Tank body excavation deviation rectifying system and method

Abstract

The invention discloses a groove body excavation deviation correction system which comprises an embedded industrial personal computer serving as a system information interaction and calculation processing center, a data acquisition unit which is in data interaction connection with the embedded industrial personal computer and is used for acquiring stratum and grooving states, a dynamic geological database and a digital twin engine. The embedded industrial personal computer is also electrically connected with a hydraulic servo mechanism, a posture adjusting mechanism and a power modulation module which are used for controlling the double-wheel milling equipment; the multi-sensor fusion I MU + laser + sound wave and model prediction control MPC is adopted, the axis precision of + / -8 mm is achieved, the stratum rigidity change is predicted 1-3 steps in advance through sound wave scanning, the control weight is dynamically adjusted, and the deviation convergence speed is increased by three times. The problem that in the prior art, adjustment depends on artificial experience or simple feedback control based on PI D, and the axis deviation is generally + / -30-50 mm after the depth exceeds 50 m is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of geological treatment technology, in particular to the field of continuous wall trench excavation control technology based on constructing an anti-seepage wall to achieve geological waterproofing, and specifically to a trench excavation correction system and method. Background Art

[0002] As a vertical barrier structure, anti-seepage walls play a key role in water conservancy, hydropower, environmental remediation, and underground engineering. They are primarily used to control seepage, prevent seepage damage, and enhance project stability. Construction techniques include grab bucket trenching, percussion drilling, saw-grooving, and high-pressure jet grouting. Materials include concrete, plastic concrete, self-setting mortar, and composite geomembranes to adapt to diverse geological conditions and anti-seepage requirements. Currently, anti-seepage walls are widely used in dam foundation reinforcement, contaminated site isolation, and embankment leakage control. They are particularly effective in deep overburden or complex strata. By forming a continuous, closed barrier, they effectively block groundwater seepage paths, reduce uplift pressure, and prevent soil piping and the spread of chemical contaminants. With technological advances, the depth, thickness, and precision requirements of anti-seepage walls are increasing. For example, in pumped-storage power stations, walls can reach depths exceeding 100 meters and must be tightly integrated with the bedrock. Furthermore, anti-seepage walls also provide structural support, such as by synergizing with support systems in deep foundation pits, further expanding their engineering value.

[0003] Trenching, a core process in anti-seepage wall construction, directly determines the wall's continuity and anti-seepage effectiveness. However, practical operations still face numerous technical bottlenecks, particularly the problem of slot hole deviation, which can cause deviation from the designed trajectory. Complex strata, such as gravel layers, heavily weathered bedrock, or alternating soft and hard strata, can easily cause misalignment in the trenching equipment's guidance. For example, grab buckets or percussion drills can easily "drift" in hard rock, while uneven lateral resistance in loose layers can lead to trench wall collapse or partial hole expansion. Deviation problems often arise from sudden changes in geological conditions, inaccurate equipment guidance systems, or improper operating parameters, such as uneven drilling pressure distribution and delayed correction response. While existing inclinometer technology can provide real-time feedback on deviation angles, high-frequency dynamic corrections are difficult to implement due to equipment cost and construction efficiency limitations. In actual projects, slot hole deviation exceeding 2‰ can result in poor overlap between adjacent slot sections, insufficient effective wall thickness, and even the formation of weak seepage zones. For example, the anti-seepage wall of a reservoir experienced a cumulative 1.5-meter deviation in slot holes, causing partial deviation from the designed axis. This required secondary reinforcement grouting to correct the problem. Furthermore, traditional deviation correction relies on manual experience and lacks intelligent control methods. This issue is particularly acute in deep trenches or high-precision projects. There is an urgent need to integrate new technologies such as real-time geological perception and adaptive deviation correction algorithms to improve trenching quality control. To this end, the present invention is designed to address the issue of trenching trajectory deviation from the designed axis. Summary of the Invention

[0004] In order to solve the problems of loose overlap, insufficient effective wall thickness, and even the formation of weak seepage zones and even anti-seepage failure at the overlaps caused by the deviation between the actual trenching axis and the designed axis during trenching construction of the conventional anti-seepage wall, the present application provides a trench excavation correction system and method, which can achieve at least one of the following technical effects:

[0005] 1. This invention achieves an axis accuracy of ±8mm through multi-sensor fusion of IMU, laser, and acoustic wave technology, along with model predictive control (MPC). Acoustic scanning predicts formation stiffness changes 1-3 steps in advance, dynamically adjusts control weights, and triples the deviation convergence speed. This effectively overcomes the existing technology's reliance on manual experience adjustment or simple PID-based feedback control, which generally results in axis deviations of ±30-50mm at depths exceeding 50m.

[0006] 2. The present invention can adjust the milling speed by 0.8-2.5 m / h and the thrust by 50-200 kN in real time according to the formation stiffness, improving the efficiency by 40% in hard rock sections. The MPC is optimized every 10 m / s, the time consumption for deviation correction accounts for less than 15%, and the unmanned intervention rate of the entire process is ≥85%. It effectively solves the problem of low average efficiency caused by frequent shutdowns in exploration in complex formations encountered by existing technologies.

[0007] 3. The present invention is based on acoustic wave early warning and can identify risks such as faults and caves 10-30 meters in advance with a low missed reporting rate. Combined with digital twin simulation, it can effectively reduce the accident rate and greatly reduce existing accidents such as hole collapse and drill jamming, thereby avoiding potential economic losses.

[0008] 4. The present invention adjusts the current excavation power dynamically through real-time detection of the stratum, which can reduce the energy consumption of the excavation equipment on the one hand, and effectively protect the tool from large impacts on the other hand, thereby extending the life of the tool.

[0009] In order to achieve the above objectives, the technical solutions adopted in this application are:

[0010] A slot excavation correction system includes an embedded industrial computer serving as the system information exchange and computing processing center, a data acquisition unit interactively connected to the embedded industrial computer and used to collect data on strata and slotting status, a dynamic geological database, and a digital twin engine. The embedded industrial computer is also electrically connected to a hydraulic servo mechanism, a posture adjustment mechanism, and a power modulation module for controlling a dual-wheel milling device.

[0011] The data acquisition unit includes an IMU attitude detection module and a laser ranging module, each used to detect the attitude and position of the dual-wheel milling equipment, and a first acoustic wave scanning module for collecting current geological conditions; the IMU attitude detection module and the first acoustic wave scanning module are both integrated in the head of the dual-wheel milling equipment, the laser ranging module includes at least one set of laser transmitters and receivers respectively installed on the ground on both sides of the slot, a pressure-resistant transparent conduit for guiding the laser beam into the slot is provided below the laser transmitter, a self-cleaning optical window is installed at the bottom of the pressure-resistant transparent conduit, and a laser ranging target is installed on the head of the dual-wheel milling equipment; the attitude adjustment mechanism consists of a pitch mechanism, a yaw mechanism and an axial mechanism.

[0012] Preferably, the IMU attitude detection module is composed of an integrated three-axis gyroscope and a three-axis accelerometer, the laser ranging module includes at least six groups of laser ranging arrays composed of cross-laser rangefinders, and the first acoustic wave scanning module includes at least six groups distributed in a circular array within the cutter shield of the double-wheel milling equipment; the adjustment angles of the pitch mechanism and the yaw mechanism are both ±5°, and the adjustment range of the axial mechanism is ±200mm.

[0013] Further preferably, it also includes a second acoustic wave scanning module, which includes a vertical acoustic wave array installed in the slot through an auxiliary bracket, with an interval of 0.6-1 meter between two adjacent vertical acoustic wave units, and the emitted acoustic wave frequency is 10kHz-50kHz.

[0014] The present invention further provides an intelligent deviation correction method for trough excavation based on the deviation correction system provided above, which specifically includes the following steps:

[0015] Step STP100, geological radar scanning, using GPR geological radar to transmit high-frequency electromagnetic waves and receive reflected signals from the stratum interface, and obtain the stratum structure through dielectric constant difference analysis;

[0016] Step STP200, acoustic stratum scanning, uses the second acoustic scanning module to transmit 50kHz low-frequency acoustic waves to scan the bottom layer, generates a global stiffness distribution map by receiving the reflected signals, and uploads the data to the digital twin engine; the piezoelectric ceramic transducer of the first acoustic scanning module transmits 500kHz high-frequency acoustic pulses, penetrates the mud and enters the near-field stratum, captures the reflected signals of P waves and shear waves S waves through the array receiver, records the travel time and amplitude, inverts the real-time mechanical parameters, and inputs them into the embedded industrial computer;

[0017] Step STP300: Data fusion and establishment of digital twin model M0. The attitude data of the dual-wheel milling equipment collected by the IMU attitude detection module, the acoustic data B collected by the second acoustic scanning module, and the position data collected by the laser ranging module are synchronized in time and space through a high-precision clock, and the coordinates are unified through the ICP algorithm to establish the digital twin model M0 of the stratum in the current construction area. After normal excavation, the large-scale stratum trend is updated every 10 minutes. The first acoustic scanning module monitors the 5m stratum geology around the milling head in real time, and dynamically corrects the digital twin model M0. t+1 ;

[0018] Step STP400, design a model predictive controller, and establish the milling system dynamic equation to predict N by designing a model predictive controller in the future. p Step position, calculate the control torque τ required by the hydraulic servo mechanism of the double-wheel milling equipment to complete the correction control The hydraulic servo mechanism is controlled by the embedded industrial computer to correct the deviation until the posture deviation δ reaches the preset value range. The dynamic equation of the milling system is as follows:

[0019]

[0020] Where q∈R 3 Represents the milling head pose vector, M(q)∈R 3×3 represents the inertia matrix; Represents the Coriolis force matrix, characterizing the motion coupling effect; G(q)∈R 3 represents the gravity component; represents the formation friction model; K d ∈R 3 ×3 Represents the elastic reaction force of the formation; δ∈R 3 Represents the pose deviation; represents the control torque; τ disturbance ∈R 3 represents the disturbance torque; the designed model predictive controller calculates the optimal control sequence U by minimizing the trajectory tracking error and the control variable change rate:

[0021]

[0022] Among them, q ref represents the reference trajectory axis of the double-wheel milling equipment design; Q represents the state deviation weight; R represents the control deviation weight; N p Represents the prediction step size; N c represents the actual control step length; q(k|t) represents the predicted posture; Δu(k|t) represents the rate of change of the control amount or the adjustment amount.

[0023] Beneficial effects:

[0024] 1. This invention achieves an axis accuracy of ±8mm through multi-sensor fusion of IMU, laser, and acoustic wave technology, along with model predictive control (MPC). Acoustic scanning predicts formation stiffness changes 1-3 steps in advance, dynamically adjusts control weights, and triples the deviation convergence speed. This effectively overcomes the existing technology's reliance on manual experience adjustment or simple PID-based feedback control, which generally results in axis deviations of ±30-50mm at depths exceeding 50m.

[0025] 2. The present invention can adjust the milling speed by 0.8-2.5 m / h and the thrust by 50-200 kN in real time according to the formation stiffness, improving the efficiency by 40% in hard rock sections. The MPC is optimized every 10 m / s, the time consumption for deviation correction accounts for less than 15%, and the unmanned intervention rate of the entire process is ≥85%. It effectively solves the problem of low average efficiency caused by frequent shutdowns in exploration in complex formations encountered by existing technologies.

[0026] 3. The present invention is based on acoustic wave early warning and can identify risks such as faults and caves 10-30 meters in advance with a low missed reporting rate. Combined with digital twin simulation, it can effectively reduce the accident rate and greatly reduce existing accidents such as hole collapse and drill jamming, thereby avoiding potential economic losses.

[0027] 4. The present invention adjusts the current excavation power dynamically through real-time detection of the stratum, which can reduce the energy consumption of the excavation equipment on the one hand, and effectively protect the tool from large impacts on the other hand, thereby extending the life of the tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative labor.

[0029] Figure 1 It is a system architecture block diagram of the present invention.

[0030] Figure 2 It is a flowchart of the present invention for performing deviation correction.

[0031] Figure 3 It is a schematic diagram of the construction structure of the present invention.

[0032] In the figure: 1-geology; 2-laser ranging module; 3-laser target prism; 4-double-wheel milling equipment; 5-first acoustic wave scanning module; 6-trough; 7-second acoustic wave scanning module. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without making any creative efforts shall fall within the scope of protection of the present application.

[0035] Example 1:

[0036] See also Figure 1 The trench excavation correction system includes an embedded industrial computer serving as the system information exchange and computing processing center, a data acquisition unit connected to the embedded industrial computer for collecting data on the strata and trenching status, a dynamic geological database, and a digital twin engine. The embedded industrial computer is also electrically connected to a hydraulic servo mechanism, a posture adjustment mechanism, and a power modulation module for controlling the dual-wheel milling device 4.

[0037] The data acquisition unit includes an IMU attitude detection module and a laser ranging module 2, which are respectively used to detect the attitude and position of the dual-wheel milling equipment 4, and a first acoustic wave scanning module 5 for collecting the current geological conditions; the IMU attitude detection module and the first acoustic wave scanning module 5 are both integrated in the head of the dual-wheel milling equipment 4, and the laser ranging module 2 includes at least one group of laser transmitters and receivers respectively installed on the ground on both sides of the slot, a pressure-resistant transparent conduit for guiding the laser beam into the slot is provided below the laser transmitter, a self-cleaning optical window is installed at the bottom of the pressure-resistant transparent conduit, and a laser ranging target is installed on the dual-wheel milling head; the attitude adjustment mechanism consists of a pitch mechanism, a yaw mechanism and an axial mechanism.

[0038] Working principle:

[0039] First, the system structure provided by this embodiment is explained. The system includes an embedded industrial computer as the core of the control layer: it serves as the information interaction and computing processing center of the entire system. It establishes and optimizes a digital twin engine that matches the current stratum through the posture data, position data, stratum data, etc. of the dual-wheel milling device 4 collected from the data acquisition unit, and makes a qualitative judgment on whether to make a correction based on different situations in the actual excavation process, as well as a quantitative judgment on the direction of correction and the amount of correction; at the same time, it stores all geological data information and establishes a dynamic geological database. A perception layer that provides objective, real, and real-time data streams to the control layer, that is, a data acquisition unit that serves as a front-end information acquisition unit, including an IMU posture detection module and a laser ranging module 2 for detecting the posture and position of the dual-wheel milling device 4, and a first acoustic wave scanning module 5 for acquiring the current geological conditions. The first acoustic wave scanning module 5 mainly acquires the structure of the near-layer stratum adjacent to the milling head of the dual-wheel milling device 4, which plays a role in predicting in advance to avoid hole collapse and drill jamming. For details, see Figure 3 As shown, the first acoustic scanning module 5 can feed back the information data of the stratum to be excavated to the embedded industrial computer as the control layer. Then, after information processing, the embedded industrial computer sends a control instruction to the power modulation module in the execution layer, thereby adjusting the output power of the double-wheel milling device 4 to adapt to the current stratum, avoiding excessive output power that increases energy consumption, and avoiding too little output power that causes drill jamming. Similarly, the IMU posture detection module and the laser ranging module 2 can monitor the current posture state of the double-wheel milling device 4 in real time, and compare the actual posture state with the preset construction axis through the embedded industrial computer. If a deviation occurs, a control instruction is sent to the hydraulic servo mechanism and posture adjustment mechanism as the execution layer, so as to adjust the posture and movement direction of the double-wheel milling device 4, thereby achieving the purpose of timely correction. It is worth noting that, since different double-wheel milling devices 4 have different control methods, the hydraulic servo mechanism and the posture adjustment mechanism will also be different. The two can be independent working mechanisms or integrated mechanisms. In this embodiment, the hydraulic servo mechanism and the posture adjustment mechanism are described separately only based on functional distinction, which makes it easier for technical personnel in this field to understand. For actual structural settings, the two can be set up as a complete set of mechanisms.

[0040] See also Figure 3As shown, when trenching is required for the geological structure 1, the system can first load the mechanical parameters and the designed trajectory (or motion axis) of the trenching excavation, and initialize the built-in correction mechanism or algorithm of the embedded industrial computer; it should be noted here that the role of loading the mechanical parameters of the actual trenching excavation is to enable the system to effectively control the current machine and avoid the execution / control instructions issued by the system exceeding the executable range of the existing machine. The designed trajectory is the trenching trajectory preset by the system, that is, the theoretical optimal trajectory for constructing the anti-seepage wall, so that after the trenching, the continuous wall cast in the trench body 6 can be perfectly matched with the node piles cast in advance to form an integrated concrete structure and achieve the best anti-seepage effect. The purpose of the correction mechanism or algorithm built into the initial embedded industrial computer is to set the current geological conditions to the initialization state, that is, the theoretical geological conditions, before actual construction. However, the initialization correction mechanism is based on theoretical or preset geology, which does not correspond to the stratum geology of the actual trenching construction. Therefore, after initialization, the geological conditions are updated based on the information collected by the data acquisition unit, including the update of the dynamic geological database and the digital twin engine, so that the information collected by the entire system before work is matched with the stratum geology of the current construction, so as to achieve the most accurate control of the actual construction progress, construction status and correction. At this point, the system realizes accurate and timely correction based on the collected real-time stratum geological information, the mechanical equipment information of the actuator and the correction mechanism algorithm built into the embedded industrial computer. There are many ways to implement the correction mechanism and algorithm, and the preferred algorithm scheme will be explained in detail in the subsequent embodiments.

[0041] Example 2:

[0042] This embodiment optimizes the system structure in many aspects based on Example 1. Specifically, the IMU attitude detection module is composed of an integrated three-axis gyroscope and a three-axis accelerometer, with an accuracy of 0.001°. Its working principle is to infer the posture by measuring angular velocity and linear acceleration. Its core technology is based on attitude angle integration and position integration. The attitude angle integral equation of the gyroscope is:

[0043] θ(t)=θ0+∫0 t ω(t)dt

[0044] Among them, ω is the three-axis angular velocity, θ0 is the initial attitude angle;

[0045] The position integral and the accelerometer integral equations are:

[0046] p(t)=p0+∫∫0 t a(t)dt 2

[0047] Where a is the triaxial acceleration after deducting the gravity component;

[0048] The laser ranging module includes at least six groups of laser ranging arrays composed of cross laser rangefinders arranged on both sides of the slot, with a wavelength of 532nm and a measurement accuracy of ±1mm. And a laser target prism 3 is arranged on the milling head. The first acoustic wave scanning module includes at least six groups of ultrasonic arrays distributed in a circular array in the cutter shield of the double-wheel milling equipment, with a transmission frequency of 500kHz-2MHz. The acoustic wave transmitter and receiver array are directly embedded in the cutter shield of the double-wheel milling, distributed in a ring shape, spaced 60° apart, and close to the slot wall working surface. Since the direction of stratum excavation is always downward, the 6 groups of milling head probes will rotate with the rotation of the milling head. Therefore, there are differences in the signal arrival time of different probes. In order to obtain objective and accurate information, it is necessary to fuse the multi-probe data. This embodiment is implemented using the time difference positioning method, specifically: using the signal arrival time difference Δt of the 6 groups of milling head probes i , calculate the azimuth θ and distance r of the formation anomaly, so as to realize the fusion of multi-probe data, where:

[0049]

[0050] Where c represents the speed of light, ε r is the current mud dielectric constant.

[0051] The advantages of this design are: 1. The acoustic wave transmitter and receiver array are directly embedded in the cutter shield of the double-wheel milling machine, distributed in a ring shape (6 groups, 60° apart), close to the working surface of the slot wall; 2. Real-time synchronous detection of 360° stratum changes around the milling head; In terms of protection design, a tungsten carbide protective cover with a thickness of 5-8mm is used, which can effectively resist the impact of gravel and avoid damage from external forces during the excavation process. In order to further highlight the technical advantages of this structural design, through the use of equivalent model testing, the difference between the effect of only using the first acoustic wave scanning module or the second acoustic wave scanning module 7 and the dual-position synergy is shown in Table 1 below.

[0052] parameter Notch probe only Milling head probe only Dual-location collaboration Stratigraphic modeling resolution <![CDATA[0.5m 3 ]]> <![CDATA[0.1m 3 ]]> <![CDATA[0.05m 3 ]]> Abnormal physical examination rate <h2 style=";text-align:left;direction:ltr"><![CDATA[60%(>2m<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> )]]><h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"><![CDATA[85%(>0.5m<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> )]]><h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"><![CDATA[98%(>0.2m<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> )]]><h2 style=";text-align:left;direction:ltr"> Position control accuracy (100m) ±25mm ±15mm ±8mm Time-consuming adjustment when encountering faults >30 minutes 10-15 minutes <5 minutes

[0053] Table 1 Comparison of technical effects using single acoustic scanning module and dual modules

[0054] Furthermore, the surface is coated with a mud-repelling material with a contact angle greater than 120° to reduce mud adhesion. It is worth noting that the mud-repelling material is an important improvement to ensure the accuracy of the entire system's correction. Due to the particularity of the actual working environment of the present invention, mud adhesion will greatly affect the authenticity of the collected signal. The mud-repelling material used in this embodiment varies depending on the application scenario, as shown in Table 2 below:

[0055]

[0056] Table 2 Composition of sludge-removing materials in different application scenarios

[0057] The adjustment angles of the pitch mechanism and the yaw mechanism are both ±5°, and the adjustment range of the axial mechanism is ±200mm.

[0058] Further preferably, the present embodiment also includes a second acoustic wave scanning module 7, which includes a vertical acoustic wave array installed in the slot through an auxiliary bracket, with an interval of 0.6-1 meters between two adjacent vertical acoustic wave units, and the frequency of the transmitted sound waves is 10kHz-50kHz, which is used for large-scale stratum modeling. It is worth noting that the role of the second acoustic wave scanning module 7 is not the same as that of the first acoustic wave scanning module, and the emphasis and technical problems solved by the two are not consistent; specifically, the second acoustic wave scanning module 7 uses a low frequency to solve the problem of mastering the geological structure of a new stratum, and is used to update the dynamic geological database and digital twin engine before construction, so that during the construction process, the information mastered by the system is always basically consistent with the current construction stratum geology. The disadvantage is that the accuracy of the specific geological conditions of a certain stratum depth needs to be improved. For this reason, the first acoustic wave scanning module can be combined with the second acoustic wave scanning module. 7 forms a complementary technology. Since the first acoustic scanning module is installed on the milling head, as trenching progresses, the first acoustic scanning module gradually descends with the milling head, always closely following the current construction stratum. This allows for accurate detection of the specific geology of the stratum to be excavated. For example, it can accurately detect whether the stratum to be excavated is hard rock, gravel, soft stratum, or a loose composite geological structure prone to hole collapse at a certain depth. This allows for timely adjustment of the power of the dual-wheel milling equipment, pre-adjusting the system's operating conditions to suit the current stratum geological structure, and reducing the probability of accidents such as hole collapse and drill stuck. In summary, the first acoustic scanning module uses high-frequency pulses with a main frequency of 500kHz and a pulse width of 1μs for close-range, high-resolution detection of less than 5m. The second acoustic scanning module 7 uses low-frequency pulses with a main frequency of 50kHz and a pulse width of 10μs for long-range stratum imaging. The combined design and installation at different locations enable accurate control of the construction status before and during construction, regardless of location, stratum, or geology. This solves the problems of hole collapse and drill jamming caused by inaccurate control of stratum geology at different depths in existing technologies. In order to further enhance the anti-interference capability of the first acoustic scanning module in mud environments, this embodiment also provides an adaptive frequency selection method. By installing a density sensor on the milling head, the mud density is collected in real time and fed back to the system control center. The system optimizes the frequency according to the following mechanism, thereby improving the actual anti-interference effect, as shown in Table 3 below:

[0059] mechanism <![CDATA[ Mud density (g / cm 3 )]]> Preferred frequency Physical causes 1 <1.2 500kHz Low attenuation, high resolution 2 1.2-1.4 250kHz Balancing penetration and resolution 3 >1.4 100kHz Avoid complete attenuation of high-frequency signals

[0060] Table 3 Frequency selection table for different mud densities

[0061] Example 3:

[0062] This embodiment provides an intelligent deviation correction method for trough excavation based on the deviation correction system provided in any of the above embodiments, specifically comprising the following steps:

[0063] Step STP100, geological radar scanning, using GPR geological radar to transmit high-frequency electromagnetic waves and receive reflected signals from the stratum interface, and obtain the stratum structure through dielectric constant difference analysis;

[0064] Step STP200, acoustic stratum scanning, uses the second acoustic scanning module to transmit 50kHz low-frequency acoustic waves to scan the bottom layer, generates a global stiffness distribution map by receiving the reflected signals, and uploads the data to the digital twin engine; the piezoelectric ceramic transducer of the first acoustic scanning module transmits 500kHz high-frequency acoustic pulses, penetrates the mud and enters the near-field stratum, captures the reflected signals of P waves and shear waves S waves through the array receiver, records the travel time and amplitude, inverts the real-time mechanical parameters, and inputs them into the embedded industrial computer;

[0065] Step STP300: Data fusion and establishment of digital twin model M0. The attitude data of the dual-wheel milling equipment collected by the IMU attitude detection module, the acoustic data B collected by the second acoustic scanning module, and the position data collected by the laser ranging module are synchronized in time and space through a high-precision clock, and the coordinates are unified through the ICP algorithm to establish the digital twin model M0 of the stratum in the current construction area. After normal excavation, the large-scale stratum trend is updated every 10 minutes. The first acoustic scanning module monitors the 5m stratum geology around the milling head in real time, and dynamically corrects the digital twin model M0. t+1 ;

[0066] Step STP400, design a model predictive controller, and establish the milling system dynamic equation to predict N by designing a model predictive controller in the future. p Step position, calculate the control torque τ required by the hydraulic servo mechanism of the double-wheel milling equipment to complete the correction control The hydraulic servo mechanism is controlled by the embedded industrial computer to correct the deviation until the posture deviation δ reaches the preset value range. The dynamic equation of the milling system is as follows:

[0067]

[0068] Where q∈R 3 Represents the milling head pose vector, M(q)∈R 3×3 Represents the inertia matrix, i.e. the mass and moment of inertia of the milling head, which are known quantities. The inertia parameters can be verified through no-load acceleration tests; Represents the Coriolis force matrix, characterizes the motion coupling effect, is calculated online based on the kinematic model, and is used to calculate the cross product of the milling head angular velocity and linear velocity in real time; G(q)∈R 3 Represents the gravity component, gravitational acceleration g = 9.81m / s 2 , attitude sensor feedback inclination angle θ, gravity component G x =mgsinθ, G y = mg cosθ; represents the formation friction model, including the combined resistance of mud and formation; K d ∈R 3×3 Represents the elastic reaction force of the formation, and the formation stiffness is inverted by acoustic scanning; δ∈R 3 Represents the posture deviation, which is obtained by real-time measurement of laser ranging. In this embodiment, K is updated every 0.5m. d , hard rock structure K d =MN / m, for sand layer, take K d =20MN / m; represents the control torque; τ disturbance ∈R 3 represents the disturbance torque, which has a small influencing factor and is mainly caused by geological mutations, equipment vibration, etc. It is estimated using the extended state observer ESO. The designed model predictive controller calculates the optimal control sequence U by minimizing the trajectory tracking error and the control variable change rate:

[0069]

[0070] Among them, q ref represents the reference trajectory axis of the double-wheel milling equipment design, which is a known quantity and is generated by importing the design drawings or B-spline interpolation; Q represents the state deviation weight, and R represents the control deviation weight. Both weight parameters are known and are set based on the empirical trial and error method; N p Represents the prediction step size, N c represents the actual control step size, which is set according to the system dynamic response time. Different system hardware and response speeds can set different step sizes. q(k|t) represents the predicted pose, which is obtained based on the ground prediction of the dynamic model. The recursive process is as follows:

[0071]

[0072] In each control cycle of MPC, the current state q(t) and control sequence U are used to predict the future N p step trajectory. Δu(k|t) represents the rate of change of the control variable or the adjustment amount, which can be obtained by online calculation using an interior point solver such as OSQP.

[0073] To facilitate understanding of this embodiment, see the following table 4 for the working logic of the correction.

[0074]

[0075] Table 4 Correction scenarios and functional component status table of this embodiment

[0076] According to Table 4 above, the actual construction deployment can be divided into the initialization stage, the system adaptation and learning stage, the full-speed construction stage and the exception handling stage.

[0077] The initialization phase includes initializing the parameters or states of the system's mechanical equipment and calibrating the zero position and scale factor of each sensor in the data acquisition unit, such as laser ranging, IMU, and acoustic scanning.

[0078] The system adaptation and learning phase refers to the initial stage of the system controlling the actuator to dig the trench. On the one hand, it observes whether the system is adaptable and compatible with the current geological data collection and construction. On the other hand, it is used to collect data for identification and update the digital twin engine, laying the foundation for subsequent high-power tunneling. In this embodiment, this phase is advanced at a speed of 0.5m / h, and the state deviation weight Q is adjusted to make the lateral deviation convergence speed t s <3s, the system presets an error band of ±2%. When the actual excavation depth reaches 10m, the full-speed construction stage can be carried out; of course, for better verification and adaptation, the excavation construction depth of this stage can also be defined as 15m, which can be set manually.

[0079] MPC closed-loop control is enabled during the full-speed construction phase, with an optimization solution performed every 10ms; the ground stiffness K is saved every 0.5m. d and friction coefficients while building components and keeping construction logs.

[0080] The exception handling stage is an unexpected event that runs through any of the above stages. It may not occur at all, but it may occur at any time. According to the actual construction situation, when the posture deviation exceeds 2σ (assuming σ is a historical statistical value), the following three-level response is triggered:

[0081] First level: increase the weight of Q matrix;

[0082] Level 2: Reduce speed to 50% and recalibrate;

[0083] Level 3: Emergency stop and start manual intervention.

[0084] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A trough excavation correction system, characterized by: It includes an embedded industrial computer as the system information exchange and computing processing center, a data acquisition unit connected to the embedded industrial computer for collecting data on the formation and trenching status, a dynamic geological database, and a digital twin engine. The embedded industrial computer is also electrically connected to a hydraulic servo mechanism, a posture adjustment mechanism, and a power modulation module for controlling the dual-wheel milling equipment; The data acquisition unit includes an IMU attitude detection module and a laser ranging module, each used to detect the attitude and position of the dual-wheel milling equipment, and a first acoustic wave scanning module for collecting current geological conditions; the IMU attitude detection module and the first acoustic wave scanning module are both integrated in the head of the dual-wheel milling equipment, the laser ranging module includes at least one set of laser transmitters and receivers respectively installed on the ground on both sides of the slot, a pressure-resistant transparent conduit for guiding the laser beam into the slot is provided below the laser transmitter, a self-cleaning optical window is installed at the bottom of the pressure-resistant transparent conduit, and a laser ranging target is installed on the head of the dual-wheel milling equipment; the attitude adjustment mechanism consists of a pitch mechanism, a yaw mechanism and an axial mechanism.

2. A trough excavation correction system according to claim 1, characterized in that: The IMU attitude detection module is composed of an integrated three-axis gyroscope and a three-axis accelerometer. The laser ranging module includes at least six groups of laser ranging arrays composed of cross-linked laser rangefinders. The first acoustic wave scanning module includes at least six groups distributed in a circular array within the cutter shield of the double-wheel milling equipment. The adjustment angles of the pitch mechanism and yaw mechanism are both ±5°, and the adjustment range of the axial mechanism is ±200mm.

3. The trough excavation correction system according to claim 2, characterized in that: It also includes a second acoustic wave scanning module, which includes a vertical acoustic wave array installed in the slot through an auxiliary bracket, with an interval of 0.6-1 meter between two adjacent vertical acoustic wave units, and the frequency of the emitted acoustic waves is 10kHz-50kHz.

4. An intelligent deviation correction method for trough excavation, characterized by: The correction system according to claim 3 is used to implement the method, which specifically includes the following steps: Step STP100, geological radar scanning, using GPR geological radar to transmit high-frequency electromagnetic waves and receive reflected signals from the stratum interface, and obtain the stratum structure through dielectric constant difference analysis; Step STP200, acoustic stratum scanning, uses the second acoustic scanning module to transmit 50kHz low-frequency acoustic waves to scan the bottom layer, generates a global stiffness distribution map by receiving the reflected signals, and uploads the data to the digital twin engine; the piezoelectric ceramic transducer of the first acoustic scanning module transmits 500kHz high-frequency acoustic pulses, penetrates the mud and enters the near-field stratum, captures the reflected signals of P waves and shear waves S waves through the array receiver, records the travel time and amplitude, inverts the real-time mechanical parameters, and inputs them into the embedded industrial computer; Step STP300: Data fusion and establishment of digital twin model M0. The attitude data of the dual-wheel milling equipment collected by the IMU attitude detection module, the acoustic data B collected by the second acoustic scanning module, and the position data collected by the laser ranging module are synchronized in time and space through a high-precision clock, and the coordinates are unified through the ICP algorithm to establish the digital twin model M0 of the stratum in the current construction area. After normal excavation, the large-scale stratum trend is updated every 10 minutes. The first acoustic scanning module monitors the 5m stratum geology around the milling head in real time, and dynamically corrects the digital twin model M0. t+1 ; Step STP400, design a model predictive controller, and establish the milling system dynamic equation to predict N by designing a model predictive controller in the future. p Step position, calculate the control torque τ required by the hydraulic servo mechanism of the double-wheel milling equipment to complete the correction control The hydraulic servo mechanism is controlled by the embedded industrial computer to correct the deviation until the posture deviation δ reaches the preset value range. The dynamic equation of the milling system is as follows: Where q∈R 3 Represents the milling head pose vector, M(q)∈R 3×3 represents the inertia matrix; Represents the Coriolis force matrix, characterizing the motion coupling effect; G(q)∈R 3 represents the gravity component; represents the formation friction model; K d ∈R 3×3 Represents the elastic reaction force of the formation; δ∈R 3 Represents the pose deviation; represents the control torque; τ disturbance ∈R 3 represents the disturbance torque; the designed model predictive controller calculates the optimal control sequence U by minimizing the trajectory tracking error and the control variable change rate: Among them, q ref represents the reference trajectory axis of the double-wheel milling equipment design; Q represents the state deviation weight; R represents the control deviation weight; N p Represents the prediction step size; N c represents the actual control step; q(k|t) represents the predicted posture; Δu(k|t) represents the rate of change of the control amount or the adjustment amount.

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