Shield tunnel thermal disaster diagnosis method and system based on deep learning

Abstract

The invention discloses a shield tunnel thermal disaster diagnosis method and system based on deep learning, and relates to the technical field of information technology and tunnel safety monitoring, and the method comprises the steps: collecting an infrared image of an inner arc surface temperature field of a shield tunnel segment; performing chromaticity matching on the screened infrared image to obtain a matched temperature cloud picture; inputting the temperature cloud picture into the trained thermal-stress diagnosis model, and outputting a stress cloud picture; the training process of the model comprises the following steps: constructing a numerical model for simulating the thermal performance of the shield tunnel under the thermal-seepage-force multi-field coupling action; based on the numerical model, a temperature cloud picture and a stress cloud picture of a temperature field of the inner cambered surface of the shield tunnel segment are obtained through transient simulation calculation; training a thermal-stress diagnosis model based on a convolutional neural network by using the temperature cloud picture and the stress cloud picture; and based on the stress nephogram, in combination with a preset diagnosis basis, judging a shield tunnel thermal disaster early warning level and performing early warning in time. Accurate early warning of the safety and stability of the shield tunnel structure can be achieved.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and tunnel safety monitoring technology, and in particular to a shield tunnel thermal disaster diagnosis method and system based on deep learning. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the continuous acceleration of urbanization, the problem of urban traffic congestion has become increasingly serious, which has prompted the rational development and utilization of underground space. At present, the shield tunnel technology has been continuously improved, providing solid technical support for the rapid expansion of urban networks. However, compared with the ground environment, subway tunnels accumulate a lot of heat in the tunnel due to the continuous operation of its internal electrical equipment and environmental control system, as well as the body heat emission of passengers. Due to the particularity of underground space, this heat is not easy to dissipate quickly, thus forming a long-term and continuous thermal pollution phenomenon inside the tunnel. With the continuous accumulation of thermal pollution in the tunnel, the soil temperature and air temperature around the tunnel will rise accordingly. This continuous high temperature environment will affect the shield tunnel segments, causing thermal stress. When the thermal stress is coupled with the structural force, it may cause the segments to deform. In the case of slight deformation, cracks may appear in the segments. If this condition persists for a long time and is not detected and repaired in time, it may even cause the collapse of the tunnel structure in severe cases, posing a threat to urban safety. Therefore, the management and maintenance of the thermal environment in subway tunnels is an important measure to ensure urban safety, which needs to be paid attention to and scientific response strategies should be proposed.

[0004] At present, the severity of thermal disasters in shield tunnels is usually diagnosed by testing the effectiveness of the tunnel segment structure under thermal environment (such as whether there is deformation, cracks, etc.). The current detection of tunnel structures is still at the stage of complex and tedious manual inspection. This traditional method of manual inspection has many limitations: (1) Manual inspection usually requires a lot of time, manpower and material resources, resulting in high costs; (2) It is impossible to monitor the temperature field and structural stress changes in the tunnel, resulting in the inability to timely warn of potential structural problems; (3) The tunnel area covered by manual inspection is relatively small, and the detection efficiency is low; (4) The tunnel environment is hot, humid and closed, which seriously reduces the cognitive ability of personnel. In addition, due to the limitations of the cognitive ability of the inspectors, there is a risk of missed detection and false detection, resulting in low accuracy of the final detection.

[0005] In addition, although an intelligent detection method for thermal disasters in shield tunnels has been proposed, that is, judging the severity of thermal disasters by identifying cracks on the surface of the segments, this method can only detect existing cracks on the surface of the segments, but cannot identify cracks inside the segments and potential crack occurrence areas. Its consideration is not comprehensive, resulting in poor accuracy in the final judgment of the severity of thermal disasters and difficulty in achieving accurate diagnosis. Summary of the invention

[0006] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a shield tunnel thermal disaster diagnosis method and system based on deep learning. By simulating the thermal performance of the shield tunnel under the coupling of heat, permeability and force multi-fields, the corresponding temperature cloud map and stress cloud map are obtained, and the correspondence between the temperature cloud map and the stress cloud map is learned by using a deep learning algorithm. Then, an infrared image of the actual shield tunnel segment structure is obtained, and a temperature cloud map is obtained after color map matching. Combined with the learned correspondence, the actual stress condition of the segment structure is clarified, so that potential structural problems can be warned in time, accurate diagnosis of thermal disasters in the shield tunnel can be achieved, and accurate and timely warning of the safety and stability of the shield tunnel structure can be achieved.

[0007] In a first aspect, the present invention provides a shield tunnel thermal disaster diagnosis method and system based on deep learning.

[0008] A shield tunnel thermal disaster diagnosis method based on deep learning, comprising:

[0009] Collect infrared images of the temperature field on the inner curved surface of shield tunnel segments and conduct preliminary screening of the collected images;

[0010] Perform chromaticity matching on the filtered infrared images to obtain a matching temperature cloud map;

[0011] The temperature cloud map is input into the trained thermal-stress prediction and diagnosis model, and the corresponding stress cloud map is output; wherein, the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, the temperature cloud map and stress cloud map of the temperature field of the arc surface inside the shield tunnel segment are obtained through transient simulation calculation; the temperature cloud map and stress cloud map are used to train the thermal-stress diagnosis model based on the convolutional neural network;

[0012] Based on the stress cloud map and the preset diagnostic basis, the thermal disaster warning level of the shield tunnel is determined and timely warning is issued.

[0013] In the second aspect, the present invention provides a shield tunnel thermal disaster diagnosis system based on deep learning.

[0014] A shield tunnel thermal disaster diagnosis system based on deep learning, including:

[0015] Infrared image acquisition module, used to collect infrared images of the temperature field of the inner curved surface of the shield tunnel segment and perform preliminary screening of the collected images;

[0016] The temperature cloud map matching module is used to perform chromaticity matching on the filtered infrared image to obtain a matching temperature cloud map;

[0017] The stress cloud map acquisition module is used to input the temperature cloud map into the trained thermal-stress diagnosis model and output the corresponding stress cloud map; wherein, the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, obtaining the temperature cloud map and stress cloud map of the temperature field of the arc surface of the shield tunnel segment through transient simulation calculation; using the temperature cloud map and stress cloud map to train the thermal-stress diagnosis model based on the convolutional neural network;

[0018] The thermal disaster diagnosis module is used to determine the thermal disaster warning level of the shield tunnel and issue a timely warning based on the stress cloud map and the preset diagnostic basis.

[0019] In a third aspect, the present invention also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned deep learning-based shield tunnel thermal disaster diagnosis method when executing the executable instructions stored in the memory.

[0020] In a fourth aspect, the present invention also provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned deep learning-based shield tunnel thermal disaster diagnosis method.

[0021] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned deep learning-based shield tunnel thermal disaster diagnosis method is implemented.

[0022] One or more of the above technical solutions have the following beneficial effects:

[0023] The present invention provides a shield tunnel thermal disaster diagnosis method and system based on deep learning. The method simulates the thermal performance of the shield tunnel under the action of heat-permeability-force multi-field coupling, obtains the corresponding temperature cloud map and stress cloud map, uses the deep learning algorithm to learn the corresponding relationship between the temperature cloud map and the stress cloud map, and then obtains the infrared image of the actual shield tunnel segment structure. The temperature cloud map is obtained after color map matching. Combined with the learned corresponding relationship, the actual stress situation of the segment structure is clarified, so that potential structural problems can be warned in time, the diagnosis of shield tunnel thermal disasters can be realized, and the accurate and timely warning of the safety and stability of the shield tunnel structure can be realized, avoiding the problems of consuming a lot of manpower and material resources and poor detection accuracy in traditional manual detection and diagnosis methods. Compared with the existing intelligent detection method, the problem that the internal characteristics of the segment cannot be considered, the present invention predicts the overall stress situation of the segment through the segment surface temperature field, can perform thermal disaster diagnosis and warning more comprehensively and deeply, effectively improves the efficiency and accuracy of shield tunnel thermal disaster diagnosis, and ensures the safety and stability of the shield tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 This is an overall flow chart of the shield tunnel thermal disaster diagnosis method based on deep learning according to an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of collecting infrared images of a rail vehicle in a shield tunnel according to an embodiment of the present invention;

[0027] Figure 3 It is a temperature cloud diagram of the temperature field of the semi-ring tunnel segment in the embodiment of the present invention;

[0028] Figure 4 The stress cloud diagram of thermal stress and structural mechanics of the semi-ring tunnel segment in the embodiment of the present invention;

[0029] Figure 5 It is a temperature distribution cloud diagram of the temperature field of the inner curved surface of the semi-ring tunnel segment in the embodiment of the present invention;

[0030] Figure 6 It is a stress distribution cloud diagram of thermal stress and structural mechanics of the inner arc surface of the semi-ring tunnel segment in the embodiment of the present invention;

[0031] Figure 7 Schematic diagram of the structure of the heat-stress diagnosis model based on the U-Net architecture adopted in an embodiment of the present invention.

[0032] Among them: 1. Tunnel segments; 2. Infrared cameras; 3. Rail cars. DETAILED DESCRIPTION

[0033] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0034] Embodiment 1

[0035] This embodiment provides a shield tunnel thermal disaster diagnosis method based on deep learning. Figure 1 As shown, the specific steps include:

[0036] Step S1, collecting infrared images of the temperature field of the inner curved surface of the shield tunnel segment, and performing preliminary screening on the collected images;

[0037] Step S2, performing chromaticity matching on the screened infrared image to obtain a matching temperature cloud map;

[0038] Step S3, inputting the temperature cloud map into the trained thermal-stress diagnosis model, and outputting the corresponding stress cloud map; wherein the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, obtaining the temperature cloud map and stress cloud map of the temperature field of the arc surface of the shield tunnel segment through transient simulation calculation; using the temperature cloud map and stress cloud map to train the thermal-stress diagnosis model based on the convolutional neural network;

[0039] Step S4: Based on the stress cloud diagram and in combination with the preset diagnostic basis, the thermal disaster warning level of the shield tunnel is determined and a timely warning is issued.

[0040] The following content provides a more detailed introduction to the shield tunnel thermal disaster diagnosis method based on deep learning proposed in this embodiment.

[0041] In step S1, an infrared image of the temperature field of the inner curved surface of the shield tunnel segment is collected, and the collected infrared image is preliminarily screened.

[0042] In this embodiment, the image acquisition device is an infrared imaging acquisition device, which is used to collect infrared thermal images (referred to as infrared images) of the temperature field of the inner curved surface of the shield tunnel segment. Figure 2As shown, the image acquisition device is composed of a rail vehicle 3 and an infrared camera 2 (also referred to as an infrared imager, infrared equipment, infrared thermal imager, etc.). The rail vehicle 3 equipped with the infrared camera 2 runs continuously on the subway track in the shield tunnel to collect data such as infrared images of the tunnel segments 1. The infrared thermal imager has sufficient sampling accuracy (less than ±0.5°C) and is suitable for the tunnel temperature sampling range (0-50°C or greater); the collected infrared thermal image has sufficient resolution, such as 1280×102 resolution.

[0043] Furthermore, in the above-mentioned image acquisition process, continuous acquisition or intermittent acquisition can be adopted, that is, the infrared thermal imager can continuously acquire infrared thermal images as the rail car moves, or it can stay for a certain time (such as 10s) after the rail car advances a certain depth, and acquire images during this time; in addition, the rail car is equipped with a rotating device that can enable the infrared thermal imager 2 (i.e., the infrared device) to rotate 360 ​​degrees, so that during the acquisition process, the infrared device can rotate around the horizontal axis to collect a complete temperature cloud map of the shield tunnel temperature field.

[0044] During the image acquisition process, the rail vehicle body avoids the imaging range of the infrared thermal imager to prevent the body from appearing in the image, and when the infrared device rotates one circle along the horizontal axis, there must be no structural parts blocking the plane where it is located to ensure the continuity and integrity of the shooting; the rail vehicle is equipped with a controller, a motor, and a battery, etc. The battery provides sufficient power for the infrared device, controller, motor, etc., and a remote communication module is provided in the controller, or a wireless data transmission terminal (such as Bluetooth, etc.) electrically connected to the controller is provided. Remote control is performed through the controller to drive the start and stop of the motor and infrared device, thereby realizing remote control of the rail vehicle and image acquisition.

[0045] On the basis of the above design, after the image acquisition is completed by the image acquisition device, the infrared thermal imager transmits the acquired infrared image to the cloud or remote terminal through the remote communication module of the controller or the wireless data transmission terminal, and then performs model detection on the computer of the cloud or remote terminal, so as to realize the diagnosis and timely warning of tunnel thermal disasters.

[0046] As a specific implementation method, Figure 2As shown, when the train stops running, the wheels of the rail car 3 are aligned with the subway track and the rail car 3 is placed on the track. The rail car 3 is equipped with a rotating device, one end of the rotating rod is fixedly connected to the rotating device, and the other end is fixedly installed with an infrared device (i.e., a high-definition infrared camera 2). The operation of the rotating device is controlled by adding a controller, so that the infrared device can be controlled to rotate 360°. In addition, the infrared device, motor, controller, etc. are connected to the battery to ensure that the equipment is powered. After the installation is completed, check whether the rotating device with the infrared camera 2 can operate normally; after the rail car arrives at the designated position, the operation of the rotating device is controlled by remotely controlling the controller, and the 360° rotating infrared device is used to collect continuous infrared thermal images of each pipe segment in the tunnel.

[0047] After installation and inspection, the detection system is turned on to check whether the wireless data transmission terminal connection is normal, so as to ensure the smoothness of the entire diagnostic process. After the instrument inspection is completed, the rail car is remotely started, and the PLC control system (based on the controller) on the rail car is set to pause for about 30 seconds when it reaches the predetermined position, that is, the center point of the circular tunnel, so that the infrared camera can complete the panoramic shooting of the temperature field of the entire circular tunnel, and the infrared camera can detect the arc surface temperature of the tunnel segment in the tunnel with high precision.

[0048] After the infrared images are collected, the diagnostic methods proposed later can be used to diagnose and warn of thermal disasters. The results of the warning can be used to guide actual projects, conduct tunnel maintenance, and ensure the safety and reliability of shield tunnels.

[0049] As another implementation method, in order to improve the collection efficiency, the infrared camera can be fixed, and multiple rail cars can be run simultaneously in a certain section of the tunnel to collect images. Specifically, the fixed position of the infrared camera and the number of rail cars required can be determined according to the number of segments in the shield tunnel section, and each infrared camera can be fixed at a specific position to only capture the infrared image of the temperature field of the arc surface of a segment corresponding to the current position. In this embodiment, six rail cars with different infrared camera positions are used, and the six rail cars are started in sequence to operate simultaneously to maintain the collection efficiency. In the above manner, only a single segment can be calculated, which can reduce the performance requirements for the computer and facilitate popularization and use.

[0050] Furthermore, after acquiring infrared images of the temperature field on the curved surface of the shield tunnel segment, the acquired infrared images are effectively screened. For the segment at each position, infrared images with high image clarity and no obstructions are selected through manual screening and other methods.

[0051] In step S2, the filtered infrared image is subjected to chromaticity matching to obtain a matching temperature cloud map.

[0052] Specifically, in combination with the above steps, the infrared image of the temperature field of the curved surface of a single tunnel segment collected by the infrared camera is used to obtain the temperature cloud map after chromaticity matching. Figure 5 As shown in Figure 2, after the diagnosis model is used, the stress cloud diagram of the thermal stress and structural force of the inner arc surface of the single tunnel segment is obtained, as shown in Figure 2. Figure 6 shown.

[0053] Among them, the chromaticity matching is to match the temperature reflected at the same position of the infrared thermal image (i.e., infrared image) and the temperature cloud map. In this embodiment, before using the infrared imager to capture the infrared image of the temperature field of the curved surface inside the shield tunnel segment, the color-temperature legend is adjusted to be consistent with the legend of the simulated temperature cloud map, so as to complete the rapid chromaticity matching; as another implementation method, the ratio of the color-temperature legend of a certain infrared thermal image and the temperature cloud map can be obtained first, and then the ratio is used to adjust the color-temperature legend of each infrared thermal image obtained, so as to obtain the corresponding temperature cloud map after chromaticity matching.

[0054] Through the color map matching in step S2 above, the temperature represented by the color in the acquired infrared image can be made consistent with the temperature represented by the color in the input image during training in the subsequent diagnostic model, thereby ensuring the accuracy of subsequent detection and diagnosis.

[0055] In step S3, the temperature cloud map is input into the trained thermal-stress diagnosis model, and the corresponding stress cloud map is output; wherein, the training process of the model includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the coupling of heat-permeability-force multi-fields; based on the numerical model, through transient simulation calculation, obtaining the temperature cloud map and stress cloud map of the temperature field of the arc surface inside the shield tunnel segment; using the temperature cloud map and stress cloud map to train the thermal-stress diagnosis model based on the convolutional neural network.

[0056] Specifically, the training process of the heat-stress diagnosis model based on convolutional neural network includes five parts: data set construction, data cleaning, model selection, model training, model verification and performance evaluation. The training process specifically includes:

[0057] Step S3.1, building a heat-stress diagnosis model based on convolutional neural network.

[0058] Specifically, a heat-stress diagnosis model is built based on a convolutional neural network (CNN), and the image features of the input temperature cloud map are extracted using a convolutional layer. After learning, the corresponding stress cloud map is output through a fully connected layer. Among them, the specific structure of the network model can be selected and adjusted (i.e., model selection) according to specific circumstances such as the size and number of input images. In this embodiment, a deep learning model based on a U-Net architecture is used to build a heat-stress diagnosis model. The U-Net architecture is as follows Figure 7As shown in the figure, as one of the commonly used convolutional neural network architectures, it can efficiently extract image features and restore detail information through the encoder-decoder structure and jump connections, thereby achieving high-precision image processing.

[0059] The specific structure of the heat-stress diagnosis model based on the U-Net architecture includes an input layer, an encoder, a bottleneck layer, a decoder, and an output layer connected in sequence, wherein:

[0060] (1) Input layer: The temperature cloud map is input through the input layer. The size of the temperature cloud map is set to (256, 256, 3) by default, which means that the size of the input image is 256×256 pixels and it is an RGB three-channel image. Preferably, normalization processing is added to the input layer to facilitate the effectiveness of subsequent data analysis and feature extraction.

[0061] (2) Encoder: It includes multiple convolution blocks connected in sequence. Each convolution block includes two convolution layers as downsampling layers. Each convolution block is followed by a Dropout layer and a maximum pooling layer.

[0062] Among them, the convolution layer adopts the Conv2D layer, each Conv2D layer has a specified number of filters (such as 64, 128, 256, 512, 1024, etc.), and a Mish activation layer, a regularization layer, and a Dropout layer are arranged after the convolution layer. In this embodiment, the activation function adopted by the activation layer is the Mish activation function, which adjusts the scale of the feature map after convolution, keeps the size of the feature map unchanged, and applies L2 regularization to reduce overfitting; a Dropout layer is added after each convolution block, and some neurons are randomly discarded with a probability of 0.5 to prevent overfitting of the model and improve the generalization ability of the model; the maximum pooling layer adopts the MaxPooling2D layer, whose step size is 2, which is used to reduce the size of the feature map.

[0063] Furthermore, in the above two-dimensional convolution (i.e., Conv2D layer), each element y(i,j) of the feature map Y is extracted and output, which can be expressed by the following formula:

[0064] y(i,j)=(X*K)(i,j)=∑m∑n[x(i+m,j+n)*k(m,n)];

[0065] Among them, X represents the input image or feature map; K represents the convolution kernel; x(i+m,j+n) represents the pixel value of the input image at position (i+m,j+n); k(m,n) represents the weight of the convolution kernel at position (m,n); * represents the convolution operation.

[0066] In the above maximum pooling layer (MaxPooling2D), each element y(i,j) of the output feature map Y can be expressed by the following formula:

[0067] y(i,j)=maxmx(m,n),n∈R(i,j);

[0068] Among them, x(m,n) represents the pixel value of the input feature map at position (m,n); R(i,j) represents the pooling window at position (i,j).

[0069] The above Mish activation function is:

[0070] f(x)=x·tanh(softplus(x));

[0071] softplus(x)=log(1+e x );

[0072] Among them, softplus() is the activation function, x represents the input feature map, and f(x) represents the output feature map.

[0073] (3) Bottleneck layer: It consists of two stacked convolutional layers. The number of filters in each convolutional layer is 1024, and the Mish activation function and L2 regularization are also used.

[0074] (4) Decoder: It includes multiple convolution blocks connected in sequence. However, unlike the encoder, each convolution block includes multiple upsampling layers. The upsampling layers use Conv2DTranspose layers and are concatenated with multiple downsampling layers of the encoder to gradually restore the size of the feature map.

[0075] Among them, the upsampling layer uses the Conv2DTranspose layer, also known as the transposed convolution layer, to increase the size of the feature map; the upsampled feature map is merged with the feature map extracted by the corresponding downsampling layer of the encoder through a skip connection (concatenate) operation, which helps to restore detail information; each upsampling layer is followed by two convolutional layers for ReLU activation and L2 regularization; finally, a Dropout layer is added after each convolution block.

[0076] Furthermore, the above-mentioned upsampling layer (Conv2DTranspose layer) is implemented by repeating elements, and each element y(i,j) of the feature map Y is output; the splicing operation is implemented by the above-mentioned jump connection, which can be expressed as splicing the two feature maps X1 and X2 together along a certain dimension d to form a new feature map Y.

[0077] (5) Output layer: The Conv2D layer is used, with 3 filters (corresponding to the three RGB channels) and the sigmoid function as the activation function, which is used to output the probability that each pixel belongs to the target category.

[0078] After the above sigmoid activation function, each element y(i,j) of the output feature map Y can be expressed by the following formula:

[0079] y(i,j)=1 / [e -x(i,j) 1;

[0080] Among them, x(i,j) represents the result after the convolution operation of the output layer.

[0081] Step S3.2, construct a numerical model to simulate the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force.

[0082] Specifically, COMSOL Multiphysics software is used to simulate the thermal performance of shield tunnels under the action of heat-seepage-force multi-field coupling. First, a physical model is built based on existing actual projects, and then the corresponding physical parameters are obtained through field tests to construct a physical field of heat-seepage-force coupling. The physical field is integrated into the physical model to construct a numerical simulation model (referred to as a numerical model). On this basis, the simulation results are diagnosed and analyzed based on the expert knowledge base and objective indicators, and the thermal disaster warning level is reasonably divided according to the simulation results and the actual situation.

[0083] Step S3.3, based on the numerical model, through transient simulation calculation, obtain the temperature cloud map and stress cloud map of the temperature field on the curved surface of the shield tunnel segment.

[0084] Based on the numerical simulation model verified by the actual project, a data set is constructed through transient simulation calculation. Specifically, in order to obtain a sufficient number of data sets, the temperature cloud map and stress cloud map are saved at a set time interval (such as one hour) during the transient simulation calculation process, and the total simulation time should be the full life cycle of the shield tunnel.

[0085] Preferably, the acquired data is preprocessed, including: reading a number of images (256*256*3) and normalizing them so that the value of each pixel is kept between 0 and 1; then dividing the data set into a training set and a test set, with a division ratio of 5:1.

[0086] Step S3.4, using the temperature cloud map and the stress cloud map to train a convolutional neural network-based heat-stress diagnosis model.

[0087] In this embodiment, based on the temperature cloud map and the stress cloud map, each pixel in the cloud map is annotated with temperature and stress labels. The temperature cloud map is used as the input image of the model, and the stress cloud map is used as the output image of the model. A thermal-stress diagnostic model based on a convolutional neural network is trained to establish a mapping between the temperature and stress of each pixel in the cloud map.

[0088] In the above training process, by optimizing the model test, the training is continuously iterated until the preset requirements are met, such as reaching the set number of iterations or minimizing the loss function. At this time, the training is completed and the parameters and hyperparameters of the model (including convolutional layer parameters, fully connected layer parameters, etc.) are determined. In the optimization process, optimization algorithms such as SGD and Adam optimizer and loss functions such as mean_squared_error can be used to compile the model. Among them, the Adam optimizer is an adaptive learning rate method based on first-order and second-order moment estimation, and the mean square error loss function is commonly used for regression problems, including pixel-level prediction in image segmentation tasks. Preferably, in order to further improve the accuracy of the model, RMSProp, Namdam or LAMB optimizer can be used for optimization.

[0089] Furthermore, in this embodiment, considering that the model prediction focuses on the similarity between the visual characteristics of the cloud image (such as edges, textures, local structures, etc.) and the real cloud image, the structural similarity index (SSIM) is added, and the hybrid loss function is defined by combining MSE and SSIM, and the formula is:

[0090] Loss = αMSE + β(1-SSIM);

[0091]

[0092] SSIM(x,y)=(2μ x μ y +C1)(2σ xy +C2)(μ x2 +μ y2 +C1)(σ x2 +σ y2 +C2);

[0093] In the above formula, α and β represent weight parameters; μ x and μ y Respectively represent the mean of image x and y, representing brightness information; σ x2 and σ y2 Respectively represent the variance of image x and y, representing contrast information (i.e., the intensity of the change in brightness of the image); σ xy Represents the covariance of images x and y, representing the structural similarity between the two images; C1 and C2 are constants used to avoid the situation where the denominator is 0, usually C1 = (k1L) 2 , C2=(k2L) 2 , L is the range of pixel values ​​(for example, for an 8-bit depth image, L=255), k1 and k2 are constants less than 1, usually k1=0.01 and k2=0.03.

[0094] Considering that increasing the number of model parameters can improve the model's expressiveness and learning ability, but too many parameters may also lead to model overfitting and increased computational costs, sufficient and diverse data can prevent model overfitting, but too much data will lead to increased computational costs. Therefore, through the above training, a balance is found between the number of model parameters and the amount of data to ensure a balance between model performance and computational costs.

[0095] Furthermore, the confusion matrix is ​​used as an indicator to evaluate the performance of the model, such as accuracy, and a model with better performance is selected as the diagnostic model of this embodiment.

[0096] As another implementation, the above model may select other models with image processing functions, such as convolutional neural networks, etc. The matched temperature cloud map is input into the trained heat-stress diagnosis model, the predicted stress cloud map is output, and the final diagnosis result is output according to the predicted image.

[0097] In step S4, based on the stress cloud map and in combination with the preset diagnostic basis, the thermal disaster warning level of the shield tunnel is determined and a timely warning is issued.

[0098] like Figure 3 and Figure 4 The temperature cloud map and stress cloud map of the tunnel segment shown in the figure can judge the specific stress condition of the segment through the thermal stress and structural mechanics of the entire tunnel segment and its coupling with the surrounding soil to determine whether the segment is in danger of cracking.

[0099] Specifically, according to the predicted stress cloud map, the stress corresponding to each pixel in the map is clarified, and combined with the preset diagnostic basis, the overall warning level of the area where the pixel is located is determined, so as to judge the warning level of thermal disasters in shield tunnels and issue warnings in time. Among them, the diagnostic basis refers to: dividing the warning level into four levels: blue, yellow, orange and red, and the corresponding levels are level IV, level III, level II and level I, including:

[0100] (1) Blue (Level IV) warning: The segment stress is about to reach the tensile limit (the standard value of the tensile strength of C50 concrete is 2.64 MPa), and the tunnel temperature needs to be controlled;

[0101] (2) Yellow (Level III) warning: The segment stress reaches the resistance limit and needs to be cooled quickly, otherwise the segment will crack.

[0102] (3) Orange (Level II) warning: The pipe segment is slightly cracked, there is a risk, and maintenance inspection should be arranged immediately;

[0103] (4) Red (Level I) Warning: Segment cracks are severe and may cause an accident. The tunnel is shut down for maintenance.

[0104] Embodiment 2

[0105] This embodiment provides a shield tunnel thermal disaster diagnosis system based on deep learning, including:

[0106] Infrared image acquisition module, used to collect infrared images of the temperature field of the inner curved surface of the shield tunnel segment and perform preliminary screening of the collected images;

[0107] The temperature cloud map matching module is used to perform chromaticity matching on the filtered infrared image to obtain a matching temperature cloud map;

[0108] The stress cloud map acquisition module is used to input the temperature cloud map into the trained thermal-stress diagnosis model and output the corresponding stress cloud map; wherein, the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, obtaining the temperature cloud map and stress cloud map of the temperature field of the arc surface of the shield tunnel segment through transient simulation calculation; using the temperature cloud map and stress cloud map to train the thermal-stress diagnosis model based on the convolutional neural network;

[0109] The thermal disaster diagnosis module is used to determine the thermal disaster warning level of the shield tunnel and issue a timely warning based on the stress cloud map and the preset diagnostic basis.

[0110] Embodiment 3

[0111] This embodiment provides an electronic device, including: a memory, used to store executable instructions; and a processor, used to implement the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0112] Embodiment 4

[0113] This embodiment also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0114] Embodiment 5

[0115] This embodiment provides a computer program product, which includes an executable instruction, which is a computer instruction; the executable instruction is stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instruction from the computer-readable storage medium and the processor executes the executable instruction, the electronic device executes the above method provided in this embodiment.

[0116] The steps involved in the above embodiments 2 to 5 correspond to the method embodiment 1. For the specific implementation, please refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0117] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0118] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A shield tunnel thermal disaster diagnosis method based on deep learning, characterized in that: include: Collect infrared images of the temperature field on the inner curved surface of shield tunnel segments and conduct preliminary screening of the collected images; Perform chromaticity matching on the filtered infrared images to obtain a matching temperature cloud map; The temperature cloud map is input into the trained thermal-stress prediction and diagnosis model, and the corresponding stress cloud map is output; wherein, the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, the temperature cloud map and stress cloud map of the temperature field of the arc surface inside the shield tunnel segment are obtained through transient simulation calculation; the temperature cloud map and stress cloud map are used to train the thermal-stress diagnosis model based on the convolutional neural network; Based on the stress cloud map and the preset diagnostic basis, the thermal disaster warning level of the shield tunnel is determined and timely warning is issued.

2. A shield tunnel thermal disaster diagnosis method based on deep learning as claimed in claim 1, characterized in that: A transient simulation calculation is performed on the thermal performance of the shield tunnel under the coupling of heat, permeability and force fields. During the transient simulation calculation process, the temperature cloud map and stress cloud map of the temperature field on the curved surface of the tunnel segment are obtained at set time intervals. The duration of the transient simulation calculation process is the entire life cycle of the shield tunnel.

3. A shield tunnel thermal disaster diagnosis method based on deep learning as claimed in claim 1, characterized in that: Before using an infrared imager to capture an infrared image of the temperature field on the inner curved surface of a shield tunnel segment, the color-temperature legend is adjusted to be consistent with the legend of the simulated temperature cloud map to complete rapid chromaticity matching.

4. A shield tunnel thermal disaster diagnosis method based on deep learning as claimed in claim 1, characterized in that: The ratio of the color-temperature legend of the infrared image and the temperature cloud map is obtained, and the color-temperature legend of each infrared image obtained is adjusted by using the ratio to obtain a temperature cloud map after chromaticity matching.

5. A shield tunnel thermal disaster diagnosis method based on deep learning as claimed in claim 1, characterized in that: The heat-stress diagnosis model based on convolutional neural network is built using U-Net architecture, including an input layer, an encoder, a bottleneck layer, a decoder and an output layer; The encoder includes a plurality of sequentially connected convolution blocks, each convolution block includes two convolution layers as downsampling layers, and each convolution block is followed by a Dropout layer and a maximum pooling layer; the convolution layer adopts a Conv2D layer, each Conv2D layer has a set number of filters, and a Mish activation layer, a regularization layer, and a Dropout layer are provided after the convolution layer; The decoder includes a plurality of sequentially connected convolution blocks, each of which includes a plurality of upsampling layers, and the upsampling layers are jump-connected with a plurality of downsampling layers of the encoder to gradually restore the size of the feature map.

6. A shield tunnel thermal disaster diagnosis method based on deep learning as claimed in claim 1, characterized in that: Based on the numerical model, the temperature cloud map and stress cloud map of the temperature field on the inner curved surface of the shield tunnel segment are obtained through transient simulation calculation; Based on the temperature cloud map and stress cloud map, the temperature and stress of each pixel in the cloud map are annotated. The temperature cloud map is used as the input image of the model, and the stress cloud map is used as the output image of the model to train the heat-stress diagnosis model based on the convolutional neural network. The training is repeated iteratively until the set number of iterations is met or the loss function is minimized, the model training is completed, and the mapping between the temperature and stress of each pixel in the cloud map is established; the loss function is a hybrid loss function based on the mean square error index MSE and the structural similarity index SSIM.

7. A shield tunnel thermal disaster diagnosis system based on deep learning, characterized in that: include: Infrared image acquisition module, used to collect infrared images of the temperature field of the inner curved surface of the shield tunnel segment and perform preliminary screening of the collected images; The temperature cloud map matching module is used to perform chromaticity matching on the filtered infrared image to obtain a matching temperature cloud map; The stress cloud map acquisition module is used to input the temperature cloud map into the trained thermal-stress diagnosis model and output the corresponding stress cloud map; wherein the model training process includes: constructing a numerical model for simulating the thermal performance of the shield tunnel under the multi-field coupling of heat, permeability and force; based on the numerical model, obtaining the temperature cloud map and stress cloud map of the temperature field of the arc surface of the shield tunnel segment through transient simulation calculation; using the temperature cloud map and stress cloud map to train the thermal-stress diagnosis model based on the convolutional neural network; The thermal disaster diagnosis module is used to determine the thermal disaster warning level of the shield tunnel and issue a timely warning based on the stress cloud map and the preset diagnostic basis.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the deep learning-based shield tunnel thermal disaster diagnosis method described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the deep learning-based shield tunnel thermal disaster diagnosis method described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the deep learning-based shield tunnel thermal disaster diagnosis method described in any one of claims 1 to 6 is implemented.

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