CN112329705A - Method for monitoring maximum deformation in lifting process of large-scale structure - Google Patents
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Abstract
The invention discloses a method for monitoring the maximum deformation in the lifting process of a large-scale structure, which comprises the steps of carrying out laser irradiation on the surface of the structure to form laser speckles, using a CCD camera to shoot according to a certain time interval when the structure is not lifted and in the lifting process, processing a gray-scale image obtained by shooting by using an image acquisition card to obtain pixel point coordinates of the speckles, carrying out clustering processing on coordinate data by using a K-means module in Python, selecting a clustering central point in each speckle as an approximate substitute of the position of the speckle, calculating the offset of corresponding pixel points in matlab, obtaining the maximum offset, drawing a line graph of which the offset changes along with time, and immediately stopping the machine for maintenance if the line graph has special conditions in the lifting process. The invention can improve the measurement precision of the surface deformation of the structure, simplify the setting process of the measuring device, reduce the use cost and greatly improve the measurement efficiency.
Description
Technical Field
The invention relates to a method for monitoring deformation of a large structure, in particular to a method for monitoring maximum deformation of the large structure in a lifting process.
Background
When a large-sized structure is installed and transported, the large-sized structure needs to be lifted through a lifting point on the structure or a fulcrum on the structure is lifted, the large-sized structure can be deformed under the unstable condition generated in the lifting or lifting process, and engineering accidents can be caused when the deformation of the large-sized structure exceeds a critical value.
The span of the large-scale structure is large, the allowable deformation in the lifting process is small, and the structure deformation in the lifting process of the large-scale structure is difficult to accurately measure by using a traditional displacement sensor and an angle inclination sensor. The more accurate measurement method is to measure the surface to be measured by adopting a static level gauge, but the method has complex device setting process, usually needs debugging for several days for one-time measurement, can only measure aiming at a horizontal plane, and has less application scenes.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a non-contact real-time monitoring method which can improve the deformation measurement precision, simplify the device setting process, reduce the use cost and greatly improve the measurement efficiency.
The invention discloses a method for monitoring the maximum deformation in the lifting process of a large-scale structure, which comprises the following steps:
acquiring a gray scale image of an initial state when the surface of the structure is not lifted and a state in the lifting process by using a CCD (charge coupled device) camera;
and secondly, performing data extraction and data storage on each gray-scale image obtained by the CCD camera by using SQL Server software, wherein the specific process is as follows:
firstly, SQL Server software is used for creating an image information database, and then n laser speckle pixel point position tables are established in the image information database
N cluster central point disorder table
And n cluster central point ordered tables
，
SubscriptiThe consistent tables have corresponding relations with each other;
secondly, transmitting the gray level image obtained by the CCD camera to a computer externally connected with an image acquisition card through a network or a circuit;
thirdly, the computer reads the gray level images of the laser speckles on the surface of the structure at different moments by using an image acquisition card, establishes an image coordinate system with the coordinate origin positioned at the lower left corner of the image in each gray level image, records the total number k of the laser speckles, selects a pixel point in each laser speckle image according to a set sequence as an approximate substitution position of the laser speckle position and extracts selected pixel point coordinate data, and sequentially and respectively stores each pixel point coordinate data selected from each gray level image into a laser speckle pixel point position table corresponding to each gray level image in a list form;
step three, respectively using a K-means module in Python to determine the position table of each laser speckle pixel point
Clustering the coordinate data of each pixel point, wherein the specific process is as follows:
firstly, leading in a K-means module in Python, establishing connection with an SQL Server database, and extracting a laser speckle pixel point position table
；
Secondly, the position of each laser speckle pixel point is tabulated
Dividing all pixel point coordinate data into K feature spaces with similar sizes in sequence from front to back in a list by using a K-means module respectively, and randomly selecting a point in each feature space as an initial clustering central point;
thirdly, respectively calculating a position table of each laser speckle pixel point
The distance from all the pixel points to each initial clustering center point is compared, then the distance from each pixel point to each initial clustering center point is compared, each pixel point closest to each initial clustering center point is recombined with the initial clustering center point to form a reconstruction feature space, and k reconstruction feature spaces are formed;
fourthly, calculating a table of the positions of each laser speckle pixel point
Mean value of horizontal and vertical coordinates of all pixel points in each reconstruction characteristic space
And
points of formation: (
,
) Coordinates as a new cluster center point for each reconstructed feature space;
fifthly, adopting a position table of each laser speckle pixel point
The coordinates of the new clustering center point of each reconstructed feature space replace the coordinates of the initial clustering center point in the third step, and the third step to the fifth step are repeated until the laser speckle pixel point position table
The coordinates of the new clustering center point in each reconstructed feature space are not changed any more, and the coordinates of the final clustering center point are obtained;
sixthly, the position of each laser speckle pixel point in the fifth step is tabulated
The coordinates of each final clustering central point in the cluster central point list are respectively stored into a clustering central point disorder list in an SQL database corresponding to the laser speckle pixel point position list
Performing the following steps;
fourthly, data sorting and index correspondence are carried out on the cluster central point ordered table by adopting SQL Server software, and the operation result is stored into the cluster central point ordered table
Performing the following steps;
step five, according to the cluster central point ordered list
Calculating the ordered list of each clustering center point in matlab according to the coordinate data in the table
Maximum amount of positional deviation of
And an amount of angular offset
Drawing an image of the maximum offset of the deformation area on the surface of the structure;
and step six, in the line graph of the change of the offset along with the time, if the offset is close to a preset critical deformation, the slope of the curve is too large, and the slope of the curve generates a sudden change, immediately stopping the lifting work, fixing and reinforcing the related fixed lifting point, and continuing to lift after the lifting standard is reached.
The invention has the advantages that: the method has the advantages that the deformation of each point on the large-scale structure in the lifting process is measured in real time by using a high-precision image recognition technology, the deformation is monitored in real time, engineering accidents caused by the fact that the deformation of the large-scale structure exceeds a critical value are prevented, the method can be used for solving the problem of measuring the maximum deformation of the large-scale structure in the installation and transportation processes of the large-scale structure, and the method is simple in operation, visible in result, stable, reliable and wide in adaptability.
Drawings
FIG. 1 is a flow chart of a method for monitoring maximum deformation during the lifting process of a large structure.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The working principle of the invention is as follows: 1. firstly, carrying out laser irradiation on the surface of a structure to form approximately circular speckles, wherein all rows and all columns of the speckles are parallel to each other, and the speckles are similar in size and uniform in distribution; 2. in the lifting process, the speckles can be changed in position and shape, but because the deformation of the large-scale structure belongs to tiny deformation, all rows and all columns of the speckles can still be regarded as approximately parallel in the lifting process; 3. selecting a small pixel point in each speckle as an approximate substitute of the speckle position; and 4, calculating the offset of the corresponding pixel points to approximately replace the offset of the laser speckles, and monitoring the deformation of the surface of the structure through the offset of the laser speckles.
Based on the principle, the method of the invention comprises the following steps:
as shown in fig. 1, the method for monitoring the maximum deformation amount in the lifting process of the large structure of the present invention comprises the following steps:
step one, collecting a gray level image of an initial state when the surface of a structure is not lifted and a state in the lifting process by using a CCD camera, wherein the specific process is as follows:
the method comprises the steps that firstly, laser is adopted to irradiate the surface of a structure, so that the surface of the structure generates a plurality of laser speckles which are approximately circular, the central lines of the laser speckles are parallel to each other and are uniformly distributed on the surface of the structure, the position of a CCD camera is adjusted, so that the lens plane of the CCD camera is parallel to the surface of the structure which generates the laser speckles, and the measurement error is reduced.
And secondly, shooting the surface of the structure generating the laser speckles by using a CCD camera according to a set time interval (such as 0.1S) when the structure is not lifted and in the lifting process, and obtaining a gray level image of the laser speckles on the surface of the structure at different moments.
And secondly, performing data extraction and data storage on each gray-scale image obtained by the CCD camera by using SQL Server software, wherein the specific process is as follows:
firstly, SQL Server software is used for creating an image information database, and then n laser speckle pixel point position tables are established in the image information database
N cluster central point disorder table
And n cluster central point ordered tables
，
SubscriptiThe tables that are consistent have a correspondence relationship with each other.
And secondly, transmitting the gray level image obtained by the CCD camera to a computer externally connected with an image acquisition card through a network or a circuit.
Thirdly, the computer reads the gray level images of the laser speckles on the surface of the structure at different moments by using an image acquisition card, establishes an image coordinate system with the coordinate origin positioned at the lower left corner of the image in each gray level image, records the total number k of the laser speckles, selects a pixel point in each laser speckle image as an approximate substitution position of the laser speckle position according to a set sequence (from top to bottom and from left to right, for example), extracts the coordinate data of the selected pixel point, and sequentially and respectively stores the coordinate data of each pixel point selected from each gray level image into a laser speckle pixel point position table corresponding to each gray level image in a list form, namely the coordinate of the pixel point selected from the same gray level image is stored into the same laser speckle pixel point position table.
Step three, respectively using a K-means module in Python to determine the position table of each laser speckle pixel point
Clustering the coordinate data of each pixel point, wherein the specific process is as follows:
firstly, leading in a K-means module in Python, establishing connection with an SQL Server database, and extracting a laser speckle pixel point position table
。
Secondly, the position of each laser speckle pixel point is tabulated
All the pixel point coordinate data in the cluster are divided into K feature spaces with similar sizes in sequence from front to back in the list by using a K-means module respectively, and a point is randomly selected in each feature space to serve as an initial clustering central point.
Thirdly, respectively calculating a position table of each laser speckle pixel point
The distance from all the internal pixel points to each initial clustering center point is compared, then the distance from each pixel point to each initial clustering center point is compared, each pixel point closest to each initial clustering center point is recombined with the initial clustering center point to form a reconstruction feature space, k reconstruction feature spaces are formed, and the distance formula is as follows:
in the formula
And
table for indicating any one laser speckle pixel point position
The abscissa and ordinate of any one of the pixel points,
and
table for indicating any one laser speckle pixel point position
The abscissa and the ordinate of the initial clustering center point corresponding to any one feature space.
Fourthly, calculating a table of the positions of each laser speckle pixel point
Mean value of horizontal and vertical coordinates of all pixel points in each reconstruction characteristic space
And
points of formation: (
,
) Coordinates as new cluster center point for each reconstructed feature space, where:
in the formula
、
Respectively representing the abscissa sum and the ordinate sum of all pixel points in each reconstruction space,
and representing the total number of all pixel points in each reconstruction space.jIs shown asjThe characteristic space is divided into a plurality of characteristic spaces,
is shown asjThe total number of pixel points within the feature space,wis shown asjIn a characteristic spacewThe number of the pixel points is one,
and
is shown asjIn a feature spacewAnd the horizontal and vertical coordinates of each pixel point.
Fifthly, adopting a position table of each laser speckle pixel point
The coordinates of the new clustering center point of each reconstructed feature space replace the coordinates of the initial clustering center point in the third step, and the third step to the fifth step are repeated until the laser speckle pixel point position table
The coordinates of the new cluster center point in each reconstructed feature space are not changed any more to obtain the coordinates of the final cluster center point。
Sixthly, the position of each laser speckle pixel point in the fifth step is tabulated
The coordinates of each final clustering central point in the cluster central point list are respectively stored into a clustering central point disorder list in an SQL database corresponding to the laser speckle pixel point position list
In (1).
Fourthly, data sorting and index correspondence are carried out on the cluster central point ordered table by adopting SQL Server software, and the operation result is stored into the cluster central point ordered table
Performing the following steps; the specific process is as follows:
firstly, SQL Server software is adopted to arrange a table in each cluster central point
Allocating k blank storage areas in the middle, and sequentially allocating 1-k index values;
second, respectively calculatingiZhang clustering central point disorder table
Each of the final cluster center points andi-1zhang clustering central point ordered list
The distance between the middle k coordinate points is equal to the distance between the first coordinate point and the second coordinate pointi-1Zhang clustering central point ordered list
The first coordinate point of the three coordinate points is nearest to the first coordinate pointiZhang clustering central point disorder table
Final cluster center point coordinates inAccording to the final cluster center pointi-1Zhang clustering central point ordered list
The index value of (1) is stored iniClustering the same index value position in the central point ordered list; wherein the content of the first and second substances,i= 0 structural object non-lifting state, 0 th clustering central point disorder table
The coordinates of the final cluster central point in the table are directly and sequentially stored into the 0 th cluster central point ordered table
In (1).
This step is illustrated below by way of example: suppose that the 6 th cluster center point is currently being sorted
Processing is carried out, and the 5 th cluster central point ordered list is processed
After completion of the filling, 3 pieces of coordinate data are stored in the two tables, respectively. Firstly, in the 6 th cluster center point ordered table
3 blank storage areas are distributed and index values of 1-3 are distributed in sequence; then, take out
1 st coordinate
Respectively calculate
And
of 3 coordinates inDistance, hypothesis
The nearest coordinate point of 2 is obtained as the index value 2
Finds a blank storage area with index 2, and will
Storing the position; then, for
2 nd coordinate of
3 rd coordinate
The same operation is carried out, and
and (5) completing filling.
Step five, according to the cluster central point ordered list
Calculating the ordered list of each clustering center point in matlab according to the coordinate data in the table
Maximum amount of positional deviation of
And an amount of angular offset
And drawing an image of the maximum offset of the deformation area of the surface of the structure, wherein the specific process is as follows:
step one, using the 0 th clustering central point to sequence the table
Taking the coordinates of each final clustering center point as a reference, and calculating the 0 th clustering center point ordered list by using matlab software
The coordinates of the central point of each final cluster are respectively the second oneiZhang clustering central point ordered list
Position offset between coordinates of respective cluster center points at the same position of medium index value
And an amount of angular offset
Wherein:
in the formula
、
For the 0 th cluster central point ordered table
The middle index value isjThe abscissa and ordinate of each final cluster center point,
、
is shown asiZhang clustering central point ordered list
The middle index value isjThe abscissa and ordinate of the respective cluster center point of (a),Fthe magnification factor representing the length of the image and the actual length can be selected according to the distance between the lens plane of the CCD camera and the surface of the structure, the larger the distance is,Fthe larger the value of (A), the more common the value of (B) is in the range of 2 to 20.
Secondly, obtaining an ordered list of each clustering center point by utilizing a math toolkit in the matlab
Each final cluster center point and 0 th cluster center point in the ordered list
The maximum position offset of the structure surface is compared with the final cluster center point of each same index value
And an amount of angular offset
Then sorted by the cluster central point
The shooting time corresponding to the gray level images of the laser speckles on the surface of the structure respectively is the X-axis coordinate, and the maximum position offset of the surface of the structure corresponding to each cluster central point ordered table is used as the coordinate
And an amount of angular offset
Plotting the offset over time for the ordinateA varying line graph, wherein:
in the formula
Indicating the position offset of all cluster center point coordinates at the ith time,
and the angular offset of the coordinates of the center points of all clusters at the ith moment is represented.
And step six, in the line graph of which the offset changes along with time, if the situation that the offset is close to a preset critical deformation, the curve slope is too large, the curve slope generates sudden change and the like, which indicates that the surface of the structure generates larger deformation in the lifting process, the lifting work is immediately stopped, and relevant fixed lifting points (hanging rings welded on the surface of the structure, supporting points used for hydraulic jacking and the like) are fixed and reinforced by adopting the existing method, so that the lifting is continued after the lifting standard is reached.
Claims (4)
1. A method for monitoring the maximum deformation in the lifting process of a large-scale structure is characterized by comprising the following steps:
acquiring a gray scale image of an initial state when the surface of the structure is not lifted and a state in the lifting process by using a CCD (charge coupled device) camera;
and secondly, performing data extraction and data storage on each gray-scale image obtained by the CCD camera by using SQL Server software, wherein the specific process is as follows:
firstly, SQL Server software is used for creating an image information database, and then n laser speckle pixel point position tables are established in the image information database
N cluster central point disorder table
And n cluster central point ordered tables
，
SubscriptiThe consistent tables have corresponding relations with each other;
secondly, transmitting the gray level image obtained by the CCD camera to a computer externally connected with an image acquisition card through a network or a circuit;
thirdly, the computer reads the gray level images of the laser speckles on the surface of the structure at different moments by using an image acquisition card, establishes an image coordinate system with the coordinate origin positioned at the lower left corner of the image in each gray level image, records the total number k of the laser speckles, selects a pixel point in each laser speckle image according to a set sequence as an approximate substitution position of the laser speckle position and extracts selected pixel point coordinate data, and sequentially and respectively stores each pixel point coordinate data selected from each gray level image into a laser speckle pixel point position table corresponding to each gray level image in a list form;
step three, respectively using a K-means module in Python to determine the position table of each laser speckle pixel point
Clustering the coordinate data of each pixel point, wherein the specific process is as follows:
firstly, leading in a K-means module in Python, establishing connection with an SQL Server database, and extracting a laser speckle pixel point position table
；
Secondly, the position of each laser speckle pixel point is tabulated
Dividing all pixel point coordinate data into K feature spaces with similar sizes in sequence from front to back in a list by using a K-means module respectively, and randomly selecting a point in each feature space as an initial clustering central point;
thirdly, respectively calculating a position table of each laser speckle pixel point
The distance from all the pixel points to each initial clustering center point is compared, then the distance from each pixel point to each initial clustering center point is compared, each pixel point closest to each initial clustering center point is recombined with the initial clustering center point to form a reconstruction feature space, and k reconstruction feature spaces are formed;
fourthly, calculating a table of the positions of each laser speckle pixel point
Mean value of horizontal and vertical coordinates of all pixel points in each reconstruction characteristic space
And
points of formation: (
,
) Coordinates as a new cluster center point for each reconstructed feature space;
the fifth step, adopt eachZhang laser speckle pixel point position table
The coordinates of the new clustering center point of each reconstructed feature space replace the coordinates of the initial clustering center point in the third step, and the third step to the fifth step are repeated until the laser speckle pixel point position table
The coordinates of the new clustering center point in each reconstructed feature space are not changed any more, and the coordinates of the final clustering center point are obtained;
sixthly, the position of each laser speckle pixel point in the fifth step is tabulated
The coordinates of each final clustering central point in the cluster central point list are respectively stored into a clustering central point disorder list in an SQL database corresponding to the laser speckle pixel point position list
Performing the following steps;
step four, adopting SQL Server software to perform disorder table on the clustering center point
Data sorting and index correspondence are carried out, and the operation result is stored into a cluster central point ordered list
Performing the following steps;
step five, according to the cluster central point ordered list
Calculating the ordered list of each clustering center point in matlab according to the coordinate data in the table
Maximum amount of positional deviation of
And an amount of angular offset
Drawing an image of the maximum offset of the deformation area on the surface of the structure;
and step six, in the line graph of the change of the offset along with the time, if the offset is close to a preset critical deformation, the slope of the curve is too large, and the slope of the curve generates a sudden change, immediately stopping the lifting work, fixing and reinforcing the related fixed lifting point, and continuing to lift after the lifting standard is reached.
2. The method for monitoring the maximum deformation amount in the lifting process of the large structure according to claim 1, wherein the method comprises the following steps: the specific process of the first step is as follows:
the method comprises the following steps that firstly, laser is adopted to irradiate the surface of a structure, so that a plurality of laser speckles which are approximately circular are generated on the surface of the structure, the central lines of the laser speckles are parallel to each other and are uniformly distributed on the surface of the structure, and the position of a CCD camera is adjusted, so that the lens plane of the CCD camera is parallel to the surface of the structure which generates the laser speckles;
and secondly, when the structure is not lifted and in the lifting process, shooting the surface of the structure generating the laser speckles by using a CCD camera according to a set time interval to obtain a gray level image of the laser speckles on the surface of the structure at different moments.
3. The method for monitoring the maximum deformation amount in the lifting process of the large structure according to claim 1 or 2, wherein: the fourth specific process of the step is as follows:
firstly, SQL Server software is adopted to arrange a table in each cluster central point
Allocating k blank storage areas in the middle, and sequentially allocating 1-k index values;
second, respectively calculatingiZhang clustering central point disorder table
Each of the final cluster center points andi-1zhang clustering central point ordered list
The distance between the middle k coordinate points is equal to the distance between the first coordinate point and the second coordinate pointi-1Zhang clustering central point ordered list
The first coordinate point of the three coordinate points is nearest to the first coordinate pointiZhang clustering central point disorder table
The coordinate of the final clustering center point is in the second place according to the final clustering center pointi-1Zhang clustering central point ordered list
The index value of (1) is stored iniClustering the same index value position in the central point ordered list; wherein the content of the first and second substances,i= 0 structural object non-lifting state, 0 th clustering central point disorder table
The coordinates of the final cluster central point in the table are directly and sequentially stored into the 0 th cluster central point ordered table
In (1).
4. The method for monitoring the maximum deformation in the lifting process of the large structure according to claim 3, wherein the specific process of the fifth step is as follows:
step one, using the 0 th clustering central point to sequence the table
Taking the coordinates of each final clustering center point as a reference, and calculating the 0 th clustering center point ordered list by using matlab software
The coordinates of the central point of each final cluster are respectively the second oneiZhang clustering central point ordered list
Position offset between coordinates of respective cluster center points at the same position of medium index value
And an amount of angular offset
；
Secondly, obtaining an ordered list of each clustering center point by utilizing a math toolkit in the matlab
Each final cluster center point and 0 th cluster center point in the ordered list
The maximum position offset of the structure surface is compared with the final cluster center point of each same index value
And an amount of angular offset
Then sorted by the cluster central point
The shooting time corresponding to the gray level images of the laser speckles on the surface of the structure respectively is the X-axis coordinate, and the maximum position offset of the surface of the structure corresponding to each cluster central point ordered table is used as the coordinate
And an amount of angular offset
A line graph of the offset over time is plotted for the ordinate.
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| CN113532270A (en) * | 2021-05-31 | 2021-10-22 | 天津博迈科海洋工程有限公司 | Attitude detection method for FPSO flare tower installation process |
| CN113970552A (en) * | 2021-09-26 | 2022-01-25 | 北京京仪仪器仪表研究总院有限公司 | Apple nondestructive testing method combining laser speckle and Kmeans clustering algorithm |
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