Tunnel deconstruction induced stress adjustment identification method

Abstract

The invention discloses a tunnel deconstruction induced stress adjustment identification method, and relates to the technical field of video processing. The method comprises the following steps: compressing a target inspection video to obtain a target compressed video; performing detection segmentation on the target compressed video to obtain a target segmentation result; judging whether a first characteristic value obtained by analyzing the first video clip accords with a preset characteristic value threshold value or not; if not, monitoring through a sensor group to obtain first sensing information; evaluating and analyzing the first sensing information to obtain first evaluation stress; and constructing a target stress distribution diagram, and performing stress adjustment on the target tunnel according to the target stress distribution diagram. The technical problem that stress monitoring is inaccurate and cannot be adjusted in real time in the tunnel inspection process in the prior art is solved, real-time detection and dynamic adjustment of tunnel stress are achieved by combining video image analysis and sensor data, and the technical effect of improving the accuracy of tunnel inspection and the timeliness of stress adjustment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of video processing, and particularly to a method for identifying the adjustment of induced stress in tunnel deconstruction. Background Art

[0002] During the inspection and maintenance of tunnels, stress distribution and its changes are important factors affecting the structural stability of tunnels. However, in the existing technology, the methods generally rely on manual inspection or a single sensor, which not only makes it difficult to accurately analyze the global stress state of the tunnel, but also lacks the ability of real-time dynamic adjustment. Especially in complex environments, due to the non-uniformity and dynamic changes of the tunnel stress distribution, it is difficult for traditional inspection means to timely detect potential structural problems, resulting in the difficulty of timely eliminating the potential safety hazards of the tunnel. In addition, the traditional methods lack the technical means to deeply combine the inspection video information with the sensor monitoring data, and cannot comprehensively reflect the stress state of the tunnel. Summary of the Invention

[0003] The present application provides a method for identifying the adjustment of induced stress in tunnel deconstruction, which solves the technical problems of inaccurate stress monitoring and inability to adjust in real time during the tunnel inspection process in the existing technology.

[0004] In view of the above problems, the present application provides a method for identifying the adjustment of induced stress in tunnel deconstruction.

[0005] The present application provides a method for identifying the adjustment of induced stress in tunnel deconstruction, and the method includes: Performing compression processing on the target inspection video of the target tunnel obtained by traversing the inspection to obtain a target compressed video; reading a predetermined shot detection strategy, and detecting and segmenting the target compressed video according to the predetermined shot detection strategy to obtain a target segmentation result, wherein the target segmentation result includes a first video segment of a first tunnel point; judging whether a first eigenvalue obtained by analyzing the first video segment conforms to a predetermined eigenvalue threshold; if not, arranging a sensor group at the first tunnel point, and monitoring and obtaining first sensing information through the sensor group; introducing a stress evaluation function to evaluate and analyze the first sensing information to obtain a first evaluation stress; constructing a target stress distribution map according to the first corresponding relationship between the first evaluation stress and the first tunnel point, and performing stress adjustment on the target tunnel according to the target stress distribution map.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: First, compress the target inspection video of the target tunnel obtained by traversing the inspection to obtain a target compressed video. Then, read the predetermined shot detection strategy, and detect and segment the target compressed video according to the predetermined shot detection strategy to obtain a target segmentation result, where the target segmentation result includes a first video segment of the first tunnel point. Then, determine whether the first eigenvalue obtained by analyzing the first video segment meets the predetermined eigenvalue threshold; if not, deploy a sensor group at the first tunnel point, and monitor and obtain first sensing information through the sensor group. Further, introduce a stress evaluation function to evaluate and analyze the first sensing information to obtain a first evaluation stress. Finally, construct a target stress distribution map according to the first corresponding relationship between the first evaluation stress and the first tunnel point, and perform stress adjustment on the target tunnel according to the target stress distribution map. This solves the technical problems of inaccurate stress monitoring and inability to adjust in real time during the tunnel inspection process in the prior art. By combining video image analysis and sensor data, real-time detection and dynamic adjustment of tunnel stress are realized, achieving the technical effects of improving the accuracy of tunnel inspection and the timeliness of stress adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0008] Figure 1 Schematic flow chart of a method for identifying tunnel structure-induced stress adjustment provided by an embodiment of the present application; Figure 2 Schematic flow chart of compressing a target inspection video in a method for identifying tunnel structure-induced stress adjustment provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The present application provides a method for identifying tunnel structure-induced stress adjustment, which solves the technical problems of inaccurate stress monitoring and inability to adjust in real time during the tunnel inspection process in the prior art.

[0010] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0011] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products, or devices.

[0012] Embodiment, such as Figure 1 As shown, an embodiment of the present application provides a method for identifying the adjustment of induced stress in tunnel deconstruction. Among them, the method includes: Step 100: Compress the target inspection video of the target tunnel obtained by traversing inspection to obtain a target compressed video.

[0013] The target tunnel is traversed and inspected by inspection equipment (such as high-definition cameras, drones, etc.) to collect video data inside and around the tunnel; the target inspection video of the target tunnel is compressed to obtain a target compressed video. By compressing the video, the burden of data processing can be effectively reduced, and the operation efficiency of the system can be improved.

[0014] Furthermore, as Figure 2 shown, compressing the target inspection video of the target tunnel obtained by traversing inspection to obtain a target compressed video, step 100 includes: Step 110: Encode the target inspection video to obtain a target encoded unit; step 120: Extract the first unit from the target encoded unit, where the first unit includes a first intra-frame image; step 130: Based on the first intra-frame image, form a target image sequence and use the target image sequence as the target compressed video.

[0015] Specifically, encoding the target inspection video, the encoding process is to convert the video data into a more compact format that is easy to store and transmit. Common video encoding formats include H.264, H.265 (HEVC), etc.; during the encoding process, the video is divided into multiple encoded units (such as macroblocks, coding tree units, etc.), and each encoded unit contains a certain number of video frames; extract the first unit from the target encoded unit, the first unit contains the first intra-frame image of the target video, and the first intra-frame image is a key frame of the video, usually containing relatively complete image information, which can provide preliminary visual features for subsequent video analysis; based on the first intra-frame image, form a target image sequence, and the target image sequence can represent the image content at different time points in the video, thus providing a basis for subsequent image processing and analysis; use the obtained target image sequence as the target compressed video.

[0016] Step 200: Read a predetermined shot detection strategy, and detect and segment the target compressed video according to the predetermined shot detection strategy to obtain a target segmentation result, where the target segmentation result includes a first video segment of a first tunnel position.

[0017] The predetermined shot detection strategy includes rules for video segmentation, such as the time interval for video cutting, the key frame detection method in the video, or the key detection requirements for specific areas (such as different parts of the tunnel). According to the read predetermined shot detection strategy, the target compressed video is segmented so as to divide the long video content into multiple small segments, and then the target segmentation result is obtained. Each segment contains different tunnel positions or states, that is, it includes a first video segment of a first tunnel position, and the first video segment represents the specific situation of a certain position in the tunnel.

[0018] Step 300: Determine whether the first eigenvalue obtained by analyzing the first video segment meets a predetermined eigenvalue threshold.

[0019] By analyzing the image content in the first video segment, a visual inspection is carried out to find out whether there are obvious cracks, deformations or corrosion phenomena on the tunnel surface; for the cracks found in the video segment, detailed measurements are carried out, and the characteristic values such as the length, width and depth of the cracks are recorded. Through these measured values, it can be judged whether the cracks are structural cracks and the development trend of the cracks; the extracted crack characteristic values are compared with the predetermined eigenvalue threshold. If the characteristic values meet the predetermined standard and do not exceed the danger threshold, it indicates that this area may not need to be processed immediately; if the size or development trend of the cracks exceeds the threshold, it indicates that there are greater potential safety hazards in this area.

[0020] Furthermore, determining whether the first eigenvalue obtained by analyzing the first video segment meets a predetermined eigenvalue threshold includes: Remove the first frame image of the first video segment to obtain a first preprocessed video segment, where the first preprocessed video segment includes a target frame image and a frame image sequence; perform enhancement calibration on the frame image sequence based on the target frame image to obtain a first target enhanced image; analyze and obtain the first eigenvalue of the first target enhanced image.

[0021] Specifically, the first frame image is removed from the first video segment. The first frame image usually contains initial capture information and may have significant noise due to the startup of the shooting device or environmental influence. Therefore, after removing the first frame image, the target frame image and the subsequent frame image sequence are retained to form the first preprocessed video segment. Based on the target frame image, the subsequent frame image sequence is enhanced and calibrated. The purpose of enhancement and calibration is to improve the image quality and highlight the key features on the tunnel surface, such as cracks and deformations. After image enhancement and calibration, a representative image is selected from the frame image sequence as the first target enhanced image. The first target enhanced image is analyzed to obtain its first eigenvalue.

[0022] Furthermore, analyzing the first eigenvalue of the first target enhanced image includes: Performing discrete cosine transform processing on the first target enhanced image to obtain a first target transform result; performing weighted calculation on the first texture eigenvalue and the first base color eigenvalue in the first target transform result to obtain the first eigenvalue.

[0023] Specifically, discrete cosine transform (DCT) processing is performed on the first target enhanced image. Discrete cosine transform is an image transformation method that can convert the spatial information of an image into frequency information, thereby extracting important features such as texture, shape, and edges in the image. Through DCT processing, the redundant information of the image can be effectively reduced, and the key features of the image can be enhanced, especially for details such as cracks and deformations in the tunnel. During the DCT processing, the image is divided into multiple 8x8 or larger-sized blocks, and each block is subjected to DCT transformation. The transformed result contains the energy distribution of the image block in different frequency directions. More specifically, the result of the DCT transformation is usually a set of coefficients representing the intensity of different frequency components. The low-frequency coefficients represent the smooth areas of the image, while the high-frequency coefficients represent the details in the image (such as cracks and deformations). By extracting the high-frequency coefficients in the DCT transformation result, the texture energy and contrast of the image can be calculated to obtain the first texture eigenvalue, which can reflect the richness of details such as cracks and deformations on the tunnel surface. The base color feature information is extracted from the low-frequency part of the DCT result, mainly by calculating the mean and standard deviation of the low-frequency coefficients to obtain the first base color eigenvalue, which can reflect the corrosion, pollution, etc. on the tunnel surface. The extracted first texture eigenvalue and first base color eigenvalue are weighted and calculated according to a preset weight to obtain the comprehensive first eigenvalue.

[0024] Step 400, if not, a sensor group is arranged at the first tunnel point, and first sensing information is monitored through the sensor group.

[0025] If the first eigenvalue of the first target enhanced image does not meet the predetermined eigenvalue threshold, this may indicate an abnormal situation at the first tunnel point position, and further monitoring is required. According to the actual situation and monitoring requirements of the first tunnel point position, select the appropriate type and number of sensors for deployment; conduct real-time monitoring of the first tunnel point position through the sensor group to obtain the first sensing information, which reflects the actual stress, deformation, temperature change, etc. of this tunnel point position.

[0026] Furthermore, the sensor group includes a stress sensor, a displacement sensor, and a temperature and humidity sensor.

[0027] The sensor group includes a stress sensor, a displacement sensor, and a temperature and humidity sensor. The stress sensor is used to monitor the stress changes in different parts of the tunnel structure to help identify whether there are areas with excessive stress concentration; the displacement sensor is used to detect the displacement of the tunnel surface or structure, especially the crack propagation or deformation trend; the temperature and humidity sensor can monitor the temperature and humidity changes in the tunnel interior, and the temperature and humidity changes may affect the structural performance of the tunnel or accelerate the corrosion process. Through the combined monitoring of these three sensors, the obtained first sensing information will be more comprehensive and accurate, providing multi-dimensional data support for subsequent stress assessment and adjustment.

[0028] Step 500, introduce a stress evaluation function to evaluate and analyze the first sensing information to obtain a first evaluation stress.

[0029] Input the first sensing information obtained by the sensor group, including data such as stress, displacement, temperature and humidity, into the stress evaluation function for calculation to obtain the first evaluation stress. The first evaluation stress reflects the stress state of the tunnel point position under actual monitoring conditions and can reveal potential safety hazards, such as stress concentration areas or overloaded structural parts.

[0030] Furthermore, the expression of the stress evaluation function is as follows: F(σ, δ, T, H) = k 1 *f 1 (σ) + k 2 *f 2 (δ) + k 3 *f 3 (T, H); where F(σ, δ, T, H) is the output of the stress evaluation function, representing the stress evaluation value corresponding to the tunnel point position, k 1 、k 2 、k 3 are weight coefficients used to adjust the importance of each sensing factor in the evaluation function, f 1 (σ)、f 2 (δ)、f 3(T, H) are sub - functions corresponding to stress, displacement, temperature, and humidity respectively, and the specific expressions are as follows: f 1 (σ) = σ / σ'; f 2 (δ) = δ / δ'; f 3 (T, H) = g(T) + h(H); where σ refers to the stress monitored by the stress sensor, σ' refers to the preset stress threshold, δ refers to the displacement monitored by the displacement sensor, δ' refers to the preset displacement threshold, g(T) refers to the influence of temperature on stress, and h(H) refers to the influence of humidity on stress.

[0031] f 1 (σ) is the sub - function corresponding to the stress sensor, used to calculate the ratio of the stress at the tunnel point to the preset stress threshold. σ refers to the stress monitored by the stress sensor, and σ' refers to the preset stress threshold; f 2 (δ) is the sub - function corresponding to the displacement sensor, used to calculate the ratio of the displacement at the tunnel point to the preset displacement threshold. δ refers to the displacement monitored by the displacement sensor, and δ' refers to the preset displacement threshold; f 3 (T, H) is the sub - function corresponding to the temperature - humidity sensor, used to consider the influence of temperature and humidity on the stress of the tunnel structure. Among them, g(T) is the influence function of temperature on the tunnel stress, and h(H) is the influence function of humidity on the tunnel stress. Through weighted summation, F(σ, δ, T, H) will give a comprehensive stress evaluation value, reflecting the health status of the tunnel point.

[0032] Step 600: Construct a target stress distribution map according to the first corresponding relationship between the first evaluated stress and the first tunnel point, and perform stress adjustment on the target tunnel according to the target stress distribution map.

[0033] According to the first corresponding relationship between the first evaluated stress and the first tunnel point, the stress value of each tunnel point can be mapped onto a three - dimensional space or a two - dimensional plane to form a stress distribution map. The stress distribution map shows the stress state of each point of the tunnel, thus revealing which areas may have excessive stress or stress concentration. Specifically, using the stress evaluation value (i.e., the first evaluated stress) of each point as the value in the graph, the distribution of stress is visualized through methods such as color gradients or contour lines, usually manifested as high - stress areas and low - stress areas.

[0034] By observing the target stress distribution map, areas with high stress or uneven stress distribution in the tunnel can be clearly identified. These areas usually indicate that there may be cracks, deformations, or other structural problems, so special attention or adjustment is required. According to the high - stress areas and uneven distribution shown in the target stress distribution map, corresponding stress adjustment measures are taken.

[0035] Furthermore, before adjusting the stress of the target tunnel according to the target stress distribution map, it further includes: determining a first adjacent area of the first tunnel point in the target stress distribution map, where the first adjacent area includes a first adjacent tunnel point; obtaining a first adjacent evaluation stress of the first adjacent tunnel point; determining whether a first adjacent deviation between the first adjacent evaluation stress and the first evaluation stress is within a predetermined support deviation threshold; if it is within, adding the first adjacent tunnel point to a support list, and if not, adding the first adjacent tunnel point to a non-support list; taking the ratio of the number of tunnel points in the support list to that in the non-support list, denoted as the adjacent support index; and calibrating the target stress distribution map according to the adjacent support index.

[0036] In the target stress distribution map, determine a first adjacent area of the first tunnel point. The first adjacent area includes a first adjacent tunnel point, that is, the adjacent tunnel points around the first tunnel point. The stress states of these adjacent points may be affected by the stress state of the first tunnel point. Obtain the first adjacent evaluation stress of the first adjacent tunnel point, that is, the actual stress evaluation values of these adjacent tunnel points. Compare the difference between the first adjacent evaluation stress and the first evaluation stress to calculate the first adjacent deviation. If the adjacent deviation is within the predetermined support deviation threshold, add the first adjacent tunnel point to the support list, indicating that the stress change in this area is consistent with or within the allowable range of the first tunnel point, and the original stress state can be maintained. If the adjacent deviation is not within the predetermined support deviation threshold, add the first adjacent tunnel point to the non-support list, indicating that there is a large deviation in the stress state of this area and special adjustment or reinforcement is required. Take the ratio of the number of tunnel points in the support list to that in the non-support list to obtain the adjacent support index. The adjacent support index reflects the degree of consistency of the stress states of the tunnel points in the adjacent area. The higher the adjacent support index, the more uniform the tunnel stress distribution and the less likely adjustment is necessary. A lower adjacent support index may indicate that the stress in some parts of the tunnel is too concentrated or unevenly distributed and requires key adjustment. Calibrate the target stress distribution map according to the calculated adjacent support index.

[0037] Furthermore, calibrating the target stress distribution map according to the adjacent support index includes: Determine whether the adjacent support index meets a predetermined support threshold; if not, send a stress identification warning signal; perform stress detection on the first tunnel point according to the stress identification warning signal to obtain a first detection stress; and replace the first evaluation stress with the first detection stress to calibrate the target stress distribution map.

[0038] Specifically, check whether the calculated adjacency support index meets a predetermined support threshold, which is a standard value used to determine whether the stress distribution between points of the tunnel is uniform; if the adjacency support index fails to reach the predetermined support threshold, it indicates that there may be significant stress concentration or non-uniformity in the tunnel structure, and at this time, a stress identification warning signal will be issued; after receiving the stress identification warning signal, further perform stress detection on the first tunnel point to obtain the first detected stress; replace the original first evaluated stress with the obtained first detected stress to update the target stress distribution map.

[0039] Furthermore, activate the ultrasonic device according to the stress identification warning signal, and perform stress detection on the first tunnel point through the ultrasonic device to obtain the first detected stress.

[0040] When the stress identification warning signal is triggered, the ultrasonic device is automatically activated. The ultrasonic device emits ultrasonic signals to the first tunnel point. By analyzing the reflected ultrasonic signals, the ultrasonic device can evaluate the stress state of this point; through the detection of the ultrasonic device, the first detected stress is obtained, and the first detected stress represents the actual stress state of the first tunnel point. By using the ultrasonic device for stress detection, more reliable stress data can be obtained, providing a more accurate basis for the health assessment of the tunnel structure.

[0041] In summary, the embodiments of the present application at least have the following technical effects: First, compress the target inspection video of the target tunnel obtained by traversing and inspecting to obtain the target compressed video. Then, read the predetermined shot detection strategy, and perform detection segmentation on the target compressed video according to the predetermined shot detection strategy to obtain the target segmentation result, where the target segmentation result includes the first video segment of the first tunnel point. Then, determine whether the first eigenvalue obtained by analyzing the first video segment meets the predetermined eigenvalue threshold; if not, arrange a sensor group at the first tunnel point, and monitor and obtain the first sensing information through the sensor group. Further, introduce a stress evaluation function to evaluate and analyze the first sensing information to obtain the first evaluated stress. Finally, construct a target stress distribution map according to the first corresponding relationship between the first evaluated stress and the first tunnel point, and perform stress adjustment on the target tunnel according to the target stress distribution map. This solves the technical problems in the prior art of inaccurate stress monitoring and inability to adjust in real time during the tunnel inspection process. By combining video image analysis and sensor data, real-time detection and dynamic adjustment of tunnel stress are achieved, and the technical effects of improving the accuracy of tunnel inspection and the timeliness of stress adjustment are achieved.

[0042] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0043] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0044] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A tunnel deconstruction induced stress adjustment identification method, characterized in that: The method comprises: Compressing the target inspection video of the target tunnel obtained through traversal inspection to obtain a target compressed video; Reading a predetermined shot detection strategy, and detecting and segmenting the target compressed video according to the predetermined shot detection strategy to obtain a target segmentation result, wherein the target segmentation result includes a first video clip of a first tunnel point; Determining whether a first feature value obtained by analyzing the first video clip meets a predetermined feature value threshold; If not, a sensor group is deployed at the first tunnel point, and first sensor information is obtained through monitoring by the sensor group; Introducing a stress evaluation function to evaluate and analyze the first sensing information to obtain a first evaluation stress; A target stress distribution map is constructed according to a first corresponding relationship between the first evaluation stress and the first tunnel point, and stress adjustment is performed on the target tunnel according to the target stress distribution map.

2. According to claim 1, a tunnel deconstruction induced stress adjustment identification method is characterized in that: The target inspection video of the target tunnel obtained through the traversal inspection is compressed to obtain a target compressed video, including: Encoding the target inspection video to obtain a target encoding unit; Extracting a first unit in the target coding unit, wherein the first unit includes a first intra-frame image; A target image sequence is constructed based on the first intra-frame image, and the target image sequence is used as the target compressed video.

3. According to claim 1, a tunnel deconstruction induced stress adjustment identification method is characterized in that: Determining whether a first feature value obtained by analyzing the first video clip meets a predetermined feature value threshold includes: Eliminating the first frame image of the first video segment to obtain a first preprocessed video segment, wherein the first preprocessed video segment includes a target frame image and a frame image sequence; Performing enhancement calibration on the frame image sequence based on the target frame image to obtain a first target enhanced image; The first eigenvalue of the first target enhanced image is obtained by analysis.

4. According to claim 3, a tunnel deconstruction induced stress adjustment identification method is characterized in that: Analyzing and obtaining the first eigenvalue of the first target enhanced image includes: Performing discrete cosine transform processing on the first target enhanced image to obtain a first target transformation result; A weighted calculation is performed on the first texture eigenvalue and the first primary color eigenvalue in the first target transformation result to obtain the first eigenvalue.

5. According to claim 1, a tunnel deconstruction induced stress adjustment identification method is characterized in that: The sensor group includes a stress sensor, a displacement sensor and a temperature and humidity sensor.

6. A tunnel deconstruction induced stress adjustment identification method according to claim 5, characterized in that: The expression of the stress evaluation function is as follows: F(σ, δ, T, H)=k1*f1(σ)+k2*f2(δ)+k3*f3(T, H); Among them, F(σ, δ, T, H) is the output of the stress evaluation function, which represents the stress evaluation value of the corresponding tunnel point. k1, k2, and k3 are weight coefficients used to adjust the importance of each sensing factor in the evaluation function. f1(σ), f2(δ), and f3(T, H) are sub-functions corresponding to stress, displacement, and temperature and humidity, respectively. The specific expressions are as follows: f1(σ)=σ / σ'; f2(δ)=δ / δ'; f3(T,H)=g(T)+h(H); Among them, σ refers to the stress monitored by the stress sensor, σ' refers to the preset stress threshold, δ refers to the displacement monitored by the displacement sensor, δ' refers to the preset displacement threshold, g(T) refers to the effect of temperature on stress, and h(H) refers to the effect of humidity on stress.

7. A tunnel deconstruction induced stress adjustment identification method according to claim 1, characterized in that: The target tunnel is subjected to stress adjustment according to the target stress distribution diagram, and the method further comprises: Determining a first adjacent region of the first tunnel point in the target stress distribution map, wherein the first adjacent region includes a first adjacent tunnel point; Obtaining a first adjacent evaluation stress of the first adjacent tunnel point; Determine whether the first adjacent evaluation stress and the first adjacent deviation of the first evaluation stress are within a predetermined support deviation threshold; if so, add the first adjacent tunnel point to a support list; if not, add the first adjacent tunnel point to a non-support list; The ratio of the number of tunnel points in the support list to the number of tunnel points in the non-support list is taken as the adjacency support index; The target stress distribution map is calibrated according to the adjacency support index.

8. A tunnel deconstruction induced stress adjustment identification method according to claim 7, characterized in that: Calibrating the target stress distribution map according to the adjacency support index includes: Determining whether the adjacency support index meets a predetermined support threshold; If it does not meet the requirements, a stress identification warning signal will be issued; Performing stress detection on the first tunnel point according to the stress identification warning signal to obtain a first detection stress; The first evaluation stress is replaced by the first detection stress to calibrate the target stress distribution map.

9. A tunnel deconstruction induced stress adjustment identification method according to claim 8, characterized in that: An ultrasonic device is activated according to the stress identification warning signal, and stress detection is performed on the first tunnel point by the ultrasonic device to obtain the first detection stress.

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