A bridge crack dynamic monitoring system and method based on diffusion probability model

Abstract

The present invention relates to the technical field of bridge material analysis. Disclosed are a system and method for dynamic monitoring of bridge cracks based on a diffusion probability model. The system and method comprise constructing a reflection amplitude matrix and a crack depth matrix based on ultrasonic data, obtaining contrast values, homogeneity values, and correlation values ​​in a bridge crack image based on a grayscale symbiosis algorithm, and constructing a texture feature matrix based on the contrast values, homogeneity values, and correlation values. The system constructs the reflection amplitude matrix, the crack depth matrix, and the texture feature matrix, respectively, to comprehensively characterize the dynamic change characteristics of bridge cracks, thereby resolving the issues of insufficient accuracy and reliability in crack feature extraction in the prior art. By screening a feature matrix sequence based on W comprehensive feature matrices and inputting the matrix into a pre-trained diffusion probability model, the system and method can capture the crack propagation rate in real time and monitor the dynamic changes of the cracks.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge material analysis, and more particularly to a bridge crack dynamic monitoring system and method based on a diffusion probability model. Background Art

[0002] Bridges are crucial transportation infrastructure, and cracks are a common structural defect. Early detection and dynamic monitoring are crucial for preventing structural failure and extending service life. Traditional methods for detecting bridge cracks include manual visual inspection, acoustic emission technology, ultrasonic testing, and infrared thermal imaging. However, with the rapid development of computer vision and machine learning technologies in recent years, image analysis-based bridge crack detection methods have gradually attracted widespread attention. Although existing methods for bridge crack detection using computer vision have been disclosed, some challenges remain.

[0003] For example, the Chinese patent application with publication number CN115078382A provides a bridge crack monitoring system based on video images. The system consists of a front-end system and a back-end system. The front-end system collects crack image information and sends it to the back-end system via wireless transmission. The back-end system stores and displays the crack image information, supports cloud display and precise calculation. The Chinese patent with authorization announcement number CN118730914B provides a crack monitoring device for a cross-sea bridge and a monitoring method thereof. The system includes an image acquisition unit and a central control unit. The image acquisition unit is used to collect visual image information of bridge cracks. The central control unit receives crack change parameters and image information, processes and calculates, and outputs monitoring results.

[0004] Although the use of computer vision for bridge crack detection has been disclosed in the prior art, it is mainly limited to static monitoring of bridge cracks and cannot monitor dynamic changes in cracks. In addition, the texture features of different bridge structures vary greatly, resulting in insufficient accuracy and reliability in crack feature extraction. These problems make it difficult for bridge crack detection to meet actual needs in terms of real-time and dynamic performance, while also affecting the accuracy of monitoring results and the effectiveness of bridge structure safety assessments.

[0005] In view of this, the present invention proposes a bridge crack dynamic monitoring system and method based on a diffusion probability model to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a bridge crack dynamic monitoring system and method based on a diffusion probability model.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, a bridge crack dynamic monitoring method based on a diffusion probability model is provided, comprising:

[0009] Acquire ultrasonic data and bridge crack images, construct reflection amplitude matrices and crack depth matrices based on the ultrasonic data, and obtain contrast, homogeneity, and correlation values ​​in the bridge crack images using a grayscale symbiosis algorithm;

[0010] A texture feature matrix is ​​constructed based on contrast values, homogeneity values, and correlation values; W comprehensive feature matrices are constructed based on the reflection amplitude matrix, the crack depth matrix, and the texture feature matrix; and a feature matrix sequence is determined based on the W comprehensive feature matrices;

[0011] The feature matrix sequence is input into a pre-trained diffusion probability model to obtain a crack monitoring result output by the diffusion probability model, wherein the crack monitoring result at least includes a crack propagation rate.

[0012] Furthermore, the method for obtaining ultrasonic data includes:

[0013] The bridge crack images are classified to obtain the corresponding crack classification results. If the crack classification result is a longitudinal crack, the ultrasonic probe is arranged along the length of the bridge. If the crack classification result is a transverse crack, the ultrasonic probe is arranged perpendicular to the length of the bridge. If the crack classification result is an oblique crack, the ultrasonic probe is arranged along the direction of the bridge crack.

[0014] Furthermore, the ultrasonic data includes peak occurrence time, and the method for constructing a crack depth matrix based on the ultrasonic data includes:

[0015] The crack depth value is determined according to the peak occurrence time in the ultrasonic data and the preset propagation speed, and a crack depth matrix is ​​constructed according to the crack depth value.

[0016] Furthermore, the method for obtaining the contrast value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0017] The contrast value in the direction θ and the co-occurrence probability in the direction θ of the bridge crack image are obtained according to the grayscale co-occurrence algorithm, and the corresponding contrast value is calculated according to the contrast value in the direction θ and the co-occurrence probability in the direction θ.

[0018] Furthermore, the method for obtaining the correlation value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0019] According to the grayscale symbiosis algorithm, the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j in the bridge crack image are obtained, and the corresponding correlation value is calculated based on the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j.

[0020] Furthermore, the method of constructing W comprehensive feature matrices based on the reflection amplitude matrix, the crack depth matrix and the texture feature matrix includes:

[0021] Adjust the value of K, obtain W texture feature matrices, traverse the W texture feature matrices, align the reflection amplitude matrix, crack depth matrix and texture feature matrix through mathematical transformation, perform weighted fusion on the reflection amplitude matrix, crack depth matrix and texture feature matrix, and construct W comprehensive feature matrices. K represents the value angle corresponding to the texture feature matrix.

[0022] Furthermore, the method for determining the feature matrix sequence based on the W comprehensive feature matrices includes:

[0023] The crack direction range in the bridge crack image is determined according to the Hough transform algorithm. P comprehensive feature matrices are screened out from W comprehensive feature matrices based on the crack direction range, and the P comprehensive feature matrices are used as a feature matrix sequence. The crack direction range is the range of crack extension directions in the bridge crack image.

[0024] Furthermore, the method of selecting P comprehensive feature matrices from W comprehensive feature matrices according to the crack direction range includes:

[0025] Obtain the value range corresponding to each comprehensive feature matrix, and determine whether the value range belongs to the crack direction range. If not, eliminate the corresponding comprehensive feature matrix to screen out P comprehensive feature matrices, and the value range is the value range of K.

[0026] Furthermore, the training method of the diffusion probability model includes:

[0027] Pair the historical feature matrix sequence with the corresponding historical crack monitoring result label to construct a training set. The historical feature matrix sequence is used as the input of the sub-model, and the corresponding historical crack monitoring result is used as the output of the sub-model.

[0028] The total number of diffusion steps is preset to T, and the noise increment of each step is defined as β t For each data point in the training set, the current diffusion step t and the corresponding noise data x are generated by random sampling. t , according to the current diffusion step t and the noise data x t Construct a loss function, the noise data x t The distribution form of is Gaussian distribution;

[0029] Randomly extract a batch of data points from the training set and generate corresponding noise data x for each data point t, calculate the corresponding loss function value, update the sub-model parameters through the back-propagation algorithm and minimize the loss function value as the goal, and iteratively train the sub-model to obtain the diffusion probability model.

[0030] In a second aspect, a bridge crack dynamic monitoring system based on a diffusion probability model is provided, which is used to implement the above-mentioned bridge crack dynamic monitoring method based on a diffusion probability model, including:

[0031] Data processing module: used to obtain ultrasonic data and bridge crack images, construct reflection amplitude matrix and crack depth matrix based on ultrasonic data, and obtain contrast value, homogeneity value and correlation value in bridge crack images based on grayscale symbiosis algorithm;

[0032] Matrix construction module: used to construct a texture feature matrix based on contrast values, homogeneity values, and correlation values, construct W comprehensive feature matrices based on the reflection amplitude matrix, crack depth matrix, and texture feature matrix, and determine a feature matrix sequence based on the W comprehensive feature matrices;

[0033] Monitoring module: used to input the feature matrix sequence into the pre-trained diffusion probability model to obtain the crack monitoring results output by the diffusion probability model, and the crack monitoring results at least include the crack propagation rate.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] By combining ultrasonic data and image texture features of bridge crack images, the present invention constructs a reflection amplitude matrix, a crack depth matrix, and a texture feature matrix, respectively, to comprehensively characterize the dynamic change characteristics of bridge cracks. This solves the problem of insufficient accuracy and reliability in crack feature extraction in the existing technology. By screening a feature matrix sequence based on W comprehensive feature matrices and inputting it into a pre-trained diffusion probability model, the crack propagation rate can be captured in real time, enabling monitoring of the dynamic changes of cracks, thus solving the problem of insufficient dynamic monitoring capabilities in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of a flow chart of a bridge crack dynamic monitoring method based on a diffusion probability model in the present invention;

[0037] Figure 2 Schematic diagram of the structure of a bridge crack dynamic monitoring system based on a diffusion probability model in the present invention;

[0038] Figure 3 Schematic diagram of the process of obtaining ultrasonic data in the present invention;

[0039] Figure 4Schematic diagram of the flow of the method for determining a feature matrix sequence based on W comprehensive feature matrices in the present invention. DETAILED DESCRIPTION

[0040] The following will provide a clear and complete description of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figure 1 As shown, this embodiment discloses a method for dynamic monitoring of bridge cracks based on a diffusion probability model, including:

[0043] S10: Acquire ultrasonic data and a bridge crack image, construct a reflection amplitude matrix and a crack depth matrix based on the ultrasonic data, and obtain contrast values, homogeneity values, and correlation values ​​in the bridge crack image based on a grayscale symbiosis algorithm;

[0044] In this embodiment, during the process of acquiring ultrasonic data and bridge crack images, the bridge to be monitored can be in a state of being open to traffic or closed to traffic. The bridge crack images can be acquired by using a camera installed on the bridge surface. The ultrasonic data can be acquired by arranging corresponding ultrasonic probes in a fixed manner. The fixed manner means arranging the ultrasonic probes in accordance with preset rules and positions at key parts of the bridge structure, such as crack-prone areas or structural nodes with greater stress. The acquisition can also be performed in the following manner:

[0045] like Figure 3 As shown, the method for obtaining ultrasonic data includes:

[0046] The bridge crack images are classified to obtain the corresponding crack classification results. If the crack classification result is a longitudinal crack, the ultrasonic probe is arranged along the length of the bridge. If the crack classification result is a transverse crack, the ultrasonic probe is arranged perpendicular to the length of the bridge. If the crack classification result is an oblique crack, the ultrasonic probe is arranged along the direction of the bridge crack.

[0047] It should be added that the classification of bridge crack images can be carried out using existing classification models, such as convolutional neural networks (CNN) or support vector machines (SVM). These models can classify based on features such as texture, shape, and direction of crack images. In addition, pre-trained deep learning models (such as ResNet or VGG) can be used to extract and classify features of bridge crack images to quickly obtain the specific direction and type of cracks.

[0048] It can be understood that compared with the fixed arrangement of ultrasonic probes, the advantage of arranging ultrasonic probes in the above manner is that it can ensure that the arrangement of ultrasonic probes is more targeted at the actual direction of the crack, thereby improving the accuracy of the reflection amplitude matrix and the crack depth matrix in subsequent steps. At the same time, this arrangement method can more effectively capture the texture features related to the crack direction, which helps to make the covariance value of the comprehensive feature matrix constructed in the subsequent steps more accurate, and ultimately optimize the determination of the feature matrix, thereby improving the accuracy and reliability of the diffusion probability model in crack propagation rate monitoring.

[0049] In this embodiment, the ultrasonic data includes at least the maximum amplitude value and the peak occurrence time. The maximum amplitude value refers to the maximum amplitude value in the waveform signal reflected back during the propagation of the ultrasonic signal, and the peak occurrence time refers to the time point corresponding to the maximum amplitude value in the ultrasonic signal.

[0050] Methods for constructing a reflection amplitude matrix from ultrasonic data include:

[0051]

[0052] Among them, U I Represents the reflection amplitude matrix, I M,N It is characterized by the maximum amplitude value measured by the transmission and reception of ultrasonic signals between the Mth ultrasonic probe and the Nth ultrasonic probe.

[0053] Methods for constructing a crack depth matrix from ultrasonic data include:

[0054] The crack depth value is determined according to the peak occurrence time in the ultrasonic data and the preset propagation speed, and a crack depth matrix is ​​constructed according to the crack depth value.

[0055] Methods for constructing a crack depth matrix based on crack depth values ​​include:

[0056]

[0057] Among them, U D represents the crack depth matrix, D M,N It is represented by the crack depth value corresponding to the transmission and reception of ultrasonic signals between the Mth ultrasonic probe and the Nth ultrasonic probe.

[0058] It can be understood that the above-mentioned propagation velocity refers to the speed at which ultrasound waves propagate in bridge materials, which is usually determined by the physical properties of the material (such as density and elastic modulus). Calculating the crack depth value is an existing technology and will not be elaborated on in this embodiment.

[0059] The method for obtaining the contrast value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0060] The contrast value in the direction θ and the co-occurrence probability in the direction θ of the bridge crack image are obtained according to the grayscale co-occurrence algorithm, and the corresponding contrast value is calculated according to the contrast value in the direction θ and the co-occurrence probability in the direction θ.

[0061] The method of calculating the corresponding contrast value according to the contrast value in the direction θ and the co-occurrence probability in the direction θ includes:

[0062] Cot θ =∑ i,j (ij) 2 P θ (i,j);

[0063] Among them, Cot θ is the contrast value in direction θ, P θ (i, j) represents the co-occurrence probability of a pixel pair with grayscale value i and grayscale value j in direction θ.

[0064] It should be noted that the grayscale co-occurrence algorithm refers to a method of describing the texture characteristics of an image by statistically analyzing the grayscale value relationships between pixels in the image and constructing a grayscale co-occurrence matrix. It reflects the spatial distribution patterns and mutual relationships of different grayscale values ​​in the image by calculating the grayscale value combination frequencies of pixels in the image at specific directions and distances. The larger the contrast value, the more drastic the grayscale value changes in the image and the more obvious the edge features of the cracks. In bridge crack detection, areas with larger contrast values ​​usually correspond to the significant parts of the cracks. Therefore, the larger the contrast value, the clearer the features of the crack area can be highlighted, which helps to improve the accuracy of crack detection and the effectiveness of feature extraction.

[0065] The method for obtaining the homogeneity value in the bridge crack image based on the grayscale symbiosis algorithm includes:

[0066]

[0067] Among them, Hoy θ is the homogeneity value in the direction θ.

[0068] It should be noted that in this embodiment, the larger the homogeneity value, the more uniform the grayscale distribution in the bridge crack image, and the smoother the texture change in the crack area. This usually reflects that the grayscale difference in the area around the crack is small, making it difficult to highlight the characteristics of the crack area.

[0069] The method for obtaining the correlation value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0070] According to the grayscale symbiosis algorithm, the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j in the bridge crack image are obtained, and the corresponding correlation value is calculated based on the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j.

[0071] The method for calculating the corresponding correlation value according to the mean of grayscale value i, the mean of grayscale value j, the standard deviation of grayscale value i, and the standard deviation of grayscale value j includes:

[0072]

[0073] Among them, Con θ is the correlation value in direction θ, μ i Represents the mean value of gray value i, μ j Characterizes the mean of gray value j, σ i Characterizes the standard deviation of gray value i, σ j Characterizes the standard deviation of the grayscale value j.

[0074] It should be noted that the mean of the grayscale value i refers to the expected value of the second grayscale value j when the first grayscale value is i. Similarly, the mean of the grayscale value j refers to the expected value of the second grayscale value i when the first grayscale value is j. The mean of the grayscale value i is μ i It reflects the average grayscale value of the pixels around the pixel with grayscale value i in the image. The higher μ i It shows that the pixels with gray value i tend to have higher gray value, while the pixels with lower μ i This indicates a tendency towards lower grayscale values.

[0075] In this embodiment, σ i and σ j It describes the extent of the grayscale value distribution around the pixels with grayscale values ​​i and j. A higher standard deviation means a larger range of grayscale value variation and a rougher texture; a lower standard deviation means a smaller grayscale value variation and a finer texture. From the above content, it can be seen that in this implementation, the larger the correlation value, the more consistent the variation trend of the grayscale value in the bridge crack image, and the stronger the correlation of the texture in the crack area, which helps to clearly distinguish the overall morphological characteristics of the crack. For subsequent crack monitoring, this can improve the accuracy and reliability of crack feature extraction, thereby more accurately calculating the crack propagation rate and morphological changes, and enhancing the effectiveness and accuracy of the monitoring results.

[0076] S20: constructing a texture feature matrix based on the contrast value, the homogeneity value, and the correlation value, constructing W comprehensive feature matrices based on the reflection amplitude matrix, the crack depth matrix, and the texture feature matrix, and determining a feature matrix sequence based on the W comprehensive feature matrices;

[0077] Methods for constructing a texture feature matrix based on contrast values, homogeneity values, and correlation values ​​include:

[0078] The contrast values, homogeneity values ​​and correlation values ​​in K directions of the bridge crack image are calculated according to the grayscale symbiosis algorithm. The contrast values, homogeneity values ​​and correlation values ​​in different directions are combined to construct the texture feature matrix.

[0079] For example, if K=4, then the four directions are 0°, 45°, 90°, and 135°, and the texture feature matrix is ​​as follows:

[0080]

[0081] Among them, TFM represents texture feature matrix.

[0082] The method of constructing W comprehensive feature matrices based on the reflection amplitude matrix, the crack depth matrix and the texture feature matrix includes:

[0083] Adjust the value of K, obtain W texture feature matrices, traverse the W texture feature matrices, align the reflection amplitude matrix, crack depth matrix and texture feature matrix through mathematical transformation, perform weighted fusion on the reflection amplitude matrix, crack depth matrix and texture feature matrix, and construct W comprehensive feature matrices. K represents the value angle corresponding to the texture feature matrix.

[0084] From the above content, it can be seen that the value of K can be 0°, 30°, 45° and 60°, etc., so the value of K is adjusted to construct W texture feature matrices. The purpose of the above-mentioned size alignment through mathematical transformation is to ensure that the texture feature matrices in different directions and scales are consistent in dimension and structure, so as to facilitate subsequent effective fusion with the reflection amplitude matrix and the crack depth matrix. The fusion of the reflection amplitude matrix, the crack depth matrix and the texture feature matrix requires that all matrices can be effectively weighted superimposed or operated in structure. If the matrix dimensions are inconsistent, direct operation will lead to calculation errors. The weighted fusion mentioned above refers to the weighted superposition of the corresponding elements in each matrix. It should be noted that weighted superposition refers to vector splicing, and vector splicing is to connect multiple matrices or vectors according to a certain dimension (such as column or row) to form a new matrix.

[0085] For example, the construction of the comprehensive feature matrix is ​​as follows:

[0086] U cod =w1·U I +w2·U D +w3·TFM;

[0087] Among them, Ucod is a comprehensive feature matrix, w1, w2, and w3 are all weight factors.

[0088] like Figure 4 As shown, the method for determining the feature matrix sequence based on W comprehensive feature matrices includes:

[0089] The crack direction range in the bridge crack image is determined according to the Hough transform algorithm. P comprehensive feature matrices are screened out from W comprehensive feature matrices based on the crack direction range, and the P comprehensive feature matrices are used as a feature matrix sequence. The crack direction range is the range of crack extension directions in the bridge crack image.

[0090] It is understandable that since the present invention requires dynamic detection of bridge cracks, the crack direction range in the bridge crack image is determined by the Hough transform algorithm, and P comprehensive feature matrices related to the crack extension direction are screened out from the W comprehensive feature matrices as the feature matrix sequence. This can effectively reduce the interference of irrelevant features and improve the accuracy and efficiency of dynamic crack detection. This method ensures that the feature matrix sequence can more accurately reflect the actual directional characteristics of crack extension, thereby providing more accurate and dynamic support for the calculation of crack extension rate, monitoring of morphological changes, and model prediction.

[0091] Methods for selecting P comprehensive feature matrices from W comprehensive feature matrices according to the crack direction range include:

[0092] Obtain the value range corresponding to each comprehensive feature matrix, and determine whether the value range belongs to the crack direction range. If not, eliminate the corresponding comprehensive feature matrix to screen out P comprehensive feature matrices, and the value range is the value range of K.

[0093] It can be understood that the value of K can be 0°, 30°, 45° and 60°, etc., then the crack direction range is also an angle range. For example, the crack direction range is 30° to 90°. If the K values ​​corresponding to the first comprehensive feature matrix include 30°, 45° and 60°, and the K values ​​corresponding to the second comprehensive feature matrix include 60°, 90° and 135°, then the second comprehensive feature matrix is ​​eliminated and the first comprehensive feature matrix is ​​retained. The purpose of this is to ensure that the feature matrix sequence selected from the comprehensive feature matrix is ​​highly correlated with the crack direction range, thereby effectively reducing the interference of irrelevant features on the monitoring of crack extension direction, and improving the accuracy and efficiency of dynamic detection of bridge cracks. At the same time, this method can more accurately reflect the actual directional characteristics of crack extension, and provide more accurate and dynamic support for the calculation of crack extension rate, monitoring of morphological changes and model prediction.

[0094] S30: inputting the feature matrix sequence into a pre-trained diffusion probability model to obtain a crack monitoring result output by the diffusion probability model, wherein the crack monitoring result at least includes a crack propagation rate;

[0095] The training methods for the diffusion probability model include:

[0096] Pair the historical feature matrix sequence with the corresponding historical crack monitoring result labels to construct a training set. The historical feature matrix sequence is used as the input of the sub-model, and the corresponding historical crack monitoring results are used as the output of the sub-model. The sub-model can be U-Net or Transformer.

[0097] The total number of diffusion steps is preset to T, and the noise increment of each step is defined as β t For each data point in the training set, the current diffusion step t and the corresponding noise data x are generated by random sampling. t , according to the current diffusion step t and the noise data x t Construct a loss function, the noise data x t The distribution form of is Gaussian distribution;

[0098] Randomly extract a batch of data points from the training set and generate corresponding noise data x for each data point t , calculate the corresponding loss function value, update the sub-model parameters through the back-propagation algorithm and minimize the loss function value as the goal, and iteratively train the sub-model to obtain the diffusion probability model.

[0099] It should be noted that in the training of the diffusion probability model, the design of each step is to ensure that the model can efficiently learn the mapping relationship between the crack feature sequence and the crack propagation rate. t This method can gradually perturb the complex information in the feature matrix sequence, thereby simulating the uncertainty and randomness in real bridge crack data. This process of gradually adding noise can enhance the generalization ability of the model after back propagation and adapt to crack expansion in different environments.

[0100] Generate noisy data x t The methods include:

[0101]

[0102] Among them, α t is the forward process coefficient, is the cumulative coefficient, x0 is the data point, ∈ is the standard normal distribution noise, I represents the unit matrix, α s is the cumulative coefficient corresponding to step s, Characterized by normal distribution.

[0103] According to the current diffusion step t and the noise data x t Methods for constructing loss functions include:

[0104]

[0105] in, Characterized as a single-step loss, Characterized as the total loss, It is represented as the expected value of the initial data point x0, the standard normal distribution noise ∈ and the current diffusion step t, ||·|| 2 Characterized as square norm, ∈ θ (x t ,t) represents the predicted normally distributed noise at the tth step.

[0106] It should be noted that the loss function constructed based on the current diffusion step t is achieved by comparing the difference between the output generated by the current model and the noise component in the real data. The process of minimizing the loss function value can help the model gradually restore the true structure of the characteristic matrix sequence, ensuring that the model has an accurate understanding and prediction capability of the dynamic expansion characteristics of the crack characteristic sequence.

[0107] In this embodiment, the combination of step S20 and step S30 can accurately reflect the texture features, depth features and dynamic expansion direction characteristics of bridge cracks through a comprehensive feature matrix sequence. In S20, the feature matrix sequence is constructed and screened, so that the data input into the diffusion probability model is targeted and relevant. This data preprocessing process effectively eliminates the interference of irrelevant features, thereby providing high-quality input data for the training and prediction of the diffusion probability model in S30, and improving the model's prediction accuracy and reliability for crack expansion rate and dynamic behavior. It can be understood that only by aligning the size and weighted fusion of the comprehensive feature matrix through mathematical transformation in S20, and screening out a high-correlation feature matrix sequence in combination with the crack direction range, can representative and complete input features be provided for the diffusion probability model in S30. This combination of sequential steps ensures the efficiency and accuracy of dynamic crack monitoring and provides more accurate support for real-time prediction of crack expansion rate and morphological changes.

[0108] This embodiment constructs a reflection amplitude matrix and a crack depth matrix based on ultrasonic data, obtains contrast values, homogeneity values, and correlation values ​​in the bridge crack image based on the grayscale symbiosis algorithm, constructs a texture feature matrix based on the contrast values, homogeneity values, and correlation values, constructs W comprehensive feature matrices based on the reflection amplitude matrix, crack depth matrix, and texture feature matrix, determines a feature matrix sequence based on the W comprehensive feature matrices, inputs the feature matrix sequence into a pre-trained diffusion probability model, and obtains crack monitoring results output by the diffusion probability model. This embodiment combines the image texture features of the ultrasonic data and the bridge crack image to construct a reflection amplitude matrix, a crack depth matrix, and a texture feature matrix, respectively, to comprehensively characterize the dynamic change characteristics of the bridge crack, solving the problem of insufficient accuracy and reliability of crack feature extraction in the prior art. By screening the feature matrix sequence based on the W comprehensive feature matrices and inputting it into the pre-trained diffusion probability model, the crack expansion rate can be captured in real time, and the dynamic changes of the crack can be monitored, solving the problem of insufficient dynamic monitoring capability in the prior art.

[0109] Example 2

[0110] See also Figure 2 As shown, based on the same inventive concept, this embodiment discloses a bridge crack dynamic monitoring system based on a diffusion probability model. For details not provided in this embodiment, please refer to the description of the relevant parts in Example 1. The system includes:

[0111] Data processing module: used to obtain ultrasonic data and bridge crack images, construct reflection amplitude matrix and crack depth matrix based on ultrasonic data, and obtain contrast value, homogeneity value and correlation value in bridge crack images based on grayscale symbiosis algorithm;

[0112] Methods for acquiring ultrasound data include:

[0113] The bridge crack images are classified to obtain the corresponding crack classification results. If the crack classification result is a longitudinal crack, the ultrasonic probe is arranged along the length of the bridge. If the crack classification result is a transverse crack, the ultrasonic probe is arranged perpendicular to the length of the bridge. If the crack classification result is an oblique crack, the ultrasonic probe is arranged along the direction of the bridge crack.

[0114] The method for obtaining the contrast value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0115] The contrast value in the direction θ and the co-occurrence probability in the direction θ in the bridge crack image are obtained according to the grayscale co-occurrence algorithm, and the corresponding contrast value is calculated according to the contrast value in the direction θ and the co-occurrence probability in the direction θ.

[0116] The method of calculating the corresponding contrast value according to the contrast value in the direction θ and the co-occurrence probability in the direction θ includes:

[0117] Cot θ =∑ i,j (ij) 2 P θ (i,j);

[0118] Among them, Cot θ is the contrast value in direction θ, P θ (i, j) represents the co-occurrence probability of a pixel pair with grayscale value i and grayscale value j in direction θ.

[0119] The method for obtaining the homogeneity value in the bridge crack image based on the grayscale symbiosis algorithm includes:

[0120]

[0121] Among them, Hoy θ is the homogeneity value in the direction θ.

[0122] The method for obtaining the correlation value in the bridge crack image according to the grayscale symbiosis algorithm includes:

[0123] According to the grayscale symbiosis algorithm, the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j in the bridge crack image are obtained, and the corresponding correlation value is calculated based on the mean of the grayscale value i, the mean of the grayscale value j, the standard deviation of the grayscale value i, and the standard deviation of the grayscale value j.

[0124] The method for calculating the corresponding correlation value according to the mean of grayscale value i, the mean of grayscale value j, the standard deviation of grayscale value i, and the standard deviation of grayscale value j includes:

[0125]

[0126] Among them, Con θ is the correlation value in direction θ, μ i Represents the mean value of gray value i, μ j Characterizes the mean of gray value j, σ i Characterizes the standard deviation of gray value i, σ j Characterizes the standard deviation of the grayscale value j.

[0127] Matrix construction module: used to construct a texture feature matrix based on contrast values, homogeneity values, and correlation values, construct W comprehensive feature matrices based on the reflection amplitude matrix, crack depth matrix, and texture feature matrix, and determine a feature matrix sequence based on the W comprehensive feature matrices;

[0128] Methods for constructing a texture feature matrix based on contrast values, homogeneity values, and correlation values ​​include:

[0129] The contrast values, homogeneity values ​​and correlation values ​​in K directions of the bridge crack image are calculated according to the grayscale symbiosis algorithm. The contrast values, homogeneity values ​​and correlation values ​​in different directions are combined to construct the texture feature matrix.

[0130] The method for determining the feature matrix sequence based on W comprehensive feature matrices includes:

[0131] The crack direction range in the bridge crack image is determined according to the Hough transform algorithm. P comprehensive feature matrices are screened out from W comprehensive feature matrices based on the crack direction range, and the P comprehensive feature matrices are used as a feature matrix sequence. The crack direction range is the range of crack extension directions in the bridge crack image.

[0132] Methods for selecting P comprehensive feature matrices from W comprehensive feature matrices according to the crack direction range include:

[0133] Obtain the value range corresponding to each comprehensive feature matrix, and determine whether the value range belongs to the crack direction range. If not, eliminate the corresponding comprehensive feature matrix to screen out P comprehensive feature matrices, and the value range is the value range of K.

[0134] Monitoring module: used to input the feature matrix sequence into the pre-trained diffusion probability model to obtain the crack monitoring results output by the diffusion probability model, wherein the crack monitoring results at least include the crack propagation rate;

[0135] The training methods for the diffusion probability model include:

[0136] Pair the historical feature matrix sequence with the corresponding historical crack monitoring result labels to construct a training set. The historical feature matrix sequence is used as the input of the sub-model, and the corresponding historical crack monitoring results are used as the output of the sub-model. The sub-model can be U-Net or Transformer.

[0137] The total number of diffusion steps is preset to T, and the noise increment of each step is defined as β t For each data point in the training set, the current diffusion step t and the corresponding noise data x are generated by random sampling. t , according to the current diffusion step t and the noise data x t Construct a loss function, the noise data x t The distribution form of is Gaussian distribution;

[0138] Randomly extract a batch of data points from the training set and generate corresponding noise data x for each data point t, calculate the corresponding loss function value, update the sub-model parameters through the back-propagation algorithm and minimize the loss function value as the goal, and iteratively train the sub-model to obtain the diffusion probability model.

[0139] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters, weights and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0140] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications 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.

[0147] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A bridge crack dynamic monitoring method based on a diffusion probability model, characterized in that: include: Acquire ultrasonic data and bridge crack images, construct reflection amplitude matrices and crack depth matrices based on the ultrasonic data, and obtain contrast, homogeneity, and correlation values ​​in the bridge crack images using a grayscale symbiosis algorithm; A texture feature matrix is ​​constructed based on contrast values, homogeneity values, and correlation values; W comprehensive feature matrices are constructed based on the reflection amplitude matrix, the crack depth matrix, and the texture feature matrix; and a feature matrix sequence is determined based on the W comprehensive feature matrices; Inputting the feature matrix sequence into a pre-trained diffusion probability model to obtain a crack monitoring result output by the diffusion probability model, wherein the crack monitoring result at least includes a crack propagation rate; The training method of the diffusion probability model includes: Pair the historical feature matrix sequence with the corresponding historical crack monitoring result label to construct a training set. The historical feature matrix sequence is used as the input of the sub-model, and the corresponding historical crack monitoring result is used as the output of the sub-model. The total number of diffusion steps is preset to T, and the noise increment of each step is defined as β t For each data point in the training set, the current diffusion step t and the corresponding noise data x are generated by random sampling. t , according to the current diffusion step t and the noise data x t Construct a loss function, the noise data x t The distribution form of is Gaussian distribution; Randomly extract a batch of data points from the training set and generate corresponding noise data x for each data point t , calculate the corresponding loss function value, update the sub-model parameters through the back-propagation algorithm and minimize the loss function value as the goal, and iteratively train the sub-model to obtain the diffusion probability model.

2. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 1 is characterized in that: The method for obtaining ultrasonic data includes: The bridge crack images are classified to obtain the corresponding crack classification results. If the crack classification result is a longitudinal crack, the ultrasonic probe is arranged along the length of the bridge. If the crack classification result is a transverse crack, the ultrasonic probe is arranged perpendicular to the length of the bridge. If the crack classification result is an oblique crack, the ultrasonic probe is arranged along the direction of the bridge crack.

3. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 2 is characterized in that: The ultrasonic data includes the peak occurrence time, and the method for constructing a crack depth matrix based on the ultrasonic data includes: The crack depth value is determined according to the peak occurrence time in the ultrasonic data and the preset propagation speed, and a crack depth matrix is ​​constructed according to the crack depth value.

4. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 1 is characterized in that: The method for obtaining a contrast value in a bridge crack image according to a grayscale co-occurrence algorithm includes: obtaining a co-occurrence probability in a direction θ in the bridge crack image according to the grayscale co-occurrence algorithm, and calculating a corresponding contrast value according to the co-occurrence probability in the direction θ.

5. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 1 is characterized in that: The method for obtaining the correlation value in the bridge crack image according to the grayscale symbiosis algorithm includes: According to the grayscale symbiosis algorithm, the mean of grayscale value i, the mean of grayscale value j, the standard deviation of grayscale value i, and the standard deviation of grayscale value j in the bridge crack image are obtained, and the corresponding correlation value is calculated according to the mean of grayscale value i, the mean of grayscale value j, the standard deviation of grayscale value i, and the standard deviation of grayscale value j; The mean of the grayscale value i refers to the expected value of the second grayscale value j when the first grayscale value is i, and the mean of the grayscale value j refers to the expected value of the second grayscale value i when the first grayscale value is j.

6. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 5 is characterized in that: The method for constructing W comprehensive feature matrices based on the reflection amplitude matrix, the crack depth matrix and the texture feature matrix includes: Adjust the value of K, obtain W texture feature matrices, traverse the W texture feature matrices, align the reflection amplitude matrix, crack depth matrix and texture feature matrix by mathematical transformation, perform weighted fusion on the reflection amplitude matrix, crack depth matrix and texture feature matrix, and construct W comprehensive feature matrices. The K represents the value angle corresponding to the texture feature matrix.

7. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 6 is characterized in that: The method for determining a feature matrix sequence based on W comprehensive feature matrices includes: The crack direction range in the bridge crack image is determined according to the Hough transform algorithm. P comprehensive feature matrices are screened out from W comprehensive feature matrices based on the crack direction range, and the P comprehensive feature matrices are used as a feature matrix sequence. The crack direction range is the range of crack extension directions in the bridge crack image.

8. The bridge crack dynamic monitoring method based on the diffusion probability model according to claim 7 is characterized in that: The method of selecting P comprehensive feature matrices from W comprehensive feature matrices according to the crack direction range includes: Obtain the value range corresponding to each comprehensive feature matrix, and determine whether the value range belongs to the crack direction range. If not, eliminate the corresponding comprehensive feature matrix to screen out P comprehensive feature matrices, and the value range is the value range of K.

9. A bridge crack dynamic monitoring system based on a diffusion probability model, which is used to implement a bridge crack dynamic monitoring method based on a diffusion probability model according to any one of claims 1 to 8, characterized in that: include: Data processing module: used to obtain ultrasonic data and bridge crack images, construct reflection amplitude matrix and crack depth matrix based on ultrasonic data, and obtain contrast value, homogeneity value and correlation value in bridge crack images based on grayscale symbiosis algorithm; Matrix construction module: used to construct a texture feature matrix based on contrast values, homogeneity values, and correlation values, construct W comprehensive feature matrices based on the reflection amplitude matrix, crack depth matrix, and texture feature matrix, and determine a feature matrix sequence based on the W comprehensive feature matrices; Monitoring module: used to input the feature matrix sequence into the pre-trained diffusion probability model to obtain the crack monitoring results output by the diffusion probability model, and the crack monitoring results at least include the crack propagation rate.

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