Real-time temperature field monitoring and crack early warning system and method for ultra-wide thin-walled solid high piers

Abstract

The present invention relates to the field of temperature monitoring technology, specifically to a real-time monitoring system and method for the temperature field of ultra-wide, thin-walled solid high piers and a crack early warning system. The present invention preprocesses the temperature data of a target temperature field and the image data of the target temperature field to obtain preprocessed temperature data and a preprocessed temperature image, then constructs a target temperature field model based on the preprocessed temperature data and the preprocessed temperature image, further performs crack thermal stress analysis, and creates a crack model by combining the target temperature field model and the crack thermal stress analysis, finally obtains the prediction results output by the trained crack model, and judges whether there is a risk of cracking based on the prediction results output by the crack model. If there is a risk of cracking, an early warning is issued. The present application compares the real-time image data with the normal image through the crack model, and predicts the risk of cracking in combination with the predicted temperature of the target temperature field model and issues an early warning in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature monitoring, and in particular to a system and method for real-time temperature field monitoring and crack early warning of ultra-wide, thin-walled solid high piers. Background Art

[0002] High pier structures are widely used in bridges, tunnels, and other major projects. Due to their large size, thin walls, and wide spans, they often produce complex temperature distributions in environments with temperature differences. A temperature field refers to the collection of temperatures at each point within a material system. It is a function of both time and spatial coordinates, reflecting the distribution of temperature over space and time. Temperature fields can be categorized as steady-state and unsteady-state. A steady-state temperature field is one in which the temperature at each point does not change over time, while an unsteady-state temperature field is one in which the temperature at each point changes over time.

[0003] Chinese patent publication number CN113466291A discloses an analytical method for detecting cracks in large-volume concrete based on temperature field changes. The method analyzes the crack conditions of the large-volume concrete based on the temperature field, stress field, and infrared thermal image. The crack size is calculated using a customized calibration gauge and perspective transformation formula to complete the crack analysis. However, the existing technology only analyzes existing cracks and makes it difficult to predict the occurrence of cracks based on temperature data. This results in a lack of timely prevention and poses a significant safety hazard. Summary of the Invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a real-time monitoring system and method for the temperature field of ultra-wide, thin-walled solid high piers and crack early warning.

[0005] The technical solution of the present invention:

[0006] On the one hand, the present application provides a method for real-time monitoring of the temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers, including:

[0007] Setting acquisition parameters, acquiring temperature data of the target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field respectively to obtain preprocessed temperature data and a preprocessed temperature image;

[0008] Constructing a target temperature field model based on preprocessed temperature data and preprocessed temperature images;

[0009] Conduct crack thermal stress analysis and create a crack model by combining the target temperature field model with the crack thermal stress analysis;

[0010] Collect real-time temperature data, input the real-time temperature data into the crack model in sequence, obtain the prediction results output by the trained crack model, and judge whether there is a risk of cracks based on the prediction results output by the crack model. If there is a risk of cracks, issue an early warning.

[0011] Preferably, setting acquisition parameters, acquiring temperature data of the target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field respectively to obtain preprocessed temperature data and preprocessed temperature image, including:

[0012] Create a temperature data table;

[0013] Set the acquisition parameters for each target temperature field separately; the acquisition parameters include temperature acquisition position, image acquisition position, acquisition cycle and acquisition frequency. Generally speaking, temperature data and image data are one-to-one corresponding, and image data and temperature data at the same acquisition frequency are recorded as a data pair;

[0014] Collect multiple temperature data of each target temperature field according to the acquisition parameters, and add all the collected temperature data to the temperature data table;

[0015] Multiple temperature images of each target temperature field are collected according to the collection parameters, and all the collected temperature images are added to the temperature data table; the collected temperature images include crack images and normal images.

[0016] Preferably, setting acquisition parameters, acquiring temperature data of the target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field respectively to obtain preprocessed temperature data and preprocessed temperature image, further comprising:

[0017] Select a target temperature field;

[0018] Select all temperature data of each acquisition time of the target temperature field from the temperature data table;

[0019] Calculate the average temperature of all temperature data at each acquisition time;

[0020] Set the temperature deviation threshold;

[0021] Select a temperature data and determine whether the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold;

[0022] If the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold, the temperature data is deleted;

[0023] Return to select a temperature data and determine whether the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold, until all temperature data of the target temperature field are selected;

[0024] Perform denoising on all temperature images in the temperature data table to obtain denoised images;

[0025] Perform binarization on the denoised image to obtain a binarized image;

[0026] Set a crack evaluation standard, and divide the binary image into a crack image and a normal image according to the crack evaluation standard; record both the crack image and the normal image as preprocessed images;

[0027] Return to select a target temperature field until all target temperature fields are selected.

[0028] Preferably, constructing a target temperature field model based on the preprocessed temperature data and the preprocessed temperature image includes:

[0029] Set the boundary conditions of the target temperature field;

[0030] The divergence theorem is introduced into the boundary condition through formula 1 to obtain the divergence boundary condition;

[0031]

[0032] Where F is the divergence, Fx, Fy and Fz are the direction vectors of the divergence on the x, y and z axes respectively, l x 、l y and l z are the components of the unit external normal vector of the boundary condition on the x, y and z axes, V is the temperature field space, and S is the temperature field plane;

[0033] The divergence boundary condition of the functional integral equation is discretized by formula 2, and the functional minimum is solved to obtain the finite element equation of the temperature field;

[0034]

[0035] Where T is the temperature at a temperature collection location in the temperature field, [T] is the node temperature vector, F is the divergence, k is the load generated by the internal heat source, and I is the variational function.

[0036] Preferably, a crack thermal stress analysis is performed, and a correlation model is created by combining the target temperature field model and the crack thermal stress analysis; the correlation model of the target temperature field model and the crack thermal stress is recorded as a crack model, including:

[0037] Thermal stress analysis of the temperature field is performed using formula 3;

[0038] ε=α(TL-Tre) Formula 3;

[0039] Where ε is the thermal stress, TL is the load temperature, i.e., the temperature data at the temperature collection location, and Tre is the reference temperature.

[0040] Preferably, crack thermal stress analysis is performed, and a correlation model is created by combining the target temperature field model and the crack thermal stress analysis; the correlation model of the target temperature field model and the crack thermal stress is recorded as a crack model, further comprising:

[0041] Create a crack model;

[0042] Acquiring a plurality of pre-processed image data and pre-processed temperature data;

[0043] Selecting preprocessed image data and preprocessed temperature data at the same acquisition time in the same acquisition cycle, establishing a coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time; recording the coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time as a training pair;

[0044] The preprocessed image data and preprocessed temperature data are input into the crack model as a training set to train the crack model, so that the crack model continuously learns the relationship between the image data and the temperature data, and a trained crack model is obtained.

[0045] Preferably, the preprocessed image data and the preprocessed temperature data are input as a training set into the crack model to train the crack model, so that the crack model continuously learns the relationship between the image data and the temperature data, and obtains a trained crack model, including:

[0046] Select a target temperature field;

[0047] Selecting an acquisition period of the target temperature field and acquiring a crack image and a normal image of the acquisition period;

[0048] All crack images are sorted according to the order of acquisition time, and the first crack image and the temperature data corresponding to the first crack image are obtained; the first crack image is recorded as the first image, and the temperature data corresponding to the first crack image is recorded as the first temperature;

[0049] Obtain the first N normal images and the temperature data corresponding to the first N normal images according to the order of acquisition time;

[0050] The coupling relationship between the normal image and the crack image is established according to the order of acquisition time, and the normal image, the temperature data corresponding to the normal image, the first image, the first temperature, and the coupling relationship between the normal image and the crack image are used as a training sample;

[0051] Return and select an acquisition cycle of the target temperature field and obtain the crack image and normal image of the acquisition cycle, thereby obtaining multiple training samples of the target temperature field;

[0052] All training samples are divided into test set and training set according to random proportions;

[0053] Return to select a target temperature field until all target temperature fields are selected, thereby creating a test set and training set corresponding to each target temperature field;

[0054] Inputting the training samples in the training set into the crack model to train the crack model and obtain a trained crack model; the trained crack model has the ability to predict the time node of crack generation based on the input image data and temperature data;

[0055] The test set is input into the trained crack model to determine whether the trained crack model is complete.

[0056] Preferably, collecting real-time temperature data, sequentially inputting the real-time temperature data into the crack model, and obtaining the prediction results output by the trained crack model include:

[0057] Obtain real-time temperature data and real-time image data;

[0058] Input the real-time temperature data into the target temperature field model to obtain the predicted temperature output by the target temperature field;

[0059] The predicted temperature and the implemented image data are input into the crack model to obtain the predicted results output by the crack model.

[0060] Preferably, the predicted temperature and the implemented image data are input into the crack model to obtain the predicted result output by the crack model, including:

[0061] Set similarity threshold and temperature threshold;

[0062] Determine whether there is a normal image whose similarity to the real-time image data is greater than or equal to a similarity threshold;

[0063] If the similarity between the real-time image data and the normal image is greater than or equal to the similarity threshold, the first image and the first temperature corresponding to the normal image are obtained;

[0064] Determine whether the difference between the first temperature and the predicted temperature is less than the temperature threshold;

[0065] If the difference between the first temperature and the predicted temperature is less than the temperature threshold, an alarm is issued.

[0066] On the other hand, this application also provides a real-time temperature field monitoring and crack early warning system for ultra-wide thin-walled solid high piers, including:

[0067] An acquisition component, comprising a temperature acquisition unit and an image acquisition unit;

[0068] A processing component includes a processor, a data processing unit and an image processing unit. The temperature data is processed by the data unit, the temperature image is processed by the image processing unit, and the real-time monitoring of the temperature field and crack warning method of the ultra-wide thin-walled solid high pier as described above is executed by the processor.

[0069] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0070] By preprocessing the temperature data of the target temperature field and the image data of the target temperature field, preprocessed temperature data and a preprocessed temperature image are obtained. Then, a target temperature field model is constructed based on the preprocessed temperature data and the preprocessed temperature image. A crack thermal stress analysis is performed, and a crack model is created by combining the target temperature field model with the crack thermal stress analysis. Finally, the prediction results output by the trained crack model are obtained, and it is determined whether there is a risk of cracks based on the prediction results output by the crack model. If there is a risk of cracks, an early warning is issued. This application compares real-time image data with normal images through a crack model, and predicts the risk of cracks in combination with the predicted temperature of the target temperature field model and issues an early warning in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of the real-time monitoring method for the temperature field and crack early warning of ultra-wide, thin-walled solid high piers proposed by the present invention;

[0072] Figure 2 This is a structural diagram of the real-time temperature field monitoring and crack early warning system for ultra-wide, thin-walled solid high piers proposed by the present invention.

[0073] Reference numerals

[0074] 100, acquisition component; 101, temperature acquisition unit; 102, image acquisition unit;

[0075] 200, processing component; 201, processor; 202, data processing unit; 203, image processing unit. DETAILED DESCRIPTION

[0076] Example 1, as Figure 1 As shown, the method for real-time monitoring of the temperature field and early warning of cracks of ultra-wide thin-walled solid high piers proposed by the present invention includes:

[0077] S100, setting acquisition parameters, acquiring temperature data of a target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field to obtain preprocessed temperature data and a preprocessed temperature image;

[0078] S200, constructing a target temperature field model based on the preprocessed temperature data and the preprocessed temperature image;

[0079] S300, performing crack thermal stress analysis, and creating a crack model by combining the target temperature field model with the crack thermal stress analysis;

[0080] S400, collect real-time temperature data, input the real-time temperature data into the crack model in sequence, obtain the prediction results output by the trained crack model, and determine whether there is a risk of cracks based on the prediction results output by the crack model. If there is a risk of cracks, issue an early warning.

[0081] In the present invention, the temperature data of the target temperature field and the image data of the target temperature field are preprocessed to obtain preprocessed temperature data and a preprocessed temperature image. Then, a target temperature field model is constructed based on the preprocessed temperature data and the preprocessed temperature image. Furthermore, a crack thermal stress analysis is performed, and a crack model is created by combining the target temperature field model with the crack thermal stress analysis. Finally, the prediction result output by the trained crack model is obtained, and it is judged whether there is a risk of cracks based on the prediction result output by the crack model. If there is a risk of cracks, an early warning is issued. The present application compares the real-time image data with the normal image through the crack model, and predicts the risk of cracks in combination with the predicted temperature of the target temperature field model and issues an early warning in a timely manner.

[0082] In an optional embodiment, the S100 includes:

[0083] S110, creating a temperature data table;

[0084] S120, respectively setting acquisition parameters for each target temperature field; the acquisition parameters include a temperature acquisition position, an image acquisition position, an acquisition period, and an acquisition frequency. Generally speaking, temperature data and image data are one-to-one corresponding, and image data and temperature data at the same acquisition frequency are recorded as a data pair;

[0085] Specifically, multiple temperature collection locations should be set for each target temperature field to ensure that complete temperature data of the target temperature field can be collected;

[0086] For a target temperature field, since the frequency and period of collecting temperature data and collecting image data are the same, the temperature data and image data of a target temperature field are one-to-one corresponding;

[0087] S130, collecting multiple temperature data of each target temperature field according to the collection parameters, and adding all the collected temperature data to the temperature data table;

[0088] S140 , collecting multiple temperature images of each target temperature field according to the collection parameters, and adding all the collected temperature images to a temperature data table; the collected temperature images include crack images and normal images.

[0089] It should be noted that by setting corresponding acquisition parameters for each target temperature field and collecting temperature data and image data of the target temperature field according to the acquisition parameters, the collected data is used as the data source for creating the target temperature field model. In order to improve the reliability of the target temperature field model, the number of data collected should be increased when collecting data to ensure the comprehensiveness of the data.

[0090] For example, there are target temperature fields A, B and C. The acquisition period of target temperature field A is set to 7 days, 10 days and 15 days respectively, and the acquisition frequency of target temperature field A is set to 1 time / hour. The acquisition period of target temperature field B is set to 5 days, 10 days and 15 days respectively, and the acquisition frequency of target temperature field B is set to 1 time / hour. The acquisition period of target temperature field C is set to 5 days, 10 days and 15 days respectively, and the acquisition frequency of target temperature field C is set to 1 time / hour. After the acquisition parameters are set, the temperature data and image data collected are shown in Table 1 Temperature Data Table.

[0091] Table-1

[0092]

[0093]

[0094] In an optional embodiment, the S100 further includes:

[0095] S150, selecting a target temperature field;

[0096] S151, selecting all temperature data of each acquisition time of the target temperature field from the temperature data table;

[0097] S152, calculating the average temperature of all temperature data at each acquisition time;

[0098] S153, setting a temperature deviation threshold;

[0099] Specifically, due to the different acquisition cycles, the temperature deviation threshold can be set independently for each acquisition cycle;

[0100] S154, selecting a temperature data and determining whether the difference between the temperature data and the average temperature is greater than or equal to a temperature deviation threshold;

[0101] S155, if the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold, the temperature data is deleted;

[0102] Specifically, if the difference between the temperature data and the average temperature is less than the temperature deviation threshold, the temperature data is retained;

[0103] When deleting temperature data, image data that is in the same acquisition cycle and at the same acquisition time as the temperature data should also be deleted, thereby reducing the processing load of subsequent image data preprocessing;

[0104] S156, returning to step S154, until all temperature data of the target temperature field are selected;

[0105] S157, performing denoising processing on all temperature images in the temperature data table to obtain denoised images;

[0106] S158, performing binarization processing on the denoised image to obtain a binarized image;

[0107] Specifically, binarization is to set the grayscale value of the image pixels to 0 or 255, making the entire image appear obvious black and white. During the binarization process, the grayscale value range of the image is compressed from 0 to 255 to 0 or 255. The RGB value of the black pixel is (0,0,0), and the RGB value of the white pixel is (255,255,255).

[0108] S159, setting a crack evaluation standard, and dividing the binary image into a crack image and a normal image according to the crack evaluation standard;

[0109] Specifically, the image data of the target temperature field including cracks is the crack image;

[0110] After the crack image is determined, the temperature data in the same acquisition cycle and at the same acquisition time as the crack image is recorded as the crack temperature, and the temperature data in the same acquisition cycle and at the same acquisition time as the normal image is recorded as the normal temperature;

[0111] Since the target temperature field contains cracks in its image data in the later stage of crack generation, the color of the cracks is generally displayed as black. Therefore, whether the image data contains cracks can be determined by judging the number of black pixels contained in the binary image. Therefore, step S159 includes:

[0112] K100 sets the crack threshold;

[0113] K101 selects a binary image and counts the number of black pixels in the binary image;

[0114] K102 determines whether the number of black pixels in the binary image is greater than or equal to a crack threshold;

[0115] K103: if the number of black pixels in the binary image is greater than or equal to the crack threshold, the binary image is recorded as a crack image;

[0116] K104: if the number of black pixels in the binary image is less than the crack threshold, the binary image is recorded as a normal image;

[0117] K105, return to step K100 until all the binarized images are selected;

[0118] S160, returning to step S150, until all target temperature fields are selected;

[0119] Optionally, in order to reduce the amount of data in the temperature data table, all image data and temperature data in an acquisition period that only includes normal images may be deleted.

[0120] It should be noted that for each target temperature field, since the sensor may be damaged during the data collection process, which may cause the collected temperature data to be abnormal, a temperature deviation threshold is set to remove the abnormal points contained in the temperature data, thereby ensuring the reliability of the temperature data for subsequent user target temperature field model training.

[0121] After the temperature data is preprocessed, the image data is preprocessed to divide the image data into normal images and crack images, so as to subsequently create a crack model as a comparison group.

[0122] In an optional embodiment, the step S200 includes:

[0123] S210, setting the boundary conditions of the target temperature field;

[0124] Specifically, the boundary conditions include first-type boundary conditions, second-type boundary conditions, and third-type boundary conditions. The first boundary conditions are used to determine the wall temperature boundary, the second boundary conditions are used to determine the heat flux density boundary, and the third boundary conditions are used to determine the convection heat transfer boundary.

[0125] S220, introducing the divergence theorem into the boundary condition through formula 1 to obtain the divergence boundary condition;

[0126]

[0127] Where F is the divergence, Fx, Fy and Fz are the direction vectors of the divergence on the x, y and z axes respectively, l x 、l y and l z are the components of the unit external normal vector of the boundary condition on the x, y and z axes, V is the temperature field space, and S is the temperature field plane;

[0128] Specifically, the divergence theorem is one of the key theorems of vector analysis, derived directly from Coulomb's law. It relies entirely on the inverse square law of the force between charges. Applying the divergence theorem to a metal conductor in electrostatic equilibrium leads to the conclusion that there is no net charge inside the conductor. Therefore, determining whether there is a net charge inside the conductor is an important method for testing Coulomb's law.

[0129] S230, discretizing the divergence boundary condition of the functional integral equation using Formula 2, and solving the functional minimum to obtain a finite element equation of the temperature field;

[0130]

[0131] Where T is the temperature at a temperature collection location in the temperature field, [T] is the node temperature vector, F is the divergence, k is the load generated by the internal heat source, and I is the variational function;

[0132] Specifically,

[0133] Among them, B is the derivative matrix of the shape function matrix, N is the shape function matrix, D is the elasticity matrix, h is the convective heat transfer coefficient, V is the temperature field space, S is the temperature field plane, [F e ] is the load vector, is the thermal conductivity term, is the convective heat transfer term.

[0134] It should be noted that the heat conduction term is the load generated by the internal heat source, the convection heat transfer term is the load generated by the heat flux density, and the load vector refers to the load generated during the convection heat transfer process;

[0135] Finite element analysis (FEA) is a numerical calculation method that discretizes a continuous physical system into a finite number of elements, analyzes each element, and then combines the results to obtain an approximate solution for the overall system. This method is particularly important in the study of temperature fields and thermal deformation, as it can help reveal their inherent laws. Temperature field FEA uses digital simulation methods to study the changing laws of the temperature field, thus providing a foundation for the subsequent creation of crack models.

[0136] In an optional embodiment, the S300 includes:

[0137] S310, performing thermal stress analysis on the temperature field using Formula 3;

[0138] ε=α(TL-Tre) Formula 3;

[0139] Where ε is the thermal stress, TL is the load temperature, i.e. the temperature data at the temperature collection location, and Tre is the reference temperature;

[0140] Specifically, thermal stress refers to the stress generated when an object cannot expand or contract completely freely due to external constraints and mutual constraints between its internal parts when the temperature changes. It is precisely because of the existence of thermal stress that cracks will appear in the high pier corresponding to the target temperature field during the continuous temperature change.

[0141] It should be noted that, in general, rising temperature causes objects to expand, while falling temperature causes objects to contract. If this process is repeated continuously, there is a risk of cracks forming in the objects. Therefore, by creating and training a crack model to predict the occurrence of cracks, we can take preventive measures in advance.

[0142] In an optional embodiment, the step 300 further includes:

[0143] S320, creating a crack model;

[0144] S330, acquiring a plurality of pre-processed image data and pre-processed temperature data;

[0145] Specifically, in order to improve the applicability of the crack model, preprocessed image data and preprocessed temperature data of multiple target temperature fields should be selected respectively;

[0146] S340, selecting preprocessed image data and preprocessed temperature data at the same acquisition time within the same acquisition cycle, and establishing a coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time; recording the coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time as a training pair;

[0147] S350 , inputting the preprocessed image data and the preprocessed temperature data as a training set into the crack model to train the crack model, so that the crack model continuously learns the relationship between the image data and the temperature data, and obtains a trained crack model.

[0148] It should be noted that after the crack model is created, the preprocessed temperature data and preprocessed image data are used as training samples of the crack model to train the crack model, so that the crack model can predict whether cracks will occur and the predicted time node of crack generation based on the temperature data and image data.

[0149] In an optional embodiment, the S350 includes:

[0150] S351, selecting a target temperature field;

[0151] S352, selecting an acquisition period of the target temperature field and acquiring a crack image and a normal image of the acquisition period;

[0152] S353, sorting all crack images according to the order of acquisition time, and obtaining the first crack image and the temperature data corresponding to the first crack image; recording the first crack image as the first image, and recording the temperature data corresponding to the first crack image as the first temperature;

[0153] S354, acquiring the first N normal images of the first image and the temperature data corresponding to the first N normal images according to the order of acquisition time;

[0154] S355: establishing a coupling relationship between the normal image and the crack image according to the order of acquisition time, and using the normal image, the temperature data corresponding to the normal image, the first image, the first temperature, and the coupling relationship between the normal image and the crack image as a training sample;

[0155] S356, returning to step S352, thereby obtaining multiple training samples of the target temperature field;

[0156] S357, divide all training samples into test set and training set according to random proportion;

[0157] S358, returning to step S351, until all target temperature fields are selected, thereby creating a test set and a training set corresponding to each target temperature field;

[0158] S359, inputting the training samples in the training set into the crack model to train the crack model, thereby obtaining a trained crack model; the trained crack model has the ability to predict the time point when the crack will occur based on the input image data and temperature data;

[0159] S360: Input the test set into the trained crack model to determine whether the trained crack model is complete.

[0160] It should be noted that all image data are divided into crack images and normal images through preprocessing. Therefore, the acquisition time and the first temperature of the high pier corresponding to the target temperature field are found by determining the first image, and then multiple normal images before the first image and multiple normal temperatures before the first temperature are found according to the order of acquisition time, so that the crack model can learn the transformation process of normal image-first image and normal temperature-first temperature. With a sufficient number of training samples, the crack model can predict the possible cracks and the time of crack generation based on the input normal images and normal temperatures.

[0161] In an optional embodiment, the step S400 includes:

[0162] S410, acquiring real-time temperature data and real-time image data;

[0163] S420, inputting the real-time temperature data into the target temperature field model to obtain the predicted temperature output by the target temperature field;

[0164] S430: Input the predicted temperature and real-time image data into the crack model to obtain the prediction result output by the crack model.

[0165] It should be noted that the target temperature field is combined with the real-time temperature data to obtain the predicted temperature of the target temperature field, and then the real-time image data of the target temperature field and the predicted temperature are simultaneously input into the crack model to determine whether there is a risk of cracks.

[0166] In an optional embodiment, the S430 includes:

[0167] S431, setting a similarity threshold and a temperature threshold;

[0168] S432, determining whether there is a normal image whose similarity to the real-time image data is greater than or equal to a similarity threshold;

[0169] Specifically, when executing step S432, the normal images with the same acquisition time should be compared with the real-time image data. For example, if the real-time temperature images are Image A, Image B, and Image C, and the corresponding acquisition times are 6:00 on the 1st, 7:00 on the 1st, and 8:00 on the 1st, then the comparison should be carried out one by one according to Image A being the normal image at 6:00 on the 1st, Image B being the normal image at 7:00 on the 1st, and Image C being the normal image at 8:00 on the 1st;

[0170] Since the image data has been pre-processed in the above embodiment, when executing step S432, it is only necessary to pre-process the real-time image data and compare the real-time image data with the normal image using formula 4;

[0171]

[0172] Among them, μ x is the average gray value of the normal image, μ y is the average grayscale value of the real-time image data, σ x is the standard deviation of the normal image, σ y is the standard deviation of the real-time image data, σ xy is the covariance of the normal image and the real-time image data, C1 and C2 are constants;

[0173] S433, if the similarity between the real-time image data and the normal image is greater than or equal to the similarity threshold, obtaining the first image and the first temperature corresponding to the normal image;

[0174] Specifically, since multiple real-time image data are compared with multiple normal images one by one, the subsequent steps are performed only when the similarity between each real-time image data and the corresponding normal image is greater than or equal to the similarity threshold;

[0175] S434, determining whether the difference between the first temperature and the predicted temperature is less than a temperature threshold;

[0176] S435, if the difference between the first temperature and the predicted temperature is less than the temperature threshold, an alarm is issued;

[0177] Specifically, when an alarm is issued, the collection time corresponding to the first temperature is used as the warning time.

[0178] It should be noted that by setting a similarity threshold, the similarity between the real-time image data and the normal image in the crack model is first verified. When the similarity between the real-time image data and the normal image in the crack model is greater than or equal to the similarity threshold, then based on the transformation process from normal image to crack image, it can be concluded that there is a certain risk of cracks.

[0179] Since the generation of cracks is closely related to temperature, the possibility of cracks is judged by re-verifying the temperature threshold between the predicted temperature and the first temperature. When the difference between the first temperature and the predicted temperature is less than the temperature threshold, it can be proved that the predicted temperature is extremely close to the first temperature. Then, under the same temperature data, the possibility of cracks in the target temperature field is extremely high, and an early warning needs to be issued in time.

[0180] This application sets up two stages of verification, namely the image verification stage and the temperature verification stage. Only through the two-stage verification can it be proved that there is a possibility of cracks in the target temperature field, thereby improving the accuracy of the early warning while ensuring the efficiency of the early warning.

[0181] The present application also provides a real-time monitoring system for the temperature field of an ultra-wide, thin-walled solid high pier and a crack early warning system, including a collection component 100 and a processing component 200.

[0182] The acquisition component 100 includes a temperature acquisition unit 101 and an image acquisition unit 102, and the processing component 200 includes a processor 201, a data processing unit 202, and an image processing unit 203. The temperature data is processed by the data unit, and the temperature image is processed by the image processing unit 203. The processor 201 executes the real-time monitoring of the temperature field and crack warning method of the ultra-wide thin-walled solid high pier described in Example 1.

[0183] It should be noted that the temperature data of the target temperature field is collected by the temperature acquisition unit 101, and the image data of the target temperature field is collected by the image acquisition unit 102. The collected temperature data and image data are transmitted to the processing component 200, and the data processing is completed by each unit in the processing component 200, and a crack warning is issued in a timely manner.

[0184] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. Real-time monitoring of temperature field and crack early warning method for ultra-wide thin-walled solid high pier, characterized by: include: Setting acquisition parameters, acquiring temperature data of the target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field respectively to obtain preprocessed temperature data and a preprocessed temperature image; Constructing a target temperature field model based on preprocessed temperature data and preprocessed temperature images; Conduct crack thermal stress analysis and create a crack model by combining the target temperature field model with the crack thermal stress analysis; Conduct crack thermal stress analysis and create a correlation model by combining the target temperature field model with the crack thermal stress analysis; The correlation model between the target temperature field model and the crack thermal stress is recorded as the crack model, including: Thermal stress analysis of the temperature field is performed using the following formula; ； in, It is heat stress, is the load temperature, that is, the temperature data at the temperature collection location, is the reference temperature; Perform crack thermal stress analysis and create a correlation model by combining the target temperature field model and the crack thermal stress analysis; record the correlation model of the target temperature field model and the crack thermal stress as the crack model, and also include: Create a crack model; Acquiring a plurality of pre-processed image data and pre-processed temperature data; Selecting preprocessed image data and preprocessed temperature data at the same acquisition time in the same acquisition cycle, establishing a coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time; recording the coupling relationship between the preprocessed image data and the preprocessed temperature data at the same acquisition time as a training pair; The preprocessed image data and preprocessed temperature data are input as training sets into the crack model to train the crack model, so that the crack model continuously learns the relationship between the image data and the temperature data, and obtains the trained crack model, including: Select a target temperature field; Selecting an acquisition period of the target temperature field and acquiring a crack image and a normal image of the acquisition period; All crack images are sorted according to the order of acquisition time, and the first crack image and the temperature data corresponding to the first crack image are obtained; the first crack image is recorded as the first image, and the temperature data corresponding to the first crack image is recorded as the first temperature; Obtain the first N normal images and the temperature data corresponding to the first N normal images according to the order of acquisition time; The coupling relationship between the normal image and the crack image is established according to the order of acquisition time, and the normal image, the temperature data corresponding to the normal image, the first image, the first temperature, and the coupling relationship between the normal image and the crack image are used as a training sample; Return and select an acquisition cycle of the target temperature field and obtain the crack image and normal image of the acquisition cycle, thereby obtaining multiple training samples of the target temperature field; All training samples are divided into test set and training set according to random proportions; Return to select a target temperature field until all target temperature fields are selected, thereby creating a test set and training set corresponding to each target temperature field; Inputting the training samples in the training set into the crack model to train the crack model and obtain a trained crack model; the trained crack model has the ability to predict the time node of crack generation based on the input image data and temperature data; Input the test set into the trained crack model to determine whether the trained crack model is complete; Collect real-time temperature data, input the real-time temperature data into the crack model in sequence, obtain the prediction results output by the trained crack model, and judge whether there is a risk of cracks based on the prediction results output by the crack model. If there is a risk of cracks, issue an early warning.

2. The method for real-time monitoring of temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers according to claim 1 is characterized in that: Acquisition parameters are set, and temperature data of the target temperature field and image data of the target temperature field are acquired according to the acquisition parameters. The temperature data of the target temperature field and the image of the target temperature field are preprocessed to obtain preprocessed temperature data and preprocessed temperature images, including: Create a temperature data table; Set the acquisition parameters for each target temperature field separately; the acquisition parameters include temperature acquisition position, image acquisition position, acquisition cycle and acquisition frequency. Generally speaking, temperature data and image data are one-to-one corresponding, and image data and temperature data at the same acquisition frequency are recorded as a data pair; Collect multiple temperature data of each target temperature field according to the acquisition parameters, and add all the collected temperature data to the temperature data table; Multiple temperature images of each target temperature field are collected according to the collection parameters, and all the collected temperature images are added to the temperature data table; the collected temperature images include crack images and normal images.

3. The method for real-time monitoring of temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers according to claim 2 is characterized in that: Setting acquisition parameters, acquiring temperature data of the target temperature field and image data of the target temperature field according to the acquisition parameters, and preprocessing the temperature data of the target temperature field and the image of the target temperature field respectively to obtain preprocessed temperature data and preprocessed temperature image, further comprising: Select a target temperature field; Select all temperature data of each acquisition time of the target temperature field from the temperature data table; Calculate the average temperature of all temperature data at each acquisition time; Set the temperature deviation threshold; Select a temperature data and determine whether the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold; If the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold, the temperature data is deleted; Return to select a temperature data and determine whether the difference between the temperature data and the average temperature is greater than or equal to the temperature deviation threshold, until all temperature data of the target temperature field are selected; Perform denoising on all temperature images in the temperature data table to obtain denoised images; Perform binarization on the denoised image to obtain a binarized image; Set a crack evaluation standard, and divide the binary image into a crack image and a normal image according to the crack evaluation standard; record both the crack image and the normal image as preprocessed images; Return to select a target temperature field until all target temperature fields are selected.

4. The method for real-time monitoring of temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers according to claim 3 is characterized in that: Construct a target temperature field model based on preprocessed temperature data and preprocessed temperature images, including: Set the boundary conditions of the target temperature field; The divergence theorem is introduced into the boundary condition through formula 1 to obtain the divergence boundary condition; Formula 1: in, is the divergence, 、 and are the direction vectors of the divergence on the x, y and z axes respectively, 、 and are the components of the unit external normal vector of the boundary condition on the x, y and z axes, V is the temperature field space, and S is the temperature field plane; The divergence boundary condition is discretized by the functional integral equation of formula 2, and the functional minimum is solved to obtain the finite element equation of the temperature field; Formula 2: in, is the temperature of a temperature collection location in the temperature field, is the nodal temperature vector, is the divergence, is the load generated by the internal heat source, is a variational function.

5. The method for real-time monitoring of temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers according to claim 4 is characterized in that: Collect real-time temperature data and input it into the crack model in sequence to obtain the prediction results output by the trained crack model. Based on the prediction results output by the crack model, determine whether there is a risk of cracks. If there is a risk of cracks, issue an early warning, including: Obtain real-time temperature data and real-time image data; Input the real-time temperature data into the target temperature field model to obtain the predicted temperature output by the target temperature field; The predicted temperature and real-time image data are input into the crack model to obtain the predicted results output by the crack model.

6. The method for real-time monitoring of temperature field and early warning of cracks in ultra-wide, thin-walled solid high piers according to claim 5 is characterized in that: Input the predicted temperature and real-time image data into the crack model to obtain the predicted results output by the crack model, including: Set similarity threshold and temperature threshold; Determine whether there is a normal image whose similarity to the real-time image data is greater than or equal to a similarity threshold; If the similarity between the real-time image data and the normal image is greater than or equal to the similarity threshold, the first image and the first temperature corresponding to the normal image are obtained; Determine whether the difference between the first temperature and the predicted temperature is less than the temperature threshold; If the difference between the first temperature and the predicted temperature is less than the temperature threshold, an alarm is issued.

7. Ultra-wide thin-walled solid high pier temperature field real-time monitoring and crack early warning system, characterized by: include: An acquisition component, comprising a temperature acquisition unit and an image acquisition unit; A processing component, wherein the processing component includes a processor, a data processing unit, and an image processing unit. The temperature data is processed by the data processing unit, the temperature image is processed by the image processing unit, and the real-time monitoring and crack warning method of the ultra-wide thin-walled solid high pier temperature field as described in any one of claims 1 to 6 is executed by the processor.

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