CN110532725B - Engineering structure mechanical parameter identification method and system based on digital image - Google Patents

Abstract

A method and system for identifying mechanical parameters of engineering structures based on digital images are disclosed. The method may include: step 1: calculating the calculation formula of the relevant deformation parameters; step 2: calculating the precise value of the displacement according to the initial value of the displacement and the relevant deformation parameters, and obtaining the displacement field; step 3: calculating the displacement through the strain window algorithm according to the displacement field Gradient; Step 4: Calculate the strain field according to the displacement gradient. The invention realizes the monitoring and early warning of the deformation of the structural component based on the digital image recognition technology by analyzing and comparing the deformation data of other images based on the reference image.

Description

Method and system for identifying mechanical parameters of engineering structures based on digital images

technical field

The invention relates to the field of engineering structure monitoring, and more specifically, to a method and system for identifying mechanical parameters of engineering structures based on digital images.

Background technique

In recent years, with the vigorous development of my country's construction industry, the construction scale has been continuously expanded and the construction speed has been changing with each passing day. At the same time, the large deformation of the structure due to the load will directly affect the strength and stability of the structure, causing related safety hazards in the project. At present, the conventional methods for solving structural safety problems in engineering mostly use theoretical analysis and calculation and on-site test and detection. Among them, the relevant numerical simulation using finite element theory is a typical representative of theoretical analysis and calculation, but the calculation conditions set by this method are mostly ideal stress states, and it is difficult to simulate the actual complex stress conditions of structural components. There is a certain deviation between the results obtained by numerical simulation analysis and calculation of the same structural member and the actual value; while the field test detection method usually directly measures the structure and obtains its relevant mechanical parameters, the results are highly credible Spend.

The structure will produce a certain deformation after bearing the relevant load, so the structural deformation measurement is a main test content in the on-site structural test measurement. According to whether the measuring instrument is in direct contact with the surface of the structure to be measured, the relevant measurement methods can be divided into contact and non-contact measurement methods. Among them, contact measuring instruments are represented by displacement meters, sensors and strain gauges, which have the advantages of simple operation, strong directness, and high reliability of measured data obtained, and have been used and developed all the time. Correspondingly, non-contact measurement methods use various waves such as ultrasonic waves, electromagnetic waves, and light waves for detection. Common instruments include total stations, GPS, and ultrasonic nondestructive testing instruments. And the use of the method can make up for the defects of special working environments such as high temperature, radiation and corrosiveness that are not applicable to the contact measurement method. Although this method can solve many practical engineering measurement problems, its applicable conditions, scope and accuracy still have certain limitations. Therefore, it is necessary to develop a method and system for identifying mechanical parameters of engineering structures based on digital images.

The information disclosed in the background of the present invention is only intended to deepen the understanding of the general background of the present invention, and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art known to those skilled in the art.

Contents of the invention

The present invention proposes a method and system for identifying mechanical parameters of engineering structures based on digital images, which can realize monitoring and early warning of deformation of structural components based on digital image recognition technology by analyzing and comparing deformation data of other images based on reference images.

According to one aspect of the present invention, a method for identifying mechanical parameters of engineering structures based on digital images is proposed. The method may include: step 1: calculating the calculation formula of the relevant deformation parameters; step 2: calculating the precise value of the displacement according to the initial value of the displacement and the relevant deformation parameters, and obtaining the displacement field; step 3: according to the displacement field, by The strain window algorithm calculates the displacement gradient; Step 4: Calculate the strain field according to the displacement gradient.

Preferably, the step 2 includes: Step 201: Determine the calculation sub-region, and calculate the normalized cross-correlation measurement value; Step 202: Determine the seed of the calculation sub-region according to the normalized cross-correlation measurement value point, calculate the relevant deformation parameters and accurate displacement values of the seed point; step 203: determine a plurality of uncalculated points closest to the seed point, and calculate the relevant deformation parameters corresponding to the plurality of uncalculated points; step 204 : Mark the uncalculated point with the smallest relative deformation parameter as the seed point, and calculate the exact displacement value of the seed point; Step 205: Determine a plurality of uncalculated points closest to the multiple seed points, and repeat steps 203-204; Step 206 : Repeat step 205 until the calculation sub-area does not contain uncalculated points.

Preferably, the relevant deformation parameters are calculated by formula (1):

为任意一点Q的坐标，

为变形后Q'对应的坐标，f和g分别为在指定位置的参考和当前图像灰度强度函数，f_m为参考图像子区灰度平均值，g_m为当前变形图像子区的灰度平均值。Among them, C _LS is the relevant deformation parameter,

is the coordinate of any point Q,

is the coordinate corresponding to Q' after deformation, f and g are the reference and current image gray intensity functions at the specified position respectively, f _m is the average gray value of the reference image sub-area, and g _m is the gray level of the current deformed image sub-area average value.

Preferably, the step 3 includes: performing least squares plane fitting according to the displacement field to obtain a noise-reduced displacement value; and calculating the displacement gradient according to the noise-reduced displacement value.

Preferably, the displacement gradient is:

Among them, E _xx is the displacement gradient in the x direction, _{Ex xy} is the displacement gradient 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.

According to another aspect of the present invention, a system for identifying mechanical parameters of engineering structures based on digital images is proposed, which is characterized in that the system includes: a memory storing computer-executable instructions; a processor running the The computer-executable instructions in the memory perform the following steps: step 1: calculate the calculation formula of the relevant deformation parameters; step 2: calculate the precise value of the displacement according to the initial displacement value and the relevant deformation parameters, and obtain the displacement field; step 3: according to For the displacement field, a displacement gradient is calculated by a strain window algorithm; step 4: calculating a strain field according to the displacement gradient.

Preferably, the step 2 includes: Step 201: Determine the calculation sub-region, and calculate the normalized cross-correlation measurement value; Step 202: Determine the seed of the calculation sub-region according to the normalized cross-correlation measurement value point, calculate the relevant deformation parameters and accurate displacement values of the seed point; step 203: determine a plurality of uncalculated points closest to the seed point, and calculate the relevant deformation parameters corresponding to the plurality of uncalculated points; step 204 : Mark the uncalculated point with the smallest relative deformation parameter as the seed point, and calculate the exact displacement value of the seed point; Step 205: Determine a plurality of uncalculated points closest to the multiple seed points, and repeat steps 203-204; Step 206 : Repeat step 205 until the calculation sub-area does not contain uncalculated points.

Preferably, the relevant deformation parameters are calculated by formula (1):

为任意一点Q的坐标，

为变形后Q'对应的坐标，f和g分别为在指定位置的参考和当前图像灰度强度函数，f_m为参考图像子区灰度平均值，g_m为当前变形图像子区的灰度平均值。Among them, C _LS is the relevant deformation parameter,

is the coordinate of any point Q,

is the coordinate corresponding to Q' after deformation, f and g are the reference and current image gray intensity functions at the specified position respectively, f _m is the average gray value of the reference image sub-area, and g _m is the gray level of the current deformed image sub-area average value.

Preferably, the step 3 includes: performing least squares plane fitting according to the displacement field to obtain a noise-reduced displacement value; and calculating the displacement gradient according to the noise-reduced displacement value.

Preferably, the displacement gradient is:

Among them, E _xx is the displacement gradient in the x direction, _{Ex xy} is the displacement gradient 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.

The method and apparatus of the present invention have other features and advantages which will be apparent from the accompanying drawings and the following detailed description, or which will be described in the accompanying drawings and the following description The detailed description is set forth in the detailed description, and together these drawings and detailed description serve to explain certain principles of the invention.

Description of drawings

The above and other objects, features and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail with reference to the accompanying drawings, wherein, in the exemplary embodiments of the present invention, the same reference numerals generally represent same parts.

Fig. 1 shows a flow chart of the steps of the method for identifying mechanical parameters of engineering structures based on digital images according to the present invention.

Fig. 2 shows a schematic diagram of the basic principle of the digital image correlation method.

Fig. 3 shows a schematic diagram of on-site image acquisition according to an embodiment of the present invention.

Fig. 4 shows a schematic diagram of an acquired digital image according to an embodiment of the present invention.

Fig. 5a, Fig. 5b, Fig. 5c, Fig. 5d, Fig. 5e, Fig. 5f respectively show the schematic diagrams of the deformation cloud images of the first second to the sixth second according to an embodiment of the present invention.

Fig. 6a, Fig. 6b, Fig. 6c, Fig. 6d, Fig. 6e, Fig. 6f respectively show the schematic diagrams of deformation cloud images from the 7th second to the 12th second according to an embodiment of the present invention.

Fig. 7a, Fig. 7b, Fig. 7c, Fig. 7d, Fig. 7e, and Fig. 7f respectively show schematic diagrams of deformation cloud images from the 13th second to the 18th second according to an embodiment of the present invention.

Fig. 8a, Fig. 8b, Fig. 8c, Fig. 8d, Fig. 8e, Fig. 8f respectively show the schematic diagrams of deformation cloud images of the 19th second to the 24th second according to an embodiment of the present invention.

Fig. 9a, Fig. 9b, Fig. 9c, Fig. 9d, Fig. 9e, Fig. 9f respectively show the schematic diagrams of deformation cloud images of the 25th second to the 30th second according to an embodiment of the present invention.

detailed description

The present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the invention are shown in the drawings, it should 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 method for identifying mechanical parameters of engineering structures based on digital images according to the present invention.

In this embodiment, the method for identifying mechanical parameters of engineering structures based on digital images according to the present invention may include: Step 1: Calculate the calculation formula of relevant deformation parameters; Step 2: Calculate the precise value of displacement according to the initial displacement value and relevant deformation parameters , to obtain the displacement field; Step 3: Calculate the displacement gradient through the strain window algorithm according to the displacement field; Step 4: Calculate the strain field according to the displacement gradient.

In one example, Step 2 includes: Step 201: Determine the calculation sub-area, and calculate the normalized cross-correlation measurement value; Step 202: Determine the seed point of the calculation sub-area according to the normalized cross-correlation method measurement value, and calculate the seed Relevant deformation parameters and accurate displacement values of the points; Step 203: Determine the multiple uncalculated points closest to the seed point, and calculate the relevant deformation parameters corresponding to the multiple uncalculated points; Step 204: Mark the uncalculated point with the smallest relative deformation parameter is the seed point, calculate the exact value of the displacement of the seed point; step 205: determine a plurality of uncalculated points closest to the distance from a plurality of seed points, repeat steps 203-204; step 206: repeat step 205 until the calculation sub-region does not contain Points not calculated.

In one example, the relevant deformation parameters are calculated by formula (1):

为任意一点Q的坐标，

为变形后Q'对应的坐标，f和g分别为在指定位置的参考和当前图像灰度强度函数，f_m为参考图像子区灰度平均值，g_m为当前变形图像子区的灰度平均值。Among them, C _LS is the relevant deformation parameter,

is the coordinate of any point Q,

is the coordinate corresponding to Q' after deformation, f and g are the reference and current image gray intensity functions at the specified position respectively, f _m is the average gray value of the reference image sub-area, and g _m is the gray level of the current deformed image sub-area average value.

In an example, step 3 includes: performing least squares plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating a displacement gradient according to the displacement value after noise reduction.

In one example, the displacement gradient is:

Among them, E _xx is the displacement gradient in the x direction, _{Ex xy} is the displacement gradient 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.

Specifically, as a new type of optical measurement method, the digital image correlation method has the advantages of low cost, non-contact, full field, high precision, and easy automation. It has attracted more and more attention, and it also makes up for the current civil engineering structure deformation measurement Insufficiency of traditional measurement methods.

Digital Image Correlation Method (DICM for short), also known as Digital Speckle Correlation Method (DSCM for short), is a deformation measurement method based on computer and digital image technology. This method records the speckle images on the surface of the specimen in different states, and uses a correlation matching algorithm based on the gray level of the image to track the position of the interest point on the specimen surface in the image, so as to obtain the relative deformation of the specimen surface in different states. information.

The digital image correlation method deals with two digital images recorded before and after deformation. Usually, the digital image before deformation is called "reference image", and the digital image after deformation is called "current image".

In the sub-area-based DIC algorithm, the reference image is divided into smaller areas of sub-areas or sub-windows, and assuming that the deformation inside each sub-area is uniform, and then find the word corresponding to the reference image in the current image that has occurred Deformed subsections.

Fig. 2 shows a schematic diagram of the basic principle of the digital image correlation method.

In computation, a subregion is initially a contiguous set of circular points at integer pixel positions of the reference image. As shown in Figure 2, the reference image sub-area selects a rectangular area S of (2N+1)×(2N+1) pixel range with the point P(x ₀ , y ₀ ) as the center point, and the deformed image The horizontal displacement of the center point of the sub-area is u, and the vertical displacement is v. When the deformed image sub-area undergoes translation, stretching, compression and other related deformations, the P point in the reference image sub-area is deformed and becomes the P' point in the deformed image. The corresponding relationship between the displacement before and after deformation is as follows:

P＝{uvu _x u _y v _x v _y } ^T (4)

The coordinate value (x ₀ , y ₀ ) of the center P of the initial reference image sub-area in the above formula (3), and the coordinate value (x ₀ ', y ₀ ') of the center point P' of the image sub-area after deformation. In formula (3), S is a set containing all sub-region points, and Δx and Δy are used to represent the relative position of the point relative to the center of the sub-region, and to establish the correspondence between the sub-region points in the current image and the reference image.

Equation (4) defines the generalized deformation vector P of the position and shape change of the image sub-area before and after deformation; u _x , u _y , v _x and v _y are the partial derivatives of the displacement u and v, and are the displacement of the reference image sub-area Gradient parameters, for a given subregion, all parameter values are constant. Equation (3) can also be written in matrix form:

In the above formula, ξ is an enhanced vector containing sub-area points and coordinates x, y, Δx and Δy are the distances between point Q (or Q') in the sub-area and point P (or P') in the center of the sub-area, and w is a warp function.

In order to improve the calculation efficiency and meet the calculation speed requirements of the subsequent inverse matching algorithm, it is set that the sub-region of the reference image can be deformed in the image, as shown below:

和

是变形的参考图像子区内Q点的坐标值，Q'点的坐标为

u_r、v_r为将Q、Q'置于同一图像子区时两者沿x轴、y轴的距离。式(6)中坐标变换是在参考图像中的两个不同坐标系之间进行的。The coordinates of any point Q in the initial reference image sub-area in the above formula are

and

is the coordinate value of point Q in the deformed reference image sub-area, and the coordinate of point Q' is

u _r , v _r are the distances between Q and Q' along the x-axis and y-axis when they are placed in the same image sub-region. The coordinate transformation in formula (6) is carried out between two different coordinate systems in the reference image.

The basic principle of image matching is to judge whether they are the same point by comparing the RGB color correlation of the pixel block image sub-areas of the same size centered on two points on the related images before and after deformation. The correlation coefficient for comparison here is calculated by the introduced correlation function when the DIC algorithm uses the correlation search method. This method uses two different correlation functions to find the initial value and refine it subsequently. The initial value is obtained by calculating the cross-correlation (NCC) of the normalized cross-correlation function at integer pixel positions.

The method for identifying mechanical parameters of engineering structures based on digital images according to the present invention may include:

Step 1: Calculate the calculation formula of relevant deformation parameters by formula (1);

Step 2: According to the initial value of displacement and related deformation parameters, calculate the precise value of displacement and obtain the displacement field; specifically include:

Step 201: Determine the calculation sub-area, and calculate the normalized cross-correlation method metric value by formula (7):

The functions f and g are respectively the reference and current image grayscale intensity functions at the specified position, the function f _m is the average gray value of the reference image sub-area, and g _m is the gray average value of the current deformed image sub-area:

where n(S) is the number of data points in subregion S. Since the digital image records discrete grayscale information, when using the correlation function of formula (7) to perform correlation search, the whole pixel in the sub-area is used as the search unit. The obtained results are roughly integer-pixel displacements. To achieve precise displacement measurements, the next step is to use a nonlinear optimizer to optimize these results with sub-pixel resolution by finding the minimum correlation condition, which is affected by both C _cc and C _LS Parameters have a big influence.

Step 202: Determine the seed point of the calculation sub-area according to the measured value of the normalized cross-correlation method, and calculate the relevant deformation parameters and accurate displacement values of the seed point; Step 203: Determine a plurality of uncalculated points closest to the seed point, and calculate Relevant deformation parameters corresponding to a plurality of uncalculated points; Step 204: Mark the uncalculated point with the smallest relative deformation parameter as a seed point, and calculate the exact displacement value of the seed point; Step 205: Determine the multiple uncalculated points closest to the multiple seed points To calculate points, repeat steps 203-204; Step 206: Repeat step 205 until the calculated sub-area does not contain uncalculated points.

Step 3: Carry out least squares plane fitting according to the displacement field, and obtain the displacement value after noise reduction:

According to the displacement value after noise reduction, the displacement gradient is calculated by formula (2);

Step 4: Calculating the strain field according to the displacement gradient, those skilled in the art may choose a method for calculating the strain field according to specific conditions.

By analyzing and comparing the deformation data of other images based on the reference image, the method realizes the monitoring and early warning of the deformation of the structural component based on the digital image recognition technology.

Application example

In order to facilitate the understanding of the solutions and effects of the embodiments of the present invention, a specific application example is given below. Those skilled in the art will understand that this example is only for the purpose of facilitating the understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

The method for identifying mechanical parameters of engineering structures based on digital images according to the present invention may include:

Step 1: Calculate the calculation formula of relevant deformation parameters by formula (1);

Step 2: According to the initial value of displacement and related deformation parameters, calculate the precise value of displacement and obtain the displacement field; specifically include:

Step 201: determine the calculation sub-area, and calculate the normalized cross-correlation method metric value by formula (7); the gray-scale average value _f of the reference image sub-area is formula (8), and the gray-scale average value g of the current deformed image sub-area _m is formula (9). Since the digital image records discrete grayscale information, when using the correlation function of formula (7) to perform correlation search, the whole pixel in the sub-area is used as the search unit. The resulting results are roughly integer pixel displacements, and to achieve precise displacement measurements, the next step is to use a nonlinear optimizer to optimize these results with sub-pixel resolution by finding a minimum correlation condition.

Step 202: Determine the seed point of the calculation sub-area according to the measured value of the normalized cross-correlation method, and calculate the relevant deformation parameters and accurate displacement values of the seed point; Step 203: Determine a plurality of uncalculated points closest to the seed point, and calculate Relevant deformation parameters corresponding to a plurality of uncalculated points; Step 204: Mark the uncalculated point with the smallest relative deformation parameter as a seed point, and calculate the exact displacement value of the seed point; Step 205: Determine the multiple uncalculated points closest to the multiple seed points To calculate points, repeat steps 203-204; Step 206: Repeat step 205 until the calculated sub-area does not contain uncalculated points.

Step 3: Carry out least squares plane fitting according to the displacement field to obtain the displacement value after denoising as formulas (10)-(11), and calculate the displacement gradient by formula (2) according to the displacement value after denoising.

Step 4: Calculate the strain field according to the displacement gradient.

Taking the monitoring data of the floor of viaduct bridge structure as an example to introduce. On-site monitoring was carried out on the light rail bridge during the operation period, and corresponding calculations were carried out based on the obtained relevant data. The original data collected on site need to be calculated by digital image correlation method, so that the image can be converted into recognizable results in the project. In this section, the base plate image collected from the measuring point directly below the base plate in the direction of Jinghe when the train passing directly above it is example to calculate.

Fig. 3 shows a schematic diagram of on-site image acquisition according to an embodiment of the present invention.

Fig. 4 shows a schematic diagram of an acquired digital image according to an embodiment of the present invention.

It can be seen from Figure 3 that the x direction of the image acquisition location area is parallel to the axis of the track floor, and the y direction is the cross-sectional direction of the track floor. At the same time, the x and y directions correspond to the horizontal and vertical directions of the collected digital image respectively. As shown in Figure 4.

Fig. 5a, Fig. 5b, Fig. 5c, Fig. 5d, Fig. 5e, Fig. 5f respectively show the schematic diagrams of the deformation cloud images of the first second to the sixth second according to an embodiment of the present invention.

Fig. 6a, Fig. 6b, Fig. 6c, Fig. 6d, Fig. 6e, Fig. 6f respectively show the schematic diagrams of deformation cloud images from the 7th second to the 12th second according to an embodiment of the present invention.

Fig. 7a, Fig. 7b, Fig. 7c, Fig. 7d, Fig. 7e, and Fig. 7f respectively show schematic diagrams of deformation cloud images from the 13th second to the 18th second according to an embodiment of the present invention.

Fig. 8a, Fig. 8b, Fig. 8c, Fig. 8d, Fig. 8e, Fig. 8f respectively show the schematic diagrams of deformation cloud images of the 19th second to the 24th second according to an embodiment of the present invention.

Fig. 9a, Fig. 9b, Fig. 9c, Fig. 9d, Fig. 9e, Fig. 9f respectively show the schematic diagrams of deformation cloud images of the 25th second to the 30th second according to an embodiment of the present invention.

It can be seen from the figure that the digital image correlation method software calculates the pixel deformation of the image, and the pixel size of the calculated image is converted to the actual test area size, so that the deformation unit of the calculation output result is meters. The deformation range from the first second to the eighth second is -2×10 ^-4 m to 1.5×10 ^-4 m, and the deformation range from the ninth second is (-4.5×10 ^-4 ~0)m, starting from the ninth second The amplitude gradually increases, and the interval amplitude reaches the deformation peak of (-3×10 ^-3 ～-0.5×10 ^-3 )m in the period from 13 seconds to 17 seconds, which is significantly increased compared with the data of the previous 8 seconds. Afterwards, it gradually decreased, and the deformation range returned to (-5×10 ^-4 ～0)m in the 23rd second, which was the same as the 9th second, and the deformation range in the last 7 seconds was (-4×10 ^-4 ～-1.5× 10 ^-4 )m, which is close to the first 8 seconds.

Observing the cloud chart scale, it is found that the maximum value of the cloud chart scale remains in a positive state for the first 8 seconds, and the maximum value of the cloud chart scale becomes 0 from the 9th second, and the maximum value is a negative value from the 10th to the 22nd second, and then becomes 0 again at 23 seconds Subsequently, the maximum value is restored to a positive value state. Combined with the on-site records, the light rail train entered the bridge pier in front of the measuring point at the 9th second, and left the rear bridge pier of the measuring point at the 21st second. The whole process lasted 12 seconds. The on-site records confirmed that the reason for the change of the cloud image from the 9th second to the 22nd second was caused by the passage of the train. The above situation proves that the application of digital image correlation measurement method to engineering components is feasible. The measurement of deformation of structural components due to loads can be identified and calculated by digital images.

In summary, the present invention realizes monitoring and early warning of deformation of structural components based on digital image recognition technology by analyzing and comparing deformation data of other images based on reference images.

Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

According to an embodiment of the present invention, a system for identifying mechanical parameters of engineering structures based on digital images is provided, wherein the system includes: a memory storing computer-executable instructions; a processor running the memory The computer can execute the instructions in the following steps: Step 1: Calculate the calculation formula of the relevant deformation parameters; Step 2: Calculate the precise value of the displacement according to the initial value of the displacement and the relevant deformation parameters, and obtain the displacement field; Step 3: According to the displacement field, Calculate the displacement gradient through the strain window algorithm; Step 4: Calculate the strain field according to the displacement gradient.

In one example, Step 2 includes: Step 201: Determine the calculation sub-area, and calculate the normalized cross-correlation measurement value; Step 202: Determine the seed point of the calculation sub-area according to the normalized cross-correlation method measurement value, and calculate the seed Relevant deformation parameters and accurate displacement values of the points; Step 203: Determine the multiple uncalculated points closest to the seed point, and calculate the relevant deformation parameters corresponding to the multiple uncalculated points; Step 204: Mark the uncalculated point with the smallest relative deformation parameter is the seed point, calculate the exact value of the displacement of the seed point; step 205: determine a plurality of uncalculated points closest to the distance from a plurality of seed points, repeat steps 203-204; step 206: repeat step 205 until the calculation sub-region does not contain Points not calculated.

In one example, the relevant deformation parameters are calculated by formula (1):

为任意一点Q的坐标，

为变形后Q'对应的坐标，f和g分别为在指定位置的参考和当前图像灰度强度函数，f_m为参考图像子区灰度平均值，g_m为当前变形图像子区的灰度平均值。Among them, C _LS is the relevant deformation parameter,

is the coordinate of any point Q,

is the coordinate corresponding to Q' after deformation, f and g are the reference and current image gray intensity functions at the specified position respectively, f _m is the average gray value of the reference image sub-area, and g _m is the gray level of the current deformed image sub-area average value.

In an example, step 3 includes: performing least squares plane fitting according to the displacement field to obtain a displacement value after noise reduction; and calculating a displacement gradient according to the displacement value after noise reduction.

In one example, the displacement gradient is:

Among them, E _xx is the displacement gradient in the x direction, _{Ex xy} is the displacement gradient 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.

By analyzing and comparing the deformation data of other images based on the reference image, the system realizes the monitoring and early warning of the deformation of structural components based on digital image recognition technology.

Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

Having described various embodiments of the present invention, the foregoing description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and alterations 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.

Images