TBM support shoe adaptive step-changing device, control system and method

Abstract

The application discloses a TBM supporting shoe self-adaptive step changing device, a control system and a method, relates to the technical field of tunnel boring equipment automation, and comprises a data acquisition module (a laser radar array), a data processing module (point cloud denoising and feature extraction) and a control module (path planning and hydraulic servo control). The method realizes real-time scanning of the surrounding rock surface through the laser radar, identifies the steel arch and the collapse cavity features in combination with the improved RANSAC algorithm and PointNet++, automatically plans a collision-free step changing path according to the matching conditions, and precisely controls the displacement of the oil cylinder through the PID. The problems of low positioning accuracy of traditional manual operation, lagging response of the collapse cavity and limited efficiency are solved, and the safety and efficiency of the continuous tunneling of the TBM are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automation technology for tunnel boring equipment, and in particular to an adaptive step-changing device, control system and method for TBM support shoes. Background Technology

[0002] Against the backdrop of rapid development in modern infrastructure construction, tunnel engineering, as a vital link between cities and regions, directly impacts the success or failure of the entire project through its construction efficiency and quality. Tunnel boring machines (TBMs), as high-end equipment integrating mechanization, automation, and intelligent technologies, dominate tunnel engineering projects such as mountain tunnels, subway lines, and water conservancy facilities due to their efficient, safe, and environmentally friendly construction characteristics. The working principle of a TBM relies on its precise and complex mechanical structure, among which the support shoe system plays a crucial role as a key support component.

[0003] The support shoe system, as the foundation for stable TBM tunneling, provides strong support for the machine's stability during tunneling through the precise coordination of components such as the support shoe cylinder. It not only ensures the cutterhead maintains a stable tunneling direction when breaking rock, but also effectively absorbs vibrations generated during tunneling, protecting the machine from damage. However, in actual operations, the TBM support shoe changing process often becomes a key factor restricting construction efficiency and quality.

[0004] Traditional TBM (Trailblader) boot-changing gait relies on manual observation and experience, which has the following drawbacks:

[0005] Low positioning accuracy: Manual visual inspection cannot monitor the deformation of the steel arch frame in real time, which can easily lead to collisions between the support shoe and the arch frame;

[0006] Delayed response to collapse: Identification of the collapse area relies on geological reports, making it difficult to adjust the support points of the support boots in a timely manner;

[0007] Efficiency limitations: Long changeover cycles affect the continuous tunneling speed of the TBM.

[0008] Existing technologies employ image-based visual monitoring of the support shoe position, but their three-dimensional environmental information is not accurate enough and their resistance to dust interference is poor. Therefore, there is an urgent need to develop a control system and method for TBM tunnel steel arch frame and cavity recognition and adaptive support shoe step-changing based on lidar. Summary of the Invention

[0009] The purpose of this invention is to provide a TBM support boot adaptive step-changing device, control system and method to solve the above problems.

[0010] The present invention achieves the above objectives through the following technical solutions:

[0011] A TBM support boot adaptive step-changing control device includes:

[0012] The main beam is used for guiding the device during step changes;

[0013] The propulsion mechanism includes a movable saddle frame fitted onto the main beam and propulsion cylinders for moving the saddle frame. Two propulsion cylinders are respectively disposed on both sides of the main beam, with their ends hinged to the saddle frame and the main beam, respectively.

[0014] The boot support mechanism includes boot support cylinders mounted on both sides of the saddle and boot support cylinders on their movable ends; and

[0015] The main beam is equipped with lidar arrays on both sides for scanning the left and right support shoes and the surrounding rock surface within a range of not less than 2m in front of them.

[0016] A TBM boot adaptive pacing control system includes:

[0017] The data acquisition module includes lidar arrays respectively installed on both sides of the main beam for scanning the arch frame.

[0018] The data processing module includes a point cloud denoising unit and a feature extraction unit; and

[0019] The control module includes a path planner and a hydraulic servo controller;

[0020] The point cloud denoising unit eliminates dust interference based on a reflection intensity threshold and spatiotemporal filtering.

[0021] The feature extraction unit uses an improved RANSAC algorithm to fit the position of the steel arch frame and combines it with PointNet++ to segment the point cloud clusters of the collapsed cavity;

[0022] The path planner generates a collision-free motion path based on the feature extraction results;

[0023] The hydraulic servo controller adjusts the displacement of the support shoe cylinder and the propulsion cylinder via PID control.

[0024] Preferably, the scanning range of the lidar array covers the support shoe and the arch frame area in front of it, which is no less than 2m in length.

[0025] Preferably, the point cloud denoising unit filters low reflectivity point clouds through a reflection intensity threshold and combines spatiotemporal filtering to eliminate dynamic dust interference.

[0026] Preferably, the reflection intensity threshold is >60.

[0027] Preferably, the feature extraction unit calculates the width b2 of the steel arch frame, the installation tilt angle θ, and the distances Δ1 and Δ2 between the support shoe and the first and second adjacent steel arch frames, and determines the collapse risk area based on the local point cloud density standard deviation σ.

[0028] An adaptive step-changing control method for a TBM support shoe includes the following steps:

[0029] Step 1: Collect point cloud data of the support shoe position and the arch frame range of ≥2m in front of it using a lidar array;

[0030] Step 2: Identify the location of the support boots, the characteristics of the steel arch frame, and the risk area of ​​collapse.

[0031] Step 3: Perform a matching judgment between the steel arch frame, the collapsed cavity, and the support shoe groove;

[0032] Among them, the conditions for direct support shoe are L≤0.6B, W≤0.5B, D≤0.3B, H·sinθ≤b1-b2-5mm, and the distance of the collapsed cavity center from the TBM axis is ≤0.2B; when these conditions are not met, the steel arch frame needs to be adjusted through auxiliary measures until the conditions are met.

[0033] Step 4: Generate the boot-strap step path:

[0034] The retraction distance of the hydraulic cylinder for the support shoe is greater than the cross-sectional height h of the steel arch frame;

[0035] As the hydraulic cylinder retracts, the support shoe moves horizontally along with the saddle frame, and the distance it moves is:

[0036] When Δ1+0.5B = single-cycle advance, move Δ1+0.5B;

[0037] When Δ1 + 0.5B = 0.5 times the single-cycle advance, move Δ2 + 0.5B.

[0038] Preferably, step 2 specifically comprises:

[0039] Boot positioning: Extract high reflectivity point clouds and spatially locate the boots according to their dimensions;

[0040] Positioning of steel arch frame: Calculate the width b2, inclination angle θ, and the distances Δ1 and Δ2 between the support shoe and the first and second adjacent steel arch frames;

[0041] Cavity collapse determination: When the local point cloud density standard deviation σ > the threshold σmax and is accompanied by a sudden change in negative height, it is marked as a cavity collapse risk area, and its size (length L × width W × depth D) and spatial location are calculated.

[0042] Preferably, in step 3, the triggering condition for the auxiliary measure is that any one of the following is met:

[0043] The collapsed cavity is located at the edge of the support boot, with at least one edge suspended in the air;

[0044] L or W exceeds the direct support boot width B;

[0045] D≥0.5B.

[0046] Preferably, in step 3, the auxiliary measures are selected such that the cavity volume is ≤3m³. 3 High-pressure jetting of rapid-setting concrete is performed through pre-reserved grouting holes in the TBM; the cavity volume is >3m³. 3 Formwork support was selected.

[0047] Compared with existing technologies, the advantages of this invention are as follows: By coordinating the laser radar array, data processing module, and control module, the problems of low positioning accuracy, delayed cavity collapse response, and limited efficiency in the TBM strut change process of traditional manual operation are solved, significantly improving the safety and efficiency of continuous TBM tunneling. The specific advantages are as follows:

[0048] Improving Positioning Accuracy: Traditional TBM support shoe changes rely on manual observation and experience. Human visual inspection cannot monitor steel arch deformation in real time, easily leading to collisions between the support shoe and the arch. This invention, however, uses LiDAR to scan the surrounding rock surface in real time, combined with an improved RANSAC algorithm and PointNet++ to identify steel arch and cavity features. This enables precise positioning of the support shoe, steel arch, and cavity risk areas, avoiding errors from manual operation and improving positioning accuracy.

[0049] Timely Response to Cavity Collapse: Traditional methods rely on geological reports for collapse area identification, making it difficult to adjust support points of the scaffolding in a timely manner. The feature extraction unit of this invention can determine the risk zone of cavity collapse based on the local point cloud density standard deviation σ. When a cavity collapse is detected, it can perform timely matching and judgment, and select appropriate auxiliary measures according to the size of the cavity, such as high-pressure jetting of quick-setting concrete through pre-reserved grouting holes in the TBM or using formwork support, thereby addressing the cavity collapse problem promptly and ensuring construction safety.

[0050] Improving construction efficiency: Traditional step-changing methods have long step-changing cycles, affecting the continuous tunneling speed of TBMs. The path planner of this invention generates collision-free motion paths based on feature extraction results, and the hydraulic servo controller precisely controls the cylinder displacement through PID control, enabling rapid and accurate step-changing of the support shoe, shortening the step-changing cycle, and improving the efficiency of continuous TBM tunneling.

[0051] Enhanced anti-interference capability: Existing technologies use image-based visual monitoring of the support shoe position, which lacks sufficient accuracy in providing three-dimensional environmental information and has poor resistance to dust interference. The data acquisition module of this invention employs a lidar array, and the point cloud denoising unit in the data processing module eliminates dust interference based on reflection intensity thresholds and spatiotemporal filtering. This enables accurate point cloud data acquisition in dusty environments, ensuring the stability and reliability of the system.

[0052] Achieving automated control: This invention achieves automated control of the TBM support shoe changing step, reducing manual intervention, lowering labor intensity, and improving the level of intelligent construction. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a cross-sectional view of a TBM support shoe adaptive step-changing control system described in this invention.

[0055] Figure 2 This is a top view of the TBM support boot adaptive step-changing device, control system and method described in this invention.

[0056] The annotations in the attached figures are explained as follows:

[0057] 1. Steel arch frame; 2. Support shoe; 3. LiDAR array; 4. Laser; 5. Support shoe slot; 6. Propulsion cylinder connecting lug; 7. Main beam; 8. Slide rail; 9. Support shoe cylinder; 10. Saddle frame; 11. Propulsion cylinder; 12. First adjacent steel arch frame; 13. Second adjacent steel arch frame. Detailed Implementation

[0058] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. In addition, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0059] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation", "connection", and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood through the specific circumstances.

[0060] The present invention will be further described below with reference to the accompanying drawings:

[0061] Example 1

[0062] like Figures 1-2 As shown, a TBM (Toyota Machine) boot adaptive step-changing device includes:

[0063] Main beam 7, as the core component for step-changing guidance of the entire device, is made of high-strength alloy steel. Its structural design meets the high-strength requirements of TBM in complex underground environments. The surface of main beam 7 is specially treated, which has good wear resistance and corrosion resistance, and can effectively extend its service life. In actual engineering applications, the stability of main beam 7 is directly related to the safety and accuracy of TBM support shoe 2 step-changing process. Its guiding function ensures that support shoe 2 can move along the predetermined path during step-changing, avoiding deviation or jamming.

[0064] The propulsion mechanism includes a saddle frame 10 and propulsion cylinders 11. The saddle frame 10 is mounted on the main beam 7, allowing for flexible movement. The saddle frame 10 and the main beam 7 are connected by a slide rail 8. The slide rail 8 is manufactured using high-precision machining, resulting in a low surface roughness, which reduces friction during sliding and improves the smoothness and accuracy of movement. The saddle frame 10 is also made of a high-strength alloy, possessing sufficient strength and rigidity to withstand the tension of the propulsion cylinders 11 and various loads transmitted by the support shoe 2 during operation. The propulsion mechanism comprises two propulsion cylinders 11. The saddles 10 and 11 are symmetrically arranged on both sides of the main beam 7. This symmetrical arrangement ensures that the saddle 10 is subjected to uniform force during movement and avoids uneven loading. The two ends of the propulsion cylinder 11 are connected to the saddle 10 and the main beam 7 by hinges. The hinge structure allows the cylinder to rotate freely within a certain angle range, thereby adapting to the attitude changes of the TBM under different working conditions. The propulsion cylinder 11 is hydraulically driven and has the characteristics of large output force and fast response speed. It can quickly and stably pull the saddle 10 to move on the main beam 7, realizing the step-changing operation of the TBM support shoe 2.

[0065] The support shoe 2 mechanism, with support shoe cylinders 9 mounted on both sides of the saddle 10, serves as the power transmission component between the support shoe 2 and the saddle 10. Its performance directly affects the working effect of the support shoe 2. The support shoe cylinders 9 employ a multi-stage telescopic design, allowing adjustment of the extension length of the support shoe 2 according to actual working needs, adapting to different shapes and sizes of surrounding rock surfaces. The cylinders are equipped with high-precision displacement sensors that can monitor the extension displacement of the support shoe 2 in real time, providing accurate data support for the control system. The support shoe 2 is installed at the movable end of the support shoe cylinder 9, directly connecting the TBM to the surrounding rock. The key components in contact; the surface of the support shoe 2 is designed with special anti-slip textures to increase friction with the surrounding rock, ensuring sufficient support for the TBM during tunneling; on the side of the support shoe 2 away from the saddle frame 10, a special support shoe slot 5 is provided corresponding to the steel arch frame 1 for avoidance; the size of this slot is precisely designed to perfectly match the steel arch frame 1, effectively avoiding collisions with the steel arch frame 1 during the support shoe 2's step change, protecting both the steel arch frame 1 and the support shoe 2 from damage, while ensuring the normal tunneling operation of the TBM; and

[0066] The lidar array 3 is positioned on both sides of the main beam 7. Figure 1 and Figure 2 (This is a single-sided illustration; in reality, the lidar arrays 3 on both sides are symmetrically arranged about the centerline of the main beam 7.) Their main function is to scan the left and right support shoes 2 and the surrounding rock surface within a range of no less than 2m in front of them. The lidar array 3 adopts multi-line lidar technology, which has the characteristics of high resolution and wide viewing angle, and can quickly and accurately acquire three-dimensional point cloud data of the surrounding rock surface. Through the analysis and processing of these point cloud data, the shape and structure of the surrounding rock and the installation status of the steel arch frame 1 can be grasped in real time, providing important data basis for the adaptive step change of the TBM support shoes 2. In practical applications, the lidar array 3 can promptly detect abnormalities such as cavities and cracks in the surrounding rock, providing early warning for construction personnel to take corresponding safety measures.

[0067] Example 2

[0068] A TBM boot adaptive pacing control system includes:

[0069] The data acquisition module mainly consists of lidar arrays 3 positioned on both sides of the main beam 7. The scanning range of these lidar arrays 3 is carefully designed to completely cover the support shoe 2 and the arch frame area at least 2 meters in front of it. During actual operation, the lidar arrays 3 scan this area at a high frequency, quickly acquiring a large amount of point cloud data. This point cloud data allows for the accurate reconstruction of the shape and position of the arch frame and the surface features of the surrounding rock, providing raw data support for subsequent data processing and analysis. The high resolution and real-time characteristics of this module ensure that the system can perceive changes in the surrounding environment in a timely and accurate manner, providing a reliable data foundation for the adaptive step-changing of the TBM support shoe 2.

[0070] The data processing module includes a point cloud denoising unit and a feature extraction unit;

[0071] Point Cloud Denoising Unit: In the underground tunnel construction environment, there are numerous interference factors such as dust and water vapor, which can cause a large number of noise points in the point cloud data collected by LiDAR. The point cloud denoising unit performs preliminary filtering on the collected point cloud data by setting a reflection intensity threshold (>60) to remove noise points with low reflectivity. At the same time, combined with a spatiotemporal filtering algorithm, it further eliminates dynamic dust interference. The spatiotemporal filtering algorithm considers the continuity of point cloud data in time and space. By analyzing and processing point cloud data at adjacent time points and spatial locations, it can effectively remove noise points caused by dust movement, thereby improving the quality and accuracy of point cloud data.

[0072] Feature Extraction Unit: The feature extraction unit is the core part of the data processing module. Its main function is to extract useful feature information from the denoised point cloud data. Specifically, this unit uses a series of advanced algorithms to calculate the width b2 and installation tilt angle θ of the steel arch frame 1, as well as the distances Δ1 and Δ2 between the support shoe 2 and the first adjacent steel arch frame 12 and the second adjacent steel arch frame 13. During the calculation process, point cloud normal vector analysis and other techniques are used to accurately obtain the geometric features and spatial position information of the steel arch frame 1. In addition, the feature extraction unit also determines the cavity collapse risk area based on the local point cloud density standard deviation σ. When the local point cloud density standard deviation σ exceeds the threshold σmax and is accompanied by a sudden change in negative height, it is marked as a cavity collapse risk area, and the size of the cavity (length L × width W × depth D) and its spatial distribution position relative to the support shoe 2 are calculated simultaneously. In order to more accurately fit the position of the steel arch frame 1, the feature extraction unit uses an improved RANSAC algorithm and combines it with PointNet++ to segment the cavity cavity point cloud clusters, thereby achieving accurate identification and positioning of the steel arch frame 1 and the cavity in complex environments.

[0073] The control module includes a path planner and a hydraulic servo controller;

[0074] Path Planner: Based on the results obtained from the feature extraction unit, the path planner generates a collision-free motion path for the support shoe 2 during step change. In the path generation process, the path planner comprehensively considers factors such as the position of the steel arch 1, the distribution of the collapsed cavity, and the size of the support shoe 2. It employs advanced path planning algorithms, such as A* and Dijkstra's algorithm, to search for a safe and efficient step change path in the complex underground environment. This path planner has strong adaptability and can adjust the path in real time according to environmental changes, ensuring that the support shoe 2 does not collide with obstacles such as the steel arch 1 and collapsed cavities during the step change, thus protecting the TB. Normal operation of M; Hydraulic servo controller: The hydraulic servo controller adjusts the displacement of the support shoe cylinder 9 and the propulsion cylinder 11 through PID, and its control accuracy can reach ±5mm; The PID adjustment algorithm is a classic control algorithm. By precisely adjusting the three parameters of proportional (P), integral (I), and derivative (D), it can achieve fast and stable control of the cylinder displacement; In actual operation, the hydraulic servo controller controls the extension and retraction of the support shoe cylinder 9 and the propulsion cylinder 11 in real time according to the motion path generated by the path planner, ensuring that the support shoe 2 moves accurately according to the predetermined path, and realizing the adaptive step-changing operation of the TBM support shoe 2.

[0075] In addition, this embodiment also provides a TBM support shoe adaptive step-changing control method, including the following steps:

[0076] Step 1: Using the lidar arrays 3 arranged on both sides of the main beam 7, point cloud data is collected for the location of the support shoe 2 and the arch frame range ≥2m in front of it. During the collection process, the lidar arrays 3 perform omnidirectional scanning of the target area at the set scanning frequency and angle to ensure that complete and accurate point cloud data is obtained. In order to ensure the reliability of data collection, the system also sets up a data verification mechanism to verify the collected data in real time. Once an abnormality is found, the data is immediately re-collected to ensure the accuracy of subsequent data processing and analysis.

[0077] Step 2: Identify the positioning of support shoe 2, the characteristics of steel arch frame 1, and the collapse risk area;

[0078] Positioning of Boot 2: Extract high reflectivity point cloud (metal features) from the collected point cloud data. Since boot 2 is usually made of metal and has high reflectivity, the point cloud data related to boot 2 can be accurately separated through this characteristic. Then, based on the known size information of boot 2, a three-dimensional spatial positioning algorithm is used to accurately position boot 2 in space and determine the specific position and orientation of boot 2 in the tunnel.

[0079] Positioning of steel arch frame 1: Similarly, high reflectivity point cloud (metal features) is extracted, and the width b2 of steel arch frame 1 is calculated using point cloud processing algorithms; the installation tilt angle θ of steel arch frame 1 relative to the vertical direction is obtained using point cloud normal vector analysis technology; further, the distance Δ1 between the edge of support shoe 2 and the central axis of the first adjacent steel arch frame 12, and the distance Δ2 between the edge of support shoe 2 and the central axis of the second adjacent steel arch frame 13 are calculated; the accurate acquisition of these parameters provides an important basis for subsequent support shoe 2 step-changing path planning and safety assessment;

[0080] Collapse determination: Calculate the local point cloud density standard deviation σ. When σ > threshold σmax and is accompanied by a sudden change in negative height, it is marked as a collapse risk area. At the same time, through detailed analysis of the point cloud data of the collapse area, calculate the size of the collapse cavity (length L × width W × depth D) and its spatial distribution relative to the support shoe 2. In the determination process, combine multiple data analysis methods to improve the accuracy and reliability of collapse identification and promptly detect potential safety hazards.

[0081] Step 3: Perform a matching judgment between the steel arch frame 1, the collapsed cavity, and the support shoe groove 5;

[0082] Direct support shoe 2 condition: The direct support shoe 2 condition is satisfied as follows: L≤0.6B, W≤0.5B, D≤0.3B, H·sinθ≤b1-b2 - The dimensions are 5mm, where the width of support shoe 2 is B, the width of support shoe 2 is b1, the height of support shoe 2 is H, the width of steel arch frame 1 is b2, and the installation inclination angle of the arch frame relative to the vertical direction is θ (obtained through point cloud normal vector analysis); in addition, it is required that the distance between the center of the collapsed cavity and the TBM axis is ≤0.2B; only when these conditions are met simultaneously can the support shoe 2 be operated directly to ensure that the support shoe 2 has good matching with the steel arch frame 1 and the collapsed cavity, and to ensure the stable support of the TBM and safe tunneling;

[0083] Auxiliary measures triggering conditions: Auxiliary measures need to be triggered when any of the following conditions occur: the collapsed cavity is located at the edge of the support shoe 2 and at least one edge is suspended; L or W exceeds the width B of the direct support shoe 2; D ≥ 0.5B; These triggering conditions are set in order to take corresponding measures in a timely manner when encountering complex geological conditions or non-standard installation of the steel arch frame 1, so as to ensure the normal operation of the TBM and construction safety.

[0084] Selection of auxiliary measures: Select appropriate auxiliary measures based on the size of the collapsed cavity; when the collapsed cavity volume is ≤3m³. 3 When the volume of the collapsed cavity is >3m³, high-pressure jetting of quick-setting concrete is used through the pre-reserved grouting holes of the TBM to rapidly fill the cavity and improve the stability of the surrounding rock. 3 At that time, formwork support was selected, and the collapse cavity was fully supported by the process of setting up formwork and pouring concrete to ensure the safety of the TBM in the subsequent tunneling process.

[0085] Step 4: Generate the step change path for boot support 2;

[0086] Retraction of support shoe cylinder 9: First, the retraction distance of support shoe cylinder 9 is greater than the cross-sectional height h of steel arch frame 1 to ensure that support shoe 2 will not collide with steel arch frame 1 during the step-changing process; when controlling the retraction of support shoe cylinder 9, the hydraulic servo controller precisely adjusts the displacement of the cylinder and retracts support shoe 2 according to the predetermined speed and distance to prepare for subsequent horizontal movement.

[0087] The retraction of the propulsion cylinder 11 and the movement of the support shoe 2: The retraction of the propulsion cylinder 11 drives the support shoe 2 to move horizontally with the saddle frame 10; the moving distance is determined according to different working conditions: when Δ1+0.5B = single cycle advance, the support shoe 2 moves Δ1+0.5B; when Δ1+0.5B = 0.5 times the single cycle advance, the support shoe 2 moves Δ2+0.5B; during the movement, the path planner monitors the position of the support shoe 2 and the changes in the surrounding environment in real time, and the hydraulic servo controller precisely controls the retraction amount of the propulsion cylinder 11 according to the instructions of the path planner to ensure that the support shoe 2 moves accurately along the predetermined path, realizing the adaptive step-changing operation of the TBM support shoe 2.

[0088] The steel arch frame 1, support shoe 2, lidar array 3, main beam 7, slide rail 8, support shoe cylinder 9, saddle frame 10, and propulsion cylinder 11 mentioned above are all general standard parts or components known to those skilled in the art. Their structures and principles can be learned by those skilled in the art through technical manuals or conventional experimental methods, and therefore will not be described in detail here. In actual engineering applications, the selection and installation of these components must be strictly carried out in accordance with relevant standards and specifications to ensure the normal operation and construction safety of the TBM support shoe 2 adaptive step-changing device, system, and control method.

[0089] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A TBM (Toyota Machine) boot adaptive step-changing control method, characterized in that: Using a TBM boot adaptive gait switching device, the device includes: The main beam (7) is used for step-changing guidance of the device; The propulsion mechanism includes a movable saddle (10) fitted onto the main beam (7) and propulsion cylinders (11) for moving the saddle (10). Two propulsion cylinders (11) are respectively located on both sides of the main beam (7), and both ends of the propulsion cylinders (11) are hinged to the saddle (10) and the main beam (7), respectively. The boot support mechanism includes boot support cylinders (9) installed on both sides of the saddle (10) and boot support (2) on the movable end of the boot support cylinders (9). Laser radar arrays (3) are arranged on both sides of the main beam (7) for scanning the left and right support shoes (2) and the surrounding rock surface within a range of not less than 2m in front of them. The control method is implemented through a TBM support shoe adaptive step-changing control system, which includes: The data acquisition module includes a laser radar array (3) respectively set on both sides of the main beam (7) for scanning the arch frame; The data processing module includes a point cloud denoising unit and a feature extraction unit; and The control module includes a path planner and a hydraulic servo controller; The point cloud denoising unit eliminates dust interference based on a reflection intensity threshold and spatiotemporal filtering. The feature extraction unit uses an improved RANSAC algorithm to fit the position of the steel arch frame (1) and combines it with PointNet++ to segment the point cloud clusters of the collapsed cavity; The path planner generates a collision-free motion path based on the feature extraction results; The hydraulic servo controller adjusts the displacement of the support shoe cylinder (9) and the propulsion cylinder (11) via PID control; The control method includes the following steps: Step 1: Collect point cloud data of the position of the support shoe (2) and the arch frame range ≥2m in front of it using the lidar array (3); Step 2: Identify the positioning of the support boot (2), the characteristics of the steel arch frame (1), and the collapse risk area; Step 3: Perform a matching judgment between the steel arch frame (1), the collapsed cavity, and the support shoe groove (5); specifically: Boot (2) positioning: Extract high reflectivity point cloud and perform spatial positioning according to the size of boot (2); Positioning of steel arch frame (1): Calculate the width b2, inclination angle θ and the distances Δ1 and Δ2 between the support shoe (2) and the first adjacent steel arch frame (12) and the second adjacent steel arch frame (13); Cavity collapse determination: When the local point cloud density standard deviation σ > the threshold σmax and is accompanied by a sudden change in negative height, it is marked as a cavity collapse risk area, and its size and spatial location are calculated. Its size includes length L, width W, and depth D. Among them, the conditions for satisfying the direct support shoe (2) are L≤0.6B, W≤0.5B, D≤0.3B, H·sinθ≤b1-b2-5mm, where the width of the support shoe (2) is B, the width of the support shoe groove (5) is b1, the height of the support shoe (2) is H, the width of the steel arch frame (1) is b2, and the installation inclination angle of the arch frame relative to the vertical direction is θ; and the distance between the center of the collapsed cavity and the TBM axis is ≤0.2B; when the conditions are not met, the steel arch frame (1) needs to be adjusted by auxiliary measures until the conditions are met; the triggering condition for the auxiliary measures is to satisfy any one of the following: The collapsed cavity is located at the edge of the support boot (2) and at least one edge is suspended in the air; L or W exceeds the width B of the boot (2); D≥0.5B; Step 4: Generate support boot (2) step change path: The retraction distance of the support shoe cylinder (9) is greater than the section height h of the steel arch frame (1); The propulsion cylinder (11) retracts, and the support shoe (2) moves horizontally with the saddle frame (10), the distance of which is: When Δ1 + 0.5B = single-cycle advance, move Δ1 + 0.5B; When Δ1 + 0.5B = 0.5 times the single-cycle advance, move Δ2 + 0.5B.

2. The TBM support shoe adaptive step-changing control method according to claim 1, characterized in that: The point cloud denoising unit filters low reflectivity point clouds through a reflection intensity threshold and combines spatiotemporal filtering to eliminate dynamic dust interference.

3. The TBM support shoe adaptive step-changing control method according to claim 1, characterized in that: The reflection intensity threshold is >60.

4. The TBM support shoe adaptive step-changing control method according to claim 1, characterized in that: The feature extraction unit calculates the width b2 of the steel arch frame (1), the installation tilt angle θ, and the distances Δ1 and Δ2 between the support shoe (2) and the first adjacent steel arch frame (12) and the second adjacent steel arch frame (13), and determines the collapse risk area based on the local point cloud density standard deviation σ.

5. The TBM support shoe adaptive step-changing control method according to claim 1, characterized in that: In step 3, the auxiliary measures are as follows: if the volume of the collapsed cavity is ≤3m³, high-pressure spraying of quick-setting concrete is carried out through the grouting holes reserved by the TBM; if the volume of the collapsed cavity is >3m³, formwork support is selected.

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