A method, device, medium and equipment for measuring the quantity of communication cable trays based on tunnel point cloud

Abstract

The present invention discloses a method, device, medium and equipment for measuring the number of communication cable trays based on tunnel point cloud. The method includes: obtaining tunnel point cloud data; obtaining tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data; obtaining communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud; performing clustering analysis on the communication cable tray point cloud data, counting the total number of categories, and generating the number of the communication cable trays. The present invention realizes automatic identification and measurement of communication cable trays with high robustness in a complex environment, effectively overcomes noise and occlusion, and improves the audit accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud analysis, and in particular to a method, device, medium and equipment for measuring the number of communication cable trays based on tunnel point clouds. Background Art

[0002] In the construction of tunnel projects, the construction audit of communication cable trays is an important link to ensure project quality and cost control. Traditional methods for measuring the number of cable trays mainly rely on manual measurement and drawing verification. This method is not only inefficient but also easily affected by human errors. Especially in complex tunnel environments, due to factors such as narrow space and insufficient light, it is difficult to ensure the accuracy of manual measurement, resulting in large deviations in audit results. In addition, traditional methods are difficult to update the construction progress in real time and cannot meet the requirements of modern project management for data timeliness and accuracy, seriously affecting the efficiency and reliability of construction audits.

[0003] With the continuous expansion of the scale of tunnel projects and the continuous progress of construction technology, traditional methods for measuring the number of cable trays can no longer meet the needs of modern project audits. Existing technologies lack effective utilization of tunnel point cloud data and cannot achieve automated and intelligent measurement of the number of cable trays. Although point cloud data can provide high-precision three-dimensional spatial information, its application in cable tray construction audits is limited due to complex data processing and immature algorithms. In addition, existing methods lack real-time monitoring of the installation status of cable trays during construction, cannot detect and correct problems in construction in a timely manner, and increase the difficulty and risk of audits. Therefore, developing a method for measuring the number of communication cable trays based on tunnel point clouds is of great significance for improving the efficiency and accuracy of construction audits. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, medium and equipment for measuring the number of communication cable trays based on tunnel point clouds. To achieve the above purpose, the present invention is implemented by the following technical solutions:

[0005] In the first aspect, the present invention provides a method for measuring the number of communication cable trays based on tunnel point clouds, including:

[0006] Obtain tunnel point cloud data;

[0007] According to the tunnel point cloud data, obtain tunnel wall point clouds and the plane equation of the tunnel wall point clouds;

[0008] Obtain the point cloud data of the communication cable bridge according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud; the obtaining of the point cloud data of the communication cable bridge according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes: obtaining the normal direction of the tunnel wall plane according to the plane equation of the tunnel wall point cloud; intercepting the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation according to a preset length value, and determining it as the bridge end point cloud data; clustering the bridge end point cloud data and calculating the central coordinates of each category; obtaining the bridge point cloud corresponding to each category according to the central coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, and forming the point cloud data of the communication cable bridge;

[0009] Perform clustering analysis on the point cloud data of the communication cable bridge, count the total number of categories, and generate the number of communication cable bridges.

[0010] Combined with the first aspect, optionally, the obtaining of the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data includes:

[0011] Perform plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud;

[0012] Intercept the point cloud data that is at a distance of the preset height range from the plane equation of the tunnel ground point cloud according to a preset height range, and determine the tunnel wall point cloud to be clustered;

[0013] Cluster the tunnel wall point cloud to be clustered according to density clustering, and determine the point cloud data corresponding to the category with the largest number of point clouds after clustering as the tunnel wall point cloud;

[0014] Perform plane fitting on the tunnel wall point cloud to obtain the plane equation of the tunnel wall point cloud.

[0015] Combined with the first aspect, optionally, the performing of plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud includes:

[0016] Filter the tunnel point cloud data by radius filtering to obtain the filtered point cloud;

[0017] Perform plane fitting on the filtered point cloud by the random sample consensus method to obtain the plane equation of the tunnel ground point cloud.

[0018] Combined with the first aspect, optionally, the obtaining of the bridge point cloud corresponding to each category according to the central coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, and forming the point cloud data of the communication cable bridge includes:

[0019] Determine the straight-line equation corresponding to each category according to the central coordinates of each category and the normal direction of the tunnel wall plane;

[0020] Calculate the distance between the tunnel wall point cloud and the straight-line equation corresponding to each category;

[0021] If the distance is less than a preset distance, determine the point cloud as the bridge point cloud corresponding to each category;

[0022] Superimpose the bridge point clouds corresponding to all categories to form the communication cable bridge point cloud data.

[0023] In a second aspect, the present invention provides a device for measuring the number of communication cable bridges based on tunnel point clouds, including:

[0024] A point cloud acquisition module: acquire tunnel point cloud data;

[0025] A tunnel wall point cloud analysis module: acquire the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data; the acquiring of the communication cable bridge point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes: acquiring the normal direction of the tunnel wall plane according to the plane equation of the tunnel wall point cloud; intercepting the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation according to a preset length value, and determining it as the bridge endpoint cloud data; clustering the bridge endpoint cloud data and calculating the central coordinates of each category; acquiring the bridge point cloud corresponding to each category according to the central coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud to form the communication cable bridge point cloud data;

[0026] A bridge point cloud acquisition module: acquire the communication cable bridge point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud;

[0027] A quantity measurement module: perform clustering analysis on the communication cable bridge point cloud data, count the total number of categories, and generate the number of communication cable bridges.

[0028] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for measuring the number of communication cable bridges based on tunnel point clouds as described in any one of the first aspects.

[0029] In a fourth aspect, the present invention provides a device for measuring the number of communication cable bridges based on tunnel point clouds, including:

[0030] A memory for storing instructions;

[0031] A processor for executing the instructions, such that the device performs operations for implementing the method for measuring the number of communication cable trays based on tunnel point clouds as described in any one of the first aspects.

[0032] Compared with the prior art, the beneficial effects achieved by the method, device, medium, and equipment for measuring the number of communication cable trays based on tunnel point clouds provided by the embodiments of the present invention include:

[0033] The present invention obtains tunnel point cloud data; based on the tunnel point cloud data, obtains tunnel wall point clouds and the plane equation of the tunnel wall point clouds; obtains communication cable tray point cloud data based on the tunnel wall point clouds and the plane equation of the tunnel wall point clouds; performs clustering analysis on the communication cable tray point cloud data, counts the total number of categories, and generates the number of communication cable trays. The present invention realizes automatic recognition and measurement of communication cable trays with high robustness in complex environments, effectively overcomes noise and occlusion, and improves the audit accuracy and efficiency. Description of the Drawings

[0034] Figure 1 It is a flowchart of a method for measuring the number of communication cable trays based on tunnel point clouds provided by Embodiment 1 of the present invention. Detailed Embodiments

[0035] It should be noted that, without conflict, the embodiments in this embodiment and the features in the embodiments can be combined with each other. The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1:

[0037] As Figure 1 shown, the embodiments of the present invention provide a method for measuring the number of communication cable trays based on tunnel point clouds, including:

[0038] S1: Obtain tunnel point cloud data;

[0039] Specifically, for the acquisition method of tunnel point cloud data, lidar scanning can be used. It determines the distance of the target object by emitting laser beams and measuring the time of the reflected light, thereby obtaining three-dimensional space information. In the tunnel environment, the lidar can be mounted on a vehicle or a mobile platform and quickly scanned along the tunnel, which can efficiently and accurately collect large-area tunnel point cloud data. It is less affected by light conditions and can adapt to complex and dark tunnel environments. It is also possible to perform three-dimensional reconstruction using structured light. This method projects a specific pattern onto the tunnel surface by a projector, and the camera synchronously takes pictures from different angles. According to the principle of triangulation, by analyzing the deformation of the pattern at different perspectives, the three-dimensional coordinates of each point on the object surface are calculated, and then the point cloud is generated. This method has high precision and good scanning effect for small tunnels or local details, but the measurement range is relatively limited, and there are certain requirements for ambient light.

[0040] S2: Obtain the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data;

[0041] In a specific embodiment, the obtaining of the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data includes:

[0042] S2-1: Perform plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud;

[0043] In a specific embodiment, the performing of plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud includes:

[0044] S2-1-1: Filter the tunnel point cloud data using radius filtering to obtain filtered point cloud;

[0045] S2-1-2: Perform plane fitting on the filtered point cloud using the random sample consensus method to obtain the plane equation of the tunnel ground point cloud.

[0046] Specifically, the radius filtering operation is performed using the remove_radius_outlier function in the open3D development library. Its inputs are the filtering radius and the minimum number of neighborhood points, and it returns the indices ind of the filtered point cloud and a boolean value cl (indicating whether the filtering is successful). Then, the filtered point cloud is extracted according to the indices using the select_by_index function. The radius is set based on the average density value of the point cloud, with the radius set to 10 times the average density value and the minimum number of neighborhood points set to 10. After filtering, the segment_plane function in the open3D development library is used for plane fitting. This function uses the random sample consensus algorithm to fit the plane. The core idea of the algorithm is to randomly sample a point set, assume it is the inlier of the plane model, and then calculate the plane model parameters. This process is repeated multiple times, and the plane model with the most inliers is selected as the final fitting result. In the point cloud data, the plane fitting function can identify the point set with plane characteristics and determine its plane equation, generally expressed as Ax + By + Cz + D = 0, where A, B, C, and D are the plane parameters.

[0047] S2-2: According to a preset height range, intercept the point cloud data that is at a distance of the preset height range from the plane equation of the tunnel ground point cloud, and determine the tunnel wall point cloud to be clustered;

[0048] Specifically, assume that the parameters A, B, C, and D of the plane equation are known. Convert the point cloud coordinates to homogeneous coordinate form and construct the points_homogeneous matrix. Then, through a single matrix dot product operation, use the np.dot function to calculate the signed distance from all points to the plane, and then take the absolute value using np.abs to obtain the true distance value. At the same time, use np.linalg.norm to calculate the norm of the plane normal vector to complete the calculation of the distance formula. In this way, relying on the vectorization characteristics of matrix operations, the distances from all point cloud points to the specified plane are calculated efficiently in one go, greatly improving the calculation efficiency compared to the traditional loop calculation method. After obtaining the distance between the point cloud and the plane, according to the preset height range, generally taking 1.5 meters to 2.5 meters in engineering, use the np.where condition to filter out the point cloud within the preset height range and determine it as the tunnel wall point cloud to be clustered.

[0049] S2-3: Cluster the tunnel wall point cloud to be clustered according to density clustering, and determine the point cloud data corresponding to the category with the largest number of points after clustering as the tunnel wall point cloud;

[0050] Specifically, the DBSCAN density clustering algorithm is used to cluster the point cloud. DBSCAN is a density-based spatial clustering algorithm. Its core idea is to divide the data space into high-density regions and low-density regions. Points in high-density regions are divided into different clusters, while points in low-density regions are regarded as noise points. In point cloud data, the algorithm realizes clustering by defining two key parameters: the neighborhood radius (eps) and the minimum number of points in the neighborhood (min_points). For each point, the algorithm checks the number of neighborhood points within the radius eps. If the number of neighborhood points is greater than or equal to min_points, the point is regarded as a core point, and starting from the core point, a cluster is continuously expanded. If the number of neighborhood points of a certain point is less than min_points and the point is not a directly or indirectly density-reachable point of the core point, the point is marked as a noise point. In the actual process, the cluster_dbscan function in the open3D development library is used to perform the DBSCAN clustering operation, and the cluster label to which each point belongs is returned. By analyzing the labels, the number of clusters can be obtained, and different colors can be assigned to the point clouds of different clusters for visualization.

[0051] S2-4: Perform plane fitting on the tunnel wall point cloud to obtain the plane equation of the tunnel wall point cloud.

[0052] S3: Obtain the communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud;

[0053] In a specific embodiment, the obtaining of the communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes:

[0054] S3-1: Obtain the normal direction of the tunnel wall plane according to the plane equation of the tunnel wall point cloud;

[0055] Specifically, the expression of the plane equation uses the general expression, which is expressed as Ax + By + Cz + D = 0, where A, B, C, and D are plane parameters, and the vector (A, B, C) can represent the normal direction of the tunnel wall plane.

[0056] S3-2: Intercept the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation according to a preset length value, and determine it as the bridge end point cloud data;

[0057] Specifically, in actual engineering, the length of a general cable tray is between 20 cm and 40 cm. Therefore, for the determination of the preset length value, an adaptive method is adopted. Taking 40 cm as the preset length, the point cloud data in the tunnel wall point cloud that is 40 cm away from the tunnel wall plane equation is intercepted. If the number of point clouds is less than 1000, the preset length is reduced by 1, and the point cloud interception is performed according to the new preset length until the number of intercepted point clouds is greater than 1000.

[0058] S3-3: Cluster the cable tray endpoint point cloud data and calculate the central coordinates of each category;

[0059] Specifically, after clustering is completed, for each category, the method for calculating the central coordinates is as follows: Accumulate and sum the x coordinate values of all point clouds in this category, and then divide by the number of point clouds in this category to obtain the central coordinate value of this category in the x-axis direction; Similarly, perform the same operations on the y coordinate and z coordinate, and calculate the central coordinate values in the y-axis and z-axis directions respectively. Combine the coordinate values in these three directions to determine the central coordinates of the point cloud data of this category.

[0060] S3-4: According to the central coordinates of each category, the normal direction of the tunnel wall plane, and the tunnel wall point cloud, obtain the cable tray point cloud corresponding to each category, and form the communication cable tray point cloud data.

[0061] Specifically, assume that the central coordinates of a certain category are (x0, y0, z0), and the normal direction of the tunnel wall plane is (A, B, C). Then the straight line passing through the point (x0, y0, z0) and with the direction of (A, B, C) is expressed as A*(x - x0) + B*(y - y0) + C*(z - z0) = 0. Calculate the distance between the tunnel wall point cloud and this straight line, and take the point cloud with a distance less than 0.05 m as the cable tray point cloud corresponding to this category.

[0062] S4: Perform clustering analysis on the communication cable tray point cloud data, count the total number of categories, and generate the number of communication cable trays.

[0063] The method for measuring the number of communication cable trays based on tunnel point clouds provided by the present invention realizes automatic recognition and measurement of communication cable trays with high robustness in complex environments, effectively overcomes noise and occlusion, and improves the audit accuracy and efficiency.

[0064] Embodiment 2:

[0065] The embodiment of the present invention provides a device for measuring the number of communication cable trays based on tunnel point clouds, including:

[0066] Point cloud acquisition module: Acquire tunnel point cloud data;

[0067] Tunnel wall point cloud analysis module: Obtain the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data;

[0068] Bridge point cloud acquisition module: Obtain the communication cable bridge point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud; The obtaining of the communication cable bridge point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes: obtaining the normal direction of the tunnel wall plane according to the plane equation of the tunnel wall point cloud; intercepting the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation according to a preset length value, and determining it as the bridge end point cloud data; clustering the bridge end point cloud data, and calculating the center coordinates of each category; obtaining the bridge point cloud corresponding to each category according to the center coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, and forming the communication cable bridge point cloud data;

[0069] Quantity measurement module: Perform clustering analysis on the communication cable bridge point cloud data, count the total number of categories, and generate the quantity of the communication cable bridge.

[0070] The communication cable bridge quantity measurement device based on tunnel point cloud provided by the present invention realizes automatic recognition and measurement of communication cable bridges with high robustness in complex environments, effectively overcomes noise and occlusion, and improves the audit accuracy and efficiency.

[0071] Embodiment 3:

[0072] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for measuring the quantity of communication cable bridges based on tunnel point cloud as described in Embodiment 1.

[0073] Embodiment 4:

[0074] The embodiment of the present invention provides a device for measuring the quantity of communication cable bridges based on tunnel point cloud, including:

[0075] A memory for storing instructions;

[0076] A processor for executing the instructions, so that the device performs operations to implement the method for measuring the quantity of communication cable bridges based on tunnel point cloud as described in any one of Embodiment 1.

[0077] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0079] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0081] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for counting the number of communication cable trays based on tunnel point clouds, characterized in that, Including: Obtain tunnel point cloud data; According to the tunnel point cloud data, obtain tunnel wall point cloud and the plane equation of the tunnel wall point cloud; Obtain communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud; The obtaining of the communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes: according to the plane equation of the tunnel wall point cloud, obtain the normal direction of the tunnel wall plane; according to a preset length value, intercept the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation, and determine it as the bridge end point cloud data; cluster the bridge end point cloud data, and calculate the center coordinates of each category; according to the center coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, obtain the bridge point cloud corresponding to each category, and form the communication cable tray point cloud data; Perform clustering analysis on the communication cable tray point cloud data, count the total number of categories, and generate the number of communication cable trays.

2. The method for measuring the number of communication cable bridges based on tunnel point cloud according to claim 1, wherein The obtaining of the tunnel wall point cloud and the plane equation of the tunnel wall point cloud according to the tunnel point cloud data includes: Perform plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud; According to a preset height range, intercept the point cloud data that is at a distance of the preset height range from the plane equation of the tunnel ground point cloud, and determine the tunnel wall point cloud to be clustered; Cluster the tunnel wall point cloud to be clustered according to density clustering, and determine the point cloud data corresponding to the category with the largest number of point clouds after clustering as the tunnel wall point cloud; Perform plane fitting on the tunnel wall point cloud to obtain the plane equation of the tunnel wall point cloud.

3. A method for measuring the number of communication cable trays based on tunnel point clouds according to claim 2, characterized in that, The performing of plane fitting on the tunnel point cloud data to obtain the plane equation of the tunnel ground point cloud includes: Perform filtering on the tunnel point cloud data using radius filtering to obtain filtered point cloud; Perform plane fitting on the filtered point cloud using the random sample consensus method to obtain the plane equation of the tunnel ground point cloud.

4. A method for measuring the number of communication cable trays based on tunnel point cloud according to claim 1, wherein, The obtaining of the bridge point cloud corresponding to each category according to the center coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, and forming the communication cable tray point cloud data includes: According to the center coordinates of each category and the normal direction of the tunnel wall plane, determine the straight line equation corresponding to each category; Calculate the distance between the tunnel wall point cloud and the straight line equation corresponding to each category; If the distance is less than a preset distance, then determine the point cloud as the bridge point cloud corresponding to each category; Superimpose the bridge point clouds corresponding to all categories to form the communication cable tray point cloud data.

5. A communication cable bridge quantity measurement device based on tunnel point cloud, characterized in that, Including: Point cloud acquisition module: Obtain tunnel point cloud data; Tunnel wall point cloud analysis module: According to the tunnel point cloud data, obtain tunnel wall point cloud and the plane equation of the tunnel wall point cloud; Bridge point cloud acquisition module: Obtain communication cable tray point cloud data according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud; Obtaining the point cloud data of the communication cable bridge according to the tunnel wall point cloud and the plane equation of the tunnel wall point cloud includes: obtaining the normal direction of the tunnel wall plane according to the plane equation of the tunnel wall point cloud; intercepting the point cloud data in the tunnel wall point cloud that is at a distance of the preset length value from the tunnel wall plane equation according to a preset length value, and determining it as the point cloud data of the bridge end point; clustering the point cloud data of the bridge end point, and calculating the central coordinates of each category; obtaining the bridge point cloud corresponding to each category according to the central coordinates of each category, the normal direction of the tunnel wall plane and the tunnel wall point cloud, and forming the point cloud data of the communication cable bridge. Quantity measurement module: performing clustering analysis on the point cloud data of the communication cable bridge, counting the total number of categories, and generating the quantity of the communication cable bridge.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method for measuring the quantity of a communication cable bridge based on tunnel point clouds as described in any one of claims 1-4.

7. A communication cable bridge quantity measurement device based on tunnel point cloud, characterized in that, Including: A memory for storing instructions; A processor for executing the instructions, so that the device performs the operations of implementing the method for measuring the quantity of a communication cable bridge based on tunnel point clouds as described in any one of claims 1-4.

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