Roadway deformation detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a roadway deformation detection method and device, electronic equipment and a storage medium. The method comprises the following steps: performing gridding processing on original point cloud data of a target area to obtain multiple pieces of to-be-processed point cloud data located in the same grid area, and determining grid center point cloud data according to the multiple pieces of to-be-processed point cloud data; wherein the target area comprises a roadway to be detected; for the same grid region, determining type information of the current grid region according to the grid center point cloud data of the current grid region and predetermined positioning point cloud data; taking the to-be-processed point cloud data in the grid region of which the type information is the preset type as deformation point cloud data, and determining deformation quantity data corresponding to each roadway part of the to-be-detected roadway according to the deformation point cloud data and a pre-created corresponding relationship; and according to the deformation quantity data, determining the target deformation information of the to-be-detected roadway, thereby realizing efficient and accurate detection of the roadway deformation information.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a tunnel deformation detection method, device, electronic equipment and storage medium. Background Art

[0002] During the mining process of underground projects such as coal mines, the rocks around the tunnels will be deformed due to the mining activities. If these deformations cannot be detected and handled in time, they may lead to disasters such as tunnel collapse. In order to ensure the safe production of underground projects, it is necessary to detect tunnel deformation.

[0003] The existing tunnel deformation detection methods are mainly displacement data detection methods represented by total stations, digital convergence meters, and roof separation meters, and stress data detection methods represented by borehole stress meters. However, the above technologies usually require pre-setting points on the tunnel surface, and relevant personnel patrol to determine the relative displacement or stress changes between fixed points at different times, so as to determine the degree of deformation, and cannot intuitively and accurately detect the overall changes of the tunnel. Summary of the invention

[0004] The present invention provides a tunnel deformation detection method, device, electronic equipment and storage medium, which realizes efficient and accurate detection of tunnel deformation information, so that relevant personnel can intuitively determine the deformation condition of the tunnel to be detected.

[0005] According to one aspect of the present invention, a method for detecting tunnel deformation is provided, the method comprising:

[0006] By gridding the original point cloud data of the target area, a plurality of point cloud data to be processed located in the same grid area are obtained, and the grid center point cloud data is determined according to the plurality of point cloud data to be processed associated with each grid area; wherein the target area includes the lane to be detected;

[0007] For the same grid area, the type information of the current grid area is determined according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in the initial state;

[0008] The point cloud data to be processed in the grid area whose type information is a preset type is used as the deformed point cloud data, and the deformation amount data corresponding to each lane part of the lane to be detected is determined according to the deformed point cloud data and the pre-created corresponding relationship; wherein the corresponding relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data;

[0009] According to the deformation data, the target deformation information of the lane to be inspected is determined.

[0010] According to another aspect of the present invention, a tunnel deformation detection device is provided, the device comprising:

[0011] The point cloud data acquisition module is used to obtain a plurality of to-be-processed point cloud data located in the same grid area by gridding the original point cloud data of the target area, and determine the grid center point cloud data according to the plurality of to-be-processed point cloud data associated with each grid area; wherein the target area includes the lane to be detected;

[0012] The type information determination module is used to determine the type information of the current grid area according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data for the same grid area, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state;

[0013] A deformation variable data determination module is used to use the to-be-processed point cloud data in the grid area whose type information is a preset type as deformation point cloud data, and determine the deformation variable data corresponding to each lane part of the lane to be detected based on the deformation point cloud data and the pre-created correspondence relationship; wherein the correspondence relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data;

[0014] The deformation information determination module is used to determine the target deformation information of the lane to be inspected based on the deformation amount data.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the tunnel deformation detection method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the tunnel deformation detection method of any embodiment of the present invention when executed by a processor.

[0020] According to another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein the computer program, when executed by a processor, implements a method for detecting deformation of a tunnel according to any embodiment of the present invention.

[0021] The technical solution of the embodiment of the present invention obtains multiple to-be-processed point cloud data located in the same grid area by gridding the original point cloud data of the target area, and determines the grid center point cloud data according to the multiple to-be-processed point cloud data associated with each grid area. Based on this, representative point cloud data is obtained, which is convenient for the subsequent determination of the type information of the grid area. For the same grid area, the type information of the current grid area is determined according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, so as to determine different deformation variable data according to different type information, so as to obtain more accurate deformation variable data. The to-be-processed point cloud data in the grid area with the type information of the preset type is used as the deformation point cloud data, and the deformation variable data corresponding to each lane part of the lane to be detected is determined according to the deformation point cloud data and the pre-created correspondence, and the target deformation information of the lane to be detected is determined according to the deformation variable data. It solves the problem in the prior art that tunnel deformation inspection relies on manual labor and corresponding equipment, which consumes a lot of manpower and material resources and cannot intuitively and accurately detect the overall deformation of the tunnel. It realizes efficient and accurate detection of tunnel deformation information, which not only saves manpower and material costs, but also enables relevant personnel to intuitively determine the deformation of the tunnel to be inspected and take disaster prevention measures in advance.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a lane deformation detection method provided by an embodiment of the present invention;

[0025] Figure 2 is an example diagram of a cross-sectional profile of a lane to be inspected provided by an embodiment of the present invention;

[0026] Figure 3 is an example diagram of deformation corresponding to the lane to be detected provided by an embodiment of the present invention;

[0027] Figure 4 is a flow chart of a method for determining a corresponding relationship provided by an embodiment of the present invention;

[0028] Figure 5It is a structural schematic diagram of a tunnel deformation detection device provided by an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the lane deformation detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a tunnel deformation detection method provided by the first embodiment of the present invention. This embodiment is applicable to the case of non-contact continuous deformation detection of tunnels in underground engineering. The method can be executed by a tunnel deformation detection device. The tunnel deformation detection device can be implemented in the form of hardware and / or software. The tunnel deformation detection device can be configured in electronic devices such as mobile phones, computers or servers. Figure 1 As shown, the method includes:

[0034] S110, by gridding the original point cloud data of the target area, a plurality of point cloud data to be processed located in the same grid area are obtained, and the grid center point cloud data is determined according to the plurality of point cloud data to be processed associated with each grid area; wherein the target area includes the lane to be inspected.

[0035] Among them, the target area can be a three-dimensional space area including the lane to be detected. The lane to be detected can be the lane area currently used to determine whether deformation occurs. The original point cloud data can be the three-dimensional point cloud data corresponding to the target area obtained by using three-dimensional scanning technology. The point cloud data to be processed can be the point cloud data located in the same grid area in the original point cloud data. The grid center data can be the point cloud data corresponding to the grid center point. The grid center data can represent multiple point cloud data in the same grid area. For example, if there are N point cloud data to be processed in the same grid area, then a point cloud data located at the center point of the grid area can be fitted based on these N point cloud data to be processed, that is, the grid center point cloud data.

[0036] Specifically, the target area including the lane to be inspected is scanned and processed to obtain the original point cloud data corresponding to the target area. The division size corresponding to the virtual three-dimensional grid is set according to the measurement accuracy. The original point cloud data is gridded according to the division size to obtain multiple point cloud data to be processed in each grid area. The multiple point cloud data associated with each grid area are fitted to obtain the grid center point cloud data without affecting the measurement accuracy. By using the grid center point cloud data to represent the multiple point cloud data to be processed associated with the current grid area, the calculation speed of subsequent processing can be improved and computing power can be saved.

[0037] S120. For the same grid area, the type information of the current grid area is determined according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state.

[0038] Among them, the positioning point cloud data and the grid center point cloud data are both the center point cloud data obtained after processing the point cloud data obtained by scanning the lane to be inspected at different times in the same coordinate system. Optionally, the positioning point cloud data is used as the reference point cloud data or the standard point cloud data to determine whether the point cloud data to be processed in the current grid area is deformed based on the positioning point cloud data. The type information can be understood as the type of the current grid area. Optionally, if the point cloud data to be processed in the current grid area is deformed, the current grid area can be a deformed grid area. If the point cloud data to be processed in the current grid area is not deformed, the current grid area can be a normal grid area.

[0039] Specifically, a three-dimensional scanning process is performed on the lane to be inspected in the target area in the initial state to obtain multiple point cloud data. The multiple point cloud data are gridded, and the positioning point cloud data corresponding to the point cloud data in the same grid area is determined without affecting the measurement accuracy. The distance between the positioning point cloud data and the grid center point cloud data is determined. If the data corresponding to the distance exceeds the preset point cloud data distance threshold, it means that the current grid area is a deformed grid area, that is, the point cloud data to be processed in the current grid area has been deformed. Correspondingly, if the data corresponding to the distance does not exceed the preset point cloud data distance threshold, it means that the current grid area is a normal grid area, that is, the point cloud data to be processed in the current grid area has not been deformed.

[0040] Optionally, the grid center point cloud data and the positioning point cloud data are determined based on the following method: clustering multiple point cloud data in the same grid area to obtain the cluster center point of the grid area; and using the point cloud data corresponding to the cluster center as the grid center point cloud data or the positioning point cloud data of the grid area.

[0041] The cluster center point may be a center point used to represent multiple point cloud data in the same grid area. Optionally, the cluster center point may be obtained by a preset clustering algorithm. For example, the preset clustering algorithm may be at least one of a K-means clustering algorithm, a hierarchical clustering algorithm, and a Gaussian mixture model clustering algorithm.

[0042] Specifically, a plurality of point cloud data to be processed in the same grid area are clustered according to a preset clustering algorithm to obtain a cluster center point of the current grid area. The cluster center point of the current grid area is used as the grid center point cloud data. Correspondingly, the point cloud data of the lane to be detected in the target area is determined when the lane to be detected is in an initial state. These point cloud data are gridded, and the point cloud data in the same grid are clustered to obtain a cluster center point of the current grid area. The cluster center point is used as the positioning point cloud data. By clustering the corresponding grid center point cloud data and positioning point cloud data, the calculation can be simplified, and the type information of the grid area and whether the point cloud data in the grid area is deformed can be directly determined by using the grid center point cloud data and the positioning point cloud data.

[0043] S130. The point cloud data to be processed in the grid area with type information of a preset type is used as the deformed point cloud data, and the deformation amount data corresponding to each lane part of the lane to be inspected is determined based on the deformed point cloud data and the pre-created correspondence relationship; wherein the correspondence relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data.

[0044] Among them, the preset type may be a preset grid area type. The point cloud data to be processed in the grid area of ​​the preset type is deformed. For example, in combination with the above example, the preset type may be a deformable grid area. The deformed point cloud data may be the point cloud data to be processed in the grid area of ​​the preset type. The corresponding relationship may be the relationship between the lane portion of the lane to be detected and the position information of the three-dimensional point cloud data. The three-dimensional point cloud data in the corresponding relationship may be understood as pre-acquired, standard point cloud data or reference point cloud data. The deformation variable data corresponding to the deformed point cloud data may be determined by the three-dimensional point cloud data in the corresponding relationship. The lane portion may be divided into the top portion of the lane to be detected, the bottom portion of the lane to be detected, and the lane wall of the lane to be detected. The deformation variable data may be the deformation variable data information corresponding to all the deformed point cloud data in the current lane portion. Optionally, the deformation variable data may include average deformation variable data, maximum deformation variable data, and deformation area size data.

[0045] Specifically, the point cloud data to be processed in the grid area with type information of a preset type is used as deformation point cloud data. According to the corresponding relationship, the three-dimensional point cloud data corresponding to the lane portion where the current grid area is located is determined. Based on the position information of the three-dimensional point cloud data and the deformation point cloud data, the deformation variable data such as the average deformation variable data, the maximum deformation variable data and the deformation size data corresponding to the deformation point cloud data are determined. It should be noted that the calculation method for determining the deformation variable for different lane portions is different, and therefore, the deformation variable data corresponding to different lane portions are different. Based on this, accurate detection of the deformation variable data corresponding to different lane portions is achieved, thereby improving the accuracy of the deformation information of the lane to be detected.

[0046] Optionally, based on the deformation point cloud data and a pre-created correspondence relationship, the deformation quantity data corresponding to each lane part of the lane to be inspected is determined, including: filtering the deformation point cloud data, and calculating the centroid data corresponding to the deformation point cloud data after filtering; determining the lane part corresponding to the centroid data based on the centroid data and the correspondence relationship; retrieving the deformation point cloud data associated with the centroid data, and determining the deformation quantity data of the corresponding lane part based on the deformation point cloud data and the three-dimensional point cloud data corresponding to the lane part in the correspondence relationship.

[0047] Among them, filtering the deformation point cloud data can filter out noise information in the deformation point cloud data. The centroid data represents the center of the overall position of the deformation point cloud data. Optionally, the coordinates corresponding to the centroid data can be the average value of the point cloud coordinates corresponding to all the deformation point cloud data. The tunnel part can be the bottom part, the top part or the tunnel wall part of the tunnel to be detected.

[0048] Specifically, the deformation point cloud data is filtered to remove the noise point cloud data in the deformation point cloud data and improve the accuracy of subsequent data processing. The centroid data corresponding to the deformation point cloud data after filtering is determined. According to the centroid data and the position information corresponding to the three-dimensional point cloud data in the corresponding relationship, the lane position corresponding to the centroid data is determined. The deformation point cloud data associated with the centroid data of the current lane position is retrieved, and the deformation amount data corresponding to the current lane position is determined according to the deformation point cloud data and the three-dimensional point cloud data in the corresponding relationship.

[0049] Exemplarily, a statistical filter is used to perform data filtering on the deformation point cloud data to filter out the interference of noise points. The mean coordinates are determined based on the three-dimensional coordinates of all the deformation point cloud data after filtering, that is, the three-dimensional coordinate data corresponding to the centroid data is determined. According to the centroid data and the lane position corresponding to the three-dimensional point cloud data in the corresponding relationship, it is determined that the centroid data corresponds to the lane top, bottom or lane wall of the lane to be detected. According to the deformation point cloud data and the three-dimensional point cloud data, the average deformation variable data, the maximum deformation variable data and the deformation area size corresponding to the overall deformation point cloud data of the current lane position are calculated.

[0050] S140. Determine target deformation information of the lane to be inspected based on the deformation amount data.

[0051] The target deformation information can be understood as the deformation information of the lane to be detected in the corresponding dimension. Optionally, the target deformation information can be the deformation information of the lane to be detected in the cross-sectional dimension; the target deformation information can be the deformation information of the lane to be detected in the protruding anchor dimension; the target deformation information can also be the deformation information of the lane to be detected in the lane width and lane height and other dimensions.

[0052] Specifically, by combining the deformation variable data with the original three-dimensional point cloud data, the target deformation information of the lane to be inspected in multiple dimensions can be determined, so as to achieve accurate detection of the deformation of the entire lane to be inspected. For example, the target deformation information of the lane to be inspected in the cross-sectional dimension can be determined by combining the maximum deformation variable coordinates in the deformation variable data with the contour point cloud data corresponding to the cross-sectional area of ​​the lane to be inspected. For example, the deformation variable data corresponding to the protruding anchor rod in the lane to be inspected can be determined by the deformation width dimension data in the deformation variable data. According to the deformation variable data corresponding to the protruding anchor rod and the preset deformation threshold, the target deformation information is determined, that is, whether the protruding anchor rod will affect the safe passage of vehicles or corresponding equipment.

[0053] Optionally, target deformation information of the lane to be inspected is determined based on the deformation variable data, including: determining the coordinates of the maximum deformation point of the lane to be inspected based on the deformation variable data; determining the cross-sectional deformation information of the lane to be inspected based on the coordinates of the maximum deformation point, the cross-sectional contour point cloud data of the lane to be inspected and the objective function; determining the target deformation information based on the original cross-sectional information and the cross-sectional deformation information corresponding to the lane to be inspected in the initial state.

[0054] The maximum deformation point coordinates can be understood as the three-dimensional coordinates corresponding to the point cloud data of the maximum deformation amount in the deformation point cloud data corresponding to the lane to be detected. The cross-sectional contour point cloud data can be the point cloud data corresponding to the cross section of the lane to be detected. For example, see Figure 2 , the cross-sectional profile of the lane to be inspected can be as follows Figure 2 As shown. The objective function may be a formula for calculating the area of ​​the cross section of the lane to be detected. The cross-sectional deformation information may be understood as the deformation information corresponding to the cross section in the lane to be detected. For example, the cross-sectional deformation information may be the area information of the deformed cross section output by the objective function. The original cross-sectional information may be the area information of the cross section determined based on the point cloud data collected in the initial state of the lane to be detected.

[0055] Specifically, the coordinates of the maximum deformation point at the deformation position of the lane to be inspected are determined according to the deformation variable data. The cross-sectional contour point cloud data is obtained from the to-be-processed point cloud data corresponding to the lane to be inspected. The central point cloud data of the cross-sectional contour point cloud data is determined, and the connection line between each cross-sectional contour point cloud data and the central point cloud data is determined. The angle between the connection line and the preset directed vector is calculated to realize the conversion between the cross-sectional contour point cloud data and the polar coordinates. The polar coordinates corresponding to the cross-sectional contour point cloud data are sorted, and the cross-sectional deformation information of the deformed lane to be inspected is calculated using the objective function. The cross-sectional deformation information is compared with the original cross-sectional information corresponding to the lane to be inspected in the initial state to determine the target deformation information of the lane to be inspected in the cross-sectional dimension.

[0056] Exemplarily, based on the deformation variable data, the coordinates of the maximum deformation point at the deformation position of the lane to be inspected in the target area are calculated. The PCL (Point Cloud Library) point cloud library is used to extract the point cloud according to the index to extract the cross-sectional contour point cloud data of the lane to be inspected. The cross-sectional contour point cloud data is converted into polar coordinates and sorted. The sorted polar coordinates are processed according to the objective function to obtain the cross-sectional area information of the deformed lane to be inspected, that is, the cross-sectional deformation information. The cross-sectional area information is compared with the cross-sectional area information at the position before the deformation to determine the deformation variable of the lane to be inspected in the cross-sectional dimension, that is, the target deformation information. Among them, the objective function can be expressed as follows:

[0057]

[0058] Among them, Area represents the cross-sectional area of ​​the lane to be detected, n represents the number of vertices corresponding to the cross-sectional area of ​​the lane to be detected, (x i ,y i ) represents the coordinates corresponding to the i-th vertex, (x i+1 ,y i+1 ) represents the coordinates corresponding to the i+1th vertex.

[0059] Optionally, the target deformation information of the roadway to be inspected is determined based on the deformation data, including: determining the deformation width dimension data of the roadway to be inspected based on the deformation data; determining the deformation data corresponding to the protruding anchor in the roadway to be inspected based on the deformation width dimension data; determining the target deformation information based on the deformation data corresponding to the protruding anchor and a preset deformation threshold. The anchor is a tensile member that penetrates deep into the stratum and is a device used in coal mines to support the roadway to be inspected.

[0060] Specifically, at least one deformation width dimension data corresponding to the area where deformation occurs at the top of the lane to be detected is determined based on the deformation amount data. The deformation width dimension data corresponding to the protruding anchor rod is screened out from the at least one deformation width dimension data. The deformation width dimension data of the protruding anchor rod is compared with a preset deformation threshold to determine whether the protruding anchor rod will affect the safe passage of the corresponding vehicle, that is, to determine the target deformation information of the lane to be detected in the dimension of the protruding anchor rod.

[0061] Optionally, the method also includes: determining the three-dimensional point cloud data corresponding to each part in the lane to be inspected based on the three-dimensional point cloud data of the lane to be inspected in the initial state and the part information of the lane to be inspected; obtaining the original point cloud data corresponding to each part in the lane to be inspected; and determining the target deformation information of the part based on the three-dimensional point cloud data and the original point cloud data of the same part.

[0062] The original point cloud data may be three-dimensional point cloud data corresponding to the target area obtained by using three-dimensional scanning technology.

[0063] Specifically, according to the 3D point cloud data of the lane to be inspected in the initial state and the location information of the lane to be inspected, the 3D point cloud data is divided into spatial point cloud regions to determine the 3D point cloud data corresponding to each lane location. The original point cloud data corresponding to each location in the lane to be inspected in the target area is obtained. The original point cloud data of the same location is compared with the 3D point cloud data in the initial state to determine the target deformation information of the lane location.

[0064] Exemplarily, according to the three-dimensional point cloud data of the lane to be detected in the initial state and the location information of the lane to be detected, the three-dimensional point cloud data is divided into spatial point cloud areas, and the three-dimensional point cloud data is divided into top point cloud data, bottom point cloud data, left wall point cloud data and right wall point cloud data of the lane to be detected by the Segmentation module of the PCL point cloud library. The target area including the lane to be detected is scanned and detected to obtain the original point cloud data corresponding to the lane to be detected in the target area. The K-Dimensional Tree (kd-tree) method in the PCL point cloud library is used to traverse, retrieve and compare the three-dimensional point cloud data and the original point cloud data of the same lane part, and the changed original point cloud data is used as the deformation point cloud data to determine the target deformation information corresponding to the lane part based on the deformation point cloud data. For example, the top point cloud data and the original point cloud data corresponding to the top of the lane are compared to determine the target deformation information corresponding to the top of the lane to be detected.

[0065] Optionally, the target deformation information is uploaded to the target terminal so that relevant personnel can intuitively determine the deformation of the lane to be inspected.

[0066] Specifically, the target deformation information and the corresponding point cloud data can be uploaded to the digital twin platform and visualized, such as Figure 3 As shown, Figure 3 This is an example of the deformation corresponding to the lane to be inspected. Based on this, the corresponding staff can timely grasp the deformation of the lane to be inspected, saving personnel inspections and making it easier to do disaster prevention and lane maintenance in advance.

[0067] The technical solution of this embodiment obtains multiple point cloud data to be processed located in the same grid area by gridding the original point cloud data of the target area, and determines the grid center point cloud data based on the multiple point cloud data to be processed associated with each grid area. Based on this, representative point cloud data is obtained, which is convenient for the subsequent determination of the type information of the grid area. For the same grid area, the type information of the current grid area is determined based on the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, so as to determine different deformation variable data according to different type information, so as to obtain more accurate deformation variable data. The point cloud data to be processed in the grid area with the preset type of type information is used as the deformation point cloud data, and the deformation variable data corresponding to each lane part of the lane to be detected is determined based on the deformation point cloud data and the pre-created correspondence, and the target deformation information of the lane to be detected is determined based on the deformation variable data. It solves the problem in the prior art that tunnel deformation inspection relies on manual labor and corresponding equipment, which consumes a lot of manpower and material resources and cannot intuitively and accurately detect the overall deformation of the tunnel. It realizes efficient and accurate detection of tunnel deformation information, which not only saves manpower and material costs, but also enables relevant personnel to intuitively determine the deformation of the tunnel to be inspected and take disaster prevention measures in advance.

[0068] Embodiment 2

[0069] Figure 4 This is a flow chart of a method for determining a corresponding relationship provided by the second embodiment of the present invention. This embodiment is based on the above embodiment. Before determining the target deformation amount data, a corresponding relationship between the position information of the roadway part and the three-dimensional point cloud data can be first created to determine the roadway part corresponding to the deformation point cloud data based on the corresponding relationship. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 4 As shown, the method includes:

[0070] S210, obtaining three-dimensional point cloud data of the lane to be inspected in an initial state.

[0071] Specifically, a three-dimensional scanning process is performed on the lane to be inspected in an initial state to obtain three-dimensional point cloud data corresponding to the initial state.

[0072] Exemplarily, the three-dimensional spatial position information of the lane to be inspected, namely, three-dimensional point cloud data, can be obtained by a three-dimensional imaging sensor installed at a preset position in the target area.

[0073] S220, projecting the three-dimensional point cloud data onto the cross section of the lane to be inspected to obtain two-dimensional point cloud data.

[0074] The tunnel cross section is a preset cross section of the tunnel to be detected. Optionally, the tunnel cross section can be a plane perpendicular to the tunnel axis, such as Figure 2 The two-dimensional point cloud data may be data obtained by projecting the three-dimensional point cloud data.

[0075] Specifically, for each 3D point cloud data, the distance from each 3D point cloud data to the lane cross section is calculated, and the projection position of the 3D point cloud data on the lane cross section is determined. Each 3D point cloud data is projected onto the lane cross section of the lane to be inspected to obtain the corresponding 2D point cloud data. By determining the 2D point cloud data, subsequent data processing operations can be simplified and data processing efficiency can be improved.

[0076] S230 , determining the point cloud data to be divided based on the two-dimensional point cloud data and the predefined center point.

[0077] The predefined center point may be the center point cloud data corresponding to the two-dimensional point cloud data. Optionally, the predefined center point may be the center point of the contour corresponding to the outer contour of the lane to be detected. The point cloud data to be divided may be understood as ordered two-dimensional point cloud data. The three-dimensional point cloud data corresponding to each lane portion of the lane to be detected may be determined through the ordered point cloud data to be divided.

[0078] Specifically, the outer contour point cloud data of the lane to be detected is determined based on the two-dimensional point cloud data. The outer contour center point of the outer contour point cloud data is determined based on the outer contour point cloud data, the outer contour center point is connected to the outer contour point cloud data, and the angle between the connection line and the preset directed vector is calculated to obtain the polar angle coordinates corresponding to each outer contour point cloud data. The polar angle coordinates are sorted to obtain the point cloud data to be divided.

[0079] Optionally, based on the two-dimensional point cloud data and a predefined center point, point cloud data to be divided is determined, including: scanning and processing the two-dimensional point cloud data based on a longitude and latitude scanning method to obtain outer contour point cloud data of the lane to be inspected; and orderly processing the outer contour point cloud data according to the outer contour point cloud data and the outer contour center point corresponding to the outer contour point cloud data to obtain the point cloud data to be divided.

[0080] The longitude and latitude scanning method can be understood as a method of scanning the two-dimensional point cloud data in a specific direction to identify or extract the boundary corresponding to the two-dimensional point cloud data. The outer contour point cloud data can be understood as the boundary point cloud data corresponding to the two-dimensional point cloud data. The outer contour center point can be the contour center point determined based on the outer contour point cloud data.

[0081] Specifically, the two-dimensional point cloud data is scanned and processed using the longitude and latitude scanning method to obtain the outer contour point cloud data of the lane to be inspected. At this time, the outer contour point cloud data is unordered point cloud data. Determine the outer contour center point corresponding to the outer contour point cloud data. Connect the outer contour center point and the outer contour point cloud data, and calculate the angle between the connection line and the preset directed vector to convert the outer contour point cloud data into polar coordinates. For example, if the connection line between the outer contour center point and the outer contour point cloud data is v i , the pre-set directed vector is v0(1,0), then calculate v i The angle between v0(1,0) and v0(1,0) is used to obtain the polar coordinates. The polar coordinates are sorted to achieve orderly processing of the outer contour point cloud data and obtain the point cloud data to be divided.

[0082] S240, determining the three-dimensional point cloud data corresponding to the point cloud data to be divided at each lane portion according to the point cloud data to be divided and the lane portion information of the lane to be detected.

[0083] The tunnel location information may include top location information, bottom location information and tunnel wall information corresponding to the tunnel to be detected.

[0084] Specifically, the point cloud data to be divided is divided into regions according to the point cloud data to be divided and the lane position information of the lane to be detected, combined with the preset lane cross-sectional size information, that is, the point cloud data to be divided corresponding to the top part of the lane to be detected, the point cloud data to be divided corresponding to the bottom part of the lane to be detected, and the point cloud data to be divided corresponding to the lane wall of the lane to be detected are determined. According to the correspondence between the point cloud data to be divided and the three-dimensional point cloud data, the three-dimensional point cloud data corresponding to the top part of the lane to be detected, the three-dimensional point cloud data corresponding to the bottom part of the lane to be detected, and the three-dimensional point cloud data corresponding to the lane wall of the lane to be detected are determined. Optionally, according to the three-dimensional point cloud data corresponding to each lane position, the basic lane information such as the cross-sectional area, lane width and lane height corresponding to the lane to be detected can be calculated.

[0085] S250, establishing a correspondence between the tunnel position and the position information of the corresponding three-dimensional point cloud data, so as to determine the tunnel position corresponding to the obtained deformation point cloud data according to the correspondence.

[0086] Specifically, according to the tunnel part and the three-dimensional point cloud data corresponding to each tunnel part, a corresponding relationship between the tunnel part and the position information of the three-dimensional point cloud data is established. The tunnel part corresponding to the deformation point cloud data can be determined through the corresponding relationship.

[0087] The technical solution of this embodiment obtains the three-dimensional point cloud data of the lane to be detected in the initial state, and projects the three-dimensional point cloud data onto the lane cross section of the lane to be detected to obtain the two-dimensional point cloud data. The point cloud data to be divided is determined by the two-dimensional point cloud data and the pre-defined center point, so as to determine the three-dimensional point cloud data corresponding to the point cloud data to be divided of each lane part based on the point cloud data to be divided and the lane part information of the lane to be detected. Thus, a corresponding relationship between the lane part and the position information of the corresponding three-dimensional point cloud data is established. Based on this, it is convenient to determine the lane part corresponding to the deformation point cloud data according to the comparison relationship, and provide data support for the subsequent accurate determination of the deformation amount data corresponding to each lane part, thereby accurately detecting the deformation of the lane as a whole.

[0088] Embodiment 3

[0089] Figure 5 Schematic diagram of the structure of a tunnel deformation detection device provided by the third embodiment of the present invention. Figure 5 As shown, the device includes: a point cloud data acquisition module 310, a type information determination module 320, a deformation amount data determination module 330 and a deformation information determination module 340.

[0090] The point cloud data acquisition module 310 is used to obtain multiple point cloud data to be processed located in the same grid area by gridding the original point cloud data of the target area, and determine the grid center point cloud data according to the multiple point cloud data to be processed associated with each grid area; wherein the target area includes the lane to be detected; the type information determination module 320 is used to determine the type information of the current grid area for the same grid area according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state; the deformation variable data determination module 330 is used to use the point cloud data to be processed in the grid area with type information of a preset type as deformation point cloud data, and determine the deformation variable data corresponding to each lane part of the lane to be detected according to the deformation point cloud data and the pre-created corresponding relationship; wherein the corresponding relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data; the deformation information determination module 340 is used to determine the target deformation information of the lane to be detected according to the deformation variable data.

[0091] The technical solution of this embodiment obtains multiple point cloud data to be processed located in the same grid area by gridding the original point cloud data of the target area, and determines the grid center point cloud data based on the multiple point cloud data to be processed associated with each grid area. Based on this, representative point cloud data is obtained, which is convenient for the subsequent determination of the type information of the grid area. For the same grid area, the type information of the current grid area is determined based on the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, so as to determine different deformation variable data according to different type information, so as to obtain more accurate deformation variable data. The point cloud data to be processed in the grid area with the preset type of type information is used as the deformation point cloud data, and the deformation variable data corresponding to each lane part of the lane to be detected is determined based on the deformation point cloud data and the pre-created correspondence, and the target deformation information of the lane to be detected is determined based on the deformation variable data. It solves the problem in the prior art that tunnel deformation inspection relies on manual labor and corresponding equipment, which consumes a lot of manpower and material resources and cannot intuitively and accurately detect the overall deformation of the tunnel. It realizes efficient and accurate detection of tunnel deformation information, which not only saves manpower and material costs, but also enables relevant personnel to intuitively determine the deformation of the tunnel to be inspected and take disaster prevention measures in advance.

[0092] On the basis of the above embodiments, optionally, the device also includes: a correspondence determination module, which includes: a three-dimensional point cloud data acquisition unit, used to acquire three-dimensional point cloud data of the to-be-detected lane in an initial state; a point cloud data mapping unit, used to project the three-dimensional point cloud data onto the lane cross section of the to-be-detected lane to obtain two-dimensional point cloud data; a point cloud data determination unit to be divided, used to determine the point cloud data to be divided based on the two-dimensional point cloud data and a predefined center point; a point cloud data correspondence unit, used to determine the three-dimensional point cloud data corresponding to the point cloud data to be divided of each lane part according to the point cloud data to be divided and the lane part information of the to-be-detected lane; a correspondence determination unit, used to establish a correspondence between the lane part and the position information of the corresponding three-dimensional point cloud data, so as to determine the lane part corresponding to the obtained deformation point cloud data according to the correspondence.

[0093] Optionally, a unit for determining point cloud data to be divided is used to scan and process the two-dimensional point cloud data based on the longitude and latitude scanning method to obtain the outer contour point cloud data of the lane to be inspected; according to the outer contour point cloud data and the outer contour center point corresponding to the outer contour point cloud data, the outer contour point cloud data is processed in an orderly manner to obtain the point cloud data to be divided.

[0094] Optionally, the grid center point cloud data and the positioning point cloud data are determined based on the following method: clustering multiple point cloud data in the same grid area to obtain the cluster center point of the grid area; and using the point cloud data corresponding to the cluster center as the grid center point cloud data or the positioning point cloud data of the grid area.

[0095] Optionally, the deformation variable data determination module includes: a centroid data determination unit, which is used to filter the deformation point cloud data and calculate the centroid data corresponding to the filtered deformation point cloud data; a tunnel position determination unit, which is used to determine the tunnel position corresponding to the centroid data based on the centroid data and the corresponding relationship; a deformation variable data determination unit, which is used to retrieve the deformation point cloud data associated with the centroid data, and determine the deformation variable data of the corresponding tunnel position based on the deformation point cloud data and the three-dimensional point cloud data corresponding to the tunnel position in the corresponding relationship.

[0096] Optionally, the deformation information determination module includes: a deformation point coordinate determination unit, which is used to determine the maximum deformation point coordinates of the lane to be inspected based on the deformation amount data; a cross-sectional deformation information determination unit, which is used to determine the cross-sectional deformation information of the lane to be inspected based on the maximum deformation point coordinates, the cross-sectional contour point cloud data of the lane to be inspected and the objective function; and a target deformation information determination unit, which is used to determine the target deformation information based on the original cross-sectional information and the cross-sectional deformation information corresponding to the lane to be inspected in the initial state.

[0097] Optionally, the device also includes a part deformation information determination module, which is used to determine the three-dimensional point cloud data corresponding to each part in the lane to be inspected based on the three-dimensional point cloud data of the lane to be inspected in an initial state and the part information of the lane to be inspected; obtain the original point cloud data corresponding to each part in the lane to be inspected; and determine the target deformation information of the part based on the three-dimensional point cloud data and the original point cloud data of the same part.

[0098] The tunnel deformation detection device provided in the embodiment of the present invention can execute the tunnel deformation detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0099] Embodiment 4

[0100] Figure 61 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0101] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the roadway deformation detection method.

[0104] In some embodiments, the lane deformation detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the lane deformation detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the lane deformation detection method in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The computer program for implementing the lane deformation detection method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] Embodiment 5

[0108] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute a method for detecting deformation of a roadway, the method comprising:

[0109] By gridding the original point cloud data of the target area, multiple point cloud data to be processed located in the same grid area are obtained, and the grid center point cloud data is determined according to the multiple point cloud data to be processed associated with each grid area; wherein the target area includes the lane to be detected; for the same grid area, the type information of the current grid area is determined according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state; the point cloud data to be processed in the grid area with type information of the preset type is used as deformation point cloud data, and the deformation variable data corresponding to each lane part of the lane to be detected is determined according to the deformation point cloud data and the pre-created corresponding relationship; wherein the corresponding relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data; according to the deformation variable data, the target deformation information of the lane to be detected is determined.

[0110] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0114] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0115] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting tunnel deformation, characterized in that: include: By gridding the original point cloud data of the target area, a plurality of point cloud data to be processed located in the same grid area are obtained, and the grid center point cloud data is determined according to the plurality of point cloud data to be processed associated with each grid area; wherein the target area includes the lane to be detected; For the same grid area, the type information of the current grid area is determined according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained after gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state; The point cloud data to be processed in the grid area whose type information is a preset type is used as the deformed point cloud data, and the deformation amount data corresponding to each lane part of the lane to be detected is determined according to the deformed point cloud data and the pre-created corresponding relationship; wherein the corresponding relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data; According to the deformation amount data, target deformation information of the lane to be inspected is determined.

2. The method according to claim 1, characterized in that The method further comprises: Acquire three-dimensional point cloud data of the lane to be inspected in an initial state; Projecting the three-dimensional point cloud data onto the lane cross section of the lane to be inspected to obtain two-dimensional point cloud data; Based on the two-dimensional point cloud data and a predefined center point, determining point cloud data to be divided; Determine the three-dimensional point cloud data corresponding to the point cloud data to be divided at each lane portion according to the point cloud data to be divided and the lane portion information of the lane to be detected; A correspondence between the lane portion and the position information of the corresponding three-dimensional point cloud data is established to determine the lane portion corresponding to the obtained deformation point cloud data based on the correspondence.

3. The method according to claim 2, characterized in that The step of determining the point cloud data to be divided based on the two-dimensional point cloud data and the predefined center point includes: Scanning and processing the two-dimensional point cloud data based on a longitude and latitude scanning method to obtain outer contour point cloud data of the lane to be detected; According to the outer contour point cloud data and the outer contour center point corresponding to the outer contour point cloud data, the outer contour point cloud data is processed in an orderly manner to obtain the point cloud data to be divided.

4. The method according to claim 1, characterized in that: The grid center point cloud data and the positioning point cloud data are both determined based on the following method: Clustering multiple point cloud data in the same grid area to obtain a cluster center point of the grid area; The point cloud data corresponding to the cluster center is used as the grid center point cloud data or the positioning point cloud data of the grid area.

5. The method according to claim 1, characterized in that Determining the deformation amount data corresponding to each lane portion of the lane to be inspected based on the deformation point cloud data and the pre-created corresponding relationship includes: Filtering the deformed point cloud data, and calculating the centroid data corresponding to the deformed point cloud data after filtering; Determining the lane portion corresponding to the centroid data according to the centroid data and the corresponding relationship; The deformation point cloud data associated with the centroid data is retrieved, and the deformation amount data of the corresponding lane portion is determined according to the deformation point cloud data and the three-dimensional point cloud data corresponding to the lane portion in the corresponding relationship.

6. The method according to claim 1, characterized in that Determining target deformation information of the lane to be detected based on the deformation amount data includes: Determining the coordinates of the maximum deformation point of the lane to be detected based on the deformation amount data; Determine the cross-sectional deformation information of the lane to be detected based on the coordinates of the maximum deformation point, the cross-sectional contour point cloud data of the lane to be detected and the objective function; The target deformation information is determined according to the original cross-sectional information corresponding to the lane to be inspected in an initial state and the cross-sectional deformation information.

7. The method according to claim 1, characterized in that The method further comprises: Determine the three-dimensional point cloud data corresponding to each part of the lane to be detected based on the three-dimensional point cloud data of the lane to be detected in the initial state and the part information of the lane to be detected; Acquire original point cloud data corresponding to each part in the lane to be inspected; According to the three-dimensional point cloud data of the same part and the original point cloud data, target deformation information of the part is determined.

8. A tunnel deformation detection device, characterized in that: include: The point cloud data acquisition module is used to obtain a plurality of to-be-processed point cloud data located in the same grid area by gridding the original point cloud data of the target area, and determine the grid center point cloud data according to the plurality of to-be-processed point cloud data associated with each grid area; wherein the target area includes the lane to be detected; A type information determination module is used to determine the type information of the current grid area according to the grid center point cloud data of the current grid area and the predetermined positioning point cloud data, wherein the positioning point cloud data is obtained by gridding the point cloud data of the lane to be detected in the target area when the lane to be detected is in an initial state; A deformation variable data determination module is used to use the to-be-processed point cloud data in the grid area whose type information is a preset type as deformation point cloud data, and determine the deformation variable data corresponding to each lane part of the to-be-detected lane according to the deformation point cloud data and a pre-created correspondence relationship; wherein the correspondence relationship includes the relationship between the lane part and the position information of the three-dimensional point cloud data; The deformation information determination module is used to determine the target deformation information of the lane to be detected based on the deformation amount data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the tunnel deformation detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the tunnel deformation detection method according to any one of claims 1 to 7 when executed.

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