CN110532725B - Engineering structure mechanical parameter identification method and system based on digital image - Google Patents
Engineering structure mechanical parameter identification method and system based on digital image Download PDFInfo
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Abstract
A method and a system for recognizing engineering structure mechanics parameters based on digital images are disclosed. The method can comprise the following steps: step 1: calculating a calculation formula of the related deformation parameters; step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field; and 4, step 4: and calculating a strain field according to the displacement gradient. The invention realizes the monitoring and early warning of the deformation of the structural member based on the digital image recognition technology by analyzing and comparing the deformation data of other images based on the reference image.
Description
Technical Field
The invention relates to the field of engineering structure monitoring, in particular to an engineering structure mechanical parameter identification method and system based on digital images.
Background
In recent years, with the vigorous development of the construction industry of China, the construction scale is continuously expanded, and the construction speed is changed day by day. Meanwhile, the structure generates larger deformation due to bearing load, which directly affects the strength and stability of the structure, and causes the problem of related potential safety hazards in engineering. At present, two methods, namely theoretical analysis calculation and field test detection, are mostly adopted in a conventional method for solving the problem of structural safety in engineering. The relevant numerical simulation performed by using a finite element theory is a typical representation of theoretical analysis and calculation, but the calculation conditions set in the calculation of the method are mostly in an ideal stress state, and the actual complex stress condition of the structural member is difficult to simulate, so that a certain deviation exists between a result obtained when the numerical simulation analysis and calculation are performed on the same structural member and an actual value; the field test detection method usually measures the structure directly and obtains relevant mechanical parameters, and the result has higher reliability.
The structure will deform to some extent after bearing the relevant load, so the measurement of the structural deformation is a main test content in the field structural test measurement. According to whether the measuring instrument is in direct contact with the surface of the structure to be measured or not, the related measuring methods can be divided into contact type measuring methods and non-contact type measuring methods. Among them, the contact measuring instrument is represented by a displacement meter, a sensor, a strain gauge and the like, has the advantages of simple operation, strong directness, higher reliability of obtained measured data and the like, and is continuously used and developed. Correspondingly, various waves such as ultrasonic waves, electromagnetic waves and light waves are mostly used for detection in a non-contact type measuring method, and common instruments include a total station, a GPS, an ultrasonic nondestructive detector and the like. And the method can make up the defects of special working environments such as high temperature, radiation, corrosivity and the like which are not suitable for the contact type measuring method. Although the method can solve more practical engineering measurement problems, the application conditions, the range, the precision and the like of the method still have certain limitations. Therefore, there is a need to develop a method and a system for identifying mechanical parameters of an engineering structure based on digital images.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides an engineering structure mechanical parameter identification method and system based on a digital image, which can realize monitoring and early warning of deformation of a structural member based on a digital image identification technology by analyzing and comparing deformation data of other images based on a reference image.
According to one aspect of the invention, an engineering structure mechanical parameter identification method based on digital images is provided. The method may include: step 1: calculating a calculation formula of the related deformation parameters; step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field; and 4, step 4: and calculating a strain field according to the displacement gradient.
Preferably, the step 2 includes: step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value; step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric value, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are closest to the plurality of seed points and not calculated, and repeating the steps 203-204; step 206: step 205 is repeated until no points are contained in the calculation sub-area.
Preferably, the relevant deformation parameter is calculated by equation (1):
wherein, C LS In order to be a relevant parameter of the deformation,
is the coordinate of any one point Q and,
f and g are the reference and current image gray scale intensity functions at the designated positions respectively for the coordinates corresponding to the deformed Q m As the mean value of the gray levels of the sub-regions of the reference image, g m And the gray level average value of the sub-area of the current deformation image is obtained.
Preferably, the step 3 comprises: performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating the displacement gradient according to the displacement value after the noise reduction.
Preferably, the displacement gradient is:
wherein E is xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy The y-direction displacement gradient, u the x-direction displacement value, and v the y-direction displacement value.
According to another aspect of the invention, an engineering structure mechanical parameter identification system based on digital images is provided, which is characterized by comprising: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: step 1: calculating a calculation formula of the related deformation parameters; step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field; and 4, step 4: and calculating a strain field according to the displacement gradient.
Preferably, the step 2 includes: step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value; step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric value, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the point with the minimum related deformation parameter which is not calculated as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are closest to the plurality of seed points and not calculated, and repeating the steps 203-204; step 206: step 205 is repeated until no non-computed points are contained in the compute subsection.
Preferably, the relevant deformation parameter is calculated by equation (1):
wherein, C LS In order to be able to correlate the deformation parameters,
is the coordinate of any one point Q,
for the coordinates corresponding to Q' after deformation, f and g are the reference and current images at the specified positions, respectivelyIntensity function of gray scale, f m As the mean value of the gray levels of the sub-regions of the reference image, g m The gray level average value of the sub-area of the current deformation image is obtained.
Preferably, the step 3 comprises: performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating the displacement gradient according to the displacement value after the noise reduction.
Preferably, the displacement gradient is:
wherein, E xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy The y-direction displacement gradient, u the x-direction displacement value, and v the y-direction displacement value.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings, wherein like reference numerals generally represent like parts in the exemplary embodiments of the present invention.
Fig. 1 shows a flow chart of the steps of the engineering structure mechanical parameter identification method based on digital images according to the invention.
Fig. 2 shows a schematic diagram of the basic principle of the digital image correlation method.
Fig. 3 shows a schematic diagram of a live image acquisition according to an embodiment of the invention.
FIG. 4 shows a schematic diagram of a captured digital image according to one embodiment of the present invention.
Fig. 5a, 5b, 5c, 5d, 5e, 5f show schematic diagrams of a deformed cloud from second 1 to second 6, respectively, according to an embodiment of the invention.
Fig. 6a, 6b, 6c, 6d, 6e, 6f show schematic diagrams of a deformation cloud in 7 th-12 th seconds, respectively, according to an embodiment of the invention.
Fig. 7a, 7b, 7c, 7d, 7e, 7f show schematic diagrams of a 13 th-18 th modified cloud according to an embodiment of the invention, respectively.
Fig. 8a, 8b, 8c, 8d, 8e, 8f show schematic diagrams of a 19 th-24 th modified cloud according to an embodiment of the invention, respectively.
Fig. 9a, 9b, 9c, 9d, 9e, 9f show schematic diagrams of a deformation cloud from 25 th to 30 th second, respectively, according to an embodiment of the invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are illustrated in the accompanying drawings, it is to be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow chart of the steps of the engineering structure mechanical parameter identification method based on digital images according to the invention.
In this embodiment, the method for identifying engineering structure mechanical parameters based on digital images according to the present invention may include: step 1: calculating a calculation formula of the related deformation parameters; step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field; and 4, step 4: and calculating a strain field according to the displacement gradient.
In one example, step 2 comprises: step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value; step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric values, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are not calculated and are closest to the plurality of seed points, and repeating the steps 203-204; step 206: step 205 is repeated until no uncomputed points are contained in the compute sub-region.
In one example, the associated deformation parameter is calculated by equation (1):
wherein, C LS In order to be able to correlate the deformation parameters,
is the coordinate of any one point Q,
f and g are the reference and current image gray scale intensity functions at the designated positions respectively for the coordinates corresponding to the deformed Q m As a reference image sub-region gray-scale average value, g m The gray level average value of the sub-area of the current deformation image is obtained.
In one example, step 3 comprises: performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating the displacement gradient according to the displacement value after noise reduction.
In one example, the displacement gradient is:
wherein, E xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy Is a gradient of displacement in the y direction, u isThe displacement value in the x direction, and v is the displacement value in the y direction.
Specifically, the digital image correlation method, as a novel optical measurement method, has the advantages of low cost, non-contact, full field, high precision and convenience in automation realization, is receiving more and more attention, and also makes up the defects of the traditional measurement method in the current civil engineering structure deformation measurement.
A Digital Image Correlation Method (DICM), also called Digital Speckle Correlation Method (DSCM), is a deformation measurement Method based on computer and Digital Image technology. According to the method, speckle images of the surface of the test piece in different states are recorded, and the position of an interest point on the surface of the test piece in the image is tracked by using a related matching algorithm based on image gray, so that relative deformation information of the surface of the test piece in different states is obtained.
The digital image correlation method processes two digital images recorded before and after deformation, and generally, the digital image before deformation is called a reference image, and the digital image after deformation is called a current image.
In the sub-region based DIC algorithm, the reference image is divided into smaller regions of sub-regions or sub-windows and assuming that the deformation inside each sub-region is uniform, then the sub-region where the deformation has occurred corresponding to the reference image word is found in the current image.
Fig. 2 shows a schematic diagram of the basic principle of the digital image correlation method.
In the calculation, the sub-regions are initially a continuous set of circular points, which are at integer pixel positions of the reference image. As shown in FIG. 2, the reference image sub-region is selected to be the point P (x) 0 ,y 0 ) A rectangular area S of the pixel range of (2N + 1) × (2N + 1) which is the central point, and the horizontal displacement of the central point of the sub-area of the deformed image is u and the vertical displacement value is v. When the sub-area of the deformed image is subjected to relevant deformation such as translation, stretching, compression and the like, the P point in the sub-area of the reference image is deformed into the P' point in the deformed image, and the corresponding relationship of displacement before and after deformation is as follows:
P＝{u v u x u y v x v y } T (4)
coordinate value (x) of center P of initial reference image subregion in the above formula (3) 0 ,y 0 ) Coordinate value (x) of point P' in the sub-area of the deformed image 0 ',y 0 '). In the formula (3), S is a set including all the sub-region points, Δ x and Δ y are used to indicate the relative positions of the points with respect to the center of the sub-region, and the corresponding relationship between the sub-region points in the current image and the reference image is established.
The general deformation vector P of the change of the position and the shape of the image subarea before and after deformation is defined by the formula (4); u. of x 、u y 、v x And v y The partial derivatives of the displacements u, v and at the same time the displacement gradient parameters of the sub-regions of the reference image, all parameter values being constant for a given sub-region. Equation (3) can also be written in matrix form:
xi in the above equation is an enhancement vector containing the point of the sub-region and the coordinates x, y, Δ x and Δ y are the distances between the point Q (or Q ') within the sub-region and the point P (or P') at the center of the sub-region, and w is a warping function.
In order to improve the calculation efficiency and meet the calculation speed requirement of the subsequent inverse matching algorithm, the sub-regions of the reference image are set to be deformed in the image as follows:
in the above formula, the coordinate of any point Q in the initial reference image subregion is
And
is the coordinate value of the point Q in the sub-area of the deformed reference image, and the coordinate of the point Q' is
u r 、v r The distance between Q and Q 'along the x-axis and the y-axis when the Q and Q' are placed in the same image subarea. The coordinate transformation in equation (6) is performed between two different coordinate systems in the reference image.
The basic principle of image matching is to compare the correlation of RGB colors of pixels in sub-regions of pixel block images of the same size centered on two points in related images before and after deformation to determine whether they are the same points. The correlation coefficient to be compared here is calculated by the DIC algorithm from the introduced correlation function when using the correlation search method. The method uses two different correlation functions to search an initial value and carry out subsequent refinement on the initial value. The initial value is obtained by computing the correlation (NCC) of the normalized cross-correlation function at integer pixel positions.
The engineering structure mechanical parameter identification method based on the digital image can comprise the following steps:
step 1: calculating a calculation formula of the related deformation parameters through the formula (1);
step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; the method specifically comprises the following steps:
step 201: determining a calculation subarea, and calculating a normalized cross-correlation method metric value through a formula (7):
the functions f and g are the reference and current image gray scale intensity functions at specified locations, respectively, the function f m As a reference image sub-region gray-scale average value, g m Taking the gray level average value of the current deformation image subregion:
in the above formula, n (S) is the number of data points in the sub-region S. Since the digital image records discrete gray scale information, the correlation search using the correlation function of equation (7) is performed in units of whole pixels in the sub-area. The result is a coarse integer pixel displacement and to achieve an accurate displacement measurement, the next step is to use a non-linear optimizer to optimize the results with sub-pixel resolution by finding the minimum correlation condition, which is denoted by C cc And C LS Both parameters have a greater influence.
Step 202: determining seed points of the calculation sub-area according to the metric value of the normalized cross-correlation method, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and closest to the seed points, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are closest to the plurality of seed points and not calculated, and repeating the steps 203-204; step 206: step 205 is repeated until no uncomputed points are contained in the compute sub-region.
And step 3: and performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction as follows:
calculating a displacement gradient through a formula (2) according to the displacement value after noise reduction;
and 4, step 4: and calculating the strain field according to the displacement gradient, wherein a method for calculating the strain field can be selected by a person skilled in the art according to specific situations.
The method realizes the monitoring and early warning of the deformation of the structural member based on the digital image recognition technology by analyzing and comparing the deformation data of the other images based on the reference image.
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
The engineering structure mechanical parameter identification method based on the digital image can comprise the following steps:
step 1: calculating a calculation formula of the related deformation parameter through the formula (1);
and 2, step: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; the method specifically comprises the following steps:
step 201: determining a calculation subarea, and calculating a normalized cross-correlation method metric value through a formula (7); reference image subregion gray level average value f m The gray average value g of the sub-area of the current deformation image is formula (8) m Is equation (9). Since the digital image records discrete gray scale information, the correlation search using the correlation function of equation (7) is performed in units of whole pixels in the sub-area. The result is a coarse integer pixel displacement and to achieve an accurate displacement measurement, a non-linear optimizer will be used to optimize the results with sub-pixel resolution by finding the minimum correlation condition.
Step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric values, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are not calculated and are closest to the plurality of seed points, and repeating the steps 203-204; step 206: step 205 is repeated until no uncomputed points are contained in the compute sub-region.
And step 3: and (3) performing least square plane fitting according to the displacement field to obtain displacement values after noise reduction in formulas (10) - (11), and calculating a displacement gradient through a formula (2) according to the displacement values after noise reduction.
And 4, step 4: and calculating a strain field according to the displacement gradient.
The monitoring data of the bottom plate of the overhead bridge structure are taken as an example for introduction. And carrying out on-site monitoring on the light rail bridge in the operation period, and carrying out corresponding calculation according to the obtained related data. The original data collected on site need to be calculated by a digital image correlation method, and the image can be converted into a result which can be recognized in engineering.
Fig. 3 shows a schematic diagram of a live image acquisition according to an embodiment of the invention.
FIG. 4 shows a schematic diagram of a captured digital image according to one embodiment of the present invention.
As can be seen from fig. 3, the x direction of the image capturing position area is parallel to the axis of the rail base plate, the y direction is the cross-sectional direction of the rail base plate, and the x and y directions respectively correspond to the horizontal and vertical directions of the captured digital image, and the captured digital image is as shown in fig. 4.
Fig. 5a, 5b, 5c, 5d, 5e, 5f show schematic diagrams of a deformed cloud from second 1 to second 6, respectively, according to an embodiment of the invention.
Fig. 6a, 6b, 6c, 6d, 6e, 6f show schematic diagrams of a deformation cloud in 7 th-12 th seconds, respectively, according to an embodiment of the invention.
Fig. 7a, 7b, 7c, 7d, 7e, 7f show schematic diagrams of a 13 th-18 th modified cloud according to an embodiment of the invention, respectively.
Fig. 8a, 8b, 8c, 8d, 8e, 8f show schematic diagrams of a 19 th-24 th modified cloud respectively, according to an embodiment of the invention.
Fig. 9a, 9b, 9c, 9d, 9e, 9f show schematic diagrams of a deformation cloud from 25 th to 30 th second, respectively, according to an embodiment of the invention.
As can be seen from the figure, the digital image correlation method software calculates the pixel deformation of the image, and the pixel size of the calculated image and the size of the actual test area are converted, so that the unit of the deformation of the calculation output result is meter. The deformation change interval from the 1 st second to the 8 th second is-2 x 10 -4 m to 1.5X 10 -4 m, the deformation change interval at the 9 th second is (-4.5 multiplied by 10) -4 0) m, the amplitude gradually increases from the 9 th second and reaches (-3 x 10) the interval amplitude in the time period from the 13 th second to the 17 th second -3 ～-0.5×10 -3 ) The peak deformation value of m is obviously increased compared with the data of the former 8 seconds. Then gradually decreases, and the deformation interval returns to (-5 multiplied by 10) in the 23 th second -4 About 0) m, and the deformation interval after 7 seconds is (-4X 10) m, which is the same as the 9 th second -4 ～-1.5×10 -4 ) m, close to the previous 8 seconds.
Observing the cloud picture scale, the maximum value of the cloud picture scale is kept in a positive state in the first 8 seconds, the maximum value of the cloud picture scale becomes 0 at the beginning of the 9 th second, the maximum value is a negative value from the 10 th second to the 22 th second, and the maximum value is restored to be in a positive state after the maximum value becomes 0 again in the 23 th second. And combining the field recording condition, when the light rail train enters the pier in front of the measuring point at the 9 th second, the light rail train leaves the pier behind the measuring point at the 21 st second, and the whole process lasts for 12 seconds. The on-site recording condition proves that the 9 th to 22 th seconds of the cloud chart change are caused by train passing. The above conditions prove that the digital image correlation measurement method is feasible to be applied to engineering components, and the measurement of deformation of structural components caused by load bearing can be identified and calculated by the digital image.
In summary, the invention realizes the monitoring and early warning of the deformation of the structural member based on the digital image recognition technology by analyzing and comparing the deformation data of the other images based on the reference image.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
According to an embodiment of the invention, an engineering structure mechanical parameter identification system based on digital images is provided, which is characterized by comprising: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: step 1: calculating a calculation formula of the related deformation parameters; step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field; and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field; and 4, step 4: and calculating a strain field according to the displacement gradient.
In one example, step 2 comprises: step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value; step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric values, and calculating related deformation parameters and displacement accurate values of the seed points; step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated; step 204: marking the point with the minimum related deformation parameter which is not calculated as a seed point, and calculating the displacement accurate value of the seed point; step 205: determining a plurality of points which are not calculated and are closest to the plurality of seed points, and repeating the steps 203-204; step 206: step 205 is repeated until no uncomputed points are contained in the compute sub-region.
In one example, the associated deformation parameter is calculated by equation (1):
wherein, C LS In order to be a relevant parameter of the deformation,
is the coordinate of any one point Q and,
f and g are the reference and current image gray scale intensity functions at the designated positions respectively for the coordinates corresponding to the deformed Q m As a reference image sub-region gray-scale average value, g m The gray level average value of the sub-area of the current deformation image is obtained.
In one example, step 3 comprises: performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating the displacement gradient according to the displacement value after noise reduction.
In one example, the displacement gradient is:
wherein E is xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy The y-direction displacement gradient, u the x-direction displacement value, and v the y-direction displacement value.
The system realizes the monitoring and early warning of the deformation of the structural member based on the digital image recognition technology by analyzing and comparing the deformation data of other images based on the reference image.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
While embodiments of the present invention have been described above, the above description is illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (8)
1. A method for identifying engineering structure mechanical parameters based on digital images is characterized by comprising the following steps:
step 1: calculating a calculation formula of the related deformation parameters;
step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field;
and step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field;
and 4, step 4: calculating a strain field according to the displacement gradient;
wherein the step 2 comprises:
step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value;
step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric value, and calculating related deformation parameters and displacement accurate values of the seed points;
step 203: determining a plurality of points which are not calculated and have the closest distance to the seed point, and calculating related deformation parameters corresponding to the points which are not calculated;
step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point;
step 205: determining a plurality of points which are not calculated and are closest to the plurality of seed points, and repeating the steps 203-204;
step 206: step 205 is repeated until no non-computed points are contained in the compute subsection.
2. The digital image-based engineering structure mechanical parameter identification method according to claim 1, wherein the related deformation parameters are calculated by formula (1):
wherein, C LS In order to be able to correlate the deformation parameters,
is the coordinate of any one point Q,
to deformThe coordinates corresponding to the last Q', f and g are the gray scale intensity functions of the reference and current images at the specified positions, f m As a reference image sub-region gray-scale average value, g m And the gray level average value of the sub-area of the current deformation image is obtained.
3. The digital image-based engineering structural mechanics parameter identification method of claim 1, wherein said step 3 comprises:
performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction;
and calculating the displacement gradient according to the displacement value after the noise reduction.
4. The digital image-based engineering structural mechanical parameter identification method of claim 1, wherein the displacement gradient is:
wherein E is xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy The y-direction displacement gradient, u the x-direction displacement value, and v the y-direction displacement value.
5. An engineering structure mechanical parameter identification system based on digital images is characterized by comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
step 1: calculating a calculation formula of the related deformation parameters;
step 2: calculating a displacement accurate value according to the displacement initial value and the related deformation parameter to obtain a displacement field;
and 3, step 3: calculating a displacement gradient through a strain window algorithm according to the displacement field;
and 4, step 4: calculating a strain field according to the displacement gradient;
wherein the step 2 comprises:
step 201: determining a calculation subarea and calculating a normalized cross-correlation method metric value;
step 202: determining seed points of the calculation sub-area according to the normalized cross-correlation method metric value, and calculating related deformation parameters and displacement accurate values of the seed points;
step 203: determining a plurality of points which are not calculated and closest to the seed points, and calculating related deformation parameters corresponding to the points which are not calculated;
step 204: marking the un-calculated point with the minimum related deformation parameter as a seed point, and calculating the displacement accurate value of the seed point;
step 205: determining a plurality of points which are closest to the plurality of seed points and not calculated, and repeating the steps 203-204;
step 206: step 205 is repeated until no non-computed points are contained in the compute subsection.
6. The digital image-based engineering structural mechanics parameter identification system of claim 5, wherein the associated deformation parameter is calculated by equation (1):
wherein, C LS In order to be a relevant parameter of the deformation,
is the coordinate of any one point Q and,
for the coordinates corresponding to Q' after deformation, f and g are the reference and current image gray scale intensity functions at the specified positions, respectively, f m As a reference image sub-region gray-scale average value, g m The gray level average value of the sub-area of the current deformation image is obtained.
7. The digital image-based engineered structural mechanics parameter identification system of claim 5, wherein said step 3 comprises:
performing least square plane fitting according to the displacement field to obtain a displacement value after noise reduction;
and calculating the displacement gradient according to the displacement value after the noise reduction.
8. The digital image-based engineering structural mechanics parameter identification system of claim 5, wherein said displacement gradient is:
wherein E is xx A gradient of displacement in the x direction, E xy A gradient of displacement in the xy direction, E yy Is the displacement gradient in the y-direction, u is the displacement value in the x-direction, and v is the displacement value in the y-direction.
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