Measurement method and system for mine method tunnel steel arch installation, computer equipment, storage medium and program product

Abstract

The invention relates to the technical field of tunnel arch installation and measurement, and discloses a measurement method and system for mining method tunnel steel arch installation, computer equipment, a storage medium and a program product. The technical problems that in the prior art, manual real-time measurement is needed during tunnel arching in a mining method, meanwhile, steel arches are mostly prefabricated through welding of multiple trusses, when the arches are moved, adjacent arches can be affected, the difficulty of the arching splicing process is increased, the working hours of the arching environment can reach several hours, the working procedure time is long, and the efficiency is low are solved. The method comprises the following steps: determining a reference point; acquiring data; calibrating data; de-noising the data; position prediction; pre-mounting an arch frame; processing data; identifying an arch frame; an installation suggestion; and performing iterative optimization. According to the invention, the laser radar is adopted to realize rapid positioning of tunnel contour and arch frame installation, so that the efficiency of traditional manual positioning is greatly improved; and an arch frame assembling worker is assisted to quickly assemble the arch frame, so that the working procedure efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel arch installation measurement, and particularly to a measurement method, system, computer device, storage medium and program product for the installation of steel arches in a mine method tunnel. Background Art

[0002] With the rapid development of China's economy and technology, our infrastructure construction has started to expand to the mountainous areas in the west. A series of underground projects such as traffic tunnels, water diversion tunnels, and hydropower projects have been under construction. The construction method of the mine method tunnel is a common method for constructing tunnels or underground spaces and is widely used in different geological environments. The construction process of the mine method mainly includes three links: excavation, support, and lining construction. Setting up steel arches is an important means in the support link, and efficient and rapid erection of arches contributes to construction safety. The existing installation measurement of steel arches mainly relies on manual work. To ensure that the tunnel contour meets the design requirements, multiple measurements and adjustments are required. In addition, to improve the installation efficiency inside the tunnel, steel arches are mostly prefabricated by welding multiple frames. While adjusting a single frame of the arch, it will affect the adjacent arches in a linkage manner, increasing the difficulty of the arch assembly process, and the working hours in the arch erection environment can be as long as several hours. Therefore, there is an urgent need for an automatic and rapid measurement method for the installation of steel arches in a mine method tunnel to improve the efficiency of the arch erection process and at the same time improve the safety during the tunnel construction process.

[0003] The Chinese patent document in the prior art: 202111432325.3 discloses an integrated measurement auxiliary device and method for tunnel surrounding rock stress and steel arch strain, including: a pressure cell fixing mechanism, a box body and a flexible material. The box body is used to place the pressure cell, and the flexible material is used to fill the space between the pressure cell and the box body. The pressure cell inside the box body can be in contact with the surrounding rock through the flexible material; a strain gauge fixing mechanism: including a cartridge that can be fixed to the strain gauge, and a fixing member is fixed to the cartridge. The fixing member can be fixed to the outer side of the steel arch so that the strain gauge clamped by the cartridge is pressed against the outer side of the steel arch; a jacking mechanism: including at least two telescopic driving members, and the telescopic driving members are used to be arranged between the pressure cell fixing mechanism and the strain gauge fixing mechanism and can apply a jacking force to the pressure cell fixing mechanism and the strain gauge fixing mechanism. The construction speed of the device of the present invention is fast.

[0004] However, during the implementation of the above solution, there are at least the following technical problems: During the arch erection of a mine method tunnel, manual real-time measurement is required. At the same time, steel arches are mostly prefabricated by welding multiple frames. While moving the arch, it will affect the adjacent arches, increasing the difficulty of the arch assembly process, and the working hours in the arch erection environment can be as long as several hours, with a long process time and low efficiency. Therefore, there is an urgent need to propose a measurement method, system, computer device, storage medium and program product for the installation of steel arches in a mine method tunnel. Summary of the Invention

[0005] In view of the above technical problems, the present disclosure provides a measurement method, system, computer device, storage medium, and program product for the installation of steel arch frames in a mine tunneling method, which solves the technical problems in the prior art that manual real-time measurement is required during the erection of steel arch frames in a mine tunneling method. At the same time, multiple steel arch frames are mostly prefabricated by welding multiple frames. When moving the arch frames, it will affect the adjacent arch frames, increasing the difficulty of the arch frame assembly process. The working hours for the erection environment can be as long as several hours, and the process time is long and the efficiency is low.

[0006] According to one aspect of the present disclosure, a measurement method for the installation of steel arch frames in a mine tunneling method is provided, including the following steps: (1) Determine the reference point: Determine the position of the reference point for lidar measurement in the tunnel or underground space; (2) Data acquisition: Use lidar to scan the surrounding rock of the tunnel or underground space to obtain the surrounding rock point cloud data; (3) Data calibration: Calibrate the surrounding rock point cloud data described in step (2) to obtain the calibrated surrounding rock point cloud data; (4) Data denoising: Perform denoising processing on the calibrated surrounding rock point cloud data described in step (3) to obtain the denoised surrounding rock point cloud data; (5) Position prediction: Predict the installation position of the arch frame according to the design contour surface of the tunnel and the arch frame information; (6) Pre-installation of the arch frame: Before actual installation, place the arch frame at the installation position predicted in step (5), and perform point cloud data scanning again to evaluate the installation effect; (7) Data processing: Perform denoising and preprocessing on the point cloud data of the pre-installed arch frame in step (6) to obtain the denoised and preprocessed data; (8) Arch frame recognition: Perform arch frame recognition on the denoised and preprocessed point cloud data of the pre-installed arch frame in step (7); (9) Installation suggestions: Infer and propose arch frame installation suggestions, including the moving direction and distance of different points of the arch frame; (10) Iterative optimization: Repeat steps 6 to 9 until the arch frame installation design requirements are met.

[0007] In some embodiments of the present disclosure, the data calibration in step (3) further includes the following steps: Step 3.1 Pose information: Calculate the rotation angle value of the laser line to determine the pose information at different times; Step 3.2 Distance value: Obtain the distance value of each laser point in the surrounding rock point cloud data; Step 3.3 Three-dimensional coordinate value: Convert the three-dimensional coordinate value of the laser point according to the pose described in step 3.1 and the distance value described in step 3.2; Step 3.4 Data Calibration: By acquiring lidar point cloud data, calculating the direction vector and elevation angle of each lidar beam, and then calculating the actual horizontal measurement distance to update the measurement distance and obtain the calibrated point cloud data.

[0008] In some embodiments of the present disclosure, the step (5) position prediction further includes the following steps: Step 5.1 Profile Comparison: Input the designed profile surface of the tunnel, compare the existing profile, and perform under-excavation treatment. Step 5.2 Position Prediction: Input the profile of the arch frame closest to the heading face or refer to the erection information of the arch frame in the previous cycle, and predict the position of the next adjacent arch frame, focusing on controlling the positions of the crown, shoulders, and bottom. The moving directions include the tunnel axis and the radial direction. Step 5.3 Visualization Guidance: Superimpose and display the position of the next arch frame predicted in step 5.2 on the point cloud map to facilitate the installation guidance. In some embodiments of the present disclosure, the step (8) arch frame recognition further includes the following steps: Step 8.1 Residual Data: Project the point cloud data without and with the arch frame onto a two-dimensional vector respectively, and subtract the two data bodies to obtain the residual data. Step 8.2 Grayscale Image: Convert the residual data described in step 8.1 into a grayscale image, where the two-dimensional coordinates are the grayscale pixel positions and the residual is the grayscale value. Step 8.3 Arch Frame Recognition: Solve the envelope of the grayscale values described in step 8.2. The enveloped part is the arch frame area, and then obtain the pose information of the arch frame to complete the arch frame recognition. In some embodiments of the present disclosure, the step (9) installation suggestion further includes the following steps: Step 9.1 Form New Samples: Use the moving position information to compare the changes in the overall position of the arch frame before and after the movement to form new samples. Step 9.2 Update the Sample Set: Incorporate the new samples described in step 9.1 into the sample set. Step 9.3 Move Prediction Model: Train the arch frame movement prediction model according to the sample set described in step 9.2. Step 9.4 Movement Suggestion: Give suggestions for arch frame movement based on the differences between the current situation and the design of the arch frame, including the position, direction, and distance of the moving points.

[0009] A measurement system for the installation of steel arch frames in a mining-method tunnel includes a surrounding rock data processing module and an arch frame data processing module; The surrounding rock data processing module is used to determine the position of the reference point measured by lidar in a tunnel or underground space; scan the surrounding rock of the tunnel or underground space using lidar to obtain the surrounding rock point cloud data; calibrate the obtained surrounding rock point cloud data to obtain the calibrated surrounding rock point cloud data; perform denoising processing on the calibrated surrounding rock point cloud data to obtain the denoised surrounding rock point cloud data. The arch data processing module is used to predict the installation position of the arch according to the designed contour surface of the tunnel and the arch information; place the arch at the predicted installation position and scan the point cloud data again to evaluate the installation effect; perform denoising and preprocessing on the point cloud data of the pre-installed arch to obtain the denoised and preprocessed data; identify the arch from the denoised and preprocessed point cloud data of the pre-installed arch; infer and propose arch installation suggestions, including the moving direction and distance of different points of the arch.

[0010] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0011] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0012] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0013] The beneficial effects of the present invention are as follows: The present invention uses lidar to achieve rapid positioning of the tunnel contour and arch installation, ensuring the accuracy of the arch installation position, reducing manual measurement errors, and greatly improving the efficiency of traditional manual positioning; the present invention can predict the installation position of the arch, and the position selection can better meet the design requirements of the tunnel or underground chamber; the present invention can identify the arch, and by comparing the differences in the predicted positions, determine the adjustment direction and distance of the arch; the present invention can assist the arch assembly workers in quickly assembling the arch and improve the process efficiency. The automated data processing greatly shortens the time for arch installation, improves the overall construction progress, and reduces the labor cost. Through the iterative optimization process, it is ensured that each adjustment can be closest to the design requirements to the greatest extent, avoiding unnecessary material waste. The predicted arch position is superimposed and displayed on the point cloud map, providing an intuitive operation guide for the construction team and simplifying the installation process. Using machine learning algorithms to continuously update the sample set and train the movement prediction model, the prediction ability and accuracy of the system are gradually improved. Description of the Drawings

[0014] Figure 1 It is a flow block diagram of a measurement method for installing a steel arch in a tunnel using the mining method. Detailed implementation manners

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention. Embodiment 1

[0016] This example discloses a measurement method for the installation of steel arch supports in a tunnel constructed by the mining method. Refer to Figure 1 ; It includes the following steps: (1) Determine the reference point: Determine the position of the reference point for lidar measurement in the tunnel or underground space; (2) Data acquisition: Use lidar to scan the surrounding rock of the tunnel or underground space to obtain the point cloud data of the surrounding rock; (3) Data calibration: Calibrate the point cloud data of the surrounding rock described in step (2) to obtain the calibrated point cloud data of the surrounding rock; (4) Data denoising: Perform denoising processing on the calibrated point cloud data of the surrounding rock described in step (3) to obtain the denoised point cloud data of the surrounding rock; (5) Position prediction: Predict the installation position of the arch support according to the design profile of the tunnel and the arch support information; (6) Pre-installation of the arch support: Before actual installation, place the arch support at the installation position predicted in step (5), and perform point cloud data scanning again to evaluate the installation effect; (7) Data processing: Perform denoising and preprocessing on the point cloud data of the pre-installed arch support in step (6) to obtain the denoised and preprocessed data; (8) Arch support identification: Identify the arch support from the denoised and preprocessed point cloud data of the pre-installed arch support in step (7); (9) Installation suggestions: Deduce and put forward installation suggestions for the arch support, including the moving direction and distance of different points of the arch support; (10) Iterative optimization: Repeat steps 6 to 9 until the design requirements for the installation of the arch support are met.

[0017] The data calibration in step (3) further includes the following steps: Step 3.1 Pose information: Calculate the rotation angle value of the laser line to determine the pose information at different times; Step 3.2 Distance value: Obtain the distance value of each laser point in the point cloud data of the surrounding rock; Step 3.3 Three-dimensional coordinate value: Convert the three-dimensional coordinate value of the laser point according to the pose described in step 3.1 and the distance value described in step 3.2; Step 3.4 Data correction: By obtaining the lidar point cloud data, calculate the direction vector and elevation angle of each lidar beam, and then calculate the actual horizontal measurement distance to update the measurement distance and obtain the corrected point cloud data.

[0018] The position prediction in step (5) further includes the following steps: Step 5.1 Profile comparison: Input the designed profile surface of the tunnel, compare it with the existing profile and perform under-excavation treatment. Step 5.2 Position prediction: Input the profile of the nearest ring of arch support to the face or refer to the erection information of the arch support in the previous cycle, and predict the position of the next adjacent ring of arch support, focusing on controlling the positions of the crown, shoulders, and bottom. The moving directions include the axial and radial directions of the tunnel. Step 5.3 Visualization guidance: Superimpose and display the predicted position of the next ring of arch support in step 5.2 on the point cloud map to facilitate the guidance of installation. The arch support recognition in step (8) further includes the following steps: Step 8.1 Residual data: Project the point cloud data without and with arch support onto two-dimensional vectors respectively, and subtract the two data bodies to obtain residual data. Step 8.2 Grayscale image: Convert the residual data in step 8.1 into a grayscale image, where the two-dimensional coordinates are the grayscale pixel positions and the residuals are the grayscale values. Step 8.3 Arch support recognition: Solve the envelope of the grayscale values in step 8.2. The enveloped part is the arch support area, and thus the pose information of the arch support is obtained to complete the arch support recognition. The installation suggestion in step (9) further includes the following steps: Step 9.1 Form a new sample: Use the moving position information to compare the change in the overall position of the arch support before and after moving to form a new sample. Step 9.2 Update the sample set: Incorporate the new sample in step 9.1 into the sample set. Step 9.3 Moving prediction model: Train an arch support moving prediction model based on the sample set in step 9.2. Step 9.4 Moving suggestion: Give arch support moving suggestions based on the difference between the current situation and the design of the arch support, including the position, direction, and distance of the moving points.

[0019] A measurement system for the installation of steel arch supports in mined tunnels includes a surrounding rock data processing module and an arch support data processing module; The surrounding rock data processing module is used to determine the position of the reference point measured by lidar in a tunnel or underground space; scan the surrounding rock of the tunnel or underground space using lidar to obtain surrounding rock point cloud data; calibrate the obtained surrounding rock point cloud data to obtain calibrated surrounding rock point cloud data; perform denoising processing on the calibrated surrounding rock point cloud data to obtain denoised surrounding rock point cloud data. The arch support data processing module is used to predict the installation position of the arch support according to the designed contour surface of the tunnel and the arch support information; place the arch support at the predicted installation position, and perform point cloud data scanning again to evaluate the installation effect; denoise and preprocess the point cloud data of the pre-installed arch support to obtain the denoised and preprocessed data; identify the arch support from the denoised and preprocessed point cloud data of the pre-installed arch support; infer and propose arch support installation suggestions, including the moving direction and distance of different points of the arch support.

[0020] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be completed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0021] Each of the above modules can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0022] A computer device is provided. The computer device can be a server. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the present invention is implemented.

[0023] Those skilled in the art can understand that the relevant part of the structure of the present application solution does not limit the computer device to which the present application solution is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0024] In one embodiment, a computer device is provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0025] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are implemented.

[0026] In one embodiment, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0027] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0028] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the various embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0029] Although some preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0030] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of this application and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A measuring method for the installation of steel arch in a tunnel constructed by the mining method, characterized in that, It includes the following steps: (1) Determine the reference point: Determine the position of the reference point for lidar measurement in the tunnel or underground space; (2) Data acquisition: Use lidar to scan the surrounding rock of the tunnel or underground space to obtain the surrounding rock point cloud data; (3) Data calibration: Calibrate the surrounding rock point cloud data described in step (2) to obtain the calibrated surrounding rock point cloud data; (4) Data denoising: Denoise the calibrated surrounding rock point cloud data described in step (3) to obtain the denoised surrounding rock point cloud data; (5) Position prediction: Predict the installation position of the arch frame according to the design contour surface and arch frame information of the tunnel; (6) Arch frame pre-installation: Before actual installation, place the arch frame at the installation position predicted in step (5), and scan the point cloud data again to evaluate the installation effect; (7) Data processing: Denoise and preprocess the point cloud data of the pre-installed arch frame in step (6) to obtain the denoised and preprocessed data; (8) Arch frame recognition: Recognize the arch frame from the denoised and preprocessed point cloud data of the pre-installed arch frame in step (7); (9) Installation suggestion: Infer and put forward installation suggestions for the arch frame, including the moving direction and distance of different points of the arch frame; (10) Iterative optimization: Repeat steps 6-9 until the installation design requirements of the arch frame are met.

2. The measuring method for installing steel arch in a tunnel by the mining method as claimed in claim 1, wherein: The data calibration in step (3) further includes the following steps: Step 3.1 Pose information: Calculate the rotation angle value of the laser line to determine the pose information at different times; Step 3.2 Distance value: Obtain the distance value of each laser point in the surrounding rock point cloud data; Step 3.3 Three-dimensional coordinate value: Convert the three-dimensional coordinate value of the laser point according to the pose described in step 3.1 and the distance value described in step 3.2; Step 3.4 Data correction: By obtaining the lidar point cloud data, calculate the direction vector and elevation angle of each lidar beam, and then calculate the actual horizontal measurement distance to update the measurement distance and obtain the corrected point cloud data.

3. The measuring method for installing steel arch in a tunnel by the mining method according to claim 1, characterized in that: The position prediction in step (5) further includes the following steps: Step 5.1 Contour comparison: Input the design contour surface of the tunnel, compare the existing contour and perform under-excavation treatment; Step 5.2 Position prediction: Input the contour of the arch frame closest to the heading face or refer to the arch frame erection information of the previous cycle, and predict the position of the next adjacent arch frame, focusing on controlling the positions of the arch crown, arch shoulder, and bottom. The moving directions include the tunnel axis and radial direction; Step 5.3 Visualization guidance: Superimpose and display the position of the next arch frame predicted in step 5.2 on the point cloud map to facilitate installation guidance.

4. The measuring method for installing steel arch in a mine tunneling method according to claim 1, characterized in that: The arch frame recognition in step (8) further includes the following steps: Step 8.1 Residual data: Project the point cloud data without and with the arch frame onto two-dimensional vectors respectively, and subtract the two data bodies to obtain the residual data; Step 8.2 Grayscale image: Convert the residual data described in step 8.1 into a grayscale image, where the two-dimensional coordinates are the grayscale pixel positions and the residual is the grayscale value; Step 8.3 Arch frame recognition: Solve the envelope of the grayscale value described in step 8.

2. The envelope part is the arch frame area, and then obtain the pose information of the arch frame to complete the arch frame recognition.

5. The measuring method for installing steel arch in a tunnel by the mining method according to claim 1, wherein: The installation suggestion in step (9) further includes the following steps: Step 9.1 Form a new sample: Using the moving position information, compare the change in the overall position of the arch frame before and after movement to form a new sample; Step 9.2 Update the sample set: Incorporate the new sample described in Step 9.1 into the sample set; Step 9.3 Train the arch frame movement prediction model: Train the arch frame movement prediction model according to the sample set described in Step 9.2; Step 9.4 Provide movement suggestions: Give arch frame movement suggestions based on the difference between the current status of the arch frame and the design, including the position, direction, and distance of the movement points.

6. A measurement system for the installation of steel arch in a tunnel constructed by the mining method, characterized in that, It includes a surrounding rock data processing module and an arch frame data processing module; The surrounding rock data processing module is used to determine the position of the reference point measured by the lidar in the tunnel or underground space; scan the surrounding rock of the tunnel or underground space using the lidar to obtain the surrounding rock point cloud data; calibrate the surrounding rock point cloud data to obtain the calibrated surrounding rock point cloud data; Perform denoising processing on the calibrated surrounding rock point cloud data to obtain the denoised surrounding rock point cloud data; The arch frame data processing module is used to predict the installation position of the arch frame according to the design profile of the tunnel and the arch frame information; place the arch frame at the predicted installation position and scan the point cloud data again to evaluate the installation effect; perform denoising and preprocessing on the point cloud data of the pre-installed arch frame to obtain the denoised and preprocessed data; identify the arch frame from the denoised and preprocessed point cloud data of the pre-installed arch frame; infer and propose arch frame installation suggestions, including the movement direction and distance of different points of the arch frame.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 5.

9. A computer program product, characterized in that, It includes a computer program that, when executed by the processor, implements the steps of the method described in any one of claims 1 to 5.

Images