model of the top surface of the reinforcing steel component and the required measurement data.

[0073] Preferably, in order to facilitate the installation and collection of the target balls 8, a plurality of target balls 8 are installed on the elongated rod, one end of which is fixed with a quick clamp and a universal magnetic base, and the other end is fixed on the inner stirrup of the steel bar component by the quick clamp, and the universal magnetic base is used to connect the middle part of the elongated rod with the outer stirrup of the steel bar component, and the target balls 8 extend to the outside of the steel bar component.

[0074] Preferably, the target balls 8 are installed on each surface of the steel bar component or the concrete, and at least three groups of target balls 8 are installed vertically, and each group has at least two target balls 8, and the target balls 8 in each group are installed and fixed on the same horizontal line.

[0075] The erection of the observation station should ensure that the two adjacent three-dimensional laser scanners 9 can repeatedly measure at least three same target balls 8 during the scanning process, and the repeatedly measured target balls 8 are different lines.

[0076] Preferably, the processing method of the data processing software is that the center coordinates of the mark points are calculated by automatically searching for the point cloud data of the mark points near the manually selected mark points by the program; the mark points in the adjacent scanning models are extracted, and the same mark points need to be matched during the extraction; the models are roughly spliced, and then the layout overlapping models are automatically precisely matched again to complete the precise splicing of the models.

[0077] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as limitations of the present application. The protection scope of the present application should be the technical solutions recited in the claims, including the equivalent replacement solutions of the technical features recited in the claims. That is, the equivalent replacement improvements within this range are also within the protection scope of the present application.

Claims

1. A method for precision testing of steel reinforcement components throughout the entire construction process, characterized by: The detection method comprises the following steps: S1, steel unit element detection: a plurality of first high-definition cameras (2) are installed on the side of the steel bending equipment (1), and image information of the bending area of the steel unit element is collected, and the processing system is used for analysis and comparison, so as to judge whether the bending forming steel unit element meets the precision requirement; S2, steel sheet body detection: a plurality of second high-definition cameras (5) are installed on the cloth gantry (4) of the sheet body processing equipment (3), and the image information of the sheet body positioning jig (6) on the sheet body processing equipment (3) is collected in different areas, and the processing system is used for analysis and comparison, so as to judge whether the steel sheet body skeleton meets the precision requirement; S3, steel part manufacturing process detection: the steel part manufacturing is located in the part jig (7), a plurality of target balls (8) are installed on each surface of the steel part, a plurality of observation stations are arranged on the side of the part jig (7), and the steel part is scanned by the three-dimensional laser scanner (9), the scanning result is three-dimensional modeling by the measurement calculation software, and the measurement data of the steel part is obtained; S4, steel part in-field stacking detection: the same as step 3, when the steel part is stacked in the field, the steel part is scanned by the three-dimensional laser scanner (9), and the scanning result is three-dimensional modeling by the measurement calculation software, so as to obtain the measurement data of the steel part; S5, steel part hoisting process detection: according to the steel part and the position to be installed, the position where each surface intersects is determined, a group of short-distance planar array laser sensors (10) are installed at each surface intersection, and a plurality of groups of short-distance planar array laser sensors (10) form a dynamic measurement system, so as to dynamically monitor the steel part in the hoisting process; S6, detection after concrete pouring is completed: a plurality of target balls (8) are installed on the concrete, and then the three-dimensional laser scanner (9) in the observation station is used to scan the concrete, and the scanning result is processed by the measurement calculation software, so as to obtain the measurement data of the concrete; The specific steps of step S1 are as follows: A1, according to the processing site environment and the size of the formed steel unit element, a plurality of first high-definition cameras (2) are arranged on the side of the bending equipment (1), so that the plurality of first high-definition cameras (2) can completely shoot the image of the bending formed steel unit element; A2, after the bending processing of the steel unit element is completed, the image information of the bending formed steel unit element is collected by the first high-definition camera (2); A3, the collected image information is automatically transmitted to the matching processing system for algorithm processing, so as to calculate the steel unit element detection data; The algorithm processing includes automatic splicing of a plurality of first high-definition cameras (2) images, steel contour detection based on semantic segmentation, bending area recognition and marking based on target detection, straight line detection based on connected domain, bending angle and length calculation, and the algorithm is based on deep learning and image processing, so as to calculate the bending angle and the length of the steel; A4, the actual data calculated is compared with the design data, if the error exceeds the specified allowable value, an alarm is sent to notify the operator to adjust the bending equipment (1) processing parameters according to the deviation, otherwise, the next steel unit element bending processing is carried out. The specific steps of step S2 are as follows: B1. According to the size of the reinforcing sheet body, a plurality of second high-definition cameras (5) are selected and installed on the cloth gantry (4) of the sheet body processing equipment (3) to enable the plurality of second high-definition cameras (5) to completely capture the image of the reinforcing sheet body skeleton on the positioning jig (6); B2. The sheet body processing equipment (3) starts to process the reinforcing sheet body. After the cloth gantry (4) and the cloth robot (11) complete the cloth work on the positioning jig (6), the second high-definition camera (5) collects the image information of the reinforcing sheet body skeleton; B3. The collected image information is automatically transmitted to the supporting processing system for algorithm processing, so as to calculate the skeleton line length between each node; The algorithm processing includes automatic splicing of the images of the plurality of second high-definition cameras (5), analysis of the connected domain features by the image processing method; For the angle of the bending position, the straight line of the bending area is fitted; For reinforcing length calculation, the skeleton line is directly extracted from the connected domain; For each bending position, the node is extracted, and the skeleton line length between each node is calculated, that is, the length of each segment of the reinforcing bar; B4. Compare the calculated actual data with the design data. If the error exceeds the specified allowable value, an alarm is issued to notify the operator to adjust the processing parameters of the sheet body processing equipment (3) according to the deviation. After the adjustment is completed, the measurement is collected again, and after the error is checked, the welding gantry (12) is started for welding work; In step S5, before the reinforcing part is hoisted, the positions of the short-range area array laser sensors (10) are calibrated by the total station. When the reinforcing part shakes in the air, the coordinates after the control angle is static are calculated by discrete sampling of the shaking period, so as to dynamically monitor the reinforcing part during hoisting; The specific detection method of step S6 is as follows: E1. Install a plurality of target balls (8) on the concrete, and erect a plurality of observation stations around the concrete; E2. Scan the concrete top surface by the three-dimensional laser scanner (9) in the observation point. The data of the reinforcing part is collected completely by collecting in different directions on both sides; E3. After the measurement is completed, the data is imported into the data processing software for data processing, and the three-dimensional model of the reinforcing part top and the required measurement data are calculated.

2. The method according to claim 1, characterized in that: Automatic image splicing method: input image; feature extraction; image registration; calculate homography matrix H by RANSAC; deformation and fusion; output image.

3. The method according to claim 1, characterized in that: The specific detection method of step S3 is as follows: C1. After the reinforcing part is assembled in the part jig (7), a plurality of target balls (8) are installed on each surface of the reinforcing part, and a plurality of observation stations are erected around the part jig (7); C2. Scan each target ball (8) in sequence by the three-dimensional laser scanner (9) in the observation station to obtain the data of the target ball (8) on the reinforcing part, and identify the coordinates of each target ball (8) in sequence by the measurement software and convert them to the same coordinate system; C3, the data processing software is used to calculate the three-dimensional coordinates on the outer contour edge of the transferred steel part, and the edge and surface of the steel part, visualize the drawing data, generate a three-dimensional model of the steel part, and obtain the measurement data of the steel part.

4. The method according to claim 1, characterized in that: The specific detection method of step S4 is as follows: D1, a plurality of target balls (8) are installed on each surface of the steel part, and a plurality of observation stations are erected around the part jig (7); D2, the steel part is scanned by the three-dimensional laser scanner (9) in the observation station to obtain point cloud data of the steel part; D3, the point cloud data is preliminarily optimized by software, and then data processing software is used to process data, and the center line and radius of the main steel bar of the steel part are fitted according to the scanning data of the main steel bar of the top and bottom openings; D4, each target ball (8) is spliced into a coordinate system by data processing software, the surface and edge data of the steel part are calculated, a three-dimensional model is built, and the measurement data of the steel part is obtained.

5. The method according to any one of claims 1, 3 or 4, characterized in that: The processing method of the data processing software is: the program automatically finds the point cloud data of the landmark point and calculates the center coordinates of the landmark point by manually selecting the landmark point or nearby; the landmark points in the adjacent scanning model are extracted, and the same landmark points need to be matched when extracting; the model is roughly spliced, then the layout overlapping model is automatically matched again, and the model is precisely spliced.

6. The method according to any one of claims 1, 3 or 4, characterized in that: The target balls (8) are installed on each surface of the steel part or the concrete, at least 3 groups of target balls (8) are installed vertically, at least 2 target balls (8) in each group, and the target balls (8) in each group are installed and fixed on the same horizontal line; The erection of the observation station should ensure that the two three-dimensional laser scanners (9) can repeatedly measure at least 3 same target balls (8) in the scanning process, and the repeatedly measured multiple target balls (8) are different lines.

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