CN118587256A - Digital twinning-based elevator installation process quality monitoring method - Google Patents
Digital twinning-based elevator installation process quality monitoring method Download PDFInfo
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Abstract
The invention relates to the technical field of elevator installation, in particular to a digital twinning-based elevator installation process quality monitoring method, which comprises the following steps: step 1, respectively establishing an ideal geometric model A for a plurality of assembly components, and establishing an ideal geometric model A1 in a maximum physical size state and an ideal geometric model A2 in a minimum physical size state according to the ideal geometric model A; and then, carrying out gridding and discrete processing on key feature surfaces of the ideal geometric models A, A and A2, so as to obtain an assembly component three-dimensional point cloud model B, a three-dimensional point cloud model B1 in a maximum entity size state and a three-dimensional point cloud model B2 in a minimum entity size state of the ideal geometric models A, A and A2 under an assembly coordinate system. The invention can judge the installation quality of the assembly components in the elevator well.
Description
Technical Field
The invention relates to the technical field of elevator installation, in particular to a quality monitoring method for an elevator installation process based on digital twinning.
Background
In the current elevator installation process, the quality monitoring of the process is an important guarantee of the quality. If problems are found after the elevator installation is completed, the elevator equipment is modified again, which brings high cost. In general, a constructor can quantitatively evaluate the quality of the installation process of the elevator from the project management perspective, and hope to find the installation quality problem in time and take targeted quality improvement measures. However, the elevator installation period is short, and the installation quality is mainly represented by the indexes of size, form and position tolerance and component missing equalization, which need to be verified by an accurate measurement method in the installation process. The metering means are limited in the elevator field construction process, so that the implementation of quality improvement measures is often restricted, the working space is narrow, and the precise measuring instruments of the total station and the laser plumb gauge are difficult to arrange and install. Therefore, the current elevator installation lacks quality monitoring on the continuity of the installation process, so that the effective guarantee of the elevator installation quality becomes a hidden trouble.
Disclosure of Invention
In order to solve the technical problems, the invention provides a digital twinning-based elevator installation process quality monitoring method which can judge the installation quality of an assembly component in an elevator hoistway.
The invention is realized by adopting the following technical scheme: a digital twinning-based elevator installation process quality monitoring method, the method comprising the steps of:
Step 1, respectively establishing an ideal geometric model A for a plurality of assembly components, and establishing an ideal geometric model A1 in a maximum physical size state and an ideal geometric model A2 in a minimum physical size state according to the ideal geometric model A; then, carrying out gridding and discrete processing on key feature surfaces of the ideal geometric models A, A and A2, so as to obtain an assembly component three-dimensional point cloud model B, a three-dimensional point cloud model B1 in a maximum entity size state and a three-dimensional point cloud model B2 in a minimum entity size state of the ideal geometric models A, A and A2 under an assembly coordinate system;
Step 2, multiplying a total differential transformation matrix T1 of the assembly maximum assembly tolerance with a three-dimensional point cloud model B1 to obtain a three-dimensional model C1 of the assembly state of the maximum physical size, and multiplying a total differential transformation matrix T2 of the assembly minimum assembly tolerance with a three-dimensional point cloud model B2 to obtain a three-dimensional model C2 of the assembly state of the minimum physical size;
Step 3, carrying out three-dimensional scanning on the assembly component mounted to the elevator shaft to obtain three-dimensional point cloud data X1 of the assembly component;
step 4, registering the three-dimensional point cloud data X1 to a three-dimensional point cloud model B to obtain registered three-dimensional point cloud data X2;
step 5, superposing the three-dimensional point cloud data X2, the three-dimensional model C1 and the three-dimensional model C2, and projected onto the coordinate planes respectively;
and 6, judging the installation quality of the assembly component according to the projection position of the three-dimensional point cloud data X2.
Preferably, the step 1 further specifically includes: and carrying out gridding and discrete processing on the key characteristic surfaces of the ideal geometric model A through a gridding and discrete processing module in the existing three-dimensional design software.
Preferably, the step 2 further specifically includes: the total differential transformation matrix T1 and the total differential transformation matrix T2 are established by:
(1) Establishing a differential transformation matrix T10 of the maximum assembly tolerance and a differential transformation matrix T20 of the minimum assembly tolerance for every two adjacent assembly components according to the assembly tolerance;
(2) All the differential transformation matrices T10 are multiplied together to obtain a total differential transformation matrix T1, and all the differential transformation matrices T20 are multiplied together to obtain a total differential transformation matrix T2.
Preferably, the step 4 further specifically includes: and registering the three-dimensional point cloud data X1 to the three-dimensional point cloud model B in a point cloud registration mode, so as to obtain registered three-dimensional point cloud data X2.
Preferably, the step 6 further specifically includes: if the boundary of the projected three-dimensional point cloud data X2 falls between the boundary of the three-dimensional model C1 and the boundary range of the three-dimensional model C2, judging that the installation quality of the assembly component is qualified, otherwise, judging that the assembly component is unqualified.
Preferably, if the installation quality is unqualified, registering the three-dimensional point cloud data X1 to an ideal geometric model A to obtain a deviation value of the actual position and posture of the assembly component relative to the ideal installation position of the assembly component, and then inverting the deviation value to obtain a correction value, and correcting the installation position of the assembly component according to the correction value.
Preferably, the step 3 further specifically includes: and carrying out three-dimensional scanning on the assembly component mounted in the elevator shaft through laser point cloud scanning equipment to obtain three-dimensional point cloud data X1 of the assembly component.
Preferably, the laser point cloud scanning equipment is conveyed into an elevator shaft through a lifting system to perform three-dimensional scanning;
the lifting system comprises a support, a support is fixed on the support, a winch is fixed on the side wall of the support, a suspension arm is fixed at the top of the support, a fixed pulley is hinged to one side of the suspension arm extending into an elevator shaft, a fixing frame arranged in the shaft is fixed around the fixed pulley by a winding rope of the winch, and a detection device is arranged on the fixing frame and comprises a rotary cradle head and laser point cloud scanning equipment fixed on the rotary cradle head.
The invention has the beneficial effects that:
The invention provides a digital twin-based elevator installation process quality monitoring method, which is characterized in that three-dimensional point cloud data X1 are registered to a three-dimensional point cloud model B in a point cloud registration mode, so that registered three-dimensional point cloud data X2 are obtained. Based on the principle of the closest dimension matching, if the boundary of the projected three-dimensional point cloud data X2 falls between the boundary of the three-dimensional model C1 and the boundary range of the three-dimensional model C2, judging that the installation quality of the assembly component is qualified, otherwise, judging that the assembly component is unqualified. If the installation quality is unqualified, registering the three-dimensional point cloud data X1 to an ideal geometric model A to obtain a deviation value of the actual position and the posture of the assembly component relative to the ideal installation position of the assembly component, and then inverting the deviation value to obtain a correction value, and correcting the installation position of the assembly component according to the correction value.
Drawings
Fig. 1 is a schematic illustration of an embodiment of step 1.
Fig. 2 is a schematic diagram of an embodiment of step 2.
Fig. 3 is a schematic diagram of an embodiment of step 3.
Fig. 4 is a schematic diagram of an embodiment of step 4.
Fig. 5 is a schematic diagram of an embodiment of step 5.
Fig. 6 is a schematic diagram of an embodiment of step 6.
Fig. 7 is a schematic structural view of a lifting system.
Fig. 8 is an enlarged schematic view of reference numeral a in fig. 7.
Fig. 9 is a schematic view of the mount.
Fig. 10 is an enlarged schematic view of reference symbol B in fig. 9.
Fig. 11 is a top view of the mount.
Fig. 7-11: the device comprises a 1-support, a 2-pillar, a 3-winch, a 4-boom, a 41-supporting arm, a 42-fixed pulley, a 5-fixed frame, a 51-cross arm, a 52-pulley, a 53-spring, a 54-telescopic slot, a 55-guide rail, a 56-movable rod, a 6-rotary cradle head, a 7-laser point cloud scanning device, an 8-balance arm, a 9-telescopic cylinder, a 10-rotary motor, an 11-support and a 12-adjusting bolt.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention provides a digital twinning-based elevator installation process quality monitoring method, which comprises the following steps of:
Step 1, respectively establishing an ideal geometric model A for a plurality of assembly components, and establishing an ideal geometric model A1 in a maximum physical size state and an ideal geometric model A2 in a minimum physical size state according to the ideal geometric model A; then, carrying out gridding and discrete processing on key feature surfaces of the ideal geometric models A, A and A2, so as to obtain an assembly component three-dimensional point cloud model B, a three-dimensional point cloud model B1 in a maximum entity size state and a three-dimensional point cloud model B2 in a minimum entity size state of the ideal geometric models A, A and A2 under an assembly coordinate system;
Step 2, multiplying a total differential transformation matrix T1 of the assembly maximum assembly tolerance with a three-dimensional point cloud model B1 to obtain a three-dimensional model C1 of the assembly state of the maximum physical size, and multiplying a total differential transformation matrix T2 of the assembly minimum assembly tolerance with a three-dimensional point cloud model B2 to obtain a three-dimensional model C2 of the assembly state of the minimum physical size;
Step 3, carrying out three-dimensional scanning on the assembly component mounted to the elevator shaft to obtain three-dimensional point cloud data X1 of the assembly component;
step 4, registering the three-dimensional point cloud data X1 to a three-dimensional point cloud model B to obtain registered three-dimensional point cloud data X2;
step 5, superposing the three-dimensional point cloud data X2, the three-dimensional model C1 and the three-dimensional model C2, and projected onto the coordinate planes respectively;
and 6, judging the installation quality of the assembly component according to the projection position of the three-dimensional point cloud data X2.
The step 1 is further specifically: and carrying out gridding and discrete processing on the key characteristic surfaces of the ideal geometric model A through a gridding and discrete processing module in the existing three-dimensional design software.
The step 2 is further specifically: the total differential transformation matrix T1 and the total differential transformation matrix T2 are established by:
(1) Establishing a differential transformation matrix T10 of the maximum assembly tolerance and a differential transformation matrix T20 of the minimum assembly tolerance for every two adjacent assembly components according to the assembly tolerance;
(2) All the differential transformation matrices T10 are multiplied together to obtain a total differential transformation matrix T1, and all the differential transformation matrices T20 are multiplied together to obtain a total differential transformation matrix T2.
The step 4 is further specifically: and registering the three-dimensional point cloud data X1 to the three-dimensional point cloud model B in a point cloud registration mode, so as to obtain registered three-dimensional point cloud data X2.
The step 6 is further specifically: based on the principle of the closest dimension matching, if the boundary of the projected three-dimensional point cloud data X2 falls between the boundary of the three-dimensional model C1 and the boundary range of the three-dimensional model C2, judging that the installation quality of the assembly component is qualified, otherwise, judging that the assembly component is unqualified.
The nearest size matching principle: and registering the two discrete point cloud data by utilizing a point cloud matching principle, namely through an ICP algorithm, so that the space distance between the two point clouds is minimum.
If the installation quality is unqualified, registering the three-dimensional point cloud data X1 to an ideal geometric model A to obtain a deviation value of the actual position and the posture of the assembly component relative to the ideal installation position of the assembly component, and then inverting the deviation value to obtain a correction value, and correcting the installation position of the assembly component according to the correction value.
The invention is further described with reference to the following specific examples:
the invention provides a digital twin-based elevator installation process quality monitoring method,
As in fig. 1, take component 3 as an example; step 1, respectively establishing an ideal geometric model A for a plurality of assembly components, and establishing an ideal geometric model A1 in a maximum physical size state and an ideal geometric model A2 in a minimum physical size state according to the ideal geometric model A; then, carrying out gridding and discrete processing on key feature surfaces of the ideal geometric models A, A and A2, so as to obtain an assembly component three-dimensional point cloud model B, a three-dimensional point cloud model B1 in a maximum entity size state and a three-dimensional point cloud model B2 in a minimum entity size state of the ideal geometric models A, A and A2 under an assembly coordinate system;
And carrying out gridding and discrete processing on the key characteristic surfaces of the ideal geometric model A through a gridding and discrete processing module in the existing three-dimensional design software.
Referring to fig. 2, step 2, multiplying the total differential transformation matrix T1 of the assembly maximum assembly tolerance by the three-dimensional point cloud model B1 to obtain a three-dimensional model C1 of the assembly state of the maximum physical size, and multiplying the total differential transformation matrix T2 of the assembly minimum assembly tolerance by the three-dimensional point cloud model B2 to obtain a three-dimensional model C2 of the assembly state of the minimum physical size;
the total differential transformation matrix T1 and the total differential transformation matrix T2 are established by:
(1) Establishing a differential transformation matrix T10 of the maximum assembly tolerance and a differential transformation matrix T20 of the minimum assembly tolerance for every two adjacent assembly components according to the assembly tolerance;
(2) All the differential transformation matrices T10 are multiplied together to obtain a total differential transformation matrix T1, and all the differential transformation matrices T20 are multiplied together to obtain a total differential transformation matrix T2.
Establishing differential transformation matrix of the ith assembly component relative to the (i-1) th component according to assembly toleranceComprising a matrix of differential transformations of maximum assembly tolerances between adjacent assembly componentsAnd a minimum assembly tolerance transformation differential transformation matrixObtaining a differential transformation matrix of the maximum assembly tolerance of the assembly to be assembled in the current assembly process through the multiplication of the differential transformation matrixAnd a minimum assembly tolerance differential transformation matrix. Multiplying the point cloud models B1 and B2 of the components to be assembled by corresponding differential transformation matrixes respectively, namelyAndObtaining a maximum physical size assembly state point cloud model C1 and a minimum physical size assembly state point cloud model C2;
As shown in fig. 3, step 3, performing three-dimensional scanning on an assembly component installed in an elevator hoistway to obtain three-dimensional point cloud data X1 of the assembly component;
referring to fig. 4, step 4, registering the three-dimensional point cloud data X1 to the three-dimensional point cloud model B to obtain registered three-dimensional point cloud data X2;
and registering the three-dimensional point cloud data X1 to the three-dimensional point cloud model B in a point cloud registration mode, so as to obtain registered three-dimensional point cloud data X2.
Referring to fig. 5, step 5, three-dimensional point cloud data X2, a three-dimensional model C1 and a three-dimensional model C2 are superimposed and respectively projected onto a coordinate plane;
as shown in fig. 6, in step 6, the installation quality of the assembly component is determined according to the projection position of the three-dimensional point cloud data X2.
Based on the principle of the closest dimension matching, if the boundary of the projected three-dimensional point cloud data X2 falls between the boundary of the three-dimensional model C1 and the boundary range of the three-dimensional model C2, judging that the installation quality of the assembly component is qualified, otherwise, judging that the assembly component is unqualified.
The nearest size matching principle: and registering the two discrete point cloud data by utilizing a point cloud matching principle, namely through an ICP algorithm, so that the space distance between the two point clouds is minimum.
In an embodiment of the invention, if the installation quality is unqualified, registering the three-dimensional point cloud data X1 to an ideal geometric model A to obtain a deviation value of the actual position and posture of the assembly component relative to the ideal installation position, and then inverting the deviation value to obtain a correction value, and correcting the installation position of the assembly component according to the correction value.
In an embodiment of the present invention, the step 3 further specifically includes: and carrying out three-dimensional scanning on the assembly component mounted in the elevator shaft through laser point cloud scanning equipment to obtain three-dimensional point cloud data X1 of the assembly component.
7-9, In one embodiment of the invention, the laser point cloud scanning device is conveyed into the elevator hoistway by a lifting system for three-dimensional scanning;
The lifting system comprises a support 1, a support 2 is fixed on the support 1, a winch 3 is fixed on the side wall of the support 2, a suspension arm 4 is fixed at the top of the support 2, one side of the suspension arm 4 extending into an elevator shaft is hinged with a fixed pulley 42, a fixing frame 5 arranged in the shaft is fixed on the winding rope of the winch 3 around the fixed pulley 42, a detection device is arranged on the fixing frame 5, the detection device comprises a rotary tripod head 6 and laser point cloud scanning equipment 7 fixed on the rotary tripod head 6, and the rotary tripod head 6 can rotate by 360 degrees.
In an embodiment of the present invention, as shown in fig. 9-11, the fixing frame 5 includes a pair of cross arms 51 disposed in a crossed manner, two ends of each cross arm 51 are hinged with pulleys 52 contacting with the inner wall of the elevator shaft, the pulleys 52 and the cross arms 51 are fixedly connected with the cross arms 51 through springs 53, when the fixing frame moves, the rollers contact with the elevator shaft, the springs are subjected to extrusion force in the whole process, and the stability in movement can be maintained through cooperation of the springs and the rollers.
In another embodiment of the present invention, as shown in fig. 10, on the basis of the previous embodiment, the outer end of the cross arm 51 is provided with a telescopic slot 54, the telescopic slot 54 is slidably matched with a movable rod 56, the pulley 52 is hinged with the movable rod 56, the telescopic slot 54 is internally provided with a guide rail 55 and a guide slot matched with the movable rod 56, two ends of the spring 53 are respectively fixed at the ends of the telescopic slot 54 and the movable rod 56, and the guide rail and the guide slot are designed to have guiding properties, so that the movable rod, the roller and the spring can be conveniently matched with each other, and the stability is better.
In another embodiment of the present invention, as shown in fig. 7, the support post 2 is fixed with a rotating motor 10, an output shaft of the rotating motor 10 is fixedly connected with the boom 4, a balance arm 8 positioned at the back of the boom is further arranged at the top of the support post 2, and a balancing weight is arranged in the balance arm 8.
In an embodiment of the present invention, as shown in fig. 7-8, the suspension arm 4 includes a support arm 41, the fixed pulley 42 is located below the end of the support arm 41, the fixed pulley 42 is hinged to the support arm 41 through a bracket 11, one end of the bracket 11 is fixed on the support arm 41, and the other end is fixed on the left and right ends of the fixed pulley 42.
In an embodiment of the present invention, as shown in fig. 7, a telescopic cylinder 9 is further disposed in the balance arm 8, and an output end of the telescopic cylinder 9 is fixedly connected with the support arm 41.
In an embodiment of the present invention, as shown in fig. 7, the support 1 is cross-shaped, and an adjusting bolt 12 is screwed at the bottom of the support 1.
In an embodiment of the present invention, a lifting moment limiter is further disposed in the balance arm 8.
The telescopic cylinder 9 works to drive the suspension arm 4 to extend into the upper part of the elevator shaft mouth, the winch 3 is started at the moment, the fixed frame 5 is placed in the elevator shaft, the pulley 52 slides in contact with the elevator shaft and extrudes the spring 53 through the movable rod 56, the spring 53 generates reverse force to keep balance in the falling process, and finally the fixed frame 5 is matched with the laser point cloud scanning equipment 7 on the rotary cradle head 6 to perform three-dimensional scanning on an assembly component mounted on the elevator shaft.
The above description is only of the preferred embodiments of the present application, and should not be construed as limiting the application, but rather as covering all equivalent variations and modifications according to the appended claims.
Claims (8)
1. The quality monitoring method for the elevator installation process based on digital twinning is characterized by comprising the following steps of: the method comprises the following steps:
Step 1, respectively establishing an ideal geometric model A for a plurality of assembly components, and establishing an ideal geometric model A1 in a maximum physical size state and an ideal geometric model A2 in a minimum physical size state according to the ideal geometric model A; then, carrying out gridding and discrete processing on key feature surfaces of the ideal geometric models A, A and A2, so as to obtain an assembly component three-dimensional point cloud model B, a three-dimensional point cloud model B1 in a maximum entity size state and a three-dimensional point cloud model B2 in a minimum entity size state of the ideal geometric models A, A and A2 under an assembly coordinate system;
Step 2, multiplying a total differential transformation matrix T1 of the assembly maximum assembly tolerance with a three-dimensional point cloud model B1 to obtain a three-dimensional model C1 of the assembly state of the maximum physical size, and multiplying a total differential transformation matrix T2 of the assembly minimum assembly tolerance with a three-dimensional point cloud model B2 to obtain a three-dimensional model C2 of the assembly state of the minimum physical size;
Step 3, carrying out three-dimensional scanning on the assembly component mounted to the elevator shaft to obtain three-dimensional point cloud data X1 of the assembly component;
step 4, registering the three-dimensional point cloud data X1 to a three-dimensional point cloud model B to obtain registered three-dimensional point cloud data X2;
step 5, superposing the three-dimensional point cloud data X2, the three-dimensional model C1 and the three-dimensional model C2, and projected onto the coordinate planes respectively;
and 6, judging the installation quality of the assembly component according to the projection position of the three-dimensional point cloud data X2.
2. The digital twinning-based elevator installation quality monitoring method of claim 1, wherein: the step 1 is further specifically: and carrying out gridding and discrete processing on the key characteristic surfaces of the ideal geometric model A through a gridding and discrete processing module in the existing three-dimensional design software.
3. The digital twinning-based elevator installation quality monitoring method of claim 1, wherein: the step 2 is further specifically: the total differential transformation matrix T1 and the total differential transformation matrix T2 are established by:
(1) Establishing a differential transformation matrix T10 of the maximum assembly tolerance and a differential transformation matrix T20 of the minimum assembly tolerance for every two adjacent assembly components according to the assembly tolerance;
(2) All the differential transformation matrices T10 are multiplied together to obtain a total differential transformation matrix T1, and all the differential transformation matrices T20 are multiplied together to obtain a total differential transformation matrix T2.
4. The digital twinning-based elevator installation quality monitoring method of claim 1, wherein: the step 4 is further specifically: and registering the three-dimensional point cloud data X1 to the three-dimensional point cloud model B in a point cloud registration mode, so as to obtain registered three-dimensional point cloud data X2.
5. The digital twinning-based elevator installation quality monitoring method of claim 1, wherein: the step 6 is further specifically: if the boundary of the projected three-dimensional point cloud data X2 falls between the boundary of the three-dimensional model C1 and the boundary range of the three-dimensional model C2, judging that the installation quality of the assembly component is qualified, otherwise, judging that the assembly component is unqualified.
6. The digital twinning-based elevator installation quality monitoring method of claim 5, wherein: if the installation quality is unqualified, registering the three-dimensional point cloud data X1 to an ideal geometric model A to obtain a deviation value of the actual position and the posture of the assembly component relative to the ideal installation position of the assembly component, and then inverting the deviation value to obtain a correction value, and correcting the installation position of the assembly component according to the correction value.
7. The digital twinning-based elevator installation quality monitoring method of claim 1, wherein: the step 3 is further specifically: and carrying out three-dimensional scanning on the assembly component mounted in the elevator shaft through laser point cloud scanning equipment to obtain three-dimensional point cloud data X1 of the assembly component.
8. The digital twinning-based elevator installation quality monitoring method of claim 7, wherein: conveying the laser point cloud scanning equipment into an elevator shaft through a lifting system to perform three-dimensional scanning;
the lifting system comprises a support, a support is fixed on the support, a winch is fixed on the side wall of the support, a suspension arm is fixed at the top of the support, a fixed pulley is hinged to one side of the suspension arm extending into an elevator shaft, a fixing frame arranged in the shaft is fixed around the fixed pulley by a winding rope of the winch, and a detection device is arranged on the fixing frame and comprises a rotary cradle head and laser point cloud scanning equipment fixed on the rotary cradle head.
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