Intelligent adjustment method and system for cutter head diameter based on complex loose body excavation

Abstract

The application discloses a cutter head diameter intelligent adjusting method and system based on complex loose body tunneling, relates to the technical field of intelligent tunneling, and comprises the following steps: acquiring equipment operation data and geological data, accurately extracting external dissipation power by eliminating internal loss; constructing a boundary constraint equation based on a rheological constitutive relation, introducing a variable diameter speed and a geological state parameter, combining a sliding mode observer to inversely calculate stratum rheological parameters in real time; further evaluating a working face bearing state, combining a preset settlement condition to deduce a target variable diameter track; finally, constructing an energy control model and an interconnected structure, mapping the external dissipation power to a constraint term, generating virtual potential energy based on the target variable diameter track, and outputting a variable diameter instruction to adjust the cutter head diameter through constraint solving. The application realizes dynamic identification of real geological interaction characteristics and soft closed-loop control of variable diameter energy, effectively prevents stratum instability induced by blind expansion, and significantly improves the safety adaptability of the tunneling system.

Description

Technical Field

[0001] This invention relates to the field of intelligent tunneling technology, and in particular to a method and system for intelligent adjustment of cutter head diameter based on tunneling in complex loose materials. Background Technology

[0002] As underground excavation projects extend into deeper and more complex strata, tunnel boring machines (TBMs) and pipe jacking equipment often rely on variable-diameter cutterheads to dynamically adapt to cross-sectional changes or avoid geological obstacles when traversing loose rock and soil. In recent years, cutterhead variable-diameter drive technology has gradually evolved from traditional mechanical limiting and basic electro-hydraulic proportional control towards automated adjustment based on multi-source sensor data. However, loose strata possess strong rheological properties and extremely low structural self-stabilizing ability, and the mechanical disturbances caused by diameter-changing operations can easily disrupt the initial stress balance state of the excavation face.

[0003] Existing variable diameter control technologies still have significant shortcomings: First, traditional control systems often directly equate the total input pressure of the drive mechanism to the soil reaction force, failing to effectively isolate internal physical losses such as electromechanical friction and dynamic inertia, resulting in severe distortion of the perception of actual ground interaction forces. Second, the systems largely rely on prior static geological survey data for parameter scheduling, lacking a real-time online identification mechanism for the micro-rheological parameters of the excavation face soil, leading to a significant lag in ground adaptability. Finally, conventional algorithms often employ single position or torque closed loops, severing the dual constraints of mechanical yielding and surface settlement, and lacking a physical mapping mechanism based on energy interconnection. When encountering drastic ground changes, this rigid control is prone to outputting excessive variable diameter commands, thereby inducing irreversible ground collapse or surface heave, failing to meet the inherent safety requirements of excavating complex loose bodies. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent adjustment method for the cutter head diameter based on tunneling in complex loose soil, which solves the technical problem that when adjusting the cutter head diameter of tunneling equipment under complex loose geological conditions, the lack of clear perception of the actual geological interaction forces and the lack of effective boundary constraints can easily lead to stratum instability and subsidence.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for intelligent adjustment of cutter head diameter based on tunneling in complex loose bodies, which includes: acquiring the operating data of the tunneling equipment and the geological data of the working face; calculating the total input power based on the operating data; calculating the internal loss power item by item; removing all the internal loss power from the total input power to obtain the external dissipation power.

[0008] The variable diameter velocity is extracted from the operational data, and the state parameters are extracted from the geological data. Boundary constraint equations are constructed through rheological constitutive relations. The variable diameter velocity and the state parameters are input into the boundary constraint equations. The rheological parameters are obtained by inverting the operational data using a sliding mode observer.

[0009] The bearing capacity of the working face is evaluated by combining the geological data and the rheological parameters, and the target diameter change trajectory of the tunneling equipment is generated based on the bearing capacity and the preset settlement conditions.

[0010] An energy control model is constructed, an interconnection structure is established, and the external dissipation power is used as the power constraint term of the energy control model. The target variable diameter trajectory is used as input to generate virtual potential energy. The variable diameter control command is output through constraint solution to adjust the cutter head diameter.

[0011] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on complex loose material tunneling described in this invention, the operating data includes the driving voltage, instantaneous operating current, instantaneous system oil temperature, instantaneous diameter changing speed, instantaneous diameter changing acceleration, and actual driving pressure of the diameter changing drive mechanism.

[0012] The step of calculating the internal power loss and removing all internal power loss from the total input power to obtain the external power dissipation includes calculating the total input power of the variable diameter drive mechanism based on the driving voltage and the instantaneous operating current.

[0013] Based on the instantaneous operating current and the instantaneous system oil temperature, the corresponding electromechanical conversion heat loss power is found through a preset heat loss mapping table;

[0014] Based on the instantaneous diameter change speed, the corresponding mechanical transmission friction loss power is found through the preset mechanical friction resistance curve;

[0015] Based on the instantaneous diameter change acceleration and the preset equivalent inertia parameters of the diameter change drive mechanism, the dynamic inertia loss power of the diameter change drive mechanism due to inertia during the variable speed motion is calculated.

[0016] Subtracting the electromechanical conversion heat loss power, the mechanical transmission friction loss power, and the dynamic inertia loss power from the total input power, the external dissipation power used for cutting and compressing the soil at the working face is extracted.

[0017] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on tunneling in complex loose soil as described in this invention, the geological data includes the natural moisture content, void ratio, and pressure distribution data of the overlying soil in the working face; the state parameters include pore pressure state parameters, compaction state parameters, and structural stability parameters.

[0018] Input the natural water content and the porosity into a preset pore pressure characteristic lookup table, and find the corresponding excess pore water pressure parameter as the pore pressure state parameter;

[0019] Input the natural moisture content, the void ratio, and the pressure distribution data of the overlying soil layer into a preset compaction assessment comparison table, and calculate the corresponding soil density as the compaction state parameter.

[0020] The pressure distribution data of the overlying soil layer is input into the stress-strain relationship equation constructed based on the effective stress principle for solving, and the stiffness modulus of the working face soil resisting volume compression is calculated, and the stiffness modulus is used as the structural stability parameter.

[0021] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on complex loose body tunneling described in this invention, the method includes: constructing boundary constraint equations through rheological constitutive relations, and inputting the variable diameter speed and the state parameters into the boundary constraint equations; obtaining rheological parameters by combining a sliding mode observer based on operational data inversion; and setting the yield stress parameter and plastic viscosity parameter to be identified according to the rheological constitutive relations to construct the boundary constraint equations.

[0022] By inputting the variable diameter speed and the state parameters into the boundary constraint equation, the theoretical stress of the cutter head squeezing the soil at the working face is calculated.

[0023] Based on the actual driving pressure and the preset effective pressure area of ​​the driving mechanism in the operating data, the actual stress of the cutter head squeezing the soil on the working face is calculated.

[0024] Calculate the deviation between the theoretical applied stress and the actual applied stress;

[0025] The integral update algorithm of the sliding mode observer forces the deviation value to converge to zero, and dynamically adjusts the yield stress parameter and plastic viscosity parameter to be identified during the convergence process. The yield stress parameter and plastic viscosity parameter when the deviation value converges to zero are used as the rheological parameters characterizing the real geological features.

[0026] The sliding mode observer is an adaptive sliding mode observer that forces the observation error to converge based on the sliding mode mechanism and simultaneously realizes online identification of unknown parameters.

[0027] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on complex loose body tunneling described in this invention, the following steps are included: combining the geological data and the rheological parameters to evaluate the bearing state of the working face, and generating the target diameter change trajectory of the tunneling equipment based on the bearing state and preset settlement conditions, including inputting the rheological parameters into a preset rock and soil yield criterion, calculating the ultimate passive earth pressure when the working face soil undergoes shear failure, and using the ultimate passive earth pressure to characterize the bearing state;

[0028] Obtain the preset maximum allowable surface uplift height and maximum allowable subsidence depth as the preset settlement conditions;

[0029] The mechanical constraint boundary is defined as the compressive stress generated by the cutter head diameter change not exceeding the ultimate passive earth pressure, and the geometric constraint boundary is defined as the stratum deformation caused by the cutter head diameter change not exceeding the maximum allowable uplift height and the maximum allowable collapse depth. Based on the mechanical constraint boundary and the geometric constraint boundary, a multi-objective dual-constraint optimization model is constructed to solve the boundary and generate the target diameter change trajectory that continuously transitions from the current cutter head diameter to the target cutter head diameter.

[0030] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on complex loose body tunneling described in this invention, the step of establishing an interconnection structure and using the external dissipation power as a power constraint term of the energy control model includes constructing an actuator power port in the energy control model to characterize the energy input and output of the variable diameter drive mechanism, and constructing a soil dissipation power port to characterize the energy exchange with the working face soil.

[0031] Using the Dirac algebraic structure, the interconnection structure between the power port of the actuator and the power dissipation port of the soil is established;

[0032] The external dissipation power is captured in real time through the soil dissipation power port, and the external dissipation power is mapped into a dynamic dissipation constraint quantity inside the control system using the interconnection structure. The dynamic dissipation constraint quantity is used as the power constraint term that limits the power amplitude injected into the energy control model.

[0033] The energy control model is a closed-loop control model based on the principle of energy conservation.

[0034] As a preferred embodiment of the intelligent adjustment method for cutter head diameter based on complex loose body tunneling described in this invention, the method includes: using the target diameter change trajectory as input to generate virtual potential energy, and outputting diameter change control commands through constraint solving, including: calculating the positional deviation between the current cutter head diameter and the target diameter change trajectory; inputting the positional deviation into a distance cost function; calculating the cost value of the current position relative to the target diameter change trajectory; and using the cost value as the virtual potential energy.

[0035] The virtual potential energy and the power constraint term are input together into an online optimization control algorithm based on the steepest descent principle to calculate the set of control variables that makes the virtual potential energy decrease the fastest without triggering the power constraint term.

[0036] The control variable group is converted into the flow valve opening signal of the variable diameter drive system according to the preset flow distribution rule, and the flow valve opening signal is sent to the tunneling equipment as the variable diameter control command.

[0037] Secondly, the present invention provides an intelligent adjustment system for the cutter head diameter based on tunneling in complex loose bodies, including a sensing unit that acquires the operating data of the tunneling equipment and the geological data of the working face, calculates the total input power based on the operating data, calculates the internal loss power item by item, and removes all the internal loss power from the total input power to obtain the external dissipation power.

[0038] The inversion unit extracts the variable-diameter velocity from the operational data and the state parameters from the geological data. It constructs boundary constraint equations through rheological constitutive relations and inputs the variable-diameter velocity and the state parameters into the boundary constraint equations. It then uses a sliding mode observer to invert the rheological parameters based on the operational data.

[0039] The planning unit, in conjunction with the geological data and the rheological parameters, assesses the bearing capacity of the working face and, based on the bearing capacity and preset settlement conditions, generates the target diameter-changing trajectory of the tunneling equipment.

[0040] The control unit constructs an energy control model, establishes an interconnection structure, and uses the external dissipation power as a power constraint term of the energy control model. It takes the target variable diameter trajectory as input to generate virtual potential energy, and outputs variable diameter control commands through constraint solving to adjust the cutter head diameter.

[0041] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the intelligent adjustment method for cutter head diameter based on complex loose body tunneling as described in the first aspect of the present invention.

[0042] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent adjustment method for cutter diameter based on complex loose body tunneling as described in the first aspect of the present invention.

[0043] The beneficial effects of this invention are as follows: By accurately extracting external dissipated power through the stripping of internal losses, high-fidelity perception of the actual interaction force between the equipment and loose strata is achieved; by utilizing an adaptive sliding mode observer to invert soil rheological parameters based on real-time data, the challenges of lag and difficulty in obtaining complex geological parameters are overcome; by integrating ultimate passive earth pressure and settlement tolerance to construct a dual constraint boundary, the absolute safety of the target diameter-changing trajectory is ensured; finally, an energy interconnection model is constructed based on the Dirac algebraic structure, mapping strata dissipation as dynamic power constraints and outputting compliant control commands. By addressing the issue from the perspective of energy conservation, ground instability and settlement collapse caused by diameter-changing overload are eliminated, significantly improving the inherent safety of the system. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0045] Figure 1 This is a flowchart of a method for intelligent adjustment of cutter head diameter in tunneling of complex loose materials.

[0046] Figure 2 This is a diagram of a computer device for an intelligent cutter head diameter adjustment method based on tunneling in complex loose materials. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-2 This is one embodiment of the present invention, which provides a method for intelligent adjustment of the cutter head diameter based on tunneling in complex loose materials, including the following steps:

[0051] S1: Obtain the operating data of the tunneling equipment and the geological data of the working face, calculate the total input power based on the operating data, calculate the internal loss power item by item, remove all the internal loss power from the total input power, and obtain the external dissipation power.

[0052] When performing diameter-changing operations in complex loose geological environments, existing technologies typically directly equate the total input power of the drive system to the soil reaction force, or simply use a fixed static mechanical efficiency for a crude calculation. However, the transient inertial peaks generated by the frequent start-stop of the diameter-changing mechanism and the nonlinear friction caused by oil temperature drift produce significant dynamic data noise. If this noise is not filtered out and used directly, it is easy to misjudge pure "mechanical motion lag" as "extremum of formation resistance" and output incorrect thrust. Based on this, this invention purifies the dynamic data by accurately removing the internal losses of the drive mechanism in real time, extracting the external dissipated power purely used for compressing the soil, thus providing a clean data foundation for subsequent high-order adaptive algorithms used for formation parameter inversion.

[0053] According to the law of conservation of energy in classical thermodynamics and kinetics, in a variable-diameter drive mechanism (a typical electromechanical-hydraulic coupling system), the total input electrical energy can only go in four directions: performing useful work (compressing and cutting the soil in front), being converted into heat energy dissipation (heating of the motor coils and throttling of hydraulic oil flowing through the valve ports), being converted into mechanical dissipation (work done by mechanical friction between gears, bearings, and hydraulic cylinder walls), and being converted into system kinetic energy (the inertial energy consumed in accelerating or decelerating a several-ton robotic arm). Therefore, heat energy, friction energy, and kinetic energy are selected to form a completely orthogonal set of energy dissipation within the electromechanical system, and relevant data on heat energy, friction energy, and kinetic energy are obtained.

[0054] The system collects multi-dimensional sensor signals from the bottom layer of the tunneling equipment in real time. This operational data includes the drive voltage, instantaneous operating current, instantaneous system oil temperature, instantaneous diameter change speed, instantaneous diameter change acceleration, and actual drive pressure of the variable diameter drive mechanism. The acquisition of this operational data primarily relies on the collaborative operation of a multi-source sensor network deployed at the bottom layer of the tunneling equipment and a high-speed data acquisition system. Specifically, the drive voltage and instantaneous operating current are acquired through high-frequency sampling using high-precision Hall voltage sensors and current transformers installed in the electrical control cabinet or motor drive circuit of the variable diameter mechanism. The instantaneous system oil temperature and actual drive pressure are obtained through real-time monitoring using industrial-grade temperature transmitters and high-frequency pressure sensors integrated into the hydraulic drive circuit, hydraulic cylinder cavity, and oil tank. To address the mechanical kinematics, a high-resolution linear displacement sensor (LVDT) or absolute encoder is installed at the telescopic end of the variable-diameter actuator to record position changes in real time. The position change signal is then connected to a programmable logic controller (PLC) or a low-level DSP chip. The internal digital differential algorithm performs first-order and second-order derivative calculations on time to accurately extract the instantaneous variable-diameter velocity and the instantaneous variable-diameter acceleration. Finally, these multi-dimensional physical signals are synchronously transmitted to the main control unit via a field industrial bus (such as CAN bus or EtherCAT), and after digital filtering and noise reduction, they are converted into high-fidelity operating data.

[0055] After obtaining the basic physical quantities, the power decoupling process is initiated to separate effective work from ineffective losses. Internal power losses are calculated item by item, and all internal power losses are removed from the total input power to obtain the external dissipation power. Specifically, the total input power of the variable-diameter drive mechanism is calculated based on the driving voltage and the instantaneous operating current. The calculation process follows the basic laws of electrical power physics, transforming the basic electrical parameters of the electronic control system into macroscopic input quantities in the energy dimension through multiplication operations, specifically expressed by the following formula:

[0056]

[0057] in, express Total input power at time t; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; express The driving voltage at any given moment; express The instantaneous operating current at any given moment.

[0058] After obtaining the total input power, it is necessary to analyze each of the various ineffective power dissipation paths within the mechanism.

[0059] First, based on the instantaneous operating current and the instantaneous system oil temperature, the corresponding electromechanical conversion heat loss power is determined using a preset heat loss mapping table. Since the equipment operates continuously for several hours in an underground confined space, the oil and motor temperatures will rise sharply; therefore, a dynamic temperature compensation factor is introduced. The aforementioned dynamic temperature compensation factor can correct the baseline for heat calculation in real time as the ambient temperature deteriorates. Other purely mathematical static models are simply unable to cope with this dynamic deterioration of physical properties. To engineer and computably implement the heat loss mapping table lookup process, an interpolation mapping analytical model based on the current heating effect and the aforementioned dynamic temperature compensation factor is introduced, with the calculation formula as follows:

[0060]

[0061] in, express The power loss due to heat generation during motor switching at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; This represents the equivalent fundamental resistance of the variable diameter drive mechanism coil at room temperature. Indicates the temperature coefficient of resistance; express Instantaneous operating oil temperature at any given moment; Indicates the reference temperature; express The instantaneous operating current at any given moment.

[0062] After stripping away heat loss, the damping effect caused by the relative motion of the mechanical structure is further analyzed. Based on the instantaneous diameter change speed, the corresponding mechanical transmission friction loss power is found through the preset mechanical friction resistance curve. Conventional low-pass filtering algorithms filter out friction oscillations, but ignore the physical characteristics of the hydraulic cylinder at different speeds. Furthermore, the diameter change mechanism of the tunneling machine is a typical heavy-load, low-speed motion, falling within the nonlinear "Stribeck friction curve" range. Therefore, a dynamic damping calculation model is selected. This dynamic damping calculation model combines a composite mechanism model of directional abrupt change (Coulomb friction, static friction pause at the start) and positive speed correlation (viscous friction, viscous resistance of hydraulic oil). Conventional filtering algorithms would misjudge the "Coulomb friction pause" at the start as "hitting a large rock," while the dynamic damping calculation model can accurately strip away this nonlinear resistance. The mechanical friction resistance curve integrates the characteristics of Coulomb friction and viscous friction, transforming it into the dynamic damping calculation model that can be solved in real time in engineering, specifically in the following form:

[0063]

[0064] in, express The power loss due to friction in mechanical transmission at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; This represents the basic Coulomb friction constant of the mechanism; Represents a symbolic function; express The instantaneous velocity of the change in diameter at any given moment; This represents the coefficient of viscous friction.

[0065] Furthermore, the diameter-changing action of the cutting head typically involves frequent start-stop and acceleration / deceleration processes, and the charging and discharging effect of mass inertia on power distribution must also be considered. Based on the instantaneous diameter-changing acceleration and the preset equivalent inertial parameters of the diameter-changing drive mechanism, the dynamic inertial power loss caused by inertia during the variable speed motion of the diameter-changing drive mechanism is calculated. The diameter-changing mechanism weighs several tons. According to Newton's second law, when the mechanism frequently starts, stops, or changes direction, a huge force is required to overcome the inertia of these several tons of steel. Acceleration can predict the "huge power peak required at the moment of acceleration" in advance, eliminating the work done by inertia. Other algorithms, such as smoothing average algorithms, will laggingly misreport this power peak caused by acceleration as a "sudden increase in geological resistance." The power representation derived from Newton's second law of dynamics is as follows:

[0066]

[0067] in, express Dynamic inertial loss power at any given moment; This represents the equivalent inertial parameter of the variable diameter drive mechanism; express Instantaneous diameter change acceleration at a given moment; express The instantaneous velocity of the change in diameter at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0068] Finally, a closed-loop power elimination operation is performed to obtain the net force power. The total input power is subtracted from the electromechanical conversion heat loss power, the mechanical transmission friction loss power, and the dynamic inertia loss power to extract the external dissipation power used for cutting and compressing the soil at the working face.

[0069]

[0070] in, express External power dissipation at any given moment; express Total input power at time t; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; express Dynamic inertial loss power at any given moment; express The power loss due to friction in mechanical transmission at any given moment; express The power loss due to heat generation during motor switching at any given moment.

[0071] The extracted high-purity external dissipation power filters out non-geological noise interference caused by equipment mechanical wear, ambient temperature drift, and variable speed start-stop conditions. This provides the most realistic and reliable power boundary constraints for the subsequent construction of interconnected structures and dynamic simulation of multi-target trajectories, greatly improving the safety and adaptability of operations in complex and variable loose bodies.

[0072] S2: Extract the variable diameter velocity from the operational data and extract the state parameters from the geological data. Construct boundary constraint equations through rheological constitutive relations and input the variable diameter velocity and the state parameters into the boundary constraint equations. Combine the sliding mode observer to obtain the rheological parameters based on the operational data inversion.

[0073] To obtain a true reflection of the dynamic mechanical state of the tunnel boring machine cutterhead interacting with the soil in real time, it is essential to accurately and continuously monitor the actual bearing capacity of the soil at the working face during construction operations. By designing an online inversion architecture based on a sliding mode observation mechanism, the dynamic operating data of the tunneling equipment itself can be fully utilized, transforming the mechanical actuator into a "geological sensor" that perceives the external environment. This effectively overcomes the engineering challenge of directly measuring key mechanical parameters in complex geological formations.

[0074] First, the basic environmental information is classified and extracted. The geological data includes the natural moisture content, void ratio, and overlying soil pressure distribution data of the working face soil; the state parameters include pore pressure state parameters, compaction state parameters, and structural stability parameters. These three types of state parameters characterize the macroscopic physical state of the current loose body from three dimensions: hydrology, density, and stiffness.

[0075] To achieve quantitative extraction of parameters, state parameters are extracted from the geological data. Specifically, the natural water content and the void ratio are input into a preset pore pressure characteristic lookup table to find the corresponding excess pore water pressure parameter as the pore pressure state parameter. The pore pressure characteristic lookup table is calibrated through prior engineering geotechnical tests, directly mapping the nonlinear coupling characteristics of water content and void ratio to the excess pore water pressure coefficient of the current formation, avoiding the need for solving complex partial differential equations. Furthermore, the preset pore pressure characteristic lookup table is a two-dimensional data grid constructed in advance by conducting standard consolidated undrained triaxial compression tests (CU tests) to obtain the peak excess pore water pressure of the target formation when it reaches the critical failure state under discrete initial water content and void ratio combinations. During real-time table lookup, the natural water content and porosity of the current working face are obtained. If the input coordinates happen to match a grid node, the corresponding excess pore water pressure coefficient is directly extracted. If the input coordinates are between discrete grids, a bilinear interpolation algorithm is used to extract the data of the four adjacent discrete nodes surrounding the input coordinates. The weighted average is calculated based on the distance weight to obtain the current accurate excess pore water pressure coefficient as the pore pressure state parameter, thereby achieving a precise mapping from limited experimental data to continuous working conditions.

[0076] The natural moisture content, void ratio, and overlying soil pressure distribution data are input into a pre-defined compaction assessment table to calculate the corresponding soil density as the compaction state parameter. This process uses a multi-dimensional interpolation algorithm to integrate depth-related overlying soil pressure and soil phase structure indicators, outputting a relative density characterizing the compactness of particle packing. Furthermore, the pre-defined compaction assessment table is constructed based on indoor standard compaction tests and lateral confined compression tests, and the maximum and minimum void ratio reference surfaces of the target soil layer under different overlying effective stresses are pre-calibrated. The real-time acquired pressure distribution data of the overlying soil layer is extracted and used as a depth-dimensional retrieval index to locate the two-dimensional evaluation sub-table corresponding to the current formation compressive stress. Subsequently, the natural water content and void ratio are substituted into the two-dimensional evaluation sub-table, and the actual pore state under the current compressive stress environment is calculated using a three-dimensional spline interpolation algorithm. The relative difference between the actual pore state and the aforementioned maximum and minimum void ratio reference surfaces is also calculated. Finally, the relative difference is converted into an engineering relative density percentage characterizing the compactness of particle packing and is output as the compaction state parameter.

[0077] The pressure distribution data of the overlying soil layer is input into a stress-strain equation constructed based on the effective stress principle for solution. The stiffness modulus of the working face soil resisting volume compression is calculated, and this stiffness modulus is used as a structural stability parameter. The solution process comprehensively evaluates the nonlinear compressive characteristics of the strata under the current overlying soil load, forming a complete environmental state assessment data chain. Furthermore, to eliminate engineering calculation errors caused by treating nonlinear soil as a constant stiffness body, the stress-strain equation constructed based on the effective stress principle specifically adopts the Janbu nonlinear elastic empirical formula based on stress state. Specifically, the pressure distribution data of the overlying soil layer is extracted, the effective vertical principal stress borne by the current working face soil is calculated, and substituted into the following power function analytical expression to obtain the dynamic stiffness modulus reflecting the soil under the current actual compressive state:

[0078]

[0079] in, Represents the dynamic stiffness modulus; This represents the foundation stiffness coefficient of the strata, which is obtained in advance through geological exploration and calibration. Represents the standard atmospheric pressure constant; This indicates the effective vertical principal stress borne by the soil at the current working face; The stress index represents the rate at which soil stiffness changes with stress.

[0080] Subsequently, the calculated dynamic stiffness modulus is smoothed by low-pass filtering to remove high-frequency data noise, and finally the processed continuous rock mass characteristic value is output as the structural stability parameter.

[0081] After obtaining the state parameters, they are combined with the diameter change rate in the operational data to construct a mechanical equation reflecting the physical interaction process. Here, the rheological constitutive relation is essentially a physical-mechanical model characterizing the nonlinear mapping relationship between the internal resistance stress and macroscopic deformation rate of complex loose soil under dynamic compression by a cutter head. Boundary constraint equations are constructed using the rheological constitutive relation, and the diameter change rate and the state parameters are input into these equations. Specifically, based on the rheological constitutive relation, yield stress and plastic viscosity parameters to be identified are set to construct the boundary constraint equations. The diameter change rate and the state parameters are then input into these equations to calculate the theoretical stress acting on the soil at the cutter head's working face. The theoretical stress is a predicted value calculated based on a priori mechanical model, and the analytical formula for calculating the theoretical stress is as follows:

[0082]

[0083] in, express The theoretical stress exerted by the cutter head pressing against the soil at the working face; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; This indicates the yield stress parameter to be identified in The observed estimate at time; This indicates the plastic viscosity parameter to be identified in The observed estimate at time; express The instantaneous velocity of the change in diameter at any given moment; This indicates the characteristic length scale of the calibrated cutter head contact area; Represents the dimensionless structural coupling correction coefficient; Indicates pore pressure state parameters; Indicates the compaction state parameters; This represents a structural stability parameter.

[0084] Based on the actual driving pressure and the preset effective pressure area of ​​the driving mechanism in the operating data, the actual stress exerted by the cutter head on the working face soil is calculated, and the physical transformation relationship is expressed as follows:

[0085]

[0086] in, express The actual stress exerted by the cutter head on the working face soil at all times; express The actual driving pressure at any given moment; This indicates the preset effective pressure area of ​​the drive mechanism; This represents the equivalent contact area between the cutter head mechanism and the working surface soil; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0087] This accurately converts the pressure data from the hydraulic system into pressure acting on the soil interface. Subsequently, the deviation between the theoretical and actual applied stress is calculated.

[0088]

[0089] in, express The deviation between the theoretical applied stress and the actual applied stress at a given time; express The actual stress exerted by the cutter head on the working face soil at all times; express The theoretical stress exerted by the cutter head on the working face as it compresses the soil.

[0090] The sliding mode observer's integral update algorithm forces the deviation value to converge to zero, dynamically adjusting the yield stress and plastic viscosity parameters to be identified during the convergence process. The yield stress and plastic viscosity parameters at which the deviation value converges to zero are then used as the rheological parameters characterizing the true geological features. The sliding mode observer is an adaptive sliding mode observer that forces observation error convergence based on the sliding mode mechanism and simultaneously achieves online identification of unknown parameters. The integral update algorithm fully utilizes the strong robustness of sliding mode control, and its parameter adaptive adjustment rules are as follows:

[0091]

[0092]

[0093] in, This indicates the yield stress parameter to be identified in The observed estimate at time; This represents the initial given value of the yield stress parameter; This represents the gain coefficient of the sliding mode observer; This represents a placeholder in the integration process; Represents a symbolic function; express The deviation between the theoretical applied stress and the actual applied stress at a given time; This indicates the plastic viscosity parameter to be identified in The observed estimate at time; This represents the initial setpoint for the plastic viscosity parameter; This represents the gain coefficient of the sliding mode observer; express The instantaneous velocity of the change in diameter at any given moment; This indicates the characteristic length scale of the calibrated cutter head contact area.

[0094] The value of the integral output is the decoupled true yield stress and true plastic viscosity.

[0095] By using an adaptive sliding mode observer to force convergence of nonlinear stress errors with strong robustness, not only are data jumps caused by local inhomogeneities in complex soil effectively filtered out, but the extracted rheological parameters are also ensured to fully match the actual physical state of the current working face. This lays an irreplaceable mechanical judgment foundation for the subsequent safe and smooth planning of the target variable trajectory and the implementation of energy optimization control.

[0096] S3: Combine the geological data and rheological parameters to evaluate the bearing condition of the working face, and generate the target diameter change trajectory of the tunneling equipment based on the bearing condition and preset settlement conditions.

[0097] Considering that changing the cutter head diameter in complex loose soil can easily disrupt the original mechanical balance of the strata, a fixed diameter change strategy based solely on a set time often leads to excessive surface subsidence or triggers deep-seated landslides. To ensure a smooth transition of the cutter head diameter while guaranteeing absolute construction safety, a dual constraint mechanism reflecting the ultimate bearing capacity of the environment is introduced to ensure that subsequent diameter change control commands are always within the safety envelope allowed by physical limitations and engineering specifications.

[0098] The diameter change of the cutter head does not occur within a cavity, but rather is a process in which the mechanical structure extends radially and forcibly pushes aside the surrounding solid soil. During this process, the compressive stress generated by the diameter change acts directly on the working surface of the cutter head, making the loose material in the cutter head section prone to deformation. Because the connections between soil particles in complex loose strata are extremely fragile, this strong inward-outward compression disrupts the original stress balance of the soil. If the compressive stress is within the safe range that the strata can withstand, the soil will only undergo minor elastic deformation; however, if the compressive stress is too strong, it will force the loose particles to undergo violent displacement and misalignment, destroying the structural interlocking force between particles, and directly inducing shear deformation and localized collapse of the loose material.

[0099] The bearing capacity of the working face is assessed by combining the geological data and the rheological parameters. Specifically, the rheological parameters are input into a preset soil yield criterion to calculate the ultimate passive earth pressure at which the soil at the working face undergoes shear failure. This ultimate passive earth pressure characterizes the bearing capacity. When complex loose bodies are subjected to radial compression by the cutter head, their internal nonlinear stress state is highly susceptible to triggering shear failure, leading to overall instability. If fixed pressure thresholds or empirical dead zones are used in conventional industrial control, the dynamic rheological parameters derived from the sliding mode observer cannot be coupled in real time. The set safety limits are either too conservative, causing frequent speed-limiting jams, or too aggressive, leading to instantaneous breaches of the stratum's bearing capacity limit. If neural networks or data-driven black-box prediction algorithms are introduced, there is a lack of clear physical interpretability. In the extreme and harsh conditions of underground engineering with high transient disturbances and scarce samples, "output mutations" or "computational divergence" are likely to occur.

[0100] Therefore, this invention selects the soil-rock yield criterion, which can accurately describe the classical constitutive model of shear failure in granular media (such as loose sand and fractured rock) under three-dimensional stress. By digitally reconstructing the soil-rock yield criterion into algorithm nodes, the critical point of ultimate passive earth pressure at the current tunneling depth where plastic yielding occurs can be rigorously determined using the real-time inverted yield stress. This is the critical stress threshold at which the internal structure of a complex loose body transitions from a stable state to shear failure when pushed outward by the tunneling machine cutterhead. The preset soil-rock yield criterion here adopts the extended model of the widely applicable Mohr-Coulomb strength criterion in engineering, and deeply integrates the rheological parameters obtained by dynamic inversion with the geological data. The mathematical analytical expression for calculating the critical stress threshold of ultimate passive earth pressure is as follows:

[0101]

[0102] in, express The ultimate passive earth pressure at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; Indicates the current tunneling depth The corresponding vertical effective principal stress; Indicates the current tunneling depth; Indicates the passive earth pressure coefficient; This indicates the yield stress parameter to be identified in The estimated value of the observation at time.

[0103] The critical stress threshold is not a rigid, absolute zero point that cannot be altered. Rather, it allows for mechanical activity and state adjustment of the soil within a certain safety envelope before reaching the highest critical point. In other words, when the cutter head expands outward, the surrounding loose material will inevitably undergo corresponding compression deformation due to the machine's thrust—an unavoidable objective physical law. However, as long as the deformation stress is strictly controlled within the critical stress threshold range, this deformation is a controllable, benign deformation or elastic buffering, and will not disrupt the overall stability of the soil skeleton. Therefore, it will never cause shear collapse or ground subsidence at the working face.

[0104] The ultimate passive earth pressure quantifies the limit of the deformation that loose material can withstand when it is pushed by the excavation: when the thrust generated by the change of the cutter head is too large, once it exceeds the critical stress threshold, the interlocking and friction of the particles inside the loose material will completely fail, resulting in irreversible plastic deformation of the soil, and even causing large-scale collapse.

[0105] In addition to the internal mechanical red line to prevent shear failure of the soil at the underground working face, urban underground engineering construction must also strictly control external surface deformation. Preset maximum allowable uplift height and maximum allowable subsidence depth are obtained as preset settlement conditions. Furthermore, after extracting and calibrating this external environmental specification boundary, a precise analytical mapping relationship is established between the dimensional changes of the underground cutter head and the surface deformation.

[0106] The mechanical constraint boundary is that the compressive stress generated by the cutter head's diameter change does not exceed the ultimate passive earth pressure. When the cutter head expands radially, it inevitably exerts a strong outward compressive force on the loose material surrounding the excavation channel. If the compressive stress exceeds the ultimate passive earth pressure's bearing limit, it will directly cause severe displacement of the soil particles outside the channel that were originally maintaining stress balance, resulting in a complete loss of structural interlocking force. This leads to irreversible shear failure and large-scale plastic displacement deformation, ultimately easily inducing the overall collapse of the underground excavation face and the overlying strata. Therefore, strictly limiting the degree of force exerted by the diameter change at the bottom layer ensures that the machine's operation does not cause destructive excessive deformation to the loose material outside the channel, thus preventing collapse accidents from a mechanistic perspective.

[0107] The geometric limit is defined as the deformation of the strata caused by the cutter head diameter change not exceeding the maximum allowable uplift height and maximum allowable collapse depth. Even if the compressive stress generated by the diameter change at the bottom layer is strictly controlled, the physical change in the cutter head size will inevitably cause the total volume expansion or loss of the excavation section. This underground volume change will be transmitted upward through the soil displacement field, reflected as macroscopic surface displacement. If only mechanical force is used as the sole constraint, the accumulated volume deformation can easily and silently exceed the safety tolerance of urban infrastructure, leading to cracking of existing roads, breakage of underground pipelines, or tilting of buildings. Therefore, the introduction of the geometric limit requires that every millimeter of the machine's diameter change must compromise with the macroscopic surface environment specifications, thereby achieving a comprehensive dual closed-loop protection from "microscopic mechanical anti-collapse" to "macroscopic geometric anti-settlement".

[0108] A multi-objective dual-constraint optimization model is constructed based on the mechanical and geometric constraints to solve the boundary conditions, generating the target diameter-changing trajectory that continuously transitions from the current cutter diameter to the target cutter diameter. To achieve numerical computation of this dual-boundary optimization model, a cost function and inequality constraint equations are introduced for optimization.

[0109]

[0110]

[0111]

[0112] in, This represents the cost function for trajectory optimization. express The target trajectory at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; Indicates the prediction time domain; Represents the integral time variable within the prediction time domain; express The target trajectory at any given moment; Indicates the diameter of the target cutting head; express The actual stress applied at any given moment; express The ultimate passive earth pressure at any given moment; Indicates the maximum permissible collapse depth; Represents the settlement prediction function; express The change in the cutter head diameter at any given time; Indicates the maximum permissible bulge height; This indicates the current tunneling depth.

[0113] The deformation of the strata caused by the change in cutter head diameter is fundamentally due to the fact that the physical expansion or reduction of the cutter head diameter directly changes the geometric volume of the underground excavation section. This sudden "volume expansion" or "volume loss" within the strata will rapidly propagate to the surroundings and upwards, triggering a redistribution of the displacement field of the surrounding soil, which will ultimately manifest as settlement, collapse, or uplift deformation on the macroscopic surface.

[0114] To accurately calculate the degree of this deformation, the Peck empirical formula, modified based on the principle of equivalent formation volume loss, is used as the settlement prediction function. A refined algebraic analytical expression was used to construct a lateral mapping between the excavation face loss volume and the geometry of the surface settlement trough, mapping the change in excavation area caused by diameter variation into an equivalent volume variable, and combining this with the formation volume loss rate. and settling trough width coefficient It can calculate the absolute value of the maximum deformation caused by the current diameter change action directly above the main axis in real time within microseconds. This not only solves the engineering pain point of "the trade-off between real-time performance and accuracy," but also reduces the formation volume loss rate. and settling trough width coefficient These two core engineering geological parameters are directly locked in the control closed loop, realizing efficient cross-domain constraints on the actions of underlying equipment by external macro-environmental specifications.

[0115] The settlement prediction function maps the excavation cross-sectional area variable caused by the change in cutterhead diameter to an equivalent ground loss volume, and then calculates the maximum surface deformation directly above the main axis of the working face. The mathematical expression of the settlement prediction function is as follows:

[0116]

[0117] in, Represents the settlement prediction function; Indicates the formation volume loss rate; Pi is a constant. This indicates the reference initial diameter of the tunneling equipment's cutter head before diameter reduction; express The change in the diameter of the cutter head at any given moment; Indicates the width coefficient of the settling trough; This indicates the current tunneling depth.

[0118] By using optimization algorithms such as sequential quadratic programming to process constrained integral equations in real time, reference coordinate points for variable-diameter travel that meet dual safety guarantees can be continuously output.

[0119] The ultimate passive earth pressure calculated by the soil-rock yield criterion ensures that the underground excavation face does not experience local shear collapse (microscopic non-destruction); while the improved Peck empirical formula ensures that the deformation of the overlying soil layer does not exceed environmental specifications (macroscopic non-collapse), together forming the double inequality boundary in the constraint solution algorithm. Without the soil-rock yield criterion, the variable-path trajectory may blindly pursue deformation within limits, resulting in excessive transient thrust and causing instantaneous shear liquefaction of the underground soil; without the Peck formula, the trajectory planning may only satisfy the underground force balance, but cause macroscopic pavement settlement due to excessive cumulative stratum loss.

[0120] This multi-objective trajectory deduction mechanism based on dual safety constraints endows complex geological tunneling with extremely high engineering practical value and intrinsic safety. It abandons the dangerous mode of blindly "step-by-step" diameter changes that rely solely on operator experience in traditional equipment, and seamlessly connects the microscopic shear failure mechanism of underground rock and soil with the macroscopic deformation specifications of the surface into the same mathematical framework.

[0121] S4: Construct an energy control model, establish an interconnection structure, and use the external dissipation power as the power constraint term of the energy control model. Use the target variable diameter trajectory as input to generate virtual potential energy. Output variable diameter control commands through constraint solving to adjust the cutter head diameter.

[0122] Traditional PID control based on error elimination is prone to overshoot when faced with complex geological nonlinear disturbances. Simply pursuing rapid tracking of position commands often leads to sudden changes in the output power of the mechanical drive mechanism, thereby breaking the newly established geotechnical safety boundary. Therefore, this scheme designs a control model based on port-controlled Hamiltonian theory, transforming abstract trajectory tracking into a controlled energy dissipation process in physical space.

[0123] An interconnection structure is established, and the external dissipated power is used as a power constraint term in the energy control model. An actuator power port is constructed in the energy control model to characterize the energy input and output of the variable diameter drive mechanism, and a soil dissipated power port is constructed to characterize energy exchange with the working face soil. Specifically, the operation of constructing the actuator power port is as follows: a set of conjugate variable registers dedicated to characterizing the mechanical drive side is allocated in the underlying algorithm architecture of the main control system. The actual driving pressure is extracted, multiplied by the effective pressure area of ​​the hydraulic cylinder to obtain the dynamic mechanical drive thrust, and the value of the dynamic mechanical drive thrust is defined and assigned to the potential variable (Effort) of the actuator power port. Simultaneously, the instantaneous variable diameter speed is extracted, defined, and assigned to the flow variable (Flow) of the actuator power port. Thus, the control system can accurately reconstruct and quantify the dynamic mechanical power output by the variable diameter drive mechanism in the mathematical model through the real-time product operation of these two physical quantities in the time domain.

[0124] Furthermore, the operation of constructing the soil dissipation power port is as follows: In the control network, another set of conjugate variable registers dedicated to characterizing the external geological boundary is allocated. The actual stress exerted by the cutter head on the working face soil (converted to soil reaction force through equivalent contact area) is defined and assigned to the potential variable of the soil dissipation power port. Simultaneously, the macroscopic deformation rate generated by soil compression (obtained by differentiating the settlement prediction function with the instantaneous diameter change velocity) is defined and assigned to the flow variable. This operational rule successfully reduces the dimensionality of the originally highly nonlinear soil-rock constitutive interaction process and numerically encapsulates it into a dynamic impedance dissipation node that is readable and computable by the control system.

[0125] In order to transfer energy between different physical domains, the interconnection structure between the actuator power port and the soil dissipation power port is established using the Dirac algebra structure.

[0126] Essentially, it connects the potential and current variables at both ends of the network through a lossless network topology matrix. The specific physical mapping equation is expressed as follows:

[0127]

[0128] in, The soil deformation rate represents the real-time response of the soil power dissipation port. This indicates the hydraulic driving force output from the actuator frame port; This represents a skew-symmetric interconnection matrix that reflects the lever ratio of a physical mechanical transmission mechanism. The soil reaction force represents the real-time response of the soil power dissipation port. This indicates the extension / retraction speed of the driver output from the actuator's power port; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0129] When encountering sudden changes in resistance in complex, loose strata, if conventional position PID closed-loop control with software limiting is used, the PID algorithm, based on its instinct to eliminate errors, will instantly overshoot the control quantity when the cutter head encounters dense sand, causing an increase in position deviation. This will instruct the hydraulic valve to open wide to mobilize the maximum system oil pressure for a forceful push. Even if a maximum pressure alarm line is set in the software, due to the discrete sampling delay and execution lag in conventional software judgment, the transient, brute force of the hydraulic cylinder during the tens of milliseconds "window period" between the thrust surge and the alarm trigger will have already broken through the shear limit of the fragile soil, inducing irreversible stratum collapse. If conventional model predictive control (MPC) or constraint optimization algorithms are used, although they can handle inequality power constraints, the complex nonlinear programming solution in the millisecond-level underlying hardware loop of heavy engineering machinery is prone to getting trapped in local optima or causing control interruptions due to excessive computation time.

[0130] Therefore, this solution adopts an interconnection structure based on the Dirac algebraic structure to establish a network topology matrix. Once the resistance from the geological feedback ahead suddenly increases, the conjugate rheological variables of the actuator architecture port will be immediately locked. The opening of the flow valve at the bottom layer can never output physical energy exceeding the actual bearing limit of the current stratum at the physical level, thereby forcing the robotic arm's movements to automatically and smoothly transform into "deceleration and yielding avoidance".

[0131] The external dissipated power is captured in real time through the soil dissipation power port. The external dissipated power is mapped to a dynamic dissipation constraint term within the control system using the interconnection structure. This dynamic dissipation constraint term is then used as the power constraint term limiting the power amplitude injected into the energy control model. The energy control model is a closed-loop control model based on the principle of energy conservation. Its numerical constraint mapping calculation logic is as follows:

[0132]

[0133] in, express The maximum permissible power boundary at any given time; Indicates the power safety attenuation coefficient; express External power dissipation at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0134] The energy control model ensures that, regardless of extreme deviations, the issued energy commands are always rigidly locked within the actual safe dissipation capacity that the current loose formation conditions can withstand.

[0135] After establishing the underlying energy constraint boundary, the diameter-changing mechanism is driven to run along a set safety trajectory. The target diameter-changing trajectory is used as input to generate virtual potential energy. The transition control command output through constraint solving includes calculating the positional deviation between the current cutter diameter and the target diameter-changing trajectory, inputting the positional deviation into a distance cost function, calculating the cost of the current position relative to the target diameter-changing trajectory, and using the cost as the virtual potential energy. To digitize the positional error and smoothly introduce it into the Hamiltonian energy control system, the following nonlinear quadratic potential energy function is constructed:

[0136]

[0137] in, express Virtual potential energy at any given moment; Indicates the virtual spring stiffness coefficient; express The current diameter position of the cutting head at any given moment; express The target trajectory at any given moment; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0138] To find the optimal path to reduce potential energy under constraints, the virtual potential energy and the power constraint term are input into an online optimization control algorithm based on the steepest descent principle. The algorithm calculates the set of control variables that maximizes the rate of decrease of the virtual potential energy without triggering the power constraint term. The optimization process is achieved by solving the Lagrange extremum equation with inequality constraints.

[0139]

[0140]

[0141] in, This represents the group of control variables that causes the virtual potential energy to decrease at the fastest rate. This represents the available search space for the control variables; Represents the potential energy gradient; The function representing the mapping from the control quantity to the velocity space; This indicates the hydraulic driving force output from the actuator frame port; This indicates the extension / retraction speed of the driver output from the actuator's power port; express The maximum permissible power boundary at any given time; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0142] The mapping function from the control quantity to the velocity space A linear proportional conversion model based on the underlying physical parameters of the variable-diameter drive mechanism is adopted. Specifically, the mapping function accurately converts the abstract control variables output by the optimization algorithm into the actual extension and retraction speed of the hydraulic actuator, and its specific mathematical expression is as follows:

[0143]

[0144] in, The function representing the mapping from the control quantity to the velocity space; Represents the physical transformation matrix of the driving system; This represents the available search space for the control variables; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism.

[0145] To accurately implement ideal instructions at the algorithm level onto the underlying physical execution components, the control variable group is converted into a flow valve opening signal for the variable diameter drive system according to a preset flow distribution rule. This flow valve opening signal is then sent to the tunneling equipment as the variable diameter control instruction. The specific motor conversion mapping relationship is as follows:

[0146]

[0147] in, express The actual diameter control command issued at any given moment; This represents the flow gain coefficient of a proportional multi-way valve; Indicates the reference bias compensation constant; This indicates the real-time sampling time during the dynamic operation of the variable diameter drive mechanism; This represents the group of control variables that causes the virtual potential energy to decrease at the fastest rate.

[0148] Once the command is issued, it will directly drive the hydraulic actuator to move, thereby completing the intelligent adjustment of the cutter head diameter in a closed loop under complex environments.

[0149] By using digital power constraints and transforming rigid mechanical motion control into the problem of the steepest descent of virtual potential energy, it is fundamentally guaranteed that the fragile loose strata will not be torn apart due to instantaneous overload during the process of rapidly approaching the target diameter change trajectory. It also effectively suppresses the high-frequency oscillation of the hydraulic system, building a solid defense for the precise, safe, and adaptive tunneling of complex urban underground spaces.

[0150] This embodiment also provides an intelligent cutter head diameter adjustment system for tunneling in complex loose materials, including:

[0151] The sensing unit acquires the operating data of the tunneling equipment and the geological data of the working face, calculates the total input power based on the operating data, calculates the internal loss power item by item, and removes all the internal loss power from the total input power to obtain the external dissipation power.

[0152] The inversion unit extracts the variable diameter velocity from the operational data and the state parameters from the geological data. It constructs boundary constraint equations through rheological constitutive relations and inputs the variable diameter velocity and the state parameters into the boundary constraint equations. The rheological parameters are obtained by inverting the operational data using a sliding mode observer.

[0153] The planning unit, in conjunction with the geological data and rheological parameters, assesses the bearing capacity of the working face and, based on the bearing capacity and preset settlement conditions, generates the target diameter-changing trajectory of the tunneling equipment.

[0154] The control unit constructs an energy control model, establishes an interconnection structure, and uses the external dissipation power as a power constraint term of the energy control model. It takes the target variable diameter trajectory as input to generate virtual potential energy, and outputs variable diameter control commands through constraint solving to adjust the cutter head diameter.

[0155] This embodiment also provides a computer device applicable to the intelligent adjustment method for cutter diameter in tunneling based on complex loose material, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent adjustment method for cutter diameter in tunneling based on complex loose material as proposed in the above embodiment.

[0156] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0157] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the intelligent adjustment method for the cutter head diameter in complex loose body tunneling as proposed in the above embodiments. The storage medium can be implemented by 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 Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0158] In summary, this invention achieves adaptive, smooth, and high-precision intelligent adjustment of the cutterhead diameter of a tunnel boring machine (TBM) in complex loose geological environments. This completely overcomes the blindness and command overshoot risks of traditional static estimation. Under the premise of strictly ensuring that the underground rock and soil structure is not damaged and that surface settlement deformation does not exceed the standard, it endows heavy TBMs with intrinsically safe machine-soil compliant interaction and safety control capabilities. It also: extracts the true external dissipated power from the total input power by removing internal losses and utilizing an adaptive sliding mode observer and rheological constitutive relations to online inversely derive the true rheological parameters of the working face based on dynamic operating data; constructs a dual-constraint boundary by combining ultimate passive earth pressure and surface settlement specifications to deduce an absolutely safe continuous target diameter-changing trajectory; and builds a closed-loop energy control model based on a Dirac interconnected structure, transforming external dissipated power into a strict power constraint term, driving the steepest descent optimization based on virtual potential energy to output diameter-changing commands.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent adjustment of cutter head diameter in tunneling of complex loose materials, characterized in that: This includes acquiring the operating data of the tunneling equipment and the geological data of the working face, calculating the total input power based on the operating data, calculating the internal loss power item by item, removing all the internal loss power from the total input power, and obtaining the external dissipation power. The variable diameter velocity is extracted from the operational data, and the state parameters are extracted from the geological data. Boundary constraint equations are constructed through rheological constitutive relations. The variable diameter velocity and the state parameters are input into the boundary constraint equations. The rheological parameters are obtained by inverting the operational data using a sliding mode observer. The bearing capacity of the working face is evaluated by combining the geological data and the rheological parameters, and the target diameter change trajectory of the tunneling equipment is generated based on the bearing capacity and the preset settlement conditions. An energy control model is constructed, an interconnection structure is established, and the external dissipation power is used as the power constraint term of the energy control model. The target variable diameter trajectory is used as input to generate virtual potential energy. The variable diameter control command is output through constraint solution to adjust the cutter head diameter.

2. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 1, characterized in that: The operating data includes the driving voltage, instantaneous operating current, instantaneous system oil temperature, instantaneous diameter change speed, instantaneous diameter change acceleration, and actual driving pressure of the variable diameter drive mechanism; The step of calculating the internal power loss and removing all internal power loss from the total input power to obtain the external power dissipation includes calculating the total input power of the variable diameter drive mechanism based on the driving voltage and the instantaneous operating current. Based on the instantaneous operating current and the instantaneous system oil temperature, the corresponding electromechanical conversion heat loss power is found through a preset heat loss mapping table; Based on the instantaneous diameter change speed, the corresponding mechanical transmission friction loss power is found through the preset mechanical friction resistance curve; Based on the instantaneous diameter change acceleration and the preset equivalent inertia parameters of the diameter change drive mechanism, the dynamic inertia loss power of the diameter change drive mechanism due to inertia during the variable speed motion is calculated. Subtracting the electromechanical conversion heat loss power, the mechanical transmission friction loss power, and the dynamic inertia loss power from the total input power, the external dissipation power used for cutting and compressing the soil at the working face is extracted.

3. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 2, characterized in that: The geological data includes the natural moisture content, void ratio, and pressure distribution data of the overlying soil in the working face; the state parameters include pore pressure state parameters, compaction state parameters, and structural stability parameters. Input the natural water content and the porosity into a preset pore pressure characteristic lookup table, and find the corresponding excess pore water pressure parameter as the pore pressure state parameter; Input the natural moisture content, the void ratio, and the pressure distribution data of the overlying soil layer into a preset compaction assessment comparison table, and calculate the corresponding soil density as the compaction state parameter. The pressure distribution data of the overlying soil layer is input into the stress-strain relationship equation constructed based on the effective stress principle for solving, and the stiffness modulus of the working face soil resisting volume compression is calculated, and the stiffness modulus is used as the structural stability parameter.

4. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 3, characterized in that: Boundary constraint equations are constructed by rheological constitutive relations, and the variable diameter velocity and the state parameters are input into the boundary constraint equations. Rheological parameters are obtained by inversion based on running data using a sliding mode observer. According to the rheological constitutive relations, the yield stress parameter and plastic viscosity parameter to be identified are set to construct the boundary constraint equations. By inputting the variable diameter speed and the state parameters into the boundary constraint equation, the theoretical stress of the cutter head squeezing the soil at the working face is calculated. Based on the actual driving pressure and the preset effective pressure area of ​​the driving mechanism in the operating data, the actual stress of the cutter head squeezing the soil on the working face is calculated. Calculate the deviation between the theoretical applied stress and the actual applied stress; The integral update algorithm of the sliding mode observer forces the deviation value to converge to zero, and dynamically adjusts the yield stress parameter and plastic viscosity parameter to be identified during the convergence process. The yield stress parameter and plastic viscosity parameter when the deviation value converges to zero are used as the rheological parameters characterizing the real geological features. The sliding mode observer is an adaptive sliding mode observer that forces the observation error to converge based on the sliding mode mechanism and simultaneously realizes online identification of unknown parameters.

5. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 4, characterized in that: The bearing capacity of the working face is evaluated by combining the geological data and the rheological parameters. The target diameter trajectory of the tunneling equipment is generated based on the bearing capacity and the preset settlement conditions. The rheological parameters are input into the preset rock and soil yield criterion, and the ultimate passive earth pressure when the soil of the working face undergoes shear failure is calculated. The ultimate passive earth pressure is used to characterize the bearing capacity. Obtain the preset maximum allowable surface uplift height and maximum allowable subsidence depth as the preset settlement conditions; The mechanical constraint boundary is defined as the compressive stress generated by the cutter head diameter change not exceeding the ultimate passive earth pressure, and the geometric constraint boundary is defined as the stratum deformation caused by the cutter head diameter change not exceeding the maximum allowable uplift height and the maximum allowable collapse depth. Based on the mechanical constraint boundary and the geometric constraint boundary, a multi-objective dual-constraint optimization model is constructed to solve the boundary and generate the target diameter change trajectory that continuously transitions from the current cutter head diameter to the target cutter head diameter.

6. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 5, characterized in that: The step of establishing an interconnection structure and using the external dissipation power as a power constraint term in the energy control model includes constructing an actuator power port in the energy control model to characterize the energy input and output of the variable diameter drive mechanism, and constructing a soil dissipation power port to characterize the energy exchange with the working face soil. Using the Dirac algebraic structure, the interconnection structure between the power port of the actuator and the power dissipation port of the soil is established; The external dissipation power is captured in real time through the soil dissipation power port, and the external dissipation power is mapped into a dynamic dissipation constraint quantity inside the control system using the interconnection structure. The dynamic dissipation constraint quantity is used as the power constraint term that limits the power amplitude injected into the energy control model. The energy control model is a closed-loop control model based on the principle of energy conservation.

7. The intelligent adjustment method for cutter head diameter based on complex loose material tunneling as described in claim 6, characterized in that: The target diameter change trajectory is used as input to generate virtual potential energy. The diameter change control command is output through constraint solving, including calculating the positional deviation between the current cutter diameter and the target diameter change trajectory, inputting the positional deviation into the distance cost function, calculating the cost value of the current position relative to the target diameter change trajectory, and using the cost value as the virtual potential energy. The virtual potential energy and the power constraint term are input together into an online optimization control algorithm based on the steepest descent principle to calculate the control variable set that makes the virtual potential energy decrease the fastest without triggering the power constraint term. The control variable group is converted into the flow valve opening signal of the variable diameter drive system according to the preset flow distribution rule, and the flow valve opening signal is sent to the tunneling equipment as the variable diameter control command.

8. A smart cutterhead diameter adjustment system for tunneling in complex loose materials, based on the smart cutterhead diameter adjustment method for tunneling in complex loose materials according to any one of claims 1 to 7, characterized in that: This includes a sensing unit that acquires the operating data of the tunneling equipment and the geological data of the working face, calculates the total input power based on the operating data, calculates the internal loss power item by item, and removes all the internal loss power from the total input power to obtain the external dissipation power. The inversion unit extracts the variable-diameter velocity from the operational data and the state parameters from the geological data. It constructs boundary constraint equations through rheological constitutive relations and inputs the variable-diameter velocity and the state parameters into the boundary constraint equations. It then uses a sliding mode observer to invert the rheological parameters based on the operational data. The planning unit, in conjunction with the geological data and the rheological parameters, assesses the bearing capacity of the working face and, based on the bearing capacity and preset settlement conditions, generates the target diameter-changing trajectory of the tunneling equipment. The control unit constructs an energy control model, establishes an interconnection structure, and uses the external dissipation power as a power constraint term of the energy control model. It takes the target variable diameter trajectory as input to generate virtual potential energy, and outputs variable diameter control commands through constraint solving to adjust the cutter head diameter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent adjustment method for cutter head diameter based on tunneling of complex loose materials as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent adjustment method for the cutter head diameter based on tunneling of complex loose bodies as described in any one of claims 1 to 7.

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