Method, device and equipment for evaluating concrete surface deformation process and storage medium

Abstract

The invention provides a concrete surface deformation process evaluation method, device and equipment and a storage medium, and the method comprises the steps: firstly obtaining a concrete surface strain field measurement requirement, generating a speckle parameter according to the strain field measurement requirement, then obtaining speckle images collected by an image collection device at preset time intervals, and carrying out the calculation of the speckle image. Carrying out the preprocessing of the speckle image; then, displacement field data is extracted from the preprocessed speckle image sequence, a covariance matrix is constructed based on the displacement field data, the covariance matrix is operated, and whether the quality of the speckle pattern meets the requirement or not is judged based on the operation result; and finally, when it is judged that the quality of the speckle pattern meets the requirement, calculating a Lagrange strain tensor based on the displacement field data, and calculating a strain tensor used for evaluating the concrete surface deformation process based on the Lagrange strain tensor. The problem of inaccurate strain field measurement result caused by poor speckle pattern quality is solved.

Description

Technical Field

[0001] The present invention relates to the field of strain field measurement, and in particular to a method, device, equipment and storage medium for evaluating a concrete surface deformation process. Background Art

[0002] To effectively apply digital image correlation (DIC) technology in field conditions during the measurement of concrete surface strain fields, it is necessary to scientifically and rationally select and evaluate the parameters of the speckle pattern. In existing technologies, the size, density, and rate of change of the speckle pattern are often determined based on empirical methods. However, due to the complexity of field measurement conditions, the speckle pattern may appear blurred, lack contrast, or be unevenly distributed, leading to uncertainties in speckle tracking and data extraction during digital image correlation measurements.

[0003] In view of this, this application is filed. Summary of the Invention

[0004] The present invention discloses a method, device, equipment and storage medium for evaluating the deformation process of a concrete surface, aiming to solve the problem of inaccurate strain field measurement results caused by poor speckle pattern quality.

[0005] A first embodiment of the present invention provides a method for evaluating a concrete surface deformation process, comprising:

[0006] Obtaining a concrete surface strain field measurement requirement, and generating speckle parameters according to the strain field measurement requirement, wherein the speckle parameters include the size, density, and change rate of the speckle;

[0007] Acquiring a speckle image acquired by an image acquisition device at preset time intervals, and preprocessing the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters;

[0008] extracting displacement field data from the preprocessed speckle image sequence, constructing a covariance matrix based on the displacement field data, calculating the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the calculation result;

[0009] When it is determined that the quality of the speckle pattern meets the requirement, a Lagrangian strain tensor is calculated based on the displacement field data, and a strain tensor for evaluating the deformation process of the concrete surface is calculated based on the Lagrangian strain tensor.

[0010] Preferably, the displacement field data is extracted from the pre-processed speckle image sequence, specifically as follows:

[0011] Select the preprocessed initial reference image and the current deformed image, use the normalized cross-correlation matching criterion to calculate the correlation index between the images, and save the displacement field data when the correlation reaches the maximum value. The expression of the normalized cross-correlation matching criterion is:

[0012]

[0013] Among them, I1 and I2 represent the grayscale values ​​of the corresponding areas of the reference image and the current image respectively, and NCC is the correlation index.

[0014] Preferably, the constructing of a covariance matrix based on the shift data, computing the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the computing result are specifically as follows:

[0015] Selecting a reference point and n neighboring points around it in the displacement field, extracting displacement field data of the reference point and the n neighboring points, and calculating and generating a displacement modulus;

[0016] A normalized covariance value between the reference point and n neighboring points is calculated based on the displacement modulus. When the normalized covariance value is greater than a preset threshold, it is confirmed that the speckle quality meets the measurement accuracy requirement.

[0017] Preferably, the expression for calculating the displacement modulus is:

[0018]

[0019] Among them, u x (p i )u Y (p i ) are points p i displacement components in the x and y directions;

[0020] The process of calculating the normalized covariance value between the reference point and n neighboring points is:

[0021]

[0022] Normalized covariance:

[0023]

[0024] Among them, u(p0) is the displacement modulus of the reference point in all frames, u(p i ) is the displacement modulus length of all frames of the neighborhood point, n is the number of frames, Cov(u(p0),u(p i )) is the reference point p0 and the surrounding points p i The covariance between u (p0) and Represent the reference point p0 and the surrounding points p respectively i The mean of the displacement modulus over all frames t, where t represents the time index of different frames, σ u (p0) and σ u (p i ) are the reference point p0 and the surrounding points p i The standard deviation over all frames.

[0025] Preferably, the neighborhood points are selected at different scales, including distances of 5 pixels, 10 pixels, 25 pixels, 50 pixels, 100 pixels, and 200 pixels.

[0026] Preferably, the Lagrangian strain tensor is calculated based on the displacement field data, and the strain tensor used to evaluate the deformation process of the concrete surface is calculated based on the Lagrangian strain tensor, specifically:

[0027] Based on the displacement field data, the Lagrangian strain tensor is calculated as follows:

[0028]

[0029] Where, ε xx , ε yy are the normal strains along the x and y directions, ε xy is the shear strain, u and v are the components of displacement in the x and y directions, respectively.

[0030] The principal strain values ​​are obtained by solving the characteristic equation det(E-λI)=0;

[0031] Where E is the strain tensor:

[0032] A second embodiment of the present invention provides a device for evaluating a concrete surface deformation process, comprising:

[0033] a speckle parameter generating unit, configured to obtain a concrete surface strain field measurement requirement and generate speckle parameters according to the strain field measurement requirement, wherein the speckle parameters include the size, density, and change rate of the speckle;

[0034] an image preprocessing unit, configured to obtain a speckle image acquired by the image acquisition device at preset time intervals and preprocess the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters;

[0035] a covariance calculation unit, configured to extract displacement field data from the preprocessed speckle image sequence, construct a covariance matrix based on the displacement field data, calculate the covariance matrix, and determine whether the speckle pattern quality meets the requirements based on the calculation result;

[0036] A strain tensor calculation unit is configured to calculate a Lagrangian strain tensor based on the displacement field data when it is determined that the quality of the speckle pattern meets the requirements, and calculate a strain tensor for evaluating the deformation process of the concrete surface based on the Lagrangian strain tensor.

[0037] A third embodiment of the present invention provides an evaluation device for a concrete surface deformation process, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a method for evaluating a concrete surface deformation process as described in any one of the above.

[0038] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for evaluating a concrete surface deformation process as described in any one of the above items.

[0039] The present invention provides a method, device, equipment, and storage medium for evaluating the deformation process of concrete surfaces. The method first obtains concrete surface strain field measurement requirements and generates speckle parameters based on the strain field measurement requirements. Then, speckle images acquired by an image acquisition device at preset intervals are acquired and preprocessed. Displacement field data is extracted from the preprocessed speckle image sequence, a covariance matrix is ​​constructed based on the displacement field data, the covariance matrix is ​​calculated, and a determination is made based on the calculation result whether the speckle pattern quality meets the requirements. Finally, if the speckle pattern quality meets the requirements, a Lagrangian strain tensor is calculated based on the displacement field data, and a strain tensor used to evaluate the concrete surface deformation process is calculated based on the Lagrangian strain tensor. This method solves the problem of inaccurate strain field measurements due to poor speckle pattern quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 1 is a flow chart of a method for evaluating a concrete surface deformation process provided by a first embodiment of the present invention;

[0041] Figure 2 is a nine-point distribution diagram in the nine-point covariance of the present invention, wherein the concentric circle radii are 10 pixels, 25 pixels, 50 pixels, 100 pixels, and 200 pixels;

[0042] Figure 3 The DIC results of the present invention are compared with the instrument measurement results;

[0043] Figure 4 1 is a schematic diagram of a module of a device for evaluating a concrete surface deformation process provided by a second embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0046] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0047] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0048] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0049] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0050] The "first" and "second" mentioned in the embodiments are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0051] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0052] The present invention discloses a method, device, equipment and storage medium for evaluating the deformation process of a concrete surface, aiming to solve the problem of inaccurate strain field measurement results caused by poor speckle pattern quality.

[0053] See also Figure 1 A first embodiment of the present invention provides a method for evaluating a concrete surface deformation process. The method can be performed by a device for evaluating a concrete surface deformation process (hereinafter referred to as the evaluation device), and in particular, by one or more processors within the evaluation device to implement at least the following steps:

[0054] S101, obtaining a concrete surface strain field measurement requirement, and generating speckle parameters according to the strain field measurement requirement, wherein the speckle parameters include the size, density, and change rate of the speckle;

[0055] In this embodiment, the evaluation device can be a terminal with data processing capabilities such as a server, workstation, desktop computer, laptop computer, etc., which can establish a communication connection with the image acquisition device; the evaluation device can be installed with a corresponding operating system and application software, and the functions required by this embodiment can be achieved through the combination of the operating system and application software.

[0056] Specifically, this embodiment first addresses the practical needs of measuring the surface strain field of concrete components by comprehensively analyzing the geometric characteristics and deformation range of the measured target. This analysis includes the component's dimensions, surface shape, and expected maximum strain amplitude, thereby determining the required field of view (FOV) and spatial resolution. To achieve sufficient feature contrast and tracking accuracy in the digital image correlation (DIC) algorithm, this embodiment simulates speckle images at different pixel resolutions to optimize the speckle size, ensuring that it is neither too small to be lost in noise nor too large to reduce the measurement point density. Furthermore, a statistical model is used to optimize the speckle density to ensure a uniform grayscale distribution within the given FOV while ensuring a sufficient number of feature points.

[0057] S102, acquiring a speckle image acquired by an image acquisition device at preset time intervals, and preprocessing the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters;

[0058] It should be noted that in this embodiment, after the designed digital speckle pattern is stored as a digital image, it can be accurately transferred to the concrete surface using a projector, inkjet printer, screen printing, or other imaging equipment. The specific implementation method is selected based on site conditions and is not specifically limited here. If a projector is used, a uniform speckle pattern is projected onto the concrete surface through digital control to ensure pattern continuity and uniformity. If spraying or printing technology is used, the speckle pattern is evenly applied or printed onto the concrete surface using a pre-made template or directly using digital printing equipment.

[0059] During actual operation, to ensure that the speckle pattern is truly continuous and uniform, an on-site calibration is performed after application. This calibration examines the pattern for any localized dense or sparse areas, and checks whether the pattern fully covers the intended measurement area. If any issues are identified, feedback is used to readjust the speckle parameters and pattern generation method, or adjust the accuracy of the pattern transfer device, until the requirements are met.

[0060] After installing the industrial camera, first fix the camera on a stable bracket based on the measurement area size and required resolution, with its optical axis as perpendicular to the concrete surface as possible. Then, configure the shooting interval to 1 second in the software, allowing the camera to automatically trigger the shutter at this preset duration, transmitting each frame of the speckle image to the computer storage module in real time. The sampling frequency of one frame per second adopted here not only takes into account the dynamic deformation rate of concrete during loading, but also avoids the data redundancy and storage pressure caused by excessively high frame rates. After the image is transferred to the computer, the previously calibrated camera intrinsic parameters, including focal length, principal point coordinates, and radial and tangential distortion coefficients, are automatically called to correct each frame of the image, eliminating lens distortion in the original image and ensuring that the speckle pattern maintains geometric consistency throughout the entire field of view.

[0061] In addition, this embodiment seamlessly connects the image acquisition and correction processes, and combines real-time transmission with automatic correction to construct an online preprocessing link. This allows subsequent speckle displacement extraction to obtain high-fidelity image input without manual intervention, thereby significantly improving the automation level and data consistency of the entire measurement system.

[0062] S103, extracting displacement field data from the preprocessed speckle image sequence, constructing a covariance matrix based on the displacement field data, calculating the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the calculation result;

[0063] It should be noted that first, an image is selected from the preprocessed image sequence as the initial reference image, usually the first frame captured in the unloaded state. After that, each subsequent deformed image is compared and analyzed with the reference image. The system divides the reference image into several sub-regions, each of which contains a characteristic speckle pattern. For the corresponding area in the deformed image, the system needs to find the position that best matches the sub-region in the reference image. The degree of this match is quantified by the normalized cross-correlation coefficient (NCC). The expression of the normalized cross-correlation matching criterion is:

[0064]

[0065] Among them, I1 and I2 represent the grayscale values ​​of the corresponding areas of the reference image and the current image respectively, and NCC is the correlation index.

[0066] This criterion is insensitive to changes in illumination and can maintain high matching accuracy outdoors or in environments with unstable lighting conditions, which is particularly important for on-site testing of concrete components. In practice, the system moves the reference subregion within a predetermined search range of the current image on a pixel-by-pixel basis, calculating the NCC value at each position. When the NCC reaches its maximum value (ideally close to 1), the difference between that position and the initial position is the displacement vector of the subregion.

[0067] In order to improve the computational efficiency, in this embodiment, a multi-level search strategy is adopted. A preliminary match is first performed on a coarse grid to determine the approximate displacement interval, and then a precise search is performed on a fine grid, which significantly improves the computational speed without sacrificing accuracy. In addition, to handle large deformations, the system implements an incremental tracking mechanism, that is, in addition to comparing with the initial reference image, it also compares with adjacent time frames to ensure stable tracking under large deformation conditions. The full-field displacement of the image is calculated frame by frame using the above method, and a complete displacement time history curve is established for each sub-area. At the same time, the system automatically extracts the displacement gradient field data, that is, the spatial derivative information of the displacement field. These data are the basis for the subsequent calculation of the strain field. During the implementation process, an improved finite difference algorithm is used to calculate the displacement gradient.

[0068] Furthermore, in this embodiment, based on the displacement field data acquired in the aforementioned steps, a covariance matrix is ​​constructed and calculated. The characteristics of the covariance matrix are evaluated to determine whether the speckle pattern quality meets the measurement accuracy requirements. The implementation process is as follows: First, a reference point p0 = (x0, y0) is selected in the displacement field, typically at the center of the measurement area or in a key observation area. Then, n neighborhood points are selected around this reference point (n = 8 in this embodiment). These neighborhood points are located above, below, to the left, to the right, and in four diagonal directions of the reference point, forming a nine-point grid structure. More importantly, this embodiment selects neighborhood points at different scales: 5 pixels, 10 pixels, 25 pixels, 50 pixels, 100 pixels, and 200 pixels from the reference point. This multi-scale analysis method comprehensively evaluates the performance of speckle patterns at different spatial scales.

[0069] For each selected reference point and its surrounding points, the corresponding displacement field data is extracted. The displacement of each point has two components: u x and u y , represents the displacement of the point in the x and y directions.

[0070]

[0071] Here, u x (x,y,t) and u y (x,y,t) represents the displacement components in the x and y directions respectively, and u m (x, y, t) is the displacement modulus of the point, which represents the displacement of the point on the plane (i.e., the square root of the sum of the squares of the two components).

[0072] For the reference point p0 and the neighboring point p i , extract the corresponding displacement component sequence (such as displacement changes in multiple time frames).

[0073] For each reference point p0 and surrounding points p i , first calculate the displacement modulus u(p). The displacement data u extracted from the multi-frame data x (p0,t),u y (p0,t) and u x (p i ,t),u y (p i ,t), the displacement modulus u(p i ):

[0074]

[0075] Reference point p0 and surrounding points p i The covariance between is calculated using the following formula:

[0076]

[0077] Among them, μ u (p0) and Represent the reference point p0 and the surrounding points p respectively i The bits in all frames t

[0078] Mean shift length:

[0079]

[0080] Where n is the number of frames.

[0081] Perform normalized covariance NormCov

[0082]

[0083] Among them, σ u (p0) and σ u (p i ) are the reference point p0 and the surrounding points p i Standard deviation over all frames t:

[0084]

[0085] C=(NormCov(u(p0),u(p0)) NormCov(u(p0),u(p1)) … NormCov(u(p0),u(p2)))

[0086] By normalizing, the normalized covariance between each pair of points can be obtained. The result of the normalized covariance is between the range [-1, 1], which reflects the correlation strength of the displacement modulus between the two points.

[0087] In practical applications, the present invention sets a preset threshold of 0.9. When the calculated normalized covariance value is greater than this threshold, it indicates that the displacement changes of the reference point and the neighboring points are highly correlated, and the speckle pattern quality is good, meeting the measurement accuracy requirements. Conversely, if the normalized covariance value is lower than the threshold, it indicates that the speckle quality is insufficient, possibly due to excessively dense or sparse speckles or insufficient contrast, resulting in inaccurate displacement field measurements. In this case, the speckle pattern needs to be regenerated.

[0088] For example, an industrial camera model BFS-U3-51 S5M-C is used to capture images of beam-column joint components with speckle patterns on the surface. The pixel resolution is 2448×2048.

[0089] In this example, the test was conducted on the surface of a concrete column, and 100 frames of results were selected for eight measurement points with different pixel distances from the center point (e.g. Figure 2 ), the specific distances are: 5 pixels, 10 pixels, 25 pixels, 50 pixels, 100 pixels and 200 pixels.

[0090]

[0091] According to the calculation results of the normalized covariance matrix, at closer measurement points (5 and 10 pixels), the displacement changes are relatively consistent, and the speckle quality is good. At farther points (100 pixels and above), the correlation of displacement changes decreases, but still remains around 0.95, ensuring the stability and accuracy of the displacement field.

[0092] S104: When it is determined that the speckle pattern quality meets the requirement, a Lagrangian strain tensor is calculated based on the displacement field data, and a strain tensor for evaluating the deformation process of the concrete surface is calculated based on the Lagrangian strain tensor.

[0093] In the specific implementation process, the system first calculates the displacement gradient field based on the displacement field data. The displacement gradient field contains the partial derivatives of the displacement components u and v with respect to the coordinates x and y respectively: du / dx, du / dy, dv / dx, and dv / dy. After obtaining the displacement gradient field, the Lagrangian finite strain theory is applied to calculate the components of the strain tensor. The calculation formula of the Lagrangian strain tensor is:

[0094]

[0095] Where, ε xx , ε yy are the normal strains along the x and y directions, ε xy is the shear strain, and u and v are the displacement components in the x and y directions, respectively. This approach considers not only the first-order derivative of displacement (linear deformation) but also the second-order term (nonlinear deformation), enabling more accurate strain calculations in areas of the concrete member where significant deformation may occur, such as near cracks.

[0096] It is worth noting that the strain tensor described above describes the strain state of any point on the concrete surface in any direction. However, in order to more intuitively understand the deformation characteristics of the material, especially to determine the location and direction of possible cracking in concrete, it is necessary to further solve the eigenvalues ​​and eigenvectors of the strain tensor. The characteristic equation of the strain tensor E and the identity matrix I is: det(E-λI) = 0 to obtain the principal strain value; where E is the strain tensor: Solving this characteristic equation yields two eigenvalues, λ1 and λ2, corresponding to the maximum and minimum principal strains, respectively. The direction of the maximum principal strain is perpendicular to the direction in which concrete cracks may occur, making it of great engineering significance. Furthermore, the magnitude of the principal strain directly reflects the degree of deformation in a localized area of ​​concrete and can be used to assess the stress state and safety of a component.

[0097] The above method is used to calculate the principal strain field corresponding to each frame of speckle image and compare it with the instrument measurement results (such as Figure 3 The results of the two methods are close, and the relative error is always less than 5%, which proves the feasibility of DIC strain measurement.

[0098] See also Figure 4 A second embodiment of the present invention provides a device for evaluating a concrete surface deformation process, comprising:

[0099] The speckle parameter generating unit 201 is used to obtain the concrete surface strain field measurement requirements and generate speckle parameters according to the strain field measurement requirements, wherein the speckle parameters include the size, density and change rate of the speckle;

[0100] an image preprocessing unit 202 for acquiring a speckle image acquired by an image acquisition device at preset time intervals and preprocessing the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters;

[0101] a covariance calculation unit 203 for extracting displacement field data from the preprocessed speckle image sequence, constructing a covariance matrix based on the displacement field data, calculating the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the calculation result;

[0102] The strain tensor calculation unit 204 is configured to calculate a Lagrangian strain tensor based on the displacement field data when it is determined that the quality of the speckle pattern meets the requirements, and calculate a strain tensor for evaluating the deformation process of the concrete surface based on the Lagrangian strain tensor.

[0103] A third embodiment of the present invention provides an evaluation device for a concrete surface deformation process, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a method for evaluating a concrete surface deformation process as described in any one of the above.

[0104] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for evaluating a concrete surface deformation process as described in any one of the above items.

[0105] The present invention provides a method, device, equipment, and storage medium for evaluating the deformation process of concrete surfaces. The method first obtains concrete surface strain field measurement requirements and generates speckle parameters based on the strain field measurement requirements. Then, speckle images acquired by an image acquisition device at preset intervals are acquired and preprocessed. Displacement field data is extracted from the preprocessed speckle image sequence, a covariance matrix is ​​constructed based on the displacement field data, the covariance matrix is ​​calculated, and a determination is made based on the calculation result whether the speckle pattern quality meets the requirements. Finally, if the speckle pattern quality meets the requirements, a Lagrangian strain tensor is calculated based on the displacement field data, and a strain tensor used to evaluate the concrete surface deformation process is calculated based on the Lagrangian strain tensor. This method solves the problem of inaccurate strain field measurements due to poor speckle pattern quality.

[0106] For example, the computer programs described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the device for evaluating a concrete surface deformation process. For example, the device described in the second embodiment of the present invention.

[0107] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor serves as the control center of the method for evaluating the concrete surface deformation process, and utilizes various interfaces and circuits to connect the various parts of the method for evaluating the concrete surface deformation process.

[0108] The memory can be used to store the computer program and / or module. The processor implements various functions of a method for evaluating a concrete surface deformation process by running or executing the computer program and / or module stored in the memory and accessing data stored in the memory. The memory can primarily include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function or a text conversion function); the data storage area can store data generated based on the use of the mobile phone (such as audio data or text message data). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0109] Wherein, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0110] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0111] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the deformation process of a concrete surface, characterized in that: include: Obtaining a concrete surface strain field measurement requirement, and generating speckle parameters according to the strain field measurement requirement, wherein the speckle parameters include the size, density, and change rate of the speckle; Acquiring a speckle image acquired by an image acquisition device at preset time intervals, and preprocessing the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters; extracting displacement field data from the preprocessed speckle image sequence, constructing a covariance matrix based on the displacement field data, calculating the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the calculation result; When it is determined that the quality of the speckle pattern meets the requirement, a Lagrangian strain tensor is calculated based on the displacement field data, and a strain tensor for evaluating the deformation process of the concrete surface is calculated based on the Lagrangian strain tensor.

2. The method for evaluating the deformation process of a concrete surface according to claim 1, characterized in that: The displacement field data is extracted from the preprocessed speckle image sequence, specifically: Select the preprocessed initial reference image and the current deformed image, use the normalized cross-correlation matching criterion to calculate the correlation index between the images, and save the displacement field data when the correlation reaches the maximum value. The expression of the normalized cross-correlation matching criterion is: Among them, I1 and I2 represent the grayscale values ​​of the corresponding areas of the reference image and the current image respectively, and NCC is the correlation index.

3. The method for evaluating the deformation process of a concrete surface according to claim 1, characterized in that: The constructing of a covariance matrix based on the shift data, computing the covariance matrix, and judging whether the speckle pattern quality meets the requirements based on the computing result are specifically as follows: Selecting a reference point and n neighboring points around it in the displacement field, extracting displacement field data of the reference point and the n neighboring points, and calculating and generating a displacement modulus; A normalized covariance value between the reference point and n neighboring points is calculated based on the displacement modulus. When the normalized covariance value is greater than a preset threshold, it is confirmed that the speckle quality meets the measurement accuracy requirement.

4. The method for evaluating the deformation process of a concrete surface according to claim 3, characterized in that: The expression for calculating the displacement modulus is: Among them, u x (p i )u Y (p i ) are points p i displacement components in the x and y directions; The process of calculating the normalized covariance value between the reference point and n neighboring points is: Normalized covariance: Among them, u(p0) is the displacement modulus of the reference point in all frames, u(p i ) is the displacement modulus length of all frames of the neighborhood point, n is the number of frames, Cov(u(p0),u(p i )) is the reference point p0 and the surrounding points p i The covariance between u (p0) and μ u(pi) Represent the reference point p0 and the surrounding points p respectively i The mean of the displacement modulus over all frames t, where t represents the time index of different frames, σ u (p0) and σ u (p i ) are the reference point p0 and the surrounding points p i The standard deviation over all frames.

5. The method for evaluating the deformation process of a concrete surface according to claim 4, characterized in that: The neighborhood points are selected at different scales, including distances of 5 pixels, 10 pixels, 25 pixels, 50 pixels, 100 pixels, and 200 pixels.

6. The method for evaluating the deformation process of a concrete surface according to claim 4, characterized in that: The Lagrangian strain tensor is calculated based on the displacement field data, and the strain tensor used to evaluate the deformation process of the concrete surface is calculated based on the Lagrangian strain tensor, specifically: Based on the displacement field data, the Lagrangian strain tensor is calculated as follows: Where, ε xx , ε yy are the normal strains along the x and y directions, ε xy is the shear strain, u and v are the components of displacement in the x and y directions, respectively. The principal strain values ​​are obtained by solving the characteristic equation det(E-λI)=0; Where E is the strain tensor:

7. A device for evaluating the deformation process of a concrete surface, characterized in that: include: a speckle parameter generating unit, configured to obtain a concrete surface strain field measurement requirement and generate speckle parameters according to the strain field measurement requirement, wherein the speckle parameters include the size, density, and change rate of the speckle; an image preprocessing unit, configured to obtain a speckle image acquired by the image acquisition device at preset time intervals and preprocess the speckle image, wherein the speckle image is a continuous and uniform speckle pattern formed on the concrete surface based on the speckle parameters; a covariance calculation unit, configured to extract displacement field data from the preprocessed speckle image sequence, construct a covariance matrix based on the displacement field data, calculate the covariance matrix, and determine whether the speckle pattern quality meets the requirements based on the calculation result; A strain tensor calculation unit is configured to calculate a Lagrangian strain tensor based on the displacement field data when it is determined that the quality of the speckle pattern meets the requirements, and calculate a strain tensor for evaluating the deformation process of the concrete surface based on the Lagrangian strain tensor.

8. An evaluation device for concrete surface deformation process, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement the method for evaluating the concrete surface deformation process according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement the method for evaluating the concrete surface deformation process as claimed in any one of claims 1 to 6.

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