Wood structure failure analysis method and device based on image tracking and stress prediction

Abstract

The invention discloses a wood structure failure analysis method and device based on image tracking and stress prediction, and relates to the related technical field of structural members, and the method comprises the steps: shooting a tracking identifier obtained by coating a lattice pattern on the surface of a wood structure based on a preset camera, and obtaining a preliminary image; loading the wood structure according to the VIC-3D, and collecting an image sequence of the wood structure loading process based on a preset camera; and calculating the displacement of the tracking identifier in combination with the preliminary image and the image sequence to obtain the identifier displacement. And calculating surface strain distribution through the identification displacement, and performing global stress distribution prediction on the surface strain distribution based on the stress prediction model to obtain predicted stress distribution. Local stress concentration analysis is carried out, and failure analysis is carried out on a stress concentration area. The technical problem that in the prior art, the comprehensiveness and precision of detection data of a wood structure stress distribution detection method are difficult to improve, and consequently the accuracy of failure recognition of a stress concentration area is low is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural members, in particular to a wood structure failure analysis method and device based on image tracking and stress prediction. BACKGROUND

[0002] In the study of the mechanical properties of wood structures, accurately obtaining the stress and strain distribution of the structure under real load conditions is a key link to realize the optimization design and safety evaluation of the structure. Traditional stress and strain measurement methods rely on strain gauges, mechanical measurement or simplified theoretical analysis methods. These methods have obvious limitations in terms of measurement point arrangement, data comprehensiveness and accuracy.

[0003] Therefore, in the prior art, the comprehensiveness and accuracy of the data detected by the wood structure stress distribution detection method are difficult to improve, resulting in the technical problem of low accuracy of failure identification in the stress concentration area. SUMMARY

[0004] The present application provides a wood structure failure analysis method and device based on image tracking and stress prediction, which solves the technical problem that the comprehensiveness and accuracy of the data detected by the wood structure stress distribution detection method in the prior art are difficult to improve, resulting in low accuracy of failure identification in the stress concentration area. The technical effect of accurately identifying the failure mode of the stress concentration area of the wood structure is achieved, so that engineers can optimize according to the identification results, thereby improving the overall bearing performance and durability of the wood structure.

[0005] The present application provides a wood structure failure analysis method based on image tracking and stress prediction, which comprises: capturing the tracking mark obtained by coating a dot matrix pattern on the surface of a wood structure based on a preset camera, to obtain a preliminary image; loading a load on the wood structure according to VIC-3D, and collecting an image sequence of the loading process of the wood structure based on the preset camera; calculating the displacement of the tracking mark by combining the preliminary image and the image sequence, to obtain a mark displacement; calculating the surface strain distribution through the mark displacement, predicting the global stress distribution based on a stress prediction model, and obtaining a predicted stress distribution; performing local stress concentration analysis according to the predicted stress distribution, performing failure analysis on the stress concentration area, and obtaining a wood structure optimization strategy based on the wood structure failure distribution.

[0006] In an implementation, the preset camera is obtained, including: establishing a three-dimensional model of the wood structure to perform preliminary stress analysis and obtain a preliminary global stress distribution; arranging a preset first resolution camera in a key detection area extracted based on the preliminary global stress distribution to obtain a first resolution camera coverage area; performing non-coverage analysis on the wood structure in combination with the first resolution camera coverage area, arranging a preset second resolution camera in a non-coverage area based on a second resolution camera; and obtaining the preset camera in combination with the preset first resolution camera and the preset second resolution camera.

[0007] In an implementation, the image sequence is collected, including: distributing a trigger signal generated by a signal trigger to the preset camera, performing trigger time verification according to the trigger signal, and completing external trigger configuration of the preset camera based on a time verification result; and obtaining the preliminary image by photographing through the preset camera and continuously collecting the image sequence based on a preset frame rate, where the preset camera synchronously collects the image sequence.

[0008] In an implementation, the identification displacement is obtained, including: extracting a first identification according to the tracking identification; taking the preliminary image as a displacement starting point and the image sequence as a displacement change point to perform displacement calculation of a preset unit time on a displacement size and a displacement direction of the first identification, to obtain a first identification displacement vector; performing displacement vector appending on the first identification displacement vector according to adjacent preset unit times, combining the first identification displacement vector with a first identification appended displacement vector, and obtaining the first identification displacement; and traversing the tracking identification according to the first identification displacement to obtain the identification displacement.

[0009] In an implementation, the predicted stress distribution is obtained, including: calculating a displacement gradient of the tracking identification based on the identification displacement, calculating normal strain and shear strain of the tracking identification based on the displacement gradient, and obtaining the surface strain distribution; constructing a stress prediction model based on a stress-strain mapping determined based on a wood structure material, inputting the surface strain distribution into the stress prediction model to perform stress prediction, and obtaining the predicted stress distribution.

[0010] In an implementation, the wood structure failure distribution is obtained, including: performing stress amount calculation based on the predicted stress distribution, performing stress concentration identification on the stress amount based on a stress concentration threshold, obtaining a stress concentration area, and performing failure mode analysis on the stress concentration area in combination with a failure criterion to obtain the wood structure failure distribution.

[0011] In an implementation, the wood structure failure distribution is obtained by: identifying failure of the stress concentration area based on a maximum stress criterion, a maximum strain criterion, and a fracture mechanics criterion to obtain a failure mode; and simulating failure propagation based on the failure mode to add failure propagation to the wood structure failure distribution.

[0012] The application also provides a wood structure failure analysis device based on image tracking and stress prediction, comprising: A collection device arrangement module is configured to capture tracking marks obtained by applying a dot matrix pattern on a wood structure surface based on a preset camera to obtain a preliminary image. An image collection module is configured to load a load on the wood structure according to VIC-3D and collect an image sequence of the wood structure loading process based on the preset camera. An identification displacement acquisition module is configured to calculate the displacement of the tracking marks by combining the preliminary image and the image sequence to obtain an identification displacement. A stress prediction module is configured to calculate a surface strain distribution by the identification displacement, predict a global stress distribution based on a stress prediction model, and obtain a predicted stress distribution. An optimization strategy acquisition module is configured to analyze local stress concentration based on the predicted stress distribution, analyze failure of a stress concentration area, and obtain a wood structure optimization strategy based on a wood structure failure distribution.

[0013] The wood structure failure analysis method and device based on image tracking and stress prediction provided by the application capture tracking marks obtained by applying a dot matrix pattern on a wood structure surface based on a preset camera to obtain a preliminary image, load a load on the wood structure according to VIC-3D, collect an image sequence of the wood structure loading process based on the preset camera, calculate the displacement of the tracking marks by combining the preliminary image and the image sequence to obtain an identification displacement, calculate a surface strain distribution by the identification displacement, predict a global stress distribution based on a stress prediction model, obtain a predicted stress distribution, analyze local stress concentration based on the predicted stress distribution, analyze failure of a stress concentration area, and obtain a wood structure optimization strategy based on a wood structure failure distribution. The technical problem of low accuracy of failure identification of a stress concentration area due to difficulty in improving the comprehensiveness and precision of detection data of a wood structure stress distribution detection method in the prior art is solved. The technical effect of accurate identification of a wood structure stress concentration area failure mode is achieved, which enables engineers to optimize based on the identification result, thereby improving the overall load bearing performance and durability of the wood structure. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the device according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0015] Figure 1 A flowchart of a wood structure failure analysis method based on image tracking and stress prediction provided by the embodiments of the present application is shown in the figure. Figure 2 A structural schematic diagram of a wood structure failure analysis device based on image tracking and stress prediction provided by the embodiments of the present application is shown in the figure.

[0016] Label explanation: acquisition device layout module 11, image acquisition module 12, identification displacement acquisition module 13, stress prediction module 14, optimization strategy acquisition module 15. DETAILED DESCRIPTION

[0017] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0018] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0019] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent a specific order of the object. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art in the technical field of the present application. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0020] The embodiment of the present application provides a wood structure failure analysis method and device based on image tracking and stress prediction, as shown in the following Figure 1 The method comprises the following steps. Based on the tracking mark obtained by coating a dot matrix pattern on the surface of the wood structure by using a preset camera, a preliminary image is obtained; a load is loaded on the wood structure by using VIC-3D, and an image sequence of the loading process of the wood structure is collected based on the preset camera; and the displacement of the tracking mark is calculated by combining the preliminary image and the image sequence, so that the mark displacement is obtained. On the surface of the wood structure to be measured, a uniform dot matrix pattern is sprayed, and the dot matrix pattern is used as the tracking mark of the structure. Based on the preset camera, the tracking mark is photographed to obtain a preliminary image. Then, according to VIC-3D, a controllable load is applied to the wood structure, and the load is gradually increased until the desired value. During the load increasing process, the image sequence of the loading process of the wood structure is collected based on the preset camera at a preset frame rate. Further, the displacement of the tracking mark is calculated by combining the preliminary image and the image sequence, so that the mark displacement is obtained.

[0021] The method provided by the embodiment of the present application further comprises the following steps: a three-dimensional model of the wood structure is established to perform preliminary stress analysis, so that a preliminary global stress distribution is obtained; a preset first resolution camera is arranged in a key detection area extracted based on the preliminary global stress distribution, so that a first resolution camera coverage area is obtained; the wood structure is combined with the first resolution camera coverage area to perform uncovered analysis, a preset second resolution camera is arranged in an uncovered area based on a second resolution camera, and the preset first resolution camera and the preset second resolution camera are combined to obtain the preset camera.

[0022] Obtaining a preset camera comprises: scanning a wood structure by using a three-dimensional scanning technology, and establishing a three-dimensional model of the wood structure to be measured by using a design software (such as a CAD modeling tool). A preliminary stress analysis is performed based on the three-dimensional model, stress of the wood structure is predicted by simulating an actual load condition in the CAD modeling tool, stress distribution of the wood structure to be measured under the load condition is obtained, and a preliminary global stress distribution is obtained. Then, according to the preliminary stress analysis result, a region with high surface stress or obvious stress gradient change of the wood structure is extracted as a key detection region. A preset first resolution camera is arranged in the key detection region, and a layout origin is arranged at the center of each key detection region when the preset first resolution camera is arranged. According to a preset camera layout distance, the preset first resolution camera is arranged on an extension line of each key detection region in a vertical direction of each key detection region at each layout origin. Then, camera parameters of the preset first resolution camera, including an actual size of a sensor, a focal length, a resolution and the like, are obtained, and a field of view coverage length and a width of the preset first resolution camera are obtained in combination with the preset camera layout distance, so as to obtain a field of view coverage face. The center of the field of view coverage face is overlapped with each layout origin, and field of view coverage mapping is performed on the three-dimensional model in the modeling tool, so as to map the field of view coverage face to the three-dimensional model, and a first resolution camera coverage region is obtained. The first resolution camera coverage region refers to a wood structure surface range in a field of view of a camera lens. Further, the wood structure is combined with the first resolution camera coverage region to perform uncovered analysis, and a region of the wood structure that is not covered by the camera is obtained. Further, a preset second resolution camera is arranged in an uncovered region based on a second resolution camera by using the same arrangement manner as that of the preset first resolution camera. Finally, the preset first resolution camera and the preset second resolution camera are combined to obtain the preset camera.

[0023] The method provided in the embodiments of the application further comprises: distributing a trigger signal generated by a signal trigger to the preset camera, performing trigger time checking according to the trigger signal, and completing external trigger configuration of the preset camera based on a time checking result; and obtaining the preliminary image by photographing through the preset camera, and continuously collecting the image sequence based on a preset frame rate, wherein the preset camera synchronously collects the image sequence.

[0024] The image acquisition process includes: setting up a signal trigger, which can be computer-controlled or provided by a separate hardware pulse generator. The trigger sends a unified start-up shooting command to the cameras to ensure that multiple cameras begin recording image data at the same time. The trigger signal generated by the signal trigger is distributed to the preset cameras. Subsequently, trigger time verification is performed based on the trigger signal; that is, before the formal acquisition begins, it is confirmed whether the reaction time of each camera after receiving the trigger signal is consistent, and whether there is any delay or jitter. Several test trigger signals are issued in a test environment, and the timestamps of the actual responses of each camera are recorded. The synchronization degree between cameras is determined by the actual response timestamps. When the camera's response timestamp error is within a preset delay range, the time verification passes. After the time verification passes, the external trigger configuration of the preset cameras is completed based on the time verification results. If the time verification fails, the trigger is adjusted by professional technicians. After the external trigger configuration is completed, the camera enters the shooting preparation state according to the external trigger signal. Before loading begins, a trigger signal is issued to allow the preset camera to capture one or several frames of the initial state image of the wooden structure surface, obtaining the preliminary image. Furthermore, as the load on the wooden structure increases under the VIC-3D system, the camera continuously captures an image sequence after the initial frame based on an external trigger signal until the desired maximum load is reached. During this process, a preset camera continuously acquires images based on a preset frame rate to obtain the image sequence. The preset camera simultaneously acquires the image sequence. The preset frame rate refers to the number of images captured per second during continuous shooting, which can be preset to 10fps, 50fps, or higher. A higher frame rate allows for recording more intermediate deformation steps, but also requires higher data storage and processing speeds.

[0025] The method provided in this application embodiment further includes: extracting a first identifier based on the tracking identifier; calculating the displacement magnitude and direction of the first identifier using the preliminary image as the displacement starting point and the image sequence as the displacement change point, thereby obtaining a first identifier displacement vector; appending a displacement vector to the first identifier displacement vector based on adjacent preset unit times, and combining the appended displacement vector of the first identifier with the first identifier displacement vector to obtain a first identifier displacement; and traversing the tracking identifiers to obtain the identifier displacement based on the first identifier displacement.

[0026] Obtaining the marker displacement includes: randomly selecting a point from the tracked markers as the first marker. Typically, a marker point with good contrast and clarity in the region is selected to verify and demonstrate subsequent calculation steps. Then, the position of the first marker in the initial image is used as the initial reference point for displacement calculation; at this point, the displacement of the first marker is defined as zero. Next, for each frame of the image sequence (these images correspond to different time points after the load is applied), the DIC algorithm is used to find the region in that frame that matches the first marker in the initial image. Because the wooden structure deforms and displaces under the load, the position of the first marker in the new image will change. Using the image sequence as the displacement change point, the displacement magnitude and direction of the first marker are calculated for a preset unit time, i.e., the displacement magnitude and direction of the point in the image sequence that undergoes displacement change are obtained within a preset unit time, thus obtaining the first marker displacement vector. The preset unit time is a pre-set unit calculation time, such as 1 second, 1 minute, etc. Furthermore, to obtain the cumulative displacement from the initial time to the current time, the displacement vectors calculated at each time point need to be superimposed. The displacement vector of the first identifier is appended according to adjacent preset unit time intervals, that is, the displacement change calculated at each time interval t is superimposed on the displacement at the previous moment. The first identifier-added displacement vector is combined with the first identifier displacement vector to obtain the first identifier displacement. For example, if there is a displacement vector V1 at t=1 second, and a new displacement vector V2 is calculated at t=2 seconds, then the total displacement at t=2 seconds is V1+V2. Further, based on the first identifier displacement, the displacement superposition operation is repeated to traverse the tracking identifiers to obtain the identifier displacement. The identifier displacement is the total superimposed displacement after the load is applied, and each superimposed displacement corresponds to a specific displacement direction.

[0027] The method provided in this application embodiment further includes: calculating the surface strain distribution through the identified displacement, predicting the global stress distribution of the surface strain distribution based on the stress prediction model, and obtaining the predicted stress distribution; performing local stress concentration analysis based on the predicted stress distribution, performing failure analysis on the stress concentration area, and obtaining a wood structure optimization strategy based on the wood structure failure distribution.

[0028] The surface strain distribution is calculated using the identified displacement. This surface strain distribution includes the normal strain and shear strain corresponding to each preset unit time of the identified displacement. A global stress distribution prediction is performed on the surface strain distribution based on a stress prediction model, obtaining stress data corresponding to each preset unit time to obtain the predicted stress distribution. Finally, local stress concentration analysis is performed based on the predicted stress distribution, and failure analysis is conducted on the stress concentration area. Based on the timber structure failure distribution, optimization strategies for the timber structure are obtained. After completing the stress distribution prediction and failure analysis, optimization strategies corresponding to the failure modes are obtained based on the stress distribution prediction and the corresponding failure modes. The failure modes and optimization strategies are correlated; one failure mode corresponds to one or more preset optimization strategies. For example, during the component design phase, engineers can change the layer thickness, add reinforcing materials, or improve structural details (such as node connection methods) at the point of maximum stress failure in the stress concentration area according to the corresponding optimization strategy, thereby improving the overall load-bearing capacity and durability of the timber structure. This solves the technical problem that the comprehensiveness and accuracy of the detection data in existing timber structure stress distribution detection methods are difficult to improve, leading to low accuracy in identifying failures in stress concentration areas. It enables precise identification of failure modes in stress concentration areas of timber structures, allowing engineers to optimize based on the identification results, thereby improving the overall load-bearing capacity and durability of timber structures.

[0029] The method provided in this application embodiment further includes: calculating the displacement gradient of the tracking marker based on the marker displacement, calculating the normal strain and shear strain of the tracking marker based on the displacement gradient, and obtaining the surface strain distribution; constructing a stress prediction model based on the stress-strain mapping determined by the wood structure material, inputting the surface strain distribution into the stress prediction model to perform stress prediction, and obtaining the predicted stress distribution.

[0030] The displacement gradient of the tracking marker is calculated based on the marker displacement. Specifically, the marker displacement is mapped to a coordinate system according to the displacement direction and amount, and the displacement difference at each spatial coordinate within a preset unit time is calculated to obtain the change corresponding to each coordinate axis. Subsequently, the normal strain and shear strain of the tracking marker are calculated based on the displacement gradient. The normal strain is equal to the ratio of the change corresponding to each coordinate axis to the initial coordinate of the corresponding change. For example, the normal strain in the x-direction is the ratio of the change corresponding to the x-axis to the initial coordinate of that change. The shear strain is the change in angle when an object undergoes a shape change. With the initial coordinates as (x1, y1) and the final coordinates as (x2, y2) after a preset unit time, the shear strain is ((x2-x1)*(y2-y1)-(x1-x2)*(y1-y2)) / L², where L is the displacement length of the previous preset unit. The surface strain distribution is obtained based on the normal strain and shear strain. Furthermore, by obtaining the stress-strain mapping relationship of the corresponding wood structure material, which includes the mapping relationship between normal strain and shear strain, as well as the corresponding normal stress and shear stress components, a stress prediction model is constructed based on the stress-strain mapping relationship. The stress prediction model performs the prediction of the corresponding normal stress and shear stress components based on the obtained normal strain and shear strain at each preset unit time. The surface strain distribution is input into the stress prediction model to perform stress prediction, and the predicted stress distribution is obtained. The predicted stress distribution is the stress prediction data corresponding to each preset unit time, and the stress prediction data includes the normal stress and shear stress components corresponding to each preset unit time.

[0031] The method provided in this application embodiment further includes: calculating stress based on the predicted stress distribution; identifying stress concentration based on the stress concentration threshold to obtain stress concentration areas; and performing failure mode analysis on the stress concentration areas in combination with failure criteria to obtain the failure distribution of the timber structure.

[0032] Obtaining the failure distribution of the timber structure includes: calculating stress based on the predicted stress distribution; inputting the normal stress and shear stress components from the predicted stress distribution into the stress calculation module; obtaining the resultant stress data corresponding to the normal stress and shear stress components to obtain the stress calculation result; setting a stress concentration threshold, which is the maximum stress that the timber can withstand; identifying areas where the stress exceeds the stress concentration threshold to complete stress concentration identification; and finally, performing failure mode analysis on the stress concentration areas based on failure criteria to obtain the failure distribution of the timber structure, which includes the failure mode judgment result and the corresponding location. The failure criteria include: the maximum stress criterion, which states that when the stress component in a certain direction of the material exceeds the ultimate stress (such as tensile strength or compressive strength) in that direction, fracture or failure occurs; the maximum strain criterion, which considers the material to have failed if the strain value exceeds the allowable strain limit of the material (such as the ultimate strain in the fiber direction); and the fracture mechanics criterion, which determines material failure when the stress level is sufficient to cause microcrack propagation based on the crack propagation energy release rate or stress intensity factor.

[0033] The method provided in this application embodiment further includes: identifying failure in the stress concentration region based on the maximum stress criterion, the maximum strain criterion, and the fracture mechanics criterion to obtain failure modes; and performing failure propagation simulation based on the failure modes to add the failure propagation to the failure distribution of the timber structure.

[0034] Failure modes are identified in the stress concentration region based on the maximum stress criterion, maximum strain criterion, and fracture mechanics criteria. Specifically, when the stress reaches or exceeds the tensile or compressive strength of the material, the failure mode is maximum stress failure. When the maximum strain exceeds the material's breaking strain, failure is determined according to the maximum strain criterion. When the stress intensity factor of the crack exceeds the material's critical stress intensity factor, material failure is determined according to the fracture mechanics criteria. Finally, failure propagation simulation is performed based on the identified failure modes, and the failure propagation is added to the failure distribution of the timber structure.

[0035] In the above text, refer to Figure 1 A method for analyzing timber structure failure based on image tracking and stress prediction according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes a timber structure failure analysis device based on image tracking and stress prediction according to an embodiment of the present invention.

[0036] The image tracking and stress prediction-based timber structure failure analysis device according to embodiments of the present invention solves the technical problem that existing timber structure stress distribution detection methods suffer from low accuracy in identifying failures in stress concentration areas due to the difficulty in improving the comprehensiveness and accuracy of detection data. It achieves accurate identification of failure modes in stress concentration areas of timber structures, enabling engineers to optimize based on the identification results, thereby improving the overall load-bearing capacity and durability of the timber structure. The image tracking and stress prediction-based timber structure failure analysis device includes: a data acquisition device deployment module 11, an image acquisition module 12, a displacement identification module 13, a stress prediction module 14, and an optimization strategy acquisition module 15.

[0037] The data acquisition device deployment module 11 is used to capture images of the tracking marks obtained by the dot matrix pattern coated on the surface of the wooden structure based on the preset camera, and to obtain preliminary images. Image acquisition module 12 is used to load the wooden structure according to VIC-3D and acquire image sequences of the wooden structure loading process based on the preset camera; The marker displacement acquisition module 13 is used to calculate the displacement of the tracking marker by combining the preliminary image and the image sequence, and obtain the marker displacement; The stress prediction module 14 is used to calculate the surface strain distribution through the identified displacement, and to perform global stress distribution prediction on the surface strain distribution based on the stress prediction model to obtain the predicted stress distribution. The optimization strategy acquisition module 15 is used to perform local stress concentration analysis based on the predicted stress distribution, perform failure analysis on the stress concentration area, and obtain the wood structure optimization strategy based on the wood structure failure distribution.

[0038] The specific configuration of the acquisition device deployment module 11 will be described in detail below. The acquisition device deployment module 11 may further include: obtaining a preset camera, including: establishing a three-dimensional model of the wooden structure for preliminary stress analysis to obtain a preliminary global stress distribution; deploying a preset first resolution camera in the key detection area extracted based on the preliminary global stress distribution to obtain the coverage area of ​​the first resolution camera; performing an uncovered analysis on the wooden structure in conjunction with the coverage area of ​​the first resolution camera, and deploying a preset second resolution camera in the uncovered area based on a second resolution camera; combining the preset first resolution camera and the preset second resolution camera to obtain the preset camera.

[0039] The specific configuration of the image acquisition module 12 will be described in detail below. The image acquisition module 12 further includes: acquiring an image sequence, including: distributing a trigger signal generated by a signal trigger to the preset camera; performing trigger time verification based on the trigger signal; completing the external trigger configuration of the preset camera based on the time verification result; capturing the preliminary image through the preset camera; and continuously acquiring the image sequence based on a preset frame rate, wherein the preset camera synchronously acquires the image sequence.

[0040] The specific configuration of the marker displacement acquisition module 13 will be described in detail below. The marker displacement acquisition module 13 may further include: obtaining marker displacement, including: extracting a first marker based on the tracking marker; calculating the displacement magnitude and direction of the first marker using the preliminary image as the displacement starting point and the image sequence as the displacement change point within a preset unit time interval to obtain a first marker displacement vector; appending a displacement vector to the first marker displacement vector based on adjacent preset unit time intervals; combining the appended first marker displacement vector with the first marker displacement vector to obtain the first marker displacement; and obtaining the marker displacement by traversing the tracking markers based on the first marker displacement.

[0041] The specific configuration of the stress prediction module 14 will be described in detail below. The stress prediction module 14 further includes: obtaining the predicted stress distribution, including: calculating the displacement gradient of the tracking marker based on the marker displacement, calculating the normal strain and shear strain of the tracking marker based on the displacement gradient, and obtaining the surface strain distribution; constructing a stress prediction model based on the stress-strain mapping determined by the wood structure material, and inputting the surface strain distribution into the stress prediction model to perform stress prediction, thereby obtaining the predicted stress distribution.

[0042] The specific configuration of the optimization strategy acquisition module 15 will be described in detail below. The optimization strategy acquisition module 15 further includes: obtaining the failure distribution of the timber structure, including: calculating the stress based on the predicted stress distribution, identifying stress concentration based on the stress concentration threshold, and obtaining stress concentration areas; and performing failure mode analysis on the stress concentration areas in combination with failure criteria to obtain the failure distribution of the timber structure.

[0043] The specific configuration of the optimization strategy acquisition module 15 will be described in detail below. The optimization strategy acquisition module 15 further includes: obtaining the failure distribution of the timber structure, including: identifying failures in the stress concentration area based on the maximum stress criterion, the maximum strain criterion, and the fracture mechanics criterion to obtain failure modes; performing failure propagation simulation based on the failure modes, and adding the failure propagation to the failure distribution of the timber structure.

[0044] The image tracking and stress prediction-based timber structure failure analysis device provided in this embodiment of the invention can execute the image tracking and stress prediction-based timber structure failure analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0045] Although this application makes various references to certain modules in the device according to the embodiments of this application, any number of different modules can be used and run on the user terminal and / or server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0046] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for failure analysis of timber structures based on image tracking and stress prediction, characterized in that, include: A preliminary image is obtained by photographing the tracking marks obtained by the dot matrix pattern coated on the surface of the wooden structure using a preset camera. Loading is applied to the wooden structure using VIC-3D, and image sequences of the loading process are acquired using the preset camera. The displacement of the tracking marker is calculated by combining the preliminary image and the image sequence to obtain the marker displacement; The surface strain distribution is calculated using the identified displacement, and the global stress distribution is predicted based on the stress prediction model to obtain the predicted stress distribution. Based on the predicted stress distribution, local stress concentration analysis is performed, and failure analysis is conducted on the stress concentration area. Based on the failure distribution of the timber structure, an optimization strategy for the timber structure is obtained.

2. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 1, characterized in that, Obtain preset cameras, including: A three-dimensional model of the timber structure was established for preliminary stress analysis to obtain a preliminary global stress distribution. A camera with a preset first resolution is deployed in the key detection area extracted based on the preliminary global stress distribution to obtain the coverage area of ​​the first resolution camera. An uncovered area analysis is performed on the wooden structure in conjunction with the coverage area of ​​the first resolution camera, and a preset second resolution camera is deployed in the uncovered area based on the second resolution camera; The preset camera is obtained by combining the preset first resolution camera and the preset second resolution camera.

3. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 1, characterized in that, Acquire image sequences, including: The trigger signal generated by the signal trigger is assigned to the preset camera, the trigger time is verified according to the trigger signal, and the external trigger configuration of the preset camera is completed based on the time verification result; The initial image is obtained by shooting with the preset camera, and the image sequence is continuously acquired based on the preset frame rate, wherein the preset camera acquires the image sequence synchronously.

4. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 1, characterized in that, Obtain the identifier displacement, including: Extract the first identifier based on the tracking identifier; Using the preliminary image as the displacement starting point and the image sequence as the displacement change point, the displacement magnitude and displacement direction of the first identifier are calculated for a preset unit time to obtain the displacement vector of the first identifier; The displacement vector of the first identifier is appended according to the adjacent preset unit time, and the first identifier appended displacement vector is combined with the first identifier displacement vector to obtain the first identifier displacement. The identification displacement is obtained by traversing the tracking identifiers based on the first identifier displacement.

5. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 1, characterized in that, To obtain the predicted stress distribution, including: The displacement gradient of the tracking marker is calculated based on the marker displacement, and the normal strain and shear strain of the tracking marker are calculated based on the displacement gradient to obtain the surface strain distribution; A stress prediction model is constructed based on the stress-strain mapping determined by the wood structure material. The surface strain distribution is input into the stress prediction model to predict the stress and obtain the predicted stress distribution.

6. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 1, characterized in that, Obtain the failure distribution of the timber structure, including: Stress force is calculated based on the predicted stress distribution, and stress concentration is identified based on the stress force concentration threshold to obtain the stress concentration region; Failure mode analysis was performed on the stress concentration area using failure criteria to obtain the failure distribution of the timber structure.

7. The method for timber structure failure analysis based on image tracking and stress prediction as described in claim 6, characterized in that, Obtaining the failure distribution of the timber structure includes: Failure modes are obtained by identifying the stress concentration region based on the maximum stress criterion, maximum strain criterion, and fracture mechanics criterion. Based on the failure mode, a failure propagation simulation is performed, and the failure propagation is added to the failure distribution of the timber structure.

8. A timber structure failure analysis device based on image tracking and stress prediction, characterized in that, include: The data acquisition device deployment module is used to capture images of tracking marks obtained by applying a dot matrix pattern to the surface of a wooden structure using a preset camera, thereby obtaining preliminary images. The image acquisition module is used to load the wooden structure according to VIC-3D and to acquire image sequences of the wooden structure loading process based on the preset camera; The marker displacement acquisition module is used to calculate the displacement of the tracking marker by combining the preliminary image and the image sequence, and obtain the marker displacement; The stress prediction module is used to calculate the surface strain distribution through the identified displacement, and to perform global stress distribution prediction based on the stress prediction model to obtain the predicted stress distribution. The optimization strategy acquisition module is used to perform local stress concentration analysis based on the predicted stress distribution, perform failure analysis on the stress concentration area, and obtain the wood structure optimization strategy based on the wood structure failure distribution.

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