A tunnel cross-section profile extraction analysis method and system

Abstract

The application discloses a tunnel section contour extraction analysis method and system, relates to the technical field of tunnel section analysis, and comprises the following steps: collecting a tunnel section image through an image acquisition and processing module, processing to obtain a comprehensive tunnel section image quality coefficient, and determining whether the image can be subjected to contour extraction; if not, the image is re-collected; if yes, a section contour binary image is collected through a contour extraction module; then, section point cloud related data is collected through a data acquisition module; the section point cloud related data is processed and calculated through a data processing module to obtain a tunnel deformation index coefficient; a section deformation index target value is set through a deformation analysis module, and the optimization degree proportion of the section deformation index target value is calculated; after the proportion is evaluated, a deformation grade is determined based on the evaluation result; then, different grade maintenance instructions are issued to execution personnel according to the deformation grade through a control center, and the function that tunnel deformation analysis can be performed according to a tunnel section contour image after the tunnel section contour is extracted is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel section analysis, in particular to a tunnel section contour extraction and analysis method and system. BACKGROUND

[0002] With the continuous development of the city, the pressure of ground road traffic is getting bigger and bigger, in order to reduce the pressure of ground traffic, the application of subway is getting bigger and bigger, at the same time, the advantages of convenience, speed and high capacity of subway make subway become one of the most popular public transportation tools. However, on the one hand, with the long-term use of subway tunnel, the tunnel will inevitably have problems such as aging and deformation; on the other hand, the newly added buildings near the subway tunnel may also cause the deformation of the tunnel. The aging and deformation of the tunnel may further cause the collapse of the tunnel, which seriously endangers the life and property safety of passengers and the transportation system of the whole city.

[0003] For tunnel deformation detection, the traditional method mainly relies on manual detection, which cannot guarantee accuracy and efficiency. SUMMARY

[0004] In order to solve the problems mentioned in the background, the purpose of the present application is to provide a tunnel section contour extraction and analysis method and system, which can analyze the tunnel deformation according to the tunnel section contour image after extracting the tunnel section contour.

[0005] In the first aspect, the purpose of the present application can be realized by the following technical scheme: a tunnel section contour extraction and analysis system, comprising:

[0006] An image acquisition and processing module is used to acquire a tunnel section image, pre-process the tunnel section image, perform binaryzation processing on the processed tunnel section image, obtain a tunnel section binaryzation image, obtain tunnel section image related data based on the tunnel section binaryzation image, perform image quality comprehensive calculation using the tunnel section image related data, obtain a comprehensive tunnel section image quality coefficient, set an image quality coefficient threshold, compare the comprehensive tunnel section image quality coefficient with the image quality coefficient threshold, determine whether the tunnel section binaryzation image is subjected to contour extraction according to the comparison result, if contour extraction can be performed, send a contour extraction signal to a contour extraction module, if contour extraction cannot be performed, re-acquire, wherein the tunnel section image related data includes image frame rate data and image resolution data;

[0007] A contour extraction module is used to acquire the tunnel section binaryzation image, and through the process of pixel detection and connected domain marking, the nodes of the contour curve are depicted to obtain a section contour binaryzation image;

[0008] The data acquisition module is configured to acquire the binary profile of the cross section by using a three-dimensional laser ranging method, and obtain cross section point cloud related data, wherein the cross section point cloud related data comprises cross section three-dimensional position data, reflection angle data and reflection intensity data, and the cross section point cloud related data is sent to the data processing module.

[0009] The data processing module is configured to mark the cross section point cloud related data, calculate tunnel deformation indexes by using the marked cross section point cloud related data, obtain tunnel deformation index coefficients, and send the tunnel deformation index coefficients to the deformation analysis module.

[0010] The deformation analysis module is configured to set a cross section deformation index target value, optimize the cross section deformation index coefficients, calculate an optimization degree ratio of the cross section deformation index target value obtained by optimizing the cross section deformation index coefficients, evaluate the tunnel deformation degree according to the optimization degree ratio of the cross section deformation index target value, and send different level deformation signals to the control center based on the evaluation result.

[0011] The control center is configured to send different level maintenance instructions to the execution personnel based on the different level deformation signals.

[0012] In combination with the first aspect, in some implementations of the first aspect, the system further comprises that the processing procedure of the image acquisition and processing module is as follows:

[0013] The image frame rate data is marked as Zi, and the image resolution data is marked as Fi, wherein i is a collection number label of the image acquisition and processing module, i = 1, 2, 3,..., n, and n is a total number of collection times of the image acquisition and processing module;

[0014] The comprehensive tunnel cross section image quality coefficient Lhi is calculated by using the formula , wherein Z0 is a preset standard image frame rate coefficient, F0 is a preset standard image resolution coefficient, K1 is an image frame rate related coefficient, K2 is an image resolution related coefficient, and t is a preset cross section profile related coefficient.

[0015] In combination with the first aspect, in some implementations of the first aspect, the system further comprises that the image acquisition and processing module sets an image quality coefficient threshold Lh0, and the comprehensive tunnel cross section image quality coefficient Lhi is compared with the image quality coefficient threshold Lh0:

[0016] If Lhi≥Lh0, the profile extraction signal is sent to the profile extraction module;

[0017] If Lhi<Lh0, the image is reacquired until Lhi≥Lh0, and then the profile extraction signal is sent to the profile extraction module.

[0018] With reference to the first aspect, in some implementations of the first aspect, the system further includes that the extraction process of the profile extraction module includes:

[0019] Based on the tunnel section binaryzation graph, pixel detection is performed, and a region with a pixel value of 0 is taken as a profile boundary; based on the profile boundary, single pixel points are removed to obtain a removed profile boundary; according to the removed profile boundary, connected domain marking is performed to obtain a profile connected domain; and based on the profile connected domain, a node of a profile curve is determined, and the node of the profile curve is drawn to obtain a section profile binaryzation graph.

[0020] With reference to the first aspect, in some implementations of the first aspect, the system further includes that the data processing module marks the section three-dimensional position data as Wj, marks the reflection angle data as Dj, and marks the reflection intensity data as Qj, wherein j is a data acquisition module acquisition number label, j = 1, 2, 3,..., m, and m is a total number of data acquisition module acquisition numbers.

[0021] The tunnel deformation index coefficient Bxj is calculated by using the formula , wherein D0 is a preset standard reflection angle coefficient, r1 is a section position influence coefficient, r2 is a reflection influence coefficient, and a is a preset proportion coefficient.

[0022] With reference to the first aspect, in some implementations of the first aspect, the system further includes that the deformation analysis module sets a section deformation index target value Bx0, and performs parameter optimization on the section deformation index coefficient to obtain an optimization degree proportion of the section deformation index target value, which is calculated as follows:

[0023]

[0024] , wherein Yj is the optimization degree proportion of the section deformation index target value, and β is a preset proportion correlation coefficient.

[0025] With reference to the first aspect, in some implementations of the first aspect, the system further includes that the analysis process of the deformation analysis module is as follows:

[0026] If Yj is less than 20%, the deformation analysis module sends a low-level deformation signal to the control center.

[0027] If Yj is not less than 20% but less than 55%, the deformation analysis module sends a medium-level deformation signal to the control center.

[0028] If Yj is not less than 55%, the deformation analysis module sends a high-level deformation signal to the control center.

[0029] Secondly, in order to achieve the above-mentioned purpose, the present application discloses a tunnel section profile extraction analysis method, which comprises the following steps:

[0030] The tunnel cross-section image is acquired and preprocessed. The processed tunnel cross-section image is then binarized to obtain a tunnel cross-section binarized image. Based on the tunnel cross-section binarized image, relevant data of the tunnel cross-section image is acquired. The relevant data of the tunnel cross-section image includes image frame rate data and image resolution data.

[0031] The relevant data of the tunnel cross-section image are labeled, and the image quality is comprehensively calculated using the labeled data to obtain the comprehensive tunnel cross-section image quality coefficient. The image quality coefficient threshold is set, and the comprehensive tunnel cross-section image quality coefficient is compared with the image quality coefficient threshold. Based on the comparison result, it is determined whether contour extraction is performed on the binarized tunnel cross-section image.

[0032] If contour extraction is performed, the nodes of the contour curve are depicted through pixel detection and connected component labeling to obtain a cross-sectional contour binarized image. A three-dimensional laser ranging method is used to obtain cross-sectional point cloud related data for the cross-sectional contour binarized image. The cross-sectional point cloud related data includes cross-sectional three-dimensional position data, reflection angle data, and reflection intensity data.

[0033] The relevant data of the cross-section point cloud are labeled, and the tunnel deformation index is calculated using the labeled cross-section point cloud data to obtain the tunnel deformation index coefficient. The target value of the cross-section deformation index is set, and the cross-section deformation index coefficient is calculated to optimize the parameters and obtain the optimization degree ratio of the target value of the cross-section deformation index. The level of tunnel deformation is determined according to the optimization degree ratio of the target value of the cross-section deformation index.

[0034] The beneficial effects of this invention are:

[0035] This invention acquires tunnel cross-section images through an image acquisition and processing module, processes them to obtain a comprehensive tunnel cross-section image quality coefficient, and determines whether the image can be contour extracted. If not, it is re-acquired; if so, a binarized cross-section contour image is acquired through the contour extraction module. Then, relevant cross-section point cloud data is acquired through a data acquisition module, and the tunnel deformation index coefficient is calculated through a data processing module. The target value of the cross-section deformation index is set through a deformation analysis module, and the optimization degree ratio of the target value is calculated. After evaluating the ratio, the deformation level is determined based on the evaluation results. Then, the control center issues different levels of maintenance instructions to the personnel based on the deformation level. This invention realizes the function of tunnel deformation analysis based on the tunnel cross-section contour image after extraction. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0038] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1:

[0041] The following is a description of the relevant terms used in the embodiments of this application:

[0042] Binarization: Binarization (English: Thresholding) is one of the simplest methods for image segmentation. Binarization converts a grayscale image into a binary image. Pixels with grayscale values ​​greater than a certain threshold are set as grayscale maxima, and pixels with grayscale values ​​less than this threshold are set as grayscale minima, thus achieving binarization. Depending on the threshold chosen, binarization algorithms are divided into fixed threshold and adaptive threshold methods. Commonly used binarization methods include the bimodal method, the P-parameter method, the iterative method, and the OTSU method.

[0043] Parameter optimization: Parameter optimization is a method to achieve design goals. By parameterizing the design goals and using optimization methods, the design variables are continuously adjusted so that the design results continuously approach the parameterized target values.

[0044] Pixel value: The pixel value is a value assigned by the computer when the original image is digitized. It represents the average brightness information of a small square in the original image, or the average reflectance (transmission) density information of that small square. When converting a digital image into a halftone image, the halftone dot area ratio (halftone dot percentage) is directly related to the pixel value (grayscale value) of the digital image; that is, the size of the halftone dot represents the average brightness information of a small square in the original image.

[0045] like Figure 1 As shown, a tunnel cross-section contour extraction and analysis system includes:

[0046] The image acquisition processing module, the profile extraction module, the data acquisition module, the data processing module, the deformation analysis module, and the control center;

[0047] The image acquisition processing module is configured to acquire a tunnel section image, and pre-process the tunnel section image to obtain a processed tunnel section image.

[0048] The pre-processing of the tunnel section image includes deblurring processing and noise reduction processing. Specifically, in the implementation process, the deblurring processing of the image includes image enhancement, image restoration, and super-resolution reconstruction. The noise reduction processing of the image is performed by a spatial domain filtering method, which directly analyzes the relationship between the image pixels and their neighboring pixels to achieve image noise reduction. This method smoothes the image by applying a series of filters or convolution kernels in the two-dimensional space of the image, thereby reducing the impact of noise on the image.

[0049] The processed tunnel section image is binarized to obtain a tunnel section binary image. It should be noted that the method of binarizing the processed tunnel section image includes global thresholding, otsu thresholding, local thresholding, and adaptive thresholding. Specifically, in this application, global thresholding is used for binarization because it is simple and fast. By assuming that the pixels with a brightness higher than a certain threshold in the entire image range should be marked as foreground (white), and the pixels with a brightness lower than the threshold should be marked as background (black), the tunnel section binary image is finally obtained.

[0050] Based on the tunnel section binary image, tunnel section image related data is acquired. The tunnel section image related data is marked and processed to obtain marked tunnel section image related data. The marked tunnel section image related data is used for image quality comprehensive calculation to obtain a comprehensive tunnel section image quality coefficient. The tunnel section image related data includes image frame rate data and image resolution data. Specifically, the processing process of the image acquisition processing module is as follows:

[0051] The image frame rate data is marked as Zi, and the image resolution data is marked as Fi, where i is the image acquisition processing module acquisition number label, and i = 1, 2, 3,..., n, n is the total number of image acquisition processing module acquisition times;

[0052] The comprehensive tunnel section image quality coefficient Lhi is calculated using the formula where Z0 is a preset standard image frame rate coefficient, F0 is a preset standard image resolution coefficient, K1 is an image frame rate related coefficient, K2 is an image resolution related coefficient, and t is a preset section profile related coefficient.

[0053] Further, in the specific implementation process, the preset standard image frame rate coefficient and the preset standard image resolution coefficient are obtained by multiple simulation calculations and data averaging after collecting image frame rate data and image resolution data;

[0054] In the embodiment, the image frame rate correlation coefficient and the image resolution correlation coefficient are obtained by comprehensive evaluation and calculation according to external and internal related factors, including human factors, image-related factors, and environmental-related factors, when the image frame rate data and the image resolution data are obtained by the application, the image-related factors include image definition, image color depth, and image distortion degree.

[0055] The preset cross-section contour correlation coefficient is related data that affects whether a clear contour can be drawn in the image, and is obtained by comprehensive processing after collecting multiple images that can draw a clear contour.

[0056] An image quality coefficient threshold Lh0 is set, and the comprehensive tunnel cross-section image quality coefficient Lhi obtained by calculation is compared with the image quality coefficient threshold Lh0, and whether the tunnel cross-section binary image quality meets the contour drawing standard is determined according to the comparison result, so as to determine whether to perform contour extraction.

[0057] The specific comparison process is as follows:

[0058] If Lhi≥Lh0, it indicates that the quality of the tunnel cross-section image meets the contour drawing standard, and the image acquisition and processing module sends a contour extraction signal to the contour extraction module.

[0059] If Lhi<Lh0, it indicates that the quality of the tunnel cross-section image does not meet the contour drawing standard, and the image acquisition and processing module reacquires the image until Lhi≥Lh0, and then sends a contour extraction signal to the contour extraction module.

[0060] The contour extraction module performs contour extraction after receiving the contour extraction signal sent by the image acquisition and processing module. Specifically, the extraction process of the contour extraction module includes the following steps:

[0061] Obtain the tunnel cross-section binary image in the image acquisition and processing module, perform pixel detection based on the tunnel cross-section binary image, regard the region with a pixel value of 0 as a contour boundary, remove single pixel points based on the contour boundary to obtain a removed contour boundary, perform connected domain marking according to the removed contour boundary to obtain a contour connected domain, determine nodes of a contour curve based on the contour connected domain, and draw the contour curve according to the nodes to obtain a cross-section contour binary image.

[0062] Wherein, in the specific implementation process, the pixel value of 0 is the black area on the tunnel section binary image;

[0063] The pixel detection and subsequent connected domain marking process based on the tunnel section binary image is operated by using an eight-neighborhood algorithm;

[0064] The section contour binary image is sent to the data acquisition module for data acquisition;

[0065] After receiving the section contour binary image sent by the contour extraction module, the data acquisition module determines the internal position of the section contour based on the section contour binary image, and then uses a three-dimensional laser ranging method to obtain section point cloud related data, and sends the section point cloud related data to the data processing module, wherein the section point cloud related data includes section three-dimensional position data, reflection angle data and reflection intensity data.

[0066] Specifically, in the embodiment, the data acquisition process performed by the data acquisition module is as follows:

[0067] The laser emitter emits laser to the surface of the object, and after the laser irradiates the surface of the section, the photoelectric converter converts the reflected laser signal into an electrical signal, and the three-dimensional coordinates of the surface of the object are calculated by an algorithm, so as to obtain point cloud data as section point cloud related data.

[0068] After receiving the crack related index data sent by the data acquisition module, the data processing module performs data processing, and specifically, the processing process of the data processing module includes the following steps:

[0069] The section point cloud related data is marked, and the marked section point cloud related data is used to calculate the tunnel deformation index to obtain a tunnel deformation index coefficient, and the specific process is as follows:

[0070] The section three-dimensional position data is marked as Wj, the reflection angle data is marked as Dj, and the reflection intensity data is marked as Qj, wherein j is the data acquisition module acquisition number label, and j = 1, 2, 3,..., m, and m is the total number of data acquisition module acquisition times;

[0071] The tunnel deformation index coefficient Bxj is calculated by using the formula , wherein D0 is a preset standard reflection angle coefficient, r1 is a section position influence coefficient, r2 is a reflection influence coefficient, and a is a preset proportion coefficient.

[0072] Wherein, in the embodiment, the section position influence coefficient and the reflection influence coefficient are calculated according to the comprehensive evaluation of external factors, including human factors, machine detection and environmental factors, etc., when the application obtains the three-dimensional position data, the reflection angle data and the reflection intensity data by daily acquisition.

[0073] The calculated section deformation index coefficient is sent to the deformation analysis module for deformation analysis.

[0074] The deformation analysis module receives the section deformation index coefficient sent by the data processing module, and performs deformation analysis. Specifically, the analysis process of the deformation analysis module includes the following steps:

[0075] The section deformation index target value Bx0 is set, and the section deformation index coefficient is parameter optimized so that the section deformation index coefficient gradually approaches the section deformation index target value until the section deformation index target value is obtained by optimization. The optimization degree ratio of the section deformation index target value obtained by parameter optimization of the section deformation index coefficient is calculated as follows:

[0076]

[0077] In the formula, Yj is the optimization degree ratio of the section deformation index target value, and β is a preset proportion correlation coefficient.

[0078] Based on the calculated optimization degree ratio Yj of the section deformation index target value, the tunnel deformation degree is evaluated, and different levels of deformation signals are sent to the control center based on the evaluation results. The specific process is as follows:

[0079] If Yj is less than 20%, it is determined that the tunnel deformation degree is low at this time, and the deformation analysis module sends a low-level deformation signal to the control center.

[0080] If Yj is not less than 20% but less than 55%, it is determined that the tunnel deformation degree is moderate at this time, and the deformation analysis module sends a medium-level deformation signal to the control center.

[0081] If Yj is not less than 55%, it is determined that the tunnel deformation degree is high at this time, and the deformation analysis module sends a high-level deformation signal to the control center.

[0082] The control center receives different levels of deformation signals sent by the deformation analysis module, and sends different levels of maintenance instructions for tunnel maintenance according to different levels of deformation. Specifically, the following embodiments further illustrate the application scheme:

[0083] The control center receives a low-level deformation signal and sends a first-level maintenance instruction to the execution personnel.

[0084] The control center sends a secondary maintenance instruction to the execution personnel after receiving the medium deformation signal.

[0085] The control center sends a tertiary maintenance instruction to the execution personnel after receiving the high-level deformation signal.

[0086] Embodiment two: a tunnel section profile extraction analysis method, the method comprising the following steps:

[0087] S101: acquire a tunnel section image and perform preprocessing, perform binary processing on the processed tunnel section image to obtain a tunnel section binary image, and acquire tunnel section image related data based on the tunnel section binary image, wherein the tunnel section image related data includes image frame rate data and image resolution data;

[0088] S102: mark the tunnel section image related data, use the marked tunnel section image related data to perform image quality comprehensive calculation to obtain a comprehensive tunnel section image quality coefficient, set an image quality coefficient threshold, compare the comprehensive tunnel section image quality coefficient with the image quality coefficient threshold, and determine whether to perform profile extraction on the tunnel section binary image according to the comparison result;

[0089] S103: if profile extraction is performed, draw the nodes of the profile curve through pixel detection and connected domain marking to obtain a section profile binary image, and use a three-dimensional laser ranging method to obtain section point cloud related data, wherein the section point cloud related data includes section three-dimensional position data, reflection angle data, and reflection intensity data;

[0090] S104: mark the section point cloud related data, use the marked section point cloud related data to perform tunnel deformation index calculation to obtain a tunnel deformation index coefficient, set a section deformation index target value, calculate the section deformation index coefficient to obtain an optimization degree ratio of the section deformation index target value, and determine the level of tunnel deformation according to the optimization degree ratio of the section deformation index target value.

[0091] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0092] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, and the storage medium stores a computer program, and the computer program is executed by the processor to perform the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include: electrical connections with one or more conductive wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or component.

[0093] The above formulas are all dimensionless values calculated by removing dimensions, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the real situation. The preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0094] In the description of the specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.

[0095] The basic principles, main features and advantages of the present disclosure are shown and described above. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements of the present disclosure can be made, which all fall within the scope of the claimed present disclosure.

Claims

1. A tunnel cross-section contour extraction and analysis system, characterized in that, include: Image acquisition and processing module: This module is used to acquire tunnel cross-section images, preprocess the tunnel cross-section images, binarize the processed tunnel cross-section images to obtain a tunnel cross-section binarized image, acquire relevant data of the tunnel cross-section image based on the tunnel cross-section binarized image, perform comprehensive image quality calculation using the relevant data of the tunnel cross-section image to obtain a comprehensive tunnel cross-section image quality coefficient, set an image quality coefficient threshold, compare the comprehensive tunnel cross-section image quality coefficient with the image quality coefficient threshold, and determine whether contour extraction should be performed on the tunnel cross-section binarized image based on the comparison result. If contour extraction can be performed, a contour extraction signal is sent to the contour extraction module; otherwise, re-acquisition is performed. The relevant data of the tunnel cross-section image includes image frame rate data and image resolution data. Contour extraction module: used to obtain a binary image of the tunnel cross section, and through pixel detection and connected component labeling, to depict the nodes of the contour curve and obtain a binary image of the cross section contour. Data acquisition module: used to acquire the cross-sectional profile binarized image using a three-dimensional laser ranging method to obtain cross-sectional point cloud related data, wherein the cross-sectional point cloud related data includes cross-sectional three-dimensional position data, reflection angle data and reflection intensity data, and sends the cross-sectional point cloud related data to the data processing module; Data processing module: used to mark the cross-sectional point cloud data, use the marked cross-sectional point cloud data to calculate the tunnel deformation index, obtain the tunnel deformation index coefficient, and send the tunnel deformation index coefficient to the deformation analysis module. Deformation Analysis Module: This module is used to set target values ​​for cross-sectional deformation indicators, optimize the coefficients of these indicators, calculate the degree of optimization of the target values, assess the tunnel deformation level based on this optimization degree, and send deformation signals of different levels to the control center based on the assessment results. Control Center: Used to send maintenance instructions of different levels to the personnel to perform based on different levels of deformation signals.

2. The tunnel cross-section contour extraction and analysis system according to claim 1, characterized in that, The processing procedure of the image acquisition and processing module is as follows: The image frame rate data is labeled as Zi, and the image resolution data is labeled as Fi, where i is the number of times the image acquisition and processing module acquires data, and i = 1, 2, 3, ..., n, where n is the total number of times the image acquisition and processing module acquires data. Using formula The comprehensive tunnel cross-section image quality coefficient Lhi is calculated, where Z0 is the preset standard image frame rate coefficient, F0 is the preset standard image resolution coefficient, K1 is the image frame rate correlation coefficient, K2 is the image resolution correlation coefficient, and t is the preset cross-section contour correlation coefficient.

3. The tunnel cross-section contour extraction and analysis system according to claim 2, characterized in that, The image acquisition and processing module sets an image quality coefficient threshold Lh0 and compares the overall tunnel cross-section image quality coefficient Lhi with the image quality coefficient threshold Lh0: If Lhi≥Lh0, then send a contour extraction signal to the contour extraction module; If Lhi < Lh0, the image is reacquired until Lhi ≥ Lh0, at which point the contour extraction signal is sent to the contour extraction module.

4. The tunnel cross-section contour extraction and analysis system according to claim 1, characterized in that, The extraction process of the contour extraction module includes: Pixel detection is performed based on the binarized image of the tunnel cross section. Regions with a pixel value of 0 are used as contour boundaries. Single pixels are removed based on the contour boundaries to obtain the removed contour boundaries. Connected components are marked based on the removed contour boundaries to obtain the contour connected components. The nodes of the contour curve are determined based on the contour connected components. The contour curve is drawn based on the nodes to obtain the cross section contour binarized image.

5. The tunnel cross-section contour extraction and analysis system according to claim 1, characterized in that, The data processing module marks the three-dimensional position data of the cross section as Wj, the reflection angle data as Dj, and the reflection intensity data as Qj, where j is the number of times the data acquisition module collects data, and j = 1, 2, 3, ..., m, where m is the total number of times the data acquisition module collects data. Using formula The tunnel deformation index coefficient Bxj is calculated, where D0 is the preset standard reflection angle coefficient, r1 is the cross-sectional position influence coefficient, r2 is the reflection influence coefficient, and α is the preset proportional coefficient.

6. The tunnel cross-section contour extraction and analysis system according to claim 1, characterized in that, The deformation analysis module sets the target value of the cross-sectional deformation index to Bx0, and calculates the optimization degree ratio of the target value of the cross-sectional deformation index by optimizing the coefficients of the cross-sectional deformation index as follows: In the formula, Yj represents the optimization degree of the target value of the cross-sectional deformation index, and β is the preset proportional correlation coefficient.

7. The tunnel cross-section contour extraction and analysis system according to claim 6, characterized in that, The analysis process of the deformation analysis module is as follows: If Yj is less than 20%, the deformation analysis module sends a low-level deformation signal to the control center. If Yj is not less than 20% but less than 55%, the deformation analysis module sends an intermediate deformation signal to the control center. If Yj is not less than 55%, the deformation analysis module sends an advanced deformation signal to the control center.

8. A method for extracting and analyzing the cross-sectional profile of a tunnel, characterized in that, The method includes the following steps: The tunnel cross-section image is acquired and preprocessed. The processed tunnel cross-section image is then binarized to obtain a tunnel cross-section binarized image. Based on the tunnel cross-section binarized image, relevant data of the tunnel cross-section image is acquired. The relevant data of the tunnel cross-section image includes image frame rate data and image resolution data. The relevant data of the tunnel cross-section image are labeled, and the image quality is comprehensively calculated using the labeled data to obtain the comprehensive tunnel cross-section image quality coefficient. The image quality coefficient threshold is set, and the comprehensive tunnel cross-section image quality coefficient is compared with the image quality coefficient threshold. Based on the comparison result, it is determined whether contour extraction is performed on the binarized tunnel cross-section image. If contour extraction is performed, the nodes of the contour curve are depicted through pixel detection and connected component labeling to obtain a cross-sectional contour binarized image. A three-dimensional laser ranging method is used to obtain cross-sectional point cloud related data for the cross-sectional contour binarized image. The cross-sectional point cloud related data includes cross-sectional three-dimensional position data, reflection angle data, and reflection intensity data. The relevant data of the cross-section point cloud are labeled, and the tunnel deformation index is calculated using the labeled cross-section point cloud data to obtain the tunnel deformation index coefficient. The target value of the cross-section deformation index is set, and the cross-section deformation index coefficient is calculated to optimize the parameters and obtain the optimization degree ratio of the target value of the cross-section deformation index. The level of tunnel deformation is determined according to the optimization degree ratio of the target value of the cross-section deformation index.

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