Deformation analysis method, device, equipment and storage medium for tunnel surrounding rock

Abstract

The present disclosure relates to a deformation analysis method, device, equipment and storage medium for tunnel surrounding rock. In the same deformation analysis time period, three-dimensional point cloud data of the tunnel surrounding rock collected by a three-dimensional scanner and a two-dimensional image of the tunnel surrounding rock collected by a camera are obtained; according to the image data of the two-dimensional image of the tunnel surrounding rock, it is determined whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner; if the tunnel surrounding rock is blocked or located outside the first viewing angle, the camera is controlled to adjust the second viewing angle, and a new two-dimensional image collected by the camera under the adjusted second viewing angle is obtained; based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, the deformation analysis result of the tunnel surrounding rock is determined. Therefore, when it is determined that the three-dimensional point cloud data is incomplete, the deformation analysis result of the tunnel surrounding rock is determined in combination with the new two-dimensional image and the three-dimensional point cloud data, so as to avoid erroneous deformation analysis due to incomplete three-dimensional point cloud data and improve the deformation analysis accuracy of the tunnel surrounding rock.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tunnel monitoring, and in particular to a deformation analysis method, device, equipment and storage medium for tunnel surrounding rock. Background Art

[0002] The deformation analysis of tunnel surrounding rock is an important part of tunnel monitoring engineering and is crucial to ensuring the safe operation of tunnels. During the construction and use of tunnels, due to factors such as geological conditions, construction impacts, and natural environment, the surrounding rock may deform and even pose safety hazards.

[0003] In order to perform deformation analysis on the tunnel surrounding rock, the relevant technology uses a 3D laser scanner to collect 3D point cloud data, and then compares the point cloud data obtained at different times to perform deformation analysis on the tunnel surrounding rock.

[0004] However, there may be areas that are blocked or cannot be scanned during the scanning process of the 3D laser scanner, resulting in incomplete 3D point cloud data, which in turn affects the accuracy of the deformation analysis results of the tunnel surrounding rock. Summary of the invention

[0005] In order to solve the above technical problems, the present disclosure provides a deformation analysis method, device, equipment and storage medium for tunnel surrounding rock.

[0006] In a first aspect, the present disclosure provides a method for analyzing deformation of tunnel surrounding rock, the method comprising:

[0007] In the same deformation analysis time period, the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera are obtained;

[0008] Determining, based on image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner;

[0009] If the tunnel surrounding rock is blocked or outside the first viewing angle, controlling the camera to adjust the second viewing angle, and acquiring a new two-dimensional image captured by the camera at the adjusted second viewing angle;

[0010] Based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, a deformation analysis result of the tunnel surrounding rock is determined.

[0011] In some embodiments of the present disclosure, determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on the image data of the two-dimensional image of the tunnel surrounding rock includes:

[0012] Determine the image data difference value of each adjacent pixel point in the two-dimensional image of the tunnel surrounding rock;

[0013] If the image data difference value of a preset number of adjacent pixel points is greater than a preset difference threshold, it is determined that the tunnel surrounding rock is blocked or is outside the first viewing angle of the three-dimensional scanner;

[0014] If the image data difference value of a preset number of adjacent pixel points is less than or equal to a preset difference threshold, it is determined that the tunnel surrounding rock is not blocked or is not outside the first viewing angle of the three-dimensional scanner.

[0015] In some embodiments of the present disclosure, determining the deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image includes:

[0016] Correcting the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data;

[0017] The deformation analysis result of the tunnel surrounding rock is determined based on the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data.

[0018] In some embodiments of the present disclosure, the correcting the three-dimensional point cloud data based on the new two-dimensional image to obtain the corrected three-dimensional point cloud data includes:

[0019] registering the new two-dimensional image with the three-dimensional point cloud data, and determining missing points in the three-dimensional point cloud data;

[0020] The missing points in the three-dimensional point cloud data are supplemented according to the coordinate data of the neighborhood points of the missing points in the three-dimensional point cloud data to obtain the corrected three-dimensional point cloud data.

[0021] In some embodiments of the present disclosure, the step of determining the deformation analysis result of the tunnel surrounding rock according to the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data includes:

[0022] Registering the new two-dimensional images in the different deformation analysis time periods to obtain texture difference data of the tunnel surrounding rock, and registering the corrected three-dimensional point cloud data in the different deformation analysis time periods to obtain point cloud difference data of the tunnel surrounding rock;

[0023] The texture difference data and the point cloud difference data are used as deformation analysis results of the tunnel surrounding rock.

[0024] In some embodiments of the present disclosure, it also includes:

[0025] Using a pre-trained noise recognition model, respectively perform noise recognition processing on the three-dimensional point cloud data and the two-dimensional image to determine noise points in the three-dimensional point cloud data and noise points in the two-dimensional image;

[0026] Removing noise points in the three-dimensional point cloud data and noise points in the two-dimensional image to obtain preprocessed three-dimensional point cloud data and preprocessed two-dimensional image;

[0027] Accordingly, determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on the image data of the two-dimensional image of the tunnel surrounding rock includes:

[0028] According to the image data of the preprocessed two-dimensional image of the tunnel surrounding rock, it is determined whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner.

[0029] In some embodiments of the present disclosure, it also includes:

[0030] Performing three-dimensional reconstruction on the three-dimensional point cloud data in different deformation analysis time periods to generate three-dimensional models of the tunnel surrounding rock in the different analysis time periods;

[0031] According to the different analysis time periods, the new two-dimensional image is correspondingly rendered onto the three-dimensional model to obtain a reconstructed model of the tunnel surrounding rock in different deformation analysis time periods;

[0032] Marking the deformation analysis results of the tunnel surrounding rock on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods;

[0033] The reconstructed model of the tunnel surrounding rock in the different deformation analysis time periods and the marked deformation analysis results of the tunnel surrounding rock are displayed in the actual environment of the tunnel surrounding rock in the form of virtual layers, and / or displayed in the virtual environment of the tunnel surrounding rock.

[0034] In a second aspect, the present disclosure provides a deformation analysis device for tunnel surrounding rock, the device comprising:

[0035] The first acquisition module is used to acquire the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera within the same deformation analysis time period;

[0036] An occlusion determination module, used to determine whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on image data of the two-dimensional image of the tunnel surrounding rock;

[0037] A second acquisition module, configured to control the camera to adjust a second viewing angle if the tunnel surrounding rock is blocked or outside the first viewing angle, and to acquire a new two-dimensional image captured by the camera at the adjusted second viewing angle;

[0038] The deformation analysis module is used to determine the deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image.

[0039] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the device comprising:

[0040] one or more processors;

[0041] a storage device for storing one or more programs,

[0042] When one or more programs are executed by one or more processors, the one or more processors implement the method provided in the first aspect.

[0043] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which implements the method provided in the first aspect when the computer program is executed by a processor.

[0044] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0045] The deformation analysis method, device, equipment and storage medium of the tunnel surrounding rock of the disclosed embodiment are as follows: within the same deformation analysis time period, the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera are obtained; according to the image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner is determined; if the tunnel surrounding rock is blocked or located outside the first viewing angle, the camera is controlled to adjust the second viewing angle, and a new two-dimensional image collected by the camera under the adjusted second viewing angle is obtained; based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, the deformation analysis result of the tunnel surrounding rock is determined. Thus, it is possible to analyze whether the tunnel surrounding rock is blocked or exceeds the viewing angle of the three-dimensional scanner based on the two-dimensional image collected from the tunnel surrounding rock, and then adjust the viewing angle of the camera so that the camera collects images of the area that cannot be collected by the three-dimensional scanner, and then determine the deformation analysis result of the tunnel surrounding rock in combination with the new two-dimensional image and the three-dimensional point cloud data, so as to avoid erroneous analysis of the deformation analysis result due to incomplete three-dimensional point cloud data, thereby improving the accuracy of the deformation analysis of the tunnel surrounding rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 A schematic diagram of a flow chart of a deformation analysis method for tunnel surrounding rock provided in an embodiment of the present disclosure;

[0049] Figure 2 A schematic diagram of the process of S140 provided in an embodiment of the present disclosure;

[0050] Figure 3 A schematic structural diagram of a tunnel surrounding rock deformation analysis device provided in an embodiment of the present disclosure;

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0054] In order to improve the accuracy of deformation analysis of tunnel surrounding rock, the following Figure 1 to Figure 2 The deformation analysis method of tunnel surrounding rock provided by the embodiment of the present disclosure is described. In the embodiment of the present disclosure, the deformation analysis method of tunnel surrounding rock can be executed by an electronic device or a server. The following is a specific explanation with the electronic device as the execution subject.

[0055] Figure 1 A schematic flow chart of a method for analyzing deformation of surrounding rock of a tunnel provided in an embodiment of the present disclosure is shown.

[0056] like Figure 1 As shown, the deformation analysis method of the tunnel surrounding rock may include the following steps.

[0057] S110. Within the same deformation analysis time period, three-dimensional point cloud data of the tunnel surrounding rock collected by a three-dimensional scanner and a two-dimensional image of the tunnel surrounding rock collected by a camera are obtained.

[0058] In this embodiment, in the tunnel monitoring project, it is necessary to obtain the three-dimensional point cloud data of the tunnel surrounding rock in different deformation analysis time periods, so that deformation analysis of the tunnel surrounding rock is performed based on the three-dimensional point cloud data of the tunnel surrounding rock obtained in different deformation analysis time periods. In order to avoid erroneous analysis of the deformation analysis results due to incomplete three-dimensional point cloud data, a camera is also used to collect a two-dimensional image of the tunnel surrounding rock, and then the three-dimensional point cloud data and the two-dimensional image are combined to improve the accuracy of the deformation analysis of the tunnel surrounding rock.

[0059] Specifically, within the same deformation analysis time period, the electronic device controls the three-dimensional scanner to collect three-dimensional point cloud data of different areas on the tunnel surrounding rock based on the first perspective, and at the same time, controls the camera to collect two-dimensional images of different areas on the tunnel surrounding rock based on the second perspective, and the three-dimensional point cloud data and the two-dimensional images correspond one to one in space.

[0060] In order to improve the quality of the three-dimensional point cloud data and the two-dimensional image, the three-dimensional point cloud data and the two-dimensional image may also be preprocessed. Accordingly, after S110, the method further includes: using a pre-trained noise recognition model to perform noise recognition processing on the three-dimensional point cloud data and the two-dimensional image, respectively, to determine the noise points in the three-dimensional point cloud data and the noise points in the two-dimensional image; removing the noise points in the three-dimensional point cloud data and the noise points in the two-dimensional image, to obtain the preprocessed three-dimensional point cloud data and the preprocessed two-dimensional image.

[0061] Specifically, the electronic device pre-acquires noise points that are manually identified from historical three-dimensional point cloud data and historical two-dimensional images, and uses the noise points of the historical three-dimensional point cloud data and the noise points of the historical two-dimensional images as labels, and uses the historical three-dimensional point cloud data, the historical two-dimensional images and the labels to train a neural network to obtain a noise recognition model. In this way, after the electronic device acquires the above three-dimensional point cloud data and two-dimensional images, it directly calls the pre-trained noise recognition model to identify the noise points in the three-dimensional point cloud data and the noise points in the two-dimensional image, and then removes the noise points to obtain the pre-processed three-dimensional point cloud data and the pre-processed two-dimensional image, thereby improving the quality of the three-dimensional point cloud data and the two-dimensional image.

[0062] Therefore, through automated processing and deep learning algorithms, it is possible to quickly pre-process large amounts of three-dimensional point cloud data and two-dimensional images, greatly reducing the need for manual intervention and helping to improve the efficiency of deformation analysis.

[0063] S120. Determine, based on the image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner.

[0064] It is understandable that when the tunnel surrounding rock is blocked or located outside the first viewing angle of the 3D scanner, the 3D scanner cannot collect complete 3D point cloud data of the tunnel surrounding rock. If deformation analysis is performed directly based on incomplete 3D point cloud data, the accuracy of the deformation analysis results will be affected. For this reason, the electronic device determines whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the 3D scanner by analyzing the image data of the 2D image of the tunnel surrounding rock.

[0065] For example, if a local area of ​​the tunnel surrounding rock is blocked by some equipment, the 3D scanner cannot collect the 3D point cloud data of the local area blocked by the equipment based on the first perspective; for another example, if a local area of ​​the tunnel surrounding rock is beyond the first perspective of the 3D scanner, the 3D scanner cannot collect the 3D point cloud data of the local area based on the first perspective.

[0066] In this embodiment, the specific implementation method of S120 includes but is not limited to the following methods: determining the image data difference value of each adjacent pixel point in the two-dimensional image of the tunnel surrounding rock; if the image data difference value of a preset number of adjacent pixel points is greater than a preset difference threshold, it is determined that the tunnel surrounding rock is obscured or is outside the first viewing angle of the three-dimensional scanner; if the image data difference value of a preset number of adjacent pixel points is less than or equal to the preset difference threshold, it is determined that the tunnel surrounding rock is not obscured or is not outside the first viewing angle of the three-dimensional scanner.

[0067] The adjacent pixel points may be pixel points within a four-neighborhood range or pixel points within an eight-neighborhood range.

[0068] The image data difference value may be obtained by subtracting the color values ​​of adjacent pixels, or by subtracting the texture values ​​of adjacent pixels.

[0069] It is understandable that if the electronic device determines that the image data difference value of a preset number of adjacent pixel points is greater than the preset difference threshold, it means that the difference between adjacent pixel points in the two-dimensional image is large, and this situation may be that a local area of ​​the tunnel surrounding rock is blocked, or a local area of ​​the tunnel surrounding rock is beyond the first viewing angle of the three-dimensional scanner. On the contrary, if the electronic device determines that the image data difference value of a preset number of adjacent pixel points is less than the preset difference threshold, it means that the difference between adjacent pixel points in the two-dimensional image is not large, and each area of ​​the tunnel surrounding rock is not blocked and does not exceed the first viewing angle of the three-dimensional scanner.

[0070] In other cases, after using the image data difference value of each adjacent pixel point in the two-dimensional image to determine whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner, it can also be verified in combination with the three-dimensional point cloud data. Accordingly, the method also includes: based on the coordinate data of the three-dimensional scanning data, determining whether there are mutation points or missing points in the three-dimensional scanning data; if the image data difference value of a preset number of adjacent pixel points is greater than the preset difference threshold, and there are mutation points or missing points in the three-dimensional scanning data, it is determined that the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner; on the contrary, if the image data difference value of a preset number of adjacent pixel points is less than the preset difference threshold, and there are no mutation points or missing points in the three-dimensional scanning data, it is determined that the tunnel surrounding rock is not blocked or is not located outside the first viewing angle of the three-dimensional scanner.

[0071] It is understandable that if the electronic device performs a preprocessing operation after acquiring the three-dimensional point cloud data and the two-dimensional image, it is necessary to determine whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on the image data of the preprocessed two-dimensional image of the tunnel surrounding rock. The specific method of determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner is similar to the principle described above and will not be described in detail here.

[0072] In this way, before performing deformation analysis on the tunnel surrounding rock, the texture values ​​or color values ​​of adjacent pixels in the two-dimensional image are combined to detect whether the tunnel surrounding rock is blocked or outside the first viewing angle of the three-dimensional scanner, or whether there are mutation points or missing points in the three-dimensional scanning data to assist in judgment, thereby facilitating appropriate adjustments in subsequent operations.

[0073] S130: If the tunnel surrounding rock is blocked or outside the first viewing angle, control the camera to adjust the second viewing angle, and obtain a new two-dimensional image captured by the camera at the adjusted second viewing angle.

[0074] In this embodiment, after determining that the tunnel surrounding rock is blocked or outside the first viewing angle, the electronic device can determine the blocked area or the area beyond the first viewing angle, and then, based on the blocked area or the area beyond the first viewing angle, control the camera to adjust the second viewing angle so that the camera captures a new two-dimensional image under the adjusted second viewing angle, and the electronic device obtains the new two-dimensional image.

[0075] It should be noted that when the tunnel surrounding rock is blocked or outside the first perspective, the three-dimensional scanning data collected by the three-dimensional scanner and the two-dimensional image collected by the camera are incomplete, and the first perspective of the three-dimensional scanner is not easy to adjust. Only the second perspective of the camera can be adjusted so that the camera can collect images of the area that the three-dimensional scanner cannot collect at the adjusted second perspective, thereby obtaining a complete two-dimensional image of the tunnel surrounding rock.

[0076] S140. Determine deformation analysis results of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image.

[0077] Since the new two-dimensional image is a complete two-dimensional image of the tunnel surrounding rock, although the three-dimensional point cloud data is not complete point cloud data, by combining the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, it can also compensate for the influence of the incomplete three-dimensional point cloud data on the deformation analysis results to a certain extent. Compared with the method in the prior art that only performs typing analysis based on incomplete three-dimensional point cloud data, the accuracy of deformation analysis of the tunnel surrounding rock is improved.

[0078] Among them, the deformation analysis results of the tunnel surrounding rock can include coordinate difference results and texture difference results.

[0079] It is understandable that if the electronic device performs a preprocessing operation after acquiring the three-dimensional point cloud data and the two-dimensional image, it is necessary to determine the deformation analysis results of the tunnel surrounding rock based on the preprocessed three-dimensional point cloud data and the new two-dimensional image of the tunnel surrounding rock.

[0080] A deformation analysis method for tunnel surrounding rock of the disclosed embodiment, in the same deformation analysis time period, obtains three-dimensional point cloud data of tunnel surrounding rock collected by a three-dimensional scanner and two-dimensional image of tunnel surrounding rock collected by a camera; determines whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner according to the image data of the two-dimensional image of the tunnel surrounding rock; if the tunnel surrounding rock is blocked or located outside the first viewing angle, controls the camera to adjust the second viewing angle, and obtains a new two-dimensional image collected by the camera under the adjusted second viewing angle; determines the deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image. Thus, it is possible to analyze whether the tunnel surrounding rock is blocked or exceeds the viewing angle of the three-dimensional scanner based on the two-dimensional image collected from the tunnel surrounding rock, and then adjust the viewing angle of the camera so that the camera collects images of the area that cannot be collected by the three-dimensional scanner, and then determine the deformation analysis result of the tunnel surrounding rock in combination with the new two-dimensional image and the three-dimensional point cloud data, so as to avoid erroneous analysis of the deformation analysis result due to incomplete three-dimensional point cloud data, thereby improving the accuracy of deformation analysis of the tunnel surrounding rock.

[0081] In another embodiment of the present disclosure, a process of determining the deformation analysis result of the tunnel surrounding rock is explained in detail.

[0082] Figure 2 A schematic diagram of the process of S140 provided in an embodiment of the present disclosure is shown.

[0083] like Figure 2 As shown, the specific implementation of S140 may include the following steps.

[0084] S210 , correcting the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data.

[0085] In order to make the new two-dimensional image and the three-dimensional point cloud data have spatial consistency, the new two-dimensional image is used to fill in the missing points in the three-dimensional point cloud data, so as to correct the three-dimensional point cloud data and obtain the corrected three-dimensional point cloud data.

[0086] In this embodiment, the specific implementation method of S210 includes but is not limited to the following methods: aligning the new two-dimensional image and the three-dimensional point cloud data to determine the missing points in the three-dimensional point cloud data; filling the missing points in the three-dimensional data according to the coordinate data of the neighborhood points of the missing points in the three-dimensional point cloud data to obtain corrected three-dimensional point cloud data.

[0087] Specifically, a preset feature matching algorithm (such as SIFT and SURF) is used to align the new two-dimensional image and the three-dimensional point cloud data to ensure that the new two-dimensional image and the three-dimensional point cloud data have spatial consistency. Then, points that cannot be matched with the pixel points of the new two-dimensional image are obtained from the three-dimensional point cloud data as missing points, and the coordinate data of the neighborhood points of the missing points are obtained. Then, the coordinate data of the neighborhood points of the missing points are averaged as the coordinate data of the missing points, and the missing points are filled in according to the coordinate data of the missing points to obtain the corrected three-dimensional point cloud data.

[0088] Optionally, the neighborhood points of the missing point may be four neighborhood points of the missing point, or eight neighborhood points of the missing point, which is not limited here.

[0089] In this way, by aligning the new two-dimensional image and the three-dimensional point cloud data, the three-dimensional point cloud data can be effectively fused with the new two-dimensional data to ensure the spatial consistency of the two, and the three-dimensional point cloud data can be completed based on the matching results, thereby improving the integrity and comprehensiveness of the three-dimensional point cloud data and providing a data basis for subsequent deformation analysis.

[0090] S220. Determine deformation analysis results of the tunnel surrounding rock based on new two-dimensional images and corrected three-dimensional point cloud data within different deformation analysis time periods.

[0091] In this embodiment, the specific implementation method of S220 includes but is not limited to the following methods: aligning the new two-dimensional images within different deformation analysis time periods to obtain texture difference data of the tunnel surrounding rock, and aligning the corrected three-dimensional point cloud data within different deformation analysis time periods to obtain point cloud difference data of the tunnel surrounding rock; using the texture difference data and the point cloud difference data as the deformation analysis results of the tunnel surrounding rock.

[0092] Specifically, a preset registration algorithm (such as an iterative nearest point algorithm) or a three-dimensional matching algorithm is used to register the corrected three-dimensional point cloud data within different deformation analysis time periods to calculate the difference data between the point clouds within different deformation analysis time periods, and the difference data is used as the point cloud difference data of the tunnel surrounding rock, thereby obtaining the deformation of the tunnel surrounding rock. At the same time, a preset registration algorithm (such as an iterative nearest point algorithm) is used to register new two-dimensional images within different deformation analysis time periods to calculate the difference data between the images within different deformation analysis time periods, and the difference data is used as the texture difference data of the tunnel surrounding rock. Then, the texture difference data of the tunnel surrounding rock and the difference between the point cloud data are used as the deformation analysis result of the tunnel surrounding rock.

[0093] In this way, the preset registration algorithm is used to register the point cloud data and images in different deformation analysis time periods respectively to find the point cloud difference data and texture difference data. Therefore, the deformation of the tunnel surrounding rock can be analyzed more accurately, providing reliable data support for the safe operation of the tunnel.

[0094] In yet another embodiment of the present disclosure, in order to improve user experience, the deformation analysis results of the tunnel surrounding rock can also be intuitively displayed in a three-dimensional model of the tunnel surrounding rock to facilitate user analysis and understanding.

[0095] In the embodiment of the present disclosure, after executing S140, the method further includes:

[0096] S150, performing three-dimensional reconstruction on the three-dimensional point cloud data in different deformation analysis time periods to generate three-dimensional models of the tunnel surrounding rock in different analysis time periods.

[0097] In this embodiment, the electronic device uses 3D reconstruction technology to reconstruct the 3D point cloud data within different deformation analysis time periods to obtain high-precision models within different deformation analysis time periods as the 3D models of the tunnel surrounding rock in different analysis time periods.

[0098] S160, rendering the new two-dimensional image onto the three-dimensional model according to different analysis time periods, to obtain a reconstructed model of the tunnel surrounding rock in different deformation analysis time periods.

[0099] In order to obtain a highly realistic three-dimensional model of the tunnel surrounding rock, the new two-dimensional images within the same analysis time period are rendered onto the three-dimensional model accordingly, so that the three-dimensional models of different analysis time periods have image data such as texture data and color data, and a highly realistic three-dimensional model is obtained as the reconstructed model of the tunnel surrounding rock within different deformation analysis time periods.

[0100] S170, marking the deformation analysis results of the tunnel surrounding rock on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods.

[0101] In this embodiment, since the reconstructed model is obtained by three-dimensional modeling and reconstruction of the three-dimensional point cloud data and the new two-dimensional image, and the deformation analysis results of the tunnel surrounding rock are determined by aligning the three-dimensional point cloud data and the new two-dimensional image of the tunnel surrounding rock, the electronic device can mark the deformation analysis results of the tunnel surrounding rock on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods based on the coordinate data of each point in the three-dimensional point cloud data and the two-dimensional image, so that the reconstructed model of the tunnel surrounding rock carries the deformation analysis results.

[0102] S180, displaying the reconstructed model of the tunnel surrounding rock and the marked deformation analysis results of the tunnel surrounding rock in different deformation analysis time periods in the form of virtual layers in the actual environment of the tunnel surrounding rock and / or in the virtual environment of the tunnel surrounding rock.

[0103] In this embodiment, in order to improve the user experience, the electronic device can use augmented reality technology (AR technology for short) to display the reconstructed model of the tunnel surrounding rock and the deformation analysis results of the marked tunnel surrounding rock in different deformation analysis time periods in the actual environment of the tunnel surrounding rock in the form of a virtual layer, so that the user can directly observe and analyze the deformation of different areas on the tunnel surrounding rock through AR glasses, without relying on additional physical models or two-dimensional images for auxiliary observation. In addition, the electronic device can use virtual reality technology (VR technology for short) to display the reconstructed model of the tunnel surrounding rock and the deformation analysis results of the marked tunnel surrounding rock in different deformation analysis time periods in the virtual environment of the tunnel surrounding rock, providing users with an immersive experience, so that users can enter the virtual tunnel environment by wearing VR glasses to view the deformation of different parts.

[0104] In order to provide a long-term stability assessment function, after executing S160, the method also includes: performing spatial analysis and risk analysis on the deformation analysis results of the tunnel surrounding rock according to the geological conditions of the tunnel surrounding rock in different deformation analysis time periods, and obtaining the spatial analysis results and risk analysis results of the tunnel surrounding rock in different deformation analysis time periods; marking the spatial analysis results and the risk analysis results accordingly on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods; accordingly, S180 specifically includes: displaying the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods, the marked deformation analysis results of the tunnel surrounding rock, the marked spatial analysis results and the risk analysis results in the form of virtual layers in the actual environment of the tunnel surrounding rock, and / or, displaying them in the virtual environment of the tunnel surrounding rock.

[0105] Specifically, the electronic device runs professional software with spatial analysis and risk analysis functions (such as GIS software Cesium), combines the geological conditions of the tunnel surrounding rock in different deformation analysis time periods, determines the spatial analysis results and risk analysis results of the tunnel surrounding rock in different deformation analysis time periods, and then marks the spatial analysis results and risk analysis results on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods based on the three-dimensional point cloud data and the coordinate data of each point in the two-dimensional image, thereby obtaining a reconstructed model of the tunnel surrounding rock in different deformation analysis time periods marked with the deformation analysis results, spatial analysis results and risk analysis results, and uses the marking information on the reconstructed model as the original chamber structure, which is displayed in the actual environment of the tunnel surrounding rock in the form of a virtual layer, and / or displayed in the virtual environment of the tunnel surrounding rock.

[0106] Therefore, using 3D visualization technology, the deformation analysis results, spatial analysis results, and risk analysis results are intuitively displayed on the reconstructed model, which is convenient for users to understand and analyze. At the same time, combined with virtual reality technology and augmented reality technology, it is convenient for users to enter the virtual tunnel environment to view and analyze the deformation analysis results, spatial analysis results, and risk analysis results in different areas, thereby improving the user experience and achieving real-time dynamic monitoring and early warning. In addition, the deformation analysis results, spatial analysis results, and risk analysis results can provide a research basis for the safe operation and management of the tunnel, facilitate users to access and query when conducting tunnel research, and improve the efficiency of data utilization.

[0107] The embodiment of the present disclosure also provides a tunnel surrounding rock deformation analysis device for implementing the above tunnel surrounding rock deformation analysis method. Figure 3 In the disclosed embodiment, the tunnel surrounding rock deformation analysis device may be an electronic device or a server.

[0108] Figure 3 A structural schematic diagram of a tunnel surrounding rock deformation analysis device provided by an embodiment of the present disclosure is shown.

[0109] like Figure 3 As shown, the tunnel surrounding rock deformation analysis device 300 may include:

[0110] The first acquisition module 310 is used to acquire the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera within the same deformation analysis time period;

[0111] An occlusion determination module 320, for determining whether the tunnel surrounding rock is occluded or located outside the first viewing angle of the three-dimensional scanner according to the image data of the two-dimensional image of the tunnel surrounding rock;

[0112] A second acquisition module 330 is used to control the camera to adjust a second viewing angle if the tunnel surrounding rock is blocked or outside the first viewing angle, and to acquire a new two-dimensional image captured by the camera under the adjusted second viewing angle;

[0113] The deformation analysis module 340 is used to determine the deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image.

[0114] A deformation analysis device for tunnel surrounding rock of the disclosed embodiment obtains three-dimensional point cloud data of tunnel surrounding rock collected by a three-dimensional scanner and two-dimensional image of tunnel surrounding rock collected by a camera in the same deformation analysis time period; determines whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner according to the image data of the two-dimensional image of the tunnel surrounding rock; if the tunnel surrounding rock is blocked or located outside the first viewing angle, controls the camera to adjust the second viewing angle, and obtains a new two-dimensional image collected by the camera under the adjusted second viewing angle; determines the deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image. Thus, it is possible to analyze whether the tunnel surrounding rock is blocked or exceeds the viewing angle of the three-dimensional scanner based on the two-dimensional image collected from the tunnel surrounding rock, and then adjust the viewing angle of the camera so that the camera collects images of the area that cannot be collected by the three-dimensional scanner, and then determine the deformation analysis result of the tunnel surrounding rock in combination with the new two-dimensional image and the three-dimensional point cloud data, thereby avoiding erroneous analysis of the deformation analysis result due to incomplete three-dimensional point cloud data, thereby improving the accuracy of deformation analysis of the tunnel surrounding rock.

[0115] In some embodiments of the present disclosure, the occlusion determination module 320 includes:

[0116] A first determining unit is used to determine the image data difference value of each adjacent pixel point in the two-dimensional image of the tunnel surrounding rock;

[0117] A second determination unit is configured to determine that the tunnel surrounding rock is blocked or is outside the first viewing angle of the three-dimensional scanner if a difference value of image data of a preset number of adjacent pixel points is greater than a preset difference threshold;

[0118] The third determination unit is used to determine that the tunnel surrounding rock is not blocked or is not outside the first viewing angle of the three-dimensional scanner if the image data difference value of a preset number of adjacent pixel points is less than or equal to a preset difference threshold.

[0119] In some embodiments of the present disclosure, the deformation analysis module 340 includes:

[0120] A correction unit, configured to correct the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data;

[0121] The fourth determination unit is used to determine the deformation analysis result of the tunnel surrounding rock according to the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data.

[0122] In some embodiments of the present disclosure, the correction unit is specifically configured to:

[0123] registering the new two-dimensional image with the three-dimensional point cloud data, and determining missing points in the three-dimensional point cloud data;

[0124] The missing points in the three-dimensional point cloud data are supplemented according to the coordinate data of the neighborhood points of the missing points in the three-dimensional point cloud data to obtain the corrected three-dimensional point cloud data.

[0125] In some embodiments of the present disclosure, the fourth determining unit is specifically configured to:

[0126] Registering the new two-dimensional images in the different deformation analysis time periods to obtain texture difference data of the tunnel surrounding rock, and registering the corrected three-dimensional point cloud data in the different deformation analysis time periods to obtain point cloud difference data of the tunnel surrounding rock;

[0127] The texture difference data and the point cloud difference data are used as deformation analysis results of the tunnel surrounding rock.

[0128] In some embodiments of the present disclosure, the device further includes:

[0129] A first determination module is used to perform noise recognition processing on the three-dimensional point cloud data and the two-dimensional image respectively by using a pre-trained noise recognition model to determine noise points in the three-dimensional point cloud data and noise points in the two-dimensional image;

[0130] A removal module, used to remove noise points in the three-dimensional point cloud data and noise points in the two-dimensional image to obtain pre-processed three-dimensional point cloud data and pre-processed two-dimensional image;

[0131] Accordingly, the occlusion determination module 320 is specifically configured to:

[0132] Determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner according to the image data of the preprocessed two-dimensional image of the tunnel surrounding rock;

[0133] Accordingly, the deformation analysis module 340 is specifically used for:

[0134] Based on the preprocessed three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, a deformation analysis result of the tunnel surrounding rock is determined.

[0135] It should be noted that Figure 3 The tunnel surrounding rock deformation analysis device 300 shown can perform Figure 1 to Figure 2 The various steps in the method embodiment shown in the figure are implemented Figure 1 to Figure 2 The various processes and effects in the method embodiment shown are not described in detail here.

[0136] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown.

[0137] like Figure 4 As shown, the electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0138] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0139] The memory 402 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 402 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 402 is a non-volatile solid-state memory. In a particular embodiment, the memory 402 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0140] The processor 401 reads and executes the computer program instructions stored in the memory 402 to perform the steps of the deformation analysis method of the tunnel surrounding rock provided in the embodiment of the present disclosure.

[0141] In one example, the electronic device may further include a transceiver 403 and a bus 404. Figure 4 As shown, the processor 401, the memory 402 and the transceiver 403 are connected via a bus 404 and communicate with each other.

[0142] The bus 404 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 404 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0143] The following is an embodiment of a computer-readable storage medium provided in an embodiment of the present disclosure. The computer-readable storage medium and the deformation analysis method for tunnel surrounding rock in the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the computer-readable storage medium, reference can be made to the embodiment of the deformation analysis method for tunnel surrounding rock in the above-mentioned embodiments.

[0144] This embodiment provides a storage medium containing computer executable instructions. When the computer executable instructions are executed by a computer processor, they are used to perform a deformation analysis method for tunnel surrounding rock. The method includes:

[0145] In the same deformation analysis time period, the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera are obtained;

[0146] Determining, based on image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner;

[0147] If the tunnel surrounding rock is blocked or outside the first viewing angle, controlling the camera to adjust the second viewing angle, and acquiring a new two-dimensional image captured by the camera at the adjusted second viewing angle;

[0148] Based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, a deformation analysis result of the tunnel surrounding rock is determined.

[0149] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present disclosure is not limited to the above method operations, and the computer executable instructions can also execute related operations in the deformation analysis method of tunnel surrounding rock provided in any embodiment of the present disclosure.

[0150] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present disclosure can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions to enable a computer cloud platform (which can be a personal computer, server, or network cloud platform, etc.) to execute the deformation analysis method of tunnel surrounding rock provided by each embodiment of the present disclosure.

[0151] Note that the above are only preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art will understand that the present disclosure is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present disclosure. Therefore, although the present disclosure is described in more detail through the above embodiments, the present disclosure is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present disclosure, and the scope of the present disclosure is determined by the scope of the appended claims.

Claims

1. A method for analyzing deformation of tunnel surrounding rock, characterized in that: include: In the same deformation analysis time period, the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera are obtained; Determining, based on image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner; If the tunnel surrounding rock is blocked or outside the first viewing angle, controlling the camera to adjust the second viewing angle, and acquiring a new two-dimensional image captured by the camera at the adjusted second viewing angle; Determine deformation analysis results of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image; The step of determining deformation analysis results of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image includes: Correcting the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data; Determine the deformation analysis result of the tunnel surrounding rock according to the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data; The step of correcting the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data includes: registering the new two-dimensional image with the three-dimensional point cloud data, and determining missing points in the three-dimensional point cloud data; According to the coordinate data of the neighboring points of the missing points in the three-dimensional point cloud data, the missing points are filled in the three-dimensional point cloud data to obtain the corrected three-dimensional point cloud data; Determining the deformation analysis result of the tunnel surrounding rock according to the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data includes: Registering the new two-dimensional images in the different deformation analysis time periods to obtain texture difference data of the tunnel surrounding rock, and registering the corrected three-dimensional point cloud data in the different deformation analysis time periods to obtain point cloud difference data of the tunnel surrounding rock, wherein the texture difference data of the tunnel surrounding rock is difference data between the new two-dimensional images in the different deformation analysis time periods; The texture difference data and the point cloud difference data are used as deformation analysis results of the tunnel surrounding rock.

2. The method according to claim 1, characterized in that The determining, based on the image data of the two-dimensional image of the tunnel surrounding rock, whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner comprises: Determine the image data difference value of each adjacent pixel point in the two-dimensional image of the tunnel surrounding rock; If the image data difference value of a preset number of adjacent pixel points is greater than a preset difference threshold, it is determined that the tunnel surrounding rock is blocked or is outside the first viewing angle of the three-dimensional scanner; If the image data difference value of a preset number of adjacent pixel points is less than or equal to a preset difference threshold, it is determined that the tunnel surrounding rock is not blocked or is not outside the first viewing angle of the three-dimensional scanner.

3. The method according to claim 1, characterized in that Also includes: Using a pre-trained noise recognition model, respectively perform noise recognition processing on the three-dimensional point cloud data and the two-dimensional image to determine noise points in the three-dimensional point cloud data and noise points in the two-dimensional image; Removing noise points in the three-dimensional point cloud data and noise points in the two-dimensional image to obtain preprocessed three-dimensional point cloud data and preprocessed two-dimensional image; Accordingly, determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on the image data of the two-dimensional image of the tunnel surrounding rock includes: Determining whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner according to the image data of the preprocessed two-dimensional image of the tunnel surrounding rock; Accordingly, the deformation analysis result of the tunnel surrounding rock is determined based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, including: Based on the preprocessed three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image, a deformation analysis result of the tunnel surrounding rock is determined.

4. The method according to claim 1, characterized in that Also includes: Performing three-dimensional reconstruction on the three-dimensional point cloud data in different deformation analysis time periods to generate three-dimensional models of the tunnel surrounding rock in the different deformation analysis time periods; According to the different analysis time periods, the new two-dimensional image is correspondingly rendered onto the three-dimensional model to obtain a reconstructed model of the tunnel surrounding rock in different deformation analysis time periods; Marking the deformation analysis results of the tunnel surrounding rock on the reconstructed model of the tunnel surrounding rock in different deformation analysis time periods; The reconstructed model of the tunnel surrounding rock in the different deformation analysis time periods and the marked deformation analysis results of the tunnel surrounding rock are displayed in the actual environment of the tunnel surrounding rock in the form of virtual layers, and / or displayed in the virtual environment of the tunnel surrounding rock.

5. A deformation analysis device for tunnel surrounding rock, characterized in that: include: The first acquisition module is used to acquire the three-dimensional point cloud data of the tunnel surrounding rock collected by the three-dimensional scanner and the two-dimensional image of the tunnel surrounding rock collected by the camera within the same deformation analysis time period; An occlusion determination module, used to determine whether the tunnel surrounding rock is blocked or located outside the first viewing angle of the three-dimensional scanner based on image data of the two-dimensional image of the tunnel surrounding rock; A second acquisition module, configured to control the camera to adjust a second viewing angle if the tunnel surrounding rock is blocked or outside the first viewing angle, and to acquire a new two-dimensional image captured by the camera at the adjusted second viewing angle; A deformation analysis module, used to determine a deformation analysis result of the tunnel surrounding rock based on the three-dimensional point cloud data of the tunnel surrounding rock and the new two-dimensional image; The deformation analysis module comprises: A correction unit, configured to correct the three-dimensional point cloud data based on the new two-dimensional image to obtain corrected three-dimensional point cloud data; A fourth determination unit, used to determine the deformation analysis result of the tunnel surrounding rock according to the new two-dimensional images in different deformation analysis time periods and the corrected three-dimensional point cloud data; The correction unit is specifically used for: registering the new two-dimensional image with the three-dimensional point cloud data, and determining missing points in the three-dimensional point cloud data; According to the coordinate data of the neighboring points of the missing points in the three-dimensional point cloud data, the missing points are filled in the three-dimensional point cloud data to obtain the corrected three-dimensional point cloud data; The fourth determining unit is specifically configured to: Registering the new two-dimensional images in the different deformation analysis time periods to obtain texture difference data of the tunnel surrounding rock, and registering the corrected three-dimensional point cloud data in the different deformation analysis time periods to obtain point cloud difference data of the tunnel surrounding rock, wherein the texture difference data of the tunnel surrounding rock is difference data between the new two-dimensional images in the different deformation analysis time periods; The texture difference data and the point cloud difference data are used as deformation analysis results of the tunnel surrounding rock.

6. An electronic device, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method according to any one of claims 1 to 4.

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