Undercut compensation finishing method and system combining blasting and heading machine operation
By combining blasting and tunneling machine operations, and using 3D laser scanning and BIM models to generate digital under-excavation models, the problem of accurate identification and compensation of under-excavation areas in tunnel construction has been solved, achieving efficient and accurate quality control of tunnel forming.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-03-13
AI Technical Summary
In tunnel drilling and blasting construction, existing technologies struggle to accurately identify and efficiently compensate for under-excavated areas in the tunnel. Traditional methods lack quantitative decision support, and three-dimensional detection cannot play a proactive control role in the construction closed loop.
By combining blasting and tunneling machine operations, point cloud data of the rock surface is acquired and a digital under-excavation model is generated. Precise data processing is performed using 3D laser scanning and BIM modeling to generate a digital under-excavation model to guide the compensation and repair of the tunnel.
It enables accurate identification and efficient compensation and adjustment of under-excavated areas in tunnels, significantly improving the scientific nature of decision-making and processing accuracy, shortening the processing cycle, and reducing under-excavation or over-excavation caused by human misjudgment.
Smart Images
Figure CN121654428A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of tunnel excavation technology, and more specifically, to a method and system for compensating for under-excavation by combining blasting and tunneling machine operations. Background Technology
[0002] In tunnel drilling and blasting construction, accurately controlling the excavation outline and reducing over- and under-excavation have long been technical challenges. Traditional methods typically involve drilling and blasting to form the tunnel in one step, but due to geological variations, drilling errors, and blasting disturbances, the outline is difficult to control precisely, often leading to over-excavation (increasing concrete consumption) or under-excavation (requiring secondary processing). While existing technologies attempt to introduce 3D scanning for quality inspection, this is usually limited to post-construction verification and cannot play a proactive control role in the construction closed loop. Some solutions propose reserving a smooth blasting layer or using mechanical trimming, but these methods often lack quantitative decision support: either relying on manual experience to judge the extent and depth of under-excavation, resulting in crude processing and low efficiency; or acquiring point cloud data but failing to perform in-depth intelligent processing to filter noise and extract reliable features, leading to inaccurate guidance information. Therefore, there is an urgent need for an integrated method that can deeply integrate blasting technology, 3D detection, intelligent point cloud processing, and construction guidance to achieve quantitative identification and efficient compensation and adjustment of under-excavated areas in tunnels. Summary of the Invention
[0003] The purpose of this invention is to provide a method and system for under-excavation compensation and correction that combines blasting and tunneling machine operations, so as to improve the above-mentioned problems.
[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions: On one hand, embodiments of this application provide a method for compensating for under-excavation by combining blasting and tunneling machine operations, the method comprising: Obtain input information and the BIM model of the tunnel. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. Send control commands, including commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data; A digital under-excavation model is generated based on the rock surface point cloud data from each scan and the tunnel's BIM model, and the under-excavation compensation and repair of the tunnel is completed based on the digital under-excavation model.
[0005] Secondly, embodiments of this application provide an under-excavation compensation and adjustment system combining blasting and tunneling machine operations, the system comprising: The acquisition module is used to acquire input information and the BIM model of the tunnel. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. The sending module is used to send control commands, including commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data; The generation module is used to generate a digital under-excavation model based on the rock surface point cloud data and the tunnel's BIM model from each scan, and to complete the under-excavation compensation and adjustment of the tunnel based on the digital under-excavation model.
[0006] Thirdly, embodiments of this application provide an under-excavation compensation and adjustment device that combines blasting and tunneling machine operations. The device includes a memory and a processor. The memory stores a computer program; the processor executes the computer program to implement the steps of the aforementioned under-excavation compensation and adjustment method combining blasting and tunneling machine operations.
[0007] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for compensating and correcting under-excavation by combining blasting and tunneling machine operations.
[0008] The beneficial effects of this invention are as follows: 1. This invention proactively creates a controllable under-excavation state through initial blasting and utilizes 3D laser scanning to acquire precise rock surface point cloud data. Further preprocessing steps ensure the reliability of the basic data. The resulting digital under-excavation model accurately includes quantitative indicators such as the spatial boundaries, volume, average depth, and maximum depth of each under-excavated area. This transforms subsequent compensation and adjustment operations into quantitative guidance based on precise 3D data, greatly improving the scientific rigor and processing accuracy of decision-making.
[0009] 2. This invention constructs a complete process that begins with blasting technology, connects data acquisition, intelligent processing, and final guidance. This closed-loop system integrates the traditional discrete processes of blasting, detection, manual judgment, and processing into a coherent intelligent process of controlled blasting, automatic scanning, intelligent modeling, and quantitative guidance. This significantly shortens the processing cycle and fundamentally reduces under-processing or over-excavation caused by human misjudgment, ensuring the final quality of the tunnel outline.
[0010] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description
[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic diagram of the under-excavation compensation and adjustment method combining blasting and tunneling machine operations as described in the embodiments of the present invention; Figure 2 This is a schematic diagram of the under-excavation compensation and adjustment system combining blasting and tunneling machine operations as described in this embodiment of the invention; Figure 3 This is a schematic diagram of the under-excavation compensation and repair equipment that combines blasting and tunneling machine operations as described in this embodiment of the invention. Detailed Implementation
[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0014] It should be noted that similar reference numerals or letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0015] Example 1 like Figure 1 As shown, this embodiment provides a method for compensating for under-excavation by combining blasting and tunneling machine operations. The method includes steps S1, S2 and S3.
[0016] Step S1: Obtain input information. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. In this step, the preset thickness can be customized. After the first blasting excavation, the overall state is under-excavated in a controllable manner. Step S2: Send control commands. The control commands include commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data. In this step, immediately after the blasting and excavation and muck removal in step S1, a 3D laser scanner is used to scan the rock surface after the tunnel excavation to obtain point cloud data of the rock surface. Step S3: Generate a digital under-excavation model based on the rock surface point cloud data and the tunnel's BIM model from each scan, and complete the under-excavation compensation and adjustment of the tunnel based on the digital under-excavation model.
[0017] The specific implementation steps of this step include step S31 and step S32; Step S31: Preprocess the rock surface point cloud data to obtain preprocessed rock surface point cloud data; The specific implementation steps of this step include step S311 and step S312; Step S311: Find the neighboring points of each point i in the rock surface point cloud data; take the average Euclidean distance between point i and each neighboring point to obtain the average Euclidean distance; record the absolute value of the difference between each Euclidean distance and the average Euclidean distance as the first difference of each Euclidean distance; sort all the first differences in ascending order, and take the first difference with an even sorting position as the target difference; for each target difference, collect the Euclidean distances corresponding to the first differences greater than the target difference to obtain the first Euclidean distance set corresponding to each target difference; collect the Euclidean distances corresponding to the first differences less than or equal to the target difference to obtain the second Euclidean distance set corresponding to each target difference; calculate the local average distance corresponding to point i based on the first Euclidean distance set and the second Euclidean distance set. In this step, the specific implementation steps for finding the neighboring points corresponding to each point i in the rock surface point cloud data include step S3111; Step S3111: For each point i in the rock surface point cloud data, calculate its Euclidean distance to all other points in the rock surface point cloud data; sort all the calculated Euclidean distance values in ascending order, and select the points corresponding to the first K Euclidean distance values as the K neighbor points of point i.
[0018] In step S311, the specific implementation steps for calculating the local average distance corresponding to point i based on the first Euclidean distance set and the second Euclidean distance set include step S3112. Step S3112: In the first Euclidean distance set, perform the first calculation process: Each time, select one Euclidean distance as the target Euclidean distance, calculate the absolute value of the difference between the target Euclidean distance and other Euclidean distances in the first Euclidean distance set, and calculate the mean of all absolute differences to obtain the first mean. Calculate the absolute value of the difference between the target Euclidean distance and each Euclidean distance in the second Euclidean distance set, and calculate the mean of all absolute differences to obtain the second mean. Filter the maximum value between the first and second means, and simultaneously calculate the difference between the second mean and the first mean. Record the ratio between the difference and the maximum value as the first ratio corresponding to the target Euclidean distance. When the corresponding first ratio is calculated for each Euclidean distance in the first Euclidean distance set... After comparing the values, all the first ratios are summed to obtain the first ratio sum corresponding to the first Euclidean distance set; similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum corresponding to the second Euclidean distance set; the first ratio sum is added to the second ratio sum to obtain the third ratio sum corresponding to each target difference; the third ratio sum corresponding to each target difference is analyzed, and the target difference corresponding to the largest third ratio sum is used as the boundary value; the Euclidean distance between point i and each neighboring point is compared with the boundary value, and the mean of the Euclidean distances less than or equal to the boundary value is used as the local average distance corresponding to point i; the preprocessing of the rock surface point cloud data is completed based on the local average distance corresponding to each point to obtain the preprocessed rock surface point cloud data.
[0019] Steps S311 and S3112 automatically identify and filter out abnormal distance values in the local neighborhood, thereby calculating a more robust local average distance. This improves the reliability of the preprocessed rock surface point cloud data.
[0020] Similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum corresponding to the second Euclidean distance set. This step can be expanded as follows: In the second set of Euclidean distances, each time a Euclidean distance is selected as the target Euclidean distance, the absolute value of the difference between the target Euclidean distance and other Euclidean distances in the second set is calculated, and the mean of all absolute differences is calculated to obtain the third mean. The absolute value of the difference between the target Euclidean distance and each Euclidean distance in the first set is calculated, and the mean of all absolute differences is calculated to obtain the fourth mean. The maximum value between the third and fourth means is selected, and the difference between the fourth and third means is calculated. The ratio between the difference and the maximum value is recorded as the third ratio corresponding to the target Euclidean distance. After the third ratio is calculated for each Euclidean distance in the second set of Euclidean distances, all the third ratios are added together to obtain the second ratio sum corresponding to the second set of Euclidean distances. Step S312: Calculate the mean and standard deviation of the local average distances corresponding to all points in the entire rock surface point cloud data; multiply the preset scale factor by the standard deviation to obtain the first value; sum the first value with the mean to obtain the distance threshold; compare the local average distance corresponding to each point with the distance threshold. If the local average distance is greater than the distance threshold, remove this point to obtain the preprocessed rock surface point cloud data.
[0021] In this step, the scale factor can be between 2 and 3, for example, 2.5. This step identifies and filters out discrete noise and abnormal flying points generated during the scanning process, thereby ensuring the cleanliness and reliability of the three-dimensional data at the source, and laying a solid data foundation for the digital identification and repair of tunnel under-excavation.
[0022] Step S32: Import the preprocessed rock surface point cloud data and the tunnel BIM model into the Leica Cyclone 3DR software platform. Use the Leica Cyclone 3DR software platform to generate a digital under-excavation model. The digital under-excavation model includes the spatial boundaries, volume, average depth, and maximum depth of each under-excavation area. Send the spatial boundaries, volume, average depth, and maximum depth of each under-excavation area included in the digital under-excavation model to the staff to guide them in using the tunnel boring machine to excavate the tunnel and complete the under-excavation compensation and adjustment of the tunnel.
[0023] In this step, generating a digital under-excavation model using the Leica Cyclone 3DR software platform is a standard procedure; alternatively, Autodesk ReCap software can be used. After the spatial boundaries, volume, average depth, and maximum depth of each under-excavation area included in the digital under-excavation model are sent to the staff, the staff can use the precise spatial positioning (boundaries) and quantified construction parameters (volume, depth) provided by the model to operate the tunnel boring machine to perform precise and efficient compensation and adjustment of the designated under-excavation area, ultimately ensuring that the tunnel outline meets the design requirements.
[0024] Example 2 like Figure 2 As shown, this embodiment provides an under-excavation compensation and adjustment system that combines blasting and tunneling machine operations. The system includes an acquisition module 1, a sending module 2, and a generation module 3.
[0025] The acquisition module 1 is used to acquire input information and the BIM model of the tunnel. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. Sending module 2 is used to send control commands, including commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data; The generation module 3 is used to generate a digital under-excavation model based on the rock surface point cloud data and the tunnel's BIM model from each scan, and to complete the under-excavation compensation and adjustment of the tunnel based on the digital under-excavation model.
[0026] In one specific embodiment of this disclosure, the generation module 3 further includes a preprocessing unit 31 and a sending unit 32.
[0027] Preprocessing unit 31 is used to preprocess the rock surface point cloud data to obtain preprocessed rock surface point cloud data; The sending unit 32 is used to import the preprocessed rock surface point cloud data and the tunnel BIM model into the Leica Cyclone 3DR software platform, and use the Leica Cyclone 3DR software platform to generate a digital under-excavation model. The digital under-excavation model includes the spatial boundaries, volume, average depth and maximum depth of each under-excavation area. The digital under-excavation model includes the spatial boundaries, volume, average depth and maximum depth of each under-excavation area, which are sent to the staff to guide them to use the tunnel boring machine to excavate the tunnel and complete the under-excavation compensation and adjustment of the tunnel.
[0028] In one specific embodiment of this disclosure, the preprocessing unit 31 further includes a lookup unit 311 and a first calculation unit 312.
[0029] The lookup unit 311 is used to perform a first calculation process in the first Euclidean distance set: Each time, a Euclidean distance is selected as the target Euclidean distance; the absolute value of the difference between the target Euclidean distance and other Euclidean distances in the first Euclidean distance set is calculated, and the mean of all absolute differences is calculated to obtain a first mean; the absolute value of the difference between the target Euclidean distance and each Euclidean distance in the second Euclidean distance set is calculated, and the mean of all absolute differences is calculated to obtain a second mean; the maximum value between the first and second means is selected, and the difference between the second mean and the first mean is calculated; the ratio between the difference and the maximum value is recorded as the first ratio corresponding to the target Euclidean distance; when the first Euclidean distance is selected... After calculating the corresponding first ratio for each Euclidean distance in the Euclidean distance set, all the first ratios are summed to obtain the first ratio sum for the first Euclidean distance set. Similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum for the second Euclidean distance set. The first ratio sum is added to the second ratio sum to obtain the third ratio sum for each target difference. The third ratio sum for each target difference is analyzed, and the target difference corresponding to the largest third ratio sum is taken as the threshold value. The Euclidean distance between point i and each neighboring point is compared with the threshold value, and the mean of the Euclidean distances less than or equal to the threshold value is taken as the local average distance for point i. The first calculation unit 312 is used to calculate the mean and standard deviation of the local average distances corresponding to all points in the entire rock surface point cloud data; multiply the preset scale factor by the standard deviation to obtain a first value; sum the first value with the mean to obtain a distance threshold; compare the local average distance corresponding to each point with the distance threshold, and if the local average distance is greater than the distance threshold, remove this point to obtain the preprocessed rock surface point cloud data.
[0030] In one specific embodiment of this disclosure, the search unit 311 further includes a first selection unit 3111 and a second selection unit 3112.
[0031] The first selection unit 3111 is used to calculate the Euclidean distance from each point i in the rock surface point cloud data to all other points in the rock surface point cloud data; sort all the calculated Euclidean distance values in ascending order, and select the points corresponding to the first K Euclidean distance values as the K neighbor points of point i.
[0032] In one specific embodiment of this disclosure, the first computing unit 312 further includes a second computing unit 3121.
[0033] The second calculation unit 3121 is used to perform a first calculation process in the first Euclidean distance set: Each time, a Euclidean distance is selected as the target Euclidean distance; the absolute value of the difference between the target Euclidean distance and other Euclidean distances in the first Euclidean distance set is calculated, and the mean of all absolute differences is calculated to obtain a first mean; the absolute value of the difference between the target Euclidean distance and each Euclidean distance in the second Euclidean distance set is calculated, and the mean of all absolute differences is calculated to obtain a second mean; the maximum value between the first mean and the second mean is selected, and the difference between the second mean and the first mean is calculated; the ratio between the difference and the maximum value is recorded as the first ratio corresponding to the target Euclidean distance; when the first... After calculating the first ratio for each Euclidean distance in the Euclidean distance set, all the first ratios are summed to obtain the first ratio sum for the first Euclidean distance set. Similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum for the second Euclidean distance set. The first ratio sum is added to the second ratio sum to obtain the third ratio sum for each target difference. The third ratio sum for each target difference is analyzed, and the target difference corresponding to the largest third ratio sum is taken as the threshold value. The Euclidean distance between point i and each neighboring point is compared with the threshold value, and the mean of the Euclidean distances less than or equal to the threshold value is taken as the local average distance for point i.
[0034] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.
[0035] Example 3 Corresponding to the above method embodiments, this disclosure also provides an under-excavation compensation and adjustment device that combines blasting and tunneling machine operations. The under-excavation compensation and adjustment device that combines blasting and tunneling machine operations described below and the under-excavation compensation and adjustment method that combines blasting and tunneling machine operations described above can be referred to in correspondence with each other.
[0036] Figure 3 This is a block diagram illustrating an undercut compensation and adjustment device 300 that combines blasting and tunneling machine operations according to an exemplary embodiment. Figure 3 As shown, the undercut compensation and adjustment device 300 combining blasting and tunneling machine operations may include: a processor 301 and a memory 302. The undercut compensation and adjustment device 300 combining blasting and tunneling machine operations may also include one or more of the following: a multimedia component 303, an I / O interface 304, and a communication component 305.
[0037] The processor 301 controls the overall operation of the under-excavation compensation and correction device 300, which combines blasting and tunneling machine operations, to complete all or part of the steps in the aforementioned under-excavation compensation and correction method. The memory 302 stores various types of data to support the operation of the under-excavation compensation and correction device 300. This data may include, for example, instructions for any application or method operating on the device, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 302 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 303 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 302 or transmitted via the communication component 305. The audio component also includes at least one speaker for outputting audio signals. I / O interface 304 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 305 is used for wired or wireless communication between the undercut compensation and trimming device 300 (combining blasting and tunneling machine operations) and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 305 may include a Wi-Fi module, a Bluetooth module, or an NFC module.
[0038] In an exemplary embodiment, the undercut compensation and adjustment device 300 combining blasting and tunneling machine operations may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned undercut compensation and adjustment method combining blasting and tunneling machine operations.
[0039] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the aforementioned under-excavation compensation and correction method combining blasting and tunneling machine operations. For example, the computer-readable storage medium may be the aforementioned memory 302 including program instructions, which may be executed by the processor 301 of the under-excavation compensation and correction device 300 combining blasting and tunneling machine operations to complete the aforementioned under-excavation compensation and correction method combining blasting and tunneling machine operations.
[0040] Example 4 Corresponding to the above method embodiments, this disclosure also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the under-excavation compensation and adjustment method combining blasting and tunneling machine operations described above.
[0041] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the under-excavation compensation and adjustment method combining blasting and tunneling machine operations as described in the above method embodiments.
[0042] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.
[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for compensating and correcting under-excavation by combining blasting and tunneling machine operations, characterized in that, include: Obtain input information and the BIM model of the tunnel. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. Send control commands, including commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data; A digital under-excavation model is generated based on the rock surface point cloud data from each scan and the tunnel's BIM model, and the under-excavation compensation and repair of the tunnel is completed based on the digital under-excavation model.
2. The under-excavation compensation and adjustment method combining blasting and tunneling machine operations according to claim 1, characterized in that, A digital under-excavation model is generated based on the rock surface point cloud data from each scan and the tunnel's BIM model. Under-excavation compensation and adjustment of the tunnel are then performed based on this digital under-excavation model, including: Preprocessing is performed on the rock surface point cloud data to obtain preprocessed rock surface point cloud data; The preprocessed rock surface point cloud data and the tunnel's BIM model are imported into the Leica Cyclone 3DR software platform. The Leica Cyclone 3DR software platform is then used to generate a digital under-excavation model, which includes the spatial boundaries, volume, average depth, and maximum depth of each under-excavation area. The spatial boundaries, volume, average depth, and maximum depth of each under-excavation area included in the digital under-excavation model are then sent to the staff to guide them in using a tunnel boring machine to excavate the tunnel and complete the under-excavation compensation and adjustment.
3. The under-excavation compensation and adjustment method combining blasting and tunneling machine operations according to claim 2, characterized in that, The rock surface point cloud data is preprocessed to obtain preprocessed rock surface point cloud data, including: Find the neighboring points of each point i in the rock surface point cloud data; average the Euclidean distances between point i and each neighboring point to obtain the mean Euclidean distance; record the absolute value of the difference between each Euclidean distance and the mean Euclidean distance as the first difference value of each Euclidean distance; sort all the first differences in ascending order, and take the first difference value with an even sorting position as the target difference value; for each target difference value, collect the Euclidean distances corresponding to the first differences greater than the target difference value to obtain the first Euclidean distance set corresponding to each target difference value, and collect the Euclidean distances corresponding to the first differences less than or equal to the target difference value to obtain the second Euclidean distance set corresponding to each target difference value; calculate the local average distance corresponding to point i based on the first Euclidean distance set and the second Euclidean distance set. Calculate the mean and standard deviation of the local average distances corresponding to all points in the entire rock surface point cloud data; multiply the preset scale factor by the standard deviation to obtain the first value; sum the first value with the mean to obtain the distance threshold; compare the local average distance corresponding to each point with the distance threshold, and if the local average distance is greater than the distance threshold, remove this point to obtain the preprocessed rock surface point cloud data.
4. The under-excavation compensation and adjustment method combining blasting and tunneling machine operations according to claim 3, characterized in that, Find the neighboring points of each point i in the rock surface point cloud data, including: For each point i in the rock surface point cloud data, calculate its Euclidean distance to all other points in the rock surface point cloud data; sort all the calculated Euclidean distance values in ascending order, and select the points corresponding to the first K Euclidean distance values as the K neighbor points of point i.
5. The under-excavation compensation and adjustment method combining blasting and tunneling machine operations according to claim 3, characterized in that, The local average distance corresponding to point i is calculated based on the first Euclidean distance set and the second Euclidean distance set, including: In the first set of Euclidean distances, the first calculation process is performed: Each time, a Euclidean distance is selected as the target Euclidean distance. The absolute value of the difference between the target Euclidean distance and other Euclidean distances in the first set is calculated, and the mean of all absolute differences is calculated to obtain the first mean. The absolute value of the difference between the target Euclidean distance and each Euclidean distance in the second set is calculated, and the mean of all absolute differences is calculated to obtain the second mean. The maximum value between the first and second means is selected, and the difference between the second and first means is calculated. The ratio between this difference and the maximum value is recorded as the first ratio corresponding to the target Euclidean distance. When the first set of Euclidean distances... After calculating the corresponding first ratio for each Euclidean distance in the set, all the first ratios are summed to obtain the first ratio sum corresponding to the first Euclidean distance set. Similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum corresponding to the second Euclidean distance set. The first ratio sum is added to the second ratio sum to obtain the third ratio sum corresponding to each target difference. The third ratio sum corresponding to each target difference is analyzed, and the target difference corresponding to the largest third ratio sum is taken as the threshold value. The Euclidean distance between point i and each neighboring point is compared with the threshold value, and the mean of the Euclidean distances less than or equal to the threshold value is taken as the local average distance corresponding to point i.
6. A system for compensating and adjusting under-excavation work combining blasting and tunneling machine operations, characterized in that, include: The acquisition module is used to acquire input information and the BIM model of the tunnel. The input information includes confirmation that the first blasting excavation of the tunnel has been completed. During the first blasting excavation, there is a buffer layer of preset thickness between the tunnel design outline and the blasting excavation outline. The sending module is used to send control commands, including commands to scan the rock surface after the first blasting excavation and obtain rock surface point cloud data; The generation module is used to generate a digital under-excavation model based on the rock surface point cloud data and the tunnel's BIM model from each scan, and to complete the under-excavation compensation and adjustment of the tunnel based on the digital under-excavation model.
7. The under-excavation compensation and adjustment system combining blasting and tunneling machine operations according to claim 6, characterized in that, The generation module includes: The preprocessing unit is used to preprocess the rock surface point cloud data to obtain preprocessed rock surface point cloud data. The sending unit is used to import the preprocessed rock surface point cloud data and the tunnel's BIM model into the Leica Cyclone 3DR software platform. The Leica Cyclone 3DR software platform is used to generate a digital under-excavation model, which includes the spatial boundaries, volume, average depth, and maximum depth of each under-excavation area. The spatial boundaries, volume, average depth, and maximum depth of each under-excavation area included in the digital under-excavation model are sent to the staff to guide them in using the tunnel boring machine to excavate the tunnel and complete the under-excavation compensation and adjustment of the tunnel.
8. The under-excavation compensation and adjustment system combining blasting and tunneling machine operations according to claim 7, characterized in that, The preprocessing unit includes: The lookup unit is used to find the neighboring points of each point i in the rock surface point cloud data; the average Euclidean distance between point i and each neighboring point is calculated by averaging the Euclidean distances; the absolute value of the difference between each Euclidean distance and the average Euclidean distance is recorded as the first difference of each Euclidean distance; all the first differences are sorted in ascending order, and the first differences with even-numbered sorting positions are taken as the target differences; for each target difference, the Euclidean distances corresponding to the first differences greater than the target difference are collected to obtain the first Euclidean distance set corresponding to each target difference, and the Euclidean distances corresponding to the first differences less than or equal to the target difference are collected to obtain the second Euclidean distance set corresponding to each target difference; the local average distance corresponding to point i is calculated based on the first Euclidean distance set and the second Euclidean distance set. The first calculation unit is used to calculate the mean and standard deviation of the local average distances corresponding to all points in the entire rock surface point cloud data; multiply the preset scale factor by the standard deviation to obtain a first value; sum the first value with the mean to obtain a distance threshold; compare the local average distance corresponding to each point with the distance threshold, and if the local average distance is greater than the distance threshold, remove this point to obtain the preprocessed rock surface point cloud data.
9. The under-excavation compensation and adjustment system combining blasting and tunneling machine operations according to claim 8, characterized in that, The search unit includes: The first selection unit is used to calculate the Euclidean distance from each point i in the rock surface point cloud data to all other points in the rock surface point cloud data; sort all the calculated Euclidean distance values in ascending order, and select the points corresponding to the first K Euclidean distance values as the K neighbor points of point i.
10. The under-excavation compensation and adjustment system combining blasting and tunneling machine operations according to claim 8, characterized in that, The first computing unit includes: The second calculation unit is used to perform a first calculation process within the first set of Euclidean distances: Each time, a Euclidean distance is selected as the target Euclidean distance; the absolute value of the difference between the target Euclidean distance and other Euclidean distances in the first set of Euclidean distances is calculated, and the mean of all absolute differences is calculated to obtain a first mean; the absolute value of the difference between the target Euclidean distance and each Euclidean distance in the second set of Euclidean distances is calculated, and the mean of all absolute differences is calculated to obtain a second mean; the maximum value between the first and second means is selected, and the difference between the second and first means is calculated; the ratio between the difference and the maximum value is recorded as the first ratio corresponding to the target Euclidean distance; when the first Euclidean distance... After calculating the first ratio for each Euclidean distance in the distance set, all the first ratios are summed to obtain the first ratio sum for the first Euclidean distance set. Similarly, the first calculation process is performed on the second Euclidean distance set to obtain the second ratio sum for the second Euclidean distance set. The first ratio sum is added to the second ratio sum to obtain the third ratio sum for each target difference. The third ratio sum for each target difference is analyzed, and the target difference corresponding to the largest third ratio sum is taken as the threshold value. The Euclidean distance between point i and each neighboring point is compared with the threshold value, and the mean of the Euclidean distances less than or equal to the threshold value is taken as the local average distance for point i.