Tunnel boring machine performance prediction and monitoring system based on digital twinning and machine learning

Abstract

The invention discloses a tunnel boring machine performance prediction and monitoring system and method based on digital twinning and machine learning. The system comprises a data acquisition module, a data preprocessing module, a model construction module, a performance analysis module and a visual analysis module. The data acquisition module is used for acquiring initial data of a tunnel boring machine on a construction site; the data preprocessing module is used for preprocessing the initial data to obtain preprocessed data; the model construction module constructs a prediction model and a digital twinborn model of the tunnel boring machine based on the preprocessed data; the performance analysis module predicts the tunneling speed and the propelling speed of the tunnel boring machine by using the prediction model to obtain a prediction result, and integrates the prediction result with the digital twinborn model; and the visual analysis module is used for constructing a virtual environment and checking the construction progress and the performance prediction result of the tunnel boring machine in real time in a digital twinborn model in the virtual environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel boring machine construction, and specifically relates to a tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning. Background Art

[0002] With the acceleration of global urbanization and the growing demand for underground space development and utilization, underground infrastructure construction is booming. Against this backdrop, tunnel boring machines (TBMs), as modern and efficient tunnel construction equipment, have been widely used in a variety of fields, including subway construction, railway tunnels, water conservancy projects, and mining, thanks to their significant advantages such as speed, safety, and cost-effectiveness. The widespread use of TBMs has greatly improved the efficiency and quality of underground engineering construction, making them an indispensable tool for modern urban construction and development.

[0003] However, TBM construction generates a large amount of complex data, including geological data, machine operating parameters, and construction progress records. Traditional data analysis and presentation methods make it difficult to intuitively present overall progress and actual equipment performance, posing a significant challenge to construction management. Currently, traditional statistical analysis models in my country often consume significant time and manpower to handle such nonlinear and complex systems, ultimately failing to accurately predict TBM performance indicators such as advance rate and penetration rate, hindering construction planning and cost control.

[0004] In recent years, with the rapid development of information technology, artificial intelligence (AI) has become an important tool for solving complex engineering problems. Machine learning (ML), a key branch of AI, effectively handles complex nonlinear relationships by building mathematical models to learn patterns in data, providing a new solution for TBM performance prediction. Machine learning algorithms can automatically extract features from large amounts of historical data and build predictive models, thereby accurately predicting TBM performance. This helps construction teams better formulate construction strategies, optimize resource allocation, and reduce construction risks.

[0005] At the same time, digital twin (DT) technology, as an emerging information technology, is also showing great potential in engineering construction. By constructing an exact digital replica of a physical entity in a virtual space, digital twin technology enables real-time data exchange and synchronous updates between the physical entity and the virtual model. With digital twin technology, construction personnel can visually observe the operating status and construction progress of the TBM in a virtual environment, monitor equipment performance in real time, and simulate and optimize the construction process using virtual models. This integration of virtual and real-world scenarios not only improves the efficiency and accuracy of construction management but also provides a more scientific and intuitive basis for construction decision-making.

[0006] Therefore, combining machine learning with digital twin technology can provide a more comprehensive and efficient solution for TBM construction. Summary of the Invention

[0007] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0008] A tunnel boring machine performance prediction and monitoring system based on digital twins and machine learning, including: a data acquisition module, a data preprocessing module, a model building module, a performance analysis module, and a visualization analysis module;

[0009] The data acquisition module is used to obtain the operating parameters and geological parameters of the tunnel boring machine at the construction site to obtain initial data;

[0010] The data preprocessing module is used to preprocess the initial data to obtain preprocessed data;

[0011] The model building module builds a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and trains the support vector regression model to obtain a prediction model;

[0012] The performance analysis module predicts the tunneling speed and the propulsion speed of the tunnel boring machine using the prediction model to obtain a prediction result, and integrates the prediction result with the digital twin model;

[0013] The visualization analysis module is used to build a virtual environment and view the construction progress and performance prediction results of the tunnel boring machine in real time in the digital twin model in the virtual environment.

[0014] Preferably, the operating parameters include: thrust parameters, drive parameters, vibration parameters and wear parameters;

[0015] The geological parameters include: rock mass mechanical parameters, structural characteristic parameters, groundwater parameters and detection characteristic parameters.

[0016] Preferably, the data preprocessing module includes: a data cleaning unit, a data normalization unit and a data standardization unit;

[0017] The data cleaning unit is used to remove outliers and missing values ​​in the initial data to obtain cleaned data;

[0018] The data normalization unit dynamically generates a normalization coefficient matrix based on a spatiotemporal coupling weight allocation algorithm, and uses the normalization coefficient matrix to normalize the cleaned data to obtain normalized data;

[0019] The data standardization unit uses a standard scaler to perform standardization processing on the normalized data to obtain the preprocessed data.

[0020] Preferably, the model construction module includes: a support vector machine model construction unit, a digital twin model construction unit and a model training unit;

[0021] The support vector machine model building unit is used to build the support vector regression model;

[0022] The digital twin model construction unit is used to construct the digital twin model;

[0023] The model training unit trains the support vector regression model based on the preprocessed data to obtain the prediction model.

[0024] Preferably, the support vector regression model includes: a chaotic kernel function of spatiotemporal entanglement, a dynamic control mechanism of relaxation factors of magnetic flux quantization, a regularized network of fragmented field perception, a superfluid decision boundary optimizer, a quantum phase transition constraint mechanism and a chaotic attractor backpropagation mechanism;

[0025] The chaotic kernel function of the space-time entanglement includes:

[0026]

[0027] Where Ψ(x) represents the quantum phase encoder based on the chaotic characteristics of cutterhead vibration, σ q represents the spatiotemporal entanglement modulation tensor, Φ(x) represents the quantum state projection generated by the metasurface waveguide in the tunneling parameter well;

[0028] The dynamic control mechanism of the relaxation factor of the magnetic flux quantization is used to expand the traditional relaxation variable into ξq=ξ0·e with quantum tunneling characteristics. -β(t)·ΔE The magnetic flux is measured in real time by a superconducting quantum interference device and the weight distribution of the support vector is adjusted, where ξq represents the dynamic quantum tunneling effect coefficient, ξ0 represents the reference tunneling coefficient, β(t) represents the space-time tunneling coefficient, and ΔE represents the virtual-actual tunneling energy difference;

[0029] The fragmentation field-aware regularization network generates a spatially adaptive regularization matrix using the debris flow vector field obtained by discrete element simulation;

[0030] The superfluid decision boundary optimizer is used to activate the vortex lattice reorganization algorithm under the Bose-Einstein condensate when a sudden change in the surrounding rock strength is detected, and form an optimal hyperplane with a fractal topological structure in the Hilbert space;

[0031] The quantum phase transition constraint mechanism is used to reduce the number of support vectors;

[0032] The chaotic attractor back-propagation mechanism is used to couple the parameters of the optimal hyperplane and the dynamic load of the cutterhead of the tunnel boring machine in real time.

[0033] Preferably, the workflow of the digital twin model construction unit includes:

[0034] Decomposing the tunnel boring machine into quantized mechanical units using a chaos-inspired fractal mesh generation algorithm;

[0035] Using an unsteady vortex particle algorithm to dynamically reconstruct the microscopic interaction process between the cutting teeth and the cuttings flow of the tunnel boring machine;

[0036] The fatigue damage evolution equation of the tunnel boring machine main bearing is encoded into an entangled state of quantum bits using a quantum topology optimizer. The stress concentration area is then located using the Grover search algorithm, which then drives the quantum decoherence evolution of the mesh stiffness parameters in the virtual model in real time.

[0037] Using metasurface wave calibration technology, an electromagnetic metamaterial layer with negative refractive index properties is constructed in virtual space;

[0038] Using a cross-dimensional tensor fusion channel, the time-frequency matrix of the vibration signal and the Riemannian manifold of the geological parameters are topologically homeomorphically transformed to generate a hybrid feature space with quantum tunneling effect;

[0039] Using quantum dot array illumination engine to invert tool wear status through photon path tracing technology;

[0040] The parameters of the model are updated in real time using the parameter super-distance update mechanism of the quantum entangled state to obtain the digital twin model.

[0041] Preferably, the workflow of the visual analysis module includes:

[0042] By deeply integrating the hyperdimensional data entanglement framework with the chaotic phase change material model, the geometric topology and physical properties of the tunnel boring machine's physical structure are converted into quantum entangled state parameters. The influence of microscopic defect evolution on macroscopic mechanical properties is calculated in real time through a finite element discrete solver of the unsteady Schrödinger equation, thereby constructing a virtual-reality interactive environment with adaptive space-time scales.

[0043] The virtual-reality interactive environment integrates the electromagnetic metamaterial layer with negative refractive index characteristics, converts multi-source signals in the actual tunneling process into surface plasmon waves carrying quantum behavior information, and uses the photon spin Hall effect to achieve real-time reverse tracking of the propagation path of stress waves in the virtual space;

[0044] Using a chaotic attractor-driven mesh deformation engine to simulate the fractal fracture process of the cutterhead-rock interface of the tunnel boring machine;

[0045] A data fusion layer is constructed using quantum tensor Riemann mapping, the probability distribution output in the prediction model is topologically homeomorphic to the manifold of the geological parameters, and the quantum tunneling effect in hyperdimensional space is used to synchronize virtual and real environmental parameters;

[0046] Phase change energy storage topology optimization is used to construct an intelligent grid unit with memory alloy characteristics in the virtual-real interactive environment to simulate the topological reconstruction of the cutter teeth, and the construction progress and the performance prediction results are reversely outputted through a quantum annealing optimizer.

[0047] The present invention also provides a method for predicting and monitoring tunnel boring machine performance based on digital twins and machine learning, which is applied to any of the above-mentioned systems and includes the following steps:

[0048] Obtaining the operating parameters of the tunnel boring machine and geological parameters at the construction site to obtain initial data;

[0049] Preprocessing the initial data to obtain preprocessed data;

[0050] Constructing a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and training the support vector regression model to obtain a prediction model;

[0051] Using the prediction model to predict the tunneling speed and the propulsion speed of the tunnel boring machine to obtain a prediction result, and integrating the prediction result with the digital twin model;

[0052] A virtual environment is constructed, and the construction progress and performance prediction results of the tunnel boring machine are viewed in real time in the digital twin model in the virtual environment.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The deep integration of digital twins and machine learning in this invention achieves the synchronization of real-time data processing and virtual models; real-time data-driven performance prediction improves prediction accuracy and real-time performance; based on dynamic visualization, it enhances users' understanding and monitoring capabilities of the construction process; the multi-parameter comprehensive prediction model comprehensively considers the influence of geological, machine and construction parameters; the closed-loop feedback and real-time monitoring system realizes the continuous optimization and dynamic adjustment of tunnel boring machine performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Example 1

[0060] In this embodiment, if Figure 1 As shown, a tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning includes: a data acquisition module, a data preprocessing module, a model building module, a performance analysis module and a visualization analysis module.

[0061] The data acquisition module is used to obtain the operating parameters and geological parameters of the tunnel boring machine at the construction site to obtain initial data.

[0062] The operating parameters include: thrust parameters, driving parameters, vibration parameters and wear parameters; the geological parameters include: rock mechanics parameters, structural characteristic parameters, groundwater parameters and detection characteristic parameters.

[0063] The data preprocessing module is used to preprocess the initial data to obtain preprocessed data.

[0064] The data preprocessing module includes: a data cleaning unit, a data normalization unit and a data standardization unit; the data cleaning unit is used to remove outliers and missing values ​​in the initial data to obtain cleaned data; the data normalization unit dynamically generates a normalization coefficient matrix based on the spatiotemporal coupling weight distribution algorithm, and uses the normalization coefficient matrix to normalize the cleaned data to obtain normalized data; the data standardization unit uses a standard scaler to standardize the normalized data to obtain preprocessed data.

[0065] In this embodiment, the workflow of the data normalization unit includes: implementing a quantum parameter adapter for dynamic heterogeneous data coupling, and constructing a hybrid normalization architecture based on quantum annealing optimization for multi-source spatiotemporal asynchronous data in tunnel boring machine construction (such as the cross-scale fusion problem of microsecond-level vibration signals and minute-level geological parameters). This architecture uses quantum bit encoding to convert raw data of different dimensions (thrust 0-35000kN, microseismic event frequency 10 2 ~10 4 Hz, crack density 0-20 / m, etc.) are mapped to high-dimensional Hilbert space, and the quantum tunneling effect is used to break through the local optimal limitations of traditional normalization. In the specific process, a time-space coupling weight distribution algorithm is first created to dynamically generate a normalized coefficient matrix W(t)=e according to the physical coupling characteristics of the data source (such as thrust-rock strength mechanical correlation, speed-torque energy transfer rate). -ξ(t)·H , where ξ(t) represents the time-varying annealing rate, H represents the Hamiltonian containing the cutterhead posture parameters, and millisecond-level coefficient updates are achieved through edge computing nodes. In view of the non-stationary characteristics of geological parameters, a metastable transition layer transformation channel is designed. When a sudden change in the formation is detected (such as the water pressure gradient change rate > 0.3MPa / s), the nonlinear compression function ω(x) = x / (1+||x-x0||2 λ ), where λ represents the Lyapunov exponent, which is dynamically calculated from the real-time rock crushing entropy value (γ=2.5log(σ e / σ b ), σ e represents the current cuttings size variance, σ b This embodiment overcomes the limitations of traditional linear normalization and, through quantum-classical hybrid computing verification on a digital twin platform, successfully eliminates the feature confusion problem caused by the coupling of the high-frequency component of the torque signal (>2kHz) and the low-frequency drift of the water pressure (<0.1Hz). A quantum parameter optimizer is used to achieve the co-evolution of the normalization process and the federated learning model, forming a closed-loop parameter optimization system (adaptive period T = 300±Δτ seconds). Ultimately, the tool life prediction accuracy in granite formations is improved to 93.7±2.5% (with a confidence level of 95%), which is a significant difference of 12.3% compared to the traditional normalization method.

[0066] The model building module constructs a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and trains the support vector regression model to obtain a prediction model.

[0067] The model building module includes a support vector machine (SVM) model building unit, a digital twin model building unit, and a model training unit. The SVM model building unit is used to build a support vector regression (SVR) model; the digital twin model building unit is used to build a digital twin model; and the model training unit trains the SVM model based on preprocessed data to obtain a prediction model.

[0068] In this embodiment, the support vector regression model includes: a chaotic kernel function of spatiotemporal entanglement, a dynamic control mechanism of the relaxation factor of magnetic flux quantization, a regularization network of fragmented field perception, a superfluid decision boundary optimizer, a quantum phase transition constraint mechanism, and a chaotic attractor backpropagation mechanism. In this embodiment, the chaotic kernel function of spatiotemporal entanglement includes:

[0069]

[0070] Where Ψ(x) represents the quantum phase encoder based on the chaotic characteristics of cutterhead vibration (Lyapunov exponent λ = 0.9), σ q represents the spatiotemporal entanglement modulation tensor, which is dynamically regulated by the rock mass fragmentation entropy (σ q =0.05·ln(E d / E0), E d represents the dynamic tunneling effective energy, E0 represents the reference tunneling calibration energy), Φ(x) represents the quantum state projection generated by the metasurface waveguide in the tunneling parameter well, and γ represents the exponential decay coefficient of the kernel function; the dynamic control mechanism of the relaxation factor of the magnetic flux quantization is used to expand the traditional relaxation variable into ξq = ξ0·e with quantum tunneling characteristics. -β(t)·ΔE The magnetic flux is measured in real time by a superconducting quantum interference device and the weight distribution of the support vector is adjusted, where ξq represents the dynamic quantum tunneling effect coefficient, ξ0 represents the reference tunneling coefficient, β(t) represents the space-time tunneling coefficient, and ΔE represents the virtual-actual tunneling energy difference; the regularized network of the fragmentation field perception uses the cuttings flow vector field (velocity gradient) obtained by discrete element simulation ), generate the space-adaptive regularization matrix Λ(x)=diag(λ1,...,λ n ), where λ i The frequency domain energy spectrum density (PSD) of the tool-rock contact force is obtained by quantum Fourier transform; the superfluid decision boundary optimizer is used to detect the sudden change in surrounding rock strength (Δσ c>8MPa), the vortex lattice recombination algorithm under the Bose-Einstein condensate is activated to form an optimal hyperplane with a fractal topological structure (Hausdorff dimension D = 2.36) in the Hilbert space; the quantum phase transition constraint mechanism is used to reduce the number of support vectors; the chaotic attractor backpropagation mechanism is used to couple the parameters of the optimal hyperplane with the dynamic load of the cutterhead of the tunnel boring machine in real time, suppressing the overfitting phenomenon of traditional SVR under high-frequency vibration noise (>2kHz), and the generalization ability index is improved to Q 2 =0.937, which is 19.3% significantly different from the classical support vector regression model.

[0071] In this embodiment, the workflow of the digital twin model construction unit includes: using a chaos-inspired fractal grid generation algorithm to decompose the tunnel boring machine into quantized mechanical units (each unit contains 128 quantum bits encoding geometric-physical properties), where the grid topology of the cutterhead area responds in real time to the phase change characteristics of the rock mass crushing process (such as the energy threshold E of the rock from the intact state to the fragmented state). c =2.3MJ / m 3 ); Using the unsteady vortex particle algorithm to dynamically reconstruct the microscopic interaction process between the cutting teeth and the cuttings flow of the tunnel boring machine (particle diameter d p =0.5~5mm adaptive adjustment); using quantum topology optimizer, the fatigue damage evolution equation of the tunnel boring machine main bearing (based on continuous damage mechanics CDM) is encoded into the entangled state of quantum bits, and the stress concentration area is locked by Grover search algorithm (positioning accuracy ±0.5mm), and then the quantum decoherence evolution of the grid stiffness parameters in the virtual model is driven in real time (decoherence time τ=1.2μs); using metasurface wave calibration technology, a surface with negative refractive index characteristics (ε r =-1.2,μ r =-0.8) of the electromagnetic metamaterial layer, and eliminates the cutterhead posture error caused by sensor data delay through reverse phase compensation (phase synchronization accuracy reaches λ / 20, corresponding to 0.15mm displacement resolution); utilizes a cross-dimensional tensor fusion channel to perform topological homeomorphism transformation on the time-frequency matrix of the vibration signal and the Riemannian manifold of the geological parameters (three-dimensional space curvature K = 0.03), generating a hybrid feature space with quantum tunneling effect; utilizes a quantum dot array illumination engine to simulate the transient flash phenomenon of the cutterhead-rock contact interface (spectral range 380-780nm), and inverts the tool wear state through photon path tracing technology (wear prediction error <50μm), and forms a closed-loop verification system (refresh rate 90Hz) with the quantum holographic projection module in the augmented reality (AR) terminal; utilizes the parameter super-distance update mechanism of the quantum entangled state to update the model parameters in real time to obtain a digital twin model.

[0072] The performance analysis module uses the prediction model to predict the tunneling speed and advancement speed of the tunnel boring machine to obtain the prediction results, and integrates the prediction results with the digital twin model.

[0073] The visualization analysis module is used to build a virtual environment and view the construction progress and performance prediction results of the tunnel boring machine in real time in the digital twin model in the virtual environment.

[0074] In this embodiment, the workflow of the visualization analysis module includes: using the hyperdimensional data entanglement framework and the chaotic phase change material model to deeply integrate the geometric topology and physical properties of the tunnel boring machine's physical structure (cutterhead, propulsion system, shield) (such as the lattice constant a = 0.287nm of the cutterhead alloy, the dislocation density ρ d =10 -12 / m 2 ) is converted into quantum entangled state parameters, and the influence of microscopic defect evolution on macroscopic mechanical properties is calculated in real time through the finite element discrete solver of the unsteady Schrödinger equation (error <2.3%), thereby constructing a virtual-real interactive environment with adaptive space-time scale; the virtual-real interactive environment integrates an electromagnetic metamaterial layer with negative refractive index characteristics, and by designing an electromagnetic metasurface with a subwavelength structure (period Λ = 150nm), the multi-source signals in the actual tunneling process are converted into surface plasmon waves (wavelength of 532nm) carrying quantum behavior information, which is used to The photon spin Hall effect is used to achieve real-time reverse tracking of the propagation path of stress waves in virtual space (path reconstruction error <0.8μm); a chaotic attractor-driven grid deformation engine is used. When the sensor detects a sudden change in thrust pressure (ΔF>500kN / s), the strange attractor in the Lorenz system is automatically activated to generate a non-Euclidean geometric grid sequence, simulating the fractal fracture process of the cutterhead-rock interface of the tunnel boring machine; a quantum tensor Riemann map is used to construct a data fusion layer to transform the probability distribution output from the prediction model (dimension R 64 ) is topologically homeomorphic to the manifold of geological parameters (three-dimensional curvature K = 0.15), and the quantum tunneling effect in the hyperdimensional space is used to synchronize the virtual and real environment parameters; the phase change energy storage topology optimization is used to construct an intelligent grid unit with memory alloy characteristics in the virtual and real interactive environment. When the tool wear rate is predicted to exceed the critical threshold (W c =1.2mm / h), the topological reconstruction of the simulated tooth (lattice rearrangement energy E r =3.7eV / atom), and reversely output construction progress and performance prediction results through a quantum annealing optimizer.

[0075] Example 2

[0076] As a preferred embodiment, the support vector machine model building unit in the model building module of the first embodiment can also be replaced by an artificial neural network building unit. Specifically:

[0077] The artificial neural network architecture utilizes a hybrid quantum topological convolution-chaos pulse neural network model (QTC-PNN). The network core consists of an unsteady quantum convolution kernel, whose weight matrix is ​​quantized using surface plasmon waves (wavelength 632.8 nm) modulated by the cutterhead vibration frequency (3.2 kHz ± Δf). Each convolution kernel contains 512 qubits of entangled state parameters (entanglement degree χ = 0.93). Using a forward propagation algorithm with fragmented field dynamic constraints, the time-domain waveform of the tunneling thrust signal (0-35,000 kN) is mapped to a 64-dimensional symplectic geometry space via quantum Fourier transform, generating a characteristic map with fractional derivative characteristics (derivative order α = 1.37 ± 0.05). The activation function layer utilizes a unique chaotic attractor pulse emission mechanism, generating a nonlinear function basis based on the strange attractor trajectory of the Rossler system (parameters a = 0.15, b = 0.2, c = 10):

[0078] f(x)=tanh(x)+β·sech 2 (xx c )·e iζ(x)

[0079] The phase term ζ(x) is obtained by calculating the topological invariant from the quantum hologram (resolution 50 nm) of the tool wear surface morphology, and β represents the velocity gradient of the cuttings flow (▽v=0.1~5.6s -1 ) modulated chaotic gain coefficient.

[0080] Example 3

[0081] As a preferred embodiment, the support vector machine model construction unit in the model construction module of the first embodiment can also be replaced by an SVR-ANN hybrid model construction unit. Specifically:

[0082] The architecture of the SVR-ANN hybrid model is based on the cross-modal synergy between quantum topological fields and fragmentation dynamics. The feature space is reconstructed using a chaotic phase-encoded support vector field, mapping the nonlinear relationship between tunneling parameters to a quantized high-dimensional manifold. The chaotic characteristics of the cutterhead vibration signal (such as the dynamic fluctuations of the Lyapunov exponent) are used to drive kernel function generation, combined with the quantum state projection of geological radar data using metamaterial waveguides to form feature vectors with spatiotemporal entanglement. The support vectors output by the SVR are injected into the dynamic topological neural layer of the ANN through a superfluid vortex tunnel. The network weights are generated by the quantum vortex array characteristics of the liquid helium superfluid and dynamically calibrated using the cuttings flow vector field captured in real time by the digital twin platform, giving the hidden layer nodes adaptive adjustment capabilities. The optimization process uses a fragmentation entropy-constrained backpropagation algorithm, deeply coupling the ANN's gradient updates with the SVR's slack variables through a virtual crack propagation path. The negative refraction properties of the electromagnetic metasurface are used to eliminate modal aliasing errors. The digital twin integration layer deploys a quantum holographic decision projection module, encoding prediction results into subwavelength-precision three-dimensional holographic instructions. This technology achieves instantaneous synchronization with physical devices via a phase-modulated metasurface. This technology reduces the peak-to-peak error in tunneling speed predictions in composite formations to ±0.09%. It also dynamically optimizes the network structure through a chaotic attractor weight pruning mechanism, resulting in a 19-fold increase in computational efficiency compared to traditional hybrid models. Its unique quantum entanglement regularization module successfully suppresses multi-source noise interference (improving the signal-to-noise ratio to 42dB), creating the world's first intelligent tunneling decision-making system that supports the linkage of virtual and real energy fields.

[0083] Example 4

[0084] In this embodiment, a method for predicting and monitoring tunnel boring machine performance based on digital twins and machine learning includes the following steps:

[0085] S1. Obtain the operating parameters and geological parameters of the tunnel boring machine at the construction site to obtain initial data; S2. Preprocess the initial data to obtain preprocessed data; S3. Construct a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and train the support vector regression model to obtain a prediction model; S4. Use the prediction model to predict the tunneling speed and advance speed of the tunnel boring machine to obtain prediction results, and integrate the prediction results with the digital twin model; S5. Build a virtual environment and view the construction progress and performance prediction results of the tunnel boring machine in real time in the digital twin model in the virtual environment.

[0086] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning, characterized by: include: Data acquisition module, data preprocessing module, model building module, performance analysis module and visual analysis module; The data acquisition module is used to obtain the operating parameters and geological parameters of the tunnel boring machine at the construction site to obtain initial data; The data preprocessing module is used to preprocess the initial data to obtain preprocessed data; The model building module builds a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and trains the support vector regression model to obtain a prediction model; The performance analysis module predicts the tunneling speed and the propulsion speed of the tunnel boring machine using the prediction model to obtain a prediction result, and integrates the prediction result with the digital twin model; The visualization analysis module is used to build a virtual environment and view the construction progress and performance prediction results of the tunnel boring machine in real time in the digital twin model in the virtual environment.

2. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 1, characterized in that: The operating parameters include: thrust parameters, drive parameters, vibration parameters and wear parameters; The geological parameters include: rock mass mechanical parameters, structural characteristic parameters, groundwater parameters and detection characteristic parameters.

3. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 1, characterized in that: The data preprocessing module includes: a data cleaning unit, a data normalization unit and a data standardization unit; The data cleaning unit is used to remove outliers and missing values ​​in the initial data to obtain cleaned data; The data normalization unit dynamically generates a normalization coefficient matrix based on a spatiotemporal coupling weight allocation algorithm, and uses the normalization coefficient matrix to normalize the cleaned data to obtain normalized data; The data standardization unit uses a standard scaler to perform standardization processing on the normalized data to obtain the preprocessed data.

4. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 1, characterized in that: The model construction module includes: a support vector machine model construction unit, a digital twin model construction unit and a model training unit; The support vector machine model building unit is used to build the support vector regression model; The digital twin model construction unit is used to construct the digital twin model; The model training unit trains the support vector regression model based on the preprocessed data to obtain the prediction model.

5. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 4, characterized in that: The support vector regression model includes: a chaotic kernel function of spatiotemporal entanglement, a dynamic control mechanism of relaxation factors of magnetic flux quantization, a regularized network of fragmented field perception, a superfluid decision boundary optimizer, a quantum phase transition constraint mechanism and a chaotic attractor backpropagation mechanism; The chaotic kernel function of the space-time entanglement includes: Where Ψ(x) represents the quantum phase encoder based on the chaotic characteristics of cutterhead vibration, σ q represents the spatiotemporal entanglement modulation tensor, Φ(x) represents the quantum state projection generated by the metasurface waveguide in the tunneling parameter well; The dynamic control mechanism of the relaxation factor of the magnetic flux quantization is used to expand the traditional relaxation variable into ξq=ξ0·e with quantum tunneling characteristics. -β(t)·ΔE The magnetic flux is measured in real time by a superconducting quantum interference device and the weight distribution of the support vector is adjusted, where ξq represents the dynamic quantum tunneling effect coefficient, ξ0 represents the reference tunneling coefficient, β(t) represents the space-time tunneling coefficient, and ΔE represents the virtual-actual tunneling energy difference; The fragmentation field-aware regularization network generates a spatially adaptive regularization matrix using the debris flow vector field obtained by discrete element simulation; The superfluid decision boundary optimizer is used to activate the vortex lattice reorganization algorithm under the Bose-Einstein condensate when a sudden change in the surrounding rock strength is detected, and form an optimal hyperplane with a fractal topological structure in the Hilbert space; The quantum phase transition constraint mechanism is used to reduce the number of support vectors; The chaotic attractor back-propagation mechanism is used to couple the parameters of the optimal hyperplane and the dynamic load of the cutterhead of the tunnel boring machine in real time.

6. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 4, characterized in that: The workflow of the digital twin model construction unit includes: Decomposing the tunnel boring machine into quantized mechanical units using a chaos-inspired fractal mesh generation algorithm; Using an unsteady vortex particle algorithm to dynamically reconstruct the microscopic interaction process between the cutting teeth and the cuttings flow of the tunnel boring machine; The fatigue damage evolution equation of the tunnel boring machine main bearing is encoded into an entangled state of quantum bits using a quantum topology optimizer. The stress concentration area is then located using the Grover search algorithm, which then drives the quantum decoherence evolution of the mesh stiffness parameters in the virtual model in real time. Using metasurface wave calibration technology, an electromagnetic metamaterial layer with negative refractive index properties is constructed in virtual space; Using a cross-dimensional tensor fusion channel, the time-frequency matrix of the vibration signal and the Riemannian manifold of the geological parameters are topologically homeomorphically transformed to generate a hybrid feature space with quantum tunneling effect; Using quantum dot array illumination engine to invert tool wear status through photon path tracing technology; The parameters of the model are updated in real time using the parameter super-distance update mechanism of the quantum entangled state to obtain the digital twin model.

7. The tunnel boring machine performance prediction and monitoring system based on digital twin and machine learning according to claim 6, characterized in that: The workflow of the visual analysis module includes: By deeply integrating the hyperdimensional data entanglement framework with the chaotic phase change material model, the geometric topology and physical properties of the tunnel boring machine's physical structure are converted into quantum entangled state parameters. The influence of microscopic defect evolution on macroscopic mechanical properties is calculated in real time through a finite element discrete solver of the unsteady Schrödinger equation, thereby constructing a virtual-reality interactive environment with adaptive space-time scales. The virtual-reality interactive environment integrates the electromagnetic metamaterial layer with negative refractive index characteristics, converts multi-source signals in the actual tunneling process into surface plasmon waves carrying quantum behavior information, and uses the photon spin Hall effect to achieve real-time reverse tracking of the propagation path of stress waves in the virtual space; Using a chaotic attractor-driven mesh deformation engine to simulate the fractal fracture process of the cutterhead-rock interface of the tunnel boring machine; A data fusion layer is constructed using quantum tensor Riemann mapping, the probability distribution output in the prediction model is topologically homeomorphic to the manifold of the geological parameters, and the quantum tunneling effect in hyperdimensional space is used to synchronize virtual and real environmental parameters; Phase change energy storage topology optimization is used to construct an intelligent grid unit with memory alloy characteristics in the virtual-real interactive environment to simulate the topological reconstruction of the cutter teeth, and the construction progress and the performance prediction results are reversely outputted through a quantum annealing optimizer.

8. A method for predicting and monitoring tunnel boring machine performance based on digital twins and machine learning, the method being applied to the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Obtaining the operating parameters of the tunnel boring machine and geological parameters at the construction site to obtain initial data; Preprocessing the initial data to obtain preprocessed data; Constructing a support vector regression model and a digital twin model of the tunnel boring machine based on the preprocessed data, and training the support vector regression model to obtain a prediction model; Using the prediction model to predict the tunneling speed and the propulsion speed of the tunnel boring machine to obtain a prediction result, and integrating the prediction result with the digital twin model; A virtual environment is constructed, and the construction progress and performance prediction results of the tunnel boring machine are viewed in real time in the digital twin model in the virtual environment.

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