3742 results about "Shield tunneling" patented technology

A multi-objective optimization method and system for a shield construction parameter. The method comprises: acquiring an initial shield construction parameter; and optimizing the shield construction parameter by using a multi-objective optimization algorithm, during the optimization of the shield construction parameter, on the basis of the shield construction parameter, obtaining a tunneling speed by means of performing prediction on the basis of random forest, calculating a shield tunneling energy consumption ratio on the basis of the tunneling speed and the shield construction parameter, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm. The multi-objective optimization method and system for a shield construction parameter take construction efficiency into consideration and reduce the construction cost while ensuring construction safety.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention discloses a large-diameter shield tunneling parameter optimization method and system based on digital twinning, and relates to the technical field of shield tunnel numerical simulation, machine learning and digital twinning, and the method comprises the steps: obtaining shield tunnel construction site data, and constructing a virtual digital twinning body; building a training sample set based on virtual numerical twinborn generation data; training an agent model by using the training sample set; during construction, actual stratum response data and tunneling parameter data are obtained in real time, the real-time data are input into the soil mass parameter inversion agent model, and the optimal value of the soil mass parameter is rapidly output; the optimal value is input into the virtual digital twin, and dynamic real-time updating of numerical simulation is carried out; and on the basis of the updated soil parameter data and the tunneling parameter data in the next construction step, stratum response prediction data are obtained through the stratum response prediction agent model, so that the tunneling parameter data are adjusted and optimized. According to the invention, more accurate numerical simulation and safe and efficient tunneling of the shield tunnel can be realized.

The invention discloses a seabed karst stratum tunnel shield data monitoring and analyzing method and system. The method comprises the steps that shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are synchronously collected; shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are subjected to seepage-stress coupling constraint, a hydraulic connectivity strength index is generated through fracture network seepage tensor inversion, and the filler migration risk level is judged based on pore water pressure dynamic mutation characteristics. And fusing the cutterhead torque frequency spectrum characteristic and the propulsive force fluctuation characteristic to generate a shield disturbance entropy value. According to the method, the hydraulic connectivity index is generated through seepage stress coupling inversion, the grouting path and the rheological parameters are dynamically optimized based on the fracture probability field, shield tunneling, seepage sudden change and grouting prevention and control cooperation is achieved, and the problem of prevention and control disjunction is radically solved.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel are realized.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention discloses a shield tunnel full-life structure deformation monitoring and early warning method based on digital twinning, and the method comprises the following steps: S1, constructing a tunnel-geological complex model fusing BIM and three-dimensional point cloud technology according to the point cloud data of a tunnel completion section; s2, FBG and incremental sensing are adopted, and full-life-cycle deformation real-time monitoring is conducted on the tunnel; s3, constructing an LGBM proxy model, and updating the deformation prediction of the tunnel structure in the construction period; s4, constructing a GAT-LSTM model, and predicting structural deformation of the tunnel in the service period; s5, constructing a tunnel structure deformation grading early warning method based on a strength reduction and sharp point mutation theory; and S6, constructing a tunnel monitoring and early warning system based on digital twinning. The tunnel-geological complex structure reconstruction is realized by fusing stratum information and high-precision spatial data on the basis of a digital twinning technology, the tunnel construction and service period real-time deformation monitoring and early warning method and the twinning system are provided, and the service safety and failure risk pre-warning of the tunnel structure in the whole life cycle are guaranteed.

The invention discloses a muck water content prediction method and system suitable for shield tunnel construction, and the method comprises the steps: obtaining first data corresponding to to-be-measured muck in the shield tunnel construction in real time based on a self-adaptive window; in combination with a triangle cumulative sectional area method and an AE-FPFH-ICP algorithm, performing parallel processing to generate second data and third data corresponding to the to-be-measured muck; constructing a PLS-SVR combined model, and according to the PLS-SVR combined model, respectively combining the first data, the second data and the third data, and generating fourth data corresponding to the to-be-measured muck in real time; according to the method, the system and the platform corresponding to the method, high precision and robustness are shown in muck water content detection, the constructed PLS-SVR combined model is excellent in performance when processing a complex nonlinear relation, the water content of muck can be accurately predicted in real time, adaptability is high, and the high-precision requirement of tunnel construction for water content detection is met.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention discloses a shield tunneling intelligent auxiliary method based on stratum types. The method comprises the steps that parameters of a current shield tunneling machine in the tunneling process are collected; through Pearson's correlation coefficient calculation, correlation coefficients of all the parameters and the total thrust and the cutterhead torque are obtained respectively, and a feature combination is obtained; utilizing a K-means algorithm to calculate and obtain a stratum classification result which is clustered by taking a basic combination consisting of the total thrust and the cutterhead torque and a feature combination as a feature set; a cooperative training algorithm is adopted, and dug stratum classification based on the basic combination and the feature combination is obtained under semi-supervised learning; a machine learning algorithm is utilized to carry out learning training on types of excavated stratums, unknown stratum conditions in the tunneling direction are identified, and a reference range of total thrust and cutterhead torque is given. The method can be circularly executed in the shield tunneling process, the excavated stratum classification result is continuously updated, the method has the characteristics of practical feasibility, real-time performance and the like, and important auxiliary decision-making information can be provided for shield tunneling.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention relates to a shield tunneling machine tunneling error compensation control method and system, and relates to the field of tunneling operation, and the method comprises the steps: carrying out the time domain alignment and cutting region mapping alignment of a predicted soil feature distribution sequence and a predicted tool wear distribution sequence, and carrying out the integration to obtain a predicted operation state data sequence; and performing iterative optimization analysis on tunneling control parameters of the shield tunneling machine on the basis of the predicted operation state data sequence by taking the minimum overall tunneling operation error of the cutterhead as an expected target until a preset optimization scale is met, outputting an optimal tunneling parameter sequence, and controlling the shield tunneling machine to execute tunnel construction in a future time zone. According to the method, the technical problems of insufficient tunneling precision and low operation efficiency caused by the fact that a traditional method lacks a dynamic adjustment mechanism and cannot adjust tunneling control parameters in a targeted manner according to soil texture changes and cutter wear conditions can be solved; by effectively reducing the tunneling operation error, the accuracy and the operation efficiency of the shield tunneling machine in the tunneling process are remarkably improved.

The invention belongs to the field of mud cake removal of shield tunneling machines, and discloses a shield cutterhead residue soil property sensing and intelligent mud cake removal system and method, and the system comprises a sensing module, a modifier spraying module, a regulation and control module and an intelligent terminal; the sensing module is used for collecting mechanical, chemical and thermodynamic signals in a soil bin of the shield tunneling machine in real time; the modifier spraying module is used for intelligently regulating and controlling the proportion of a modifier so as to inhibit mud cake adhesion; the regulation and control module is used for dynamically optimizing a modifier proportioning parameter and a cutterhead tunneling strategy according to the sensing data; and the intelligent terminal is used for displaying the mud cake distribution state and receiving a manual intervention instruction. According to the method, three-dimensional form dynamic analysis and accurate targeted removal of the mud cake can be realized.

The invention discloses a real-time prediction method for muck improvement parameters of a shield under-crossing existing tunnel, and the method comprises the steps: carrying out the refined preprocessing of non-under-crossing section shield tunneling data, enabling an obtained data set to be used for the training of a machine learning integrated prediction model, and obtaining a muck improver dosage integrated prediction model; meanwhile, slump experiments are carried out for different types of stratums, the optimal water content and foam injection ratio parameter range is determined, and a reliable decision basis is provided for a shield driver. The quality of a data set and the prediction precision of a machine learning model are remarkably improved through a finer data preprocessing means; the machine learning integrated prediction model fuses learning capabilities of different machine learning models, so that the prediction precision of the model is further improved; and machine learning and experimental means are combined to realize accurate and rapid prediction of the use amount of the modifier, so that safe and smooth tunneling of the shield under the existing tunnel is ensured, and the method has certain timeliness and economical efficiency.

The invention provides a shield adaptability adjusting method based on a stratum structure, which comprises the following steps: acquiring geological parameters of a construction area of a shield tunneling machine through drilling and geophysical prospecting, and arranging sensors at key positions of the shield tunneling machine to acquire construction parameters of the shield tunneling machine in real time; a convolutional neural network CNN is adopted to analyze the geological parameters to identify stratum features, and a geological model is constructed based on the identified stratum features; constructing a disaster risk index system, and predicting a disaster risk based on the geological model and the construction parameters by adopting Logistic regression and a random forest algorithm; according to the predicted disaster risk, the cutterhead configuration of the shield tunneling machine is dynamically adjusted, and tunneling parameters are optimized or a muck improvement scheme is adopted; by monitoring geological parameters, construction parameters and environmental changes in real time, a multistage early warning mechanism is adopted to trigger emergency response measures, and construction safety is guaranteed. According to the method, the safety and efficiency of shield construction can be improved, the geological disaster risk is reduced, and reliable technical guarantee is provided for underground space development.

The invention provides a shield tunnel construction period segment longitudinal dynamic floating prediction method, and relates to the field of shield tunnel segment floating prediction.The method comprises the steps that the static upward floating force and the dynamic upward floating force of a shield tunnel are calculated by considering the timeliness of slurry, and the characteristic of nonlinear change of the rigidity of a circular seam connector is considered; the method comprises the following steps: dividing bending-resistant and shearing-resistant deformation modes of a circular seam joint, further deducing bending rigidity and shearing rigidity of a tenon-type joint in each deformation stage, deducing a shield tail segment longitudinal floating prediction model of the circular seam joint in the shield tunnel construction period, and constructing a nonlinear iterative algorithm of shield tunnel longitudinal deformation according to the theories; and acquiring parameter data of actual engineering, and inputting the parameter data into the duct piece floating prediction model to obtain a duct piece floating value. By means of the method, accurate prediction of floating deformation of the shield tail segment in large shield tunneling can be achieved, an effective scientific basis is provided for large-diameter shield tunneling construction, the problems that parameter decision of shield tunneling machine operators is difficult, and the segment floating control effect is poor are solved, and structural diseases caused by too large floating of the shield tail segment are eliminated.

The invention provides a shield tunneling machine cutter cylinder state monitoring method based on a multi-sensor information fusion technology. The shield tunneling machine cutter cylinder state monitoring method comprises the steps that multi-physical field synchronous data collection is carried out; self-adaptive signal preprocessing: processing the multi-physics field time sequence signal to obtain multi-physics field synchronization time sequence data; multi-physics field feature space construction: processing the multi-physics field synchronization time sequence data to respectively obtain time domain feature data, frequency domain feature data and time-frequency domain feature data; deep collaborative feature extraction and state evolution modeling: obtaining a multi-physics field coupling feature sequence of the spatio-temporal context through multi-physics field collaborative perception feature coding; global space-time dependency collaborative modeling is adopted, and an enhanced space-time feature sequence is obtained; and state evolution trajectory interpretation and classification decision. According to the scheme, a traditional linear stacking mode of data acquisition-preprocessing-feature extraction-fusion classification is broken through, and a closed-loop intelligent sensing method with physical mechanism guidance, multi-modal data deep coupling and feature self-coevolution is constructed.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention discloses a method for predicting shield tunneling parameters under a composite stratum, which comprises the following steps of: collecting and sorting shield tunneling historical data under the composite stratum, including geological parameters and construction parameters, and performing abnormal value elimination, missing value filling and data standardization; the geological parameters and the geometric parameters are combined according to the tunneling ring number, and a quantitative geological matrix of each ring is generated; the geometric parameters represent the position of the soil layer relative to the tunnel excavation face by adopting 1-5 codes, and input dimension unification is ensured through zero filling alignment; a CNN-Attention-SVR model is constructed, and the CNN-Attention-SVR model is trained; new geological data are input into the trained model, tunneling parameters are predicted, and construction adjustment is guided; reliable technical support and decision basis are provided for tunnel construction under the composite stratum condition, and therefore the overall safety and economical efficiency of shield construction are improved.

The invention discloses a mobile tunnel detection method and system, and relates to the field of tunnel detection, and the method comprises the steps: dividing a tunnel into a shield tunnel and a mine tunnel; for a shield tunnel, obtaining a three-dimensional point cloud of the shield tunnel, and extracting bolt holes by adopting a CSF algorithm and a DBSCAN algorithm; adopting a mean-shift algorithm to obtain a central point of the bolt hole, and performing ring-dividing and block-dividing on the three-dimensional point cloud of the shield tunnel according to the central point of the bolt hole to obtain a shield tunnel point cloud after ring-dividing and block-dividing; the method comprises the following steps of: for a mine tunnel, arranging one target every preset distance along the axial direction of the tunnel by using mobile tunnel laser equipment for target arrangement to obtain a mine tunnel point cloud with target position information; generating a mine tunnel lining diagram according to the mine tunnel point cloud with the target position information; establishing an index relationship between the mine tunnel point cloud and the mine tunnel lining graph; and positioning the mine tunnel point cloud according to the index relationship to obtain the positioned mine tunnel point cloud and the like, so that the tunnel defect detection efficiency is improved.

The invention relates to the field of rock-soil particle motion trail acquisition, and discloses a rock-soil particle motion trail acquisition method and system in a shield tunneling process, and the method comprises the steps: multi-source data collection and fusion preparation, rock-soil particle preliminary identification and classification, unmarked motion tracking algorithm initialization, and real-time motion trail calculation and correction. The method comprises the steps of uncertain parameter real-time estimation, simulation and actual measurement data comparison verification, motion trail visualization and analysis report generation, and adaptive optimization and feedback control. According to the method, multi-source data acquisition and fusion preparation is made firstly, a data foundation is laid, then through links of rock-soil particle identification and classification, motion tracking algorithm initialization and the like in sequence, functions of particle motion tracking, trajectory calculation and correction, parameter estimation and the like are realized by utilizing multiple means, and through simulation comparison verification, visualization and self-adaptive optimization feedback control, the real-time performance of the system is improved. And finally, the construction efficiency and quality are improved, construction decision is assisted, construction is guaranteed, and data management is facilitated.

The invention discloses a random field theory-based deformation analysis method for a shield to pass through an existing shield tunnel, and the method comprises the following steps: 1, building a shield construction three-dimensional refined numerical model, and exporting central point coordinates of all entity units; 2, determining a statistical characteristic value of the spatial variability of the elastic modulus of the stratum; 3, constructing a three-dimensional random field model of the stratum elastic modulus; 4, importing the three-dimensional random field model of the stratum elasticity modulus into the three-dimensional refined numerical model, and carrying out numerical calculation and analysis; 5, vertical displacement, caused by shield construction, of the stratum at the axis position of the existing shield tunnel is extracted; 6, deformation of the existing shield tunnel is calculated; and 7, calculating the mean value and the standard deviation of the maximum vertical deformation of the tunnel. According to the analysis method, spatial variability characteristics of stratum mechanical parameters are considered, the random field theory and the Monte Carlo strategy are adopted to predict existing shield tunnel deformation caused by shield construction, and the analysis result is more accurate.

The invention discloses a control method for adjusting the tunneling attitude of a shield tunneling machine, particularly relates to the field of attitude control of the shield tunneling machine, and aims to solve the problem that attitude deviation caused by underground environment change cannot be timely and effectively corrected in the tunneling process of the shield tunneling machine. The method comprises the following steps of: calculating membership degrees of horizontal deviation, vertical deviation and change rates of the horizontal deviation and the vertical deviation, extracting high-frequency and low-frequency characteristics, generating independent phase signals, and calculating a judgment coefficient by using a multi-dimensional data fusion and high-dimensional exponential integration method so as to dynamically adjust the weight of a fuzzy control rule base and realize adaptive optimization of PID (Proportion Integration Differentiation) gain parameters; a master control output signal is generated based on the updated PID parameters, the thrust of the partition oil cylinders is accurately adjusted, it is ensured that the posture of the shield tunneling machine is rapidly, accurately and stably adjusted under the complex environment condition, the control precision of the tunneling direction and the construction safety are improved, and the working efficiency is improved. And meanwhile, the overall posture correction capability of the shield tunneling machine is further enhanced through dynamic adjustment of the rolling angle.

The invention provides a shield tunnel construction period segment floating amount prediction method, system and device and a medium, and belongs to the technical field of tunnel detection. The method comprises the following steps: acquiring an exploration data set and an actual measurement data set of a shield tunnel section; the exploration data set is input into a shield tunnel mechanical model to be solved, and relevant mechanical parameter values of the shield tunnel section are obtained; determining a physical control equation for reflecting the deformation characteristics of the shield tunnel section according to the Timoshensincaro beam model and related mechanical parameter values thereof; according to the actually measured data set and the physical control equation, training a neural network model to obtain a segment floating quantity prediction model; and obtaining positions of a plurality of segments to be detected in the shield tunnel section and inputting the positions into the segment floating quantity prediction model for analysis to obtain predicted floating quantities of the plurality of segments to be detected. According to the method, a physical mechanism conforming to tunnel construction can be fused into the neural network model to effectively constrain the prediction process, and the reliability of the prediction result of the segment floating amount can be improved.

The invention discloses a shield tunneling machine axis predictive control method and system, electronic equipment and a storage medium, the shield tunneling machine axis predictive control method is based on data driving modeling, system dynamic characteristics contained in shield tunneling axis control related data are fully excavated, the predictive control precision is high, and the predictive control precision is high. A GRU (Gated Recurrent Neural Network) model and an ARX (Autoregressive Regressive) model with exogenous variables are combined to construct a guide deviation dynamic characteristic model to predict the guide deviation, the advantage that the GRU is good at processing a long-interval and long-delay time sequence is fully utilized, and the correlation characteristics before and after the shield guide deviation data time sequence are fully excavated; the model not only has the global nonlinear description capability, but also has the capability of describing the time sequence correlation characteristics of the system, the dynamic characteristics of the system are more comprehensively described, and the model is a nonlinear model which is more in line with the guiding actual working characteristics of the shield tunneling machine, and is higher in generalization capability and higher in precision.

The invention discloses a shield tunnel anti-seismic toughness evaluation method and device and electronic equipment. The method comprises the following steps: establishing a grid, defining a material unit and setting a boundary, and establishing a tunnel finite element model; a seismic oscillation-input model is selected, and according to the tunnel finite element model, seismic vulnerability analysis is carried out after response is obtained; determining structure recovery information according to the result of the earthquake vulnerability analysis; determining a tunnel performance function curve and a function recovery curve by combining a function recovery function of the structure according to the structure recovery information; and performing tunnel toughness evaluation according to the function recovery curve to obtain an evaluation result of tunnel anti-seismic toughness. According to the embodiment of the invention, based on the anti-seismic toughness design, the influence of the seismic oscillation intensity and structural factors on the shield tunnel damage is evaluated, the time and cost of function recovery after the earthquake are quantitatively reflected, and the technical gap of an existing toughness evaluation method in the aspects of shield tunnel economic loss and recovery time calculation is solved; the method can be widely applied to the technical field of computers.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention discloses a non-contact real-time measurement and super-discharge prevention intelligent control method for shield tunneling deslagging. The method comprises the following steps that 1, equipment is selected and installed; 2, acquiring an image; 3, preprocessing the image; 4, constructing a CNN machine learning model; 5, setting a CNN machine learning model and an alarm mechanism; step 6, establishing a real-time monitoring and feedback system; and 7, recording and analyzing data. The CNN can automatically extract local features in complex data, and is especially suitable for image processing. In the tunneling process, the CNN can recognize useful features such as the working state of the shield tunneling machine from image data, and therefore more accurate reference is provided for super-discharge risk prediction and control. As the CNN can process a large amount of complex data and capture precision changes in the complex data, the prediction precision of the CNN is generally higher than that of a traditional method, and the precision and stability of a control system can be effectively improved by adopting the CNN in anti-super-discharge control.

The invention discloses a shield tunnel risk early warning method and system based on digital twinning and federated learning, and the method comprises the steps: a shield tunnel construction side client serves as an edge calculation terminal, and obtains basic parameters; a first shield digital twinning system, a second shield digital twinning system and a third shield digital twinning system are correspondingly constructed by the shield machine producer client side, the shield tunnel construction side client side and the third-party client side according to the basic parameters; a shield construction risk early warning model is arranged in each shield digital twin system; the shield machine production side client, the shield tunnel construction side client and the third side client are all participants, and each participant sends model parameters of the respective shield construction risk early warning model to the central server; the central server is used for averaging the parameters of the three models and issuing the average value of the parameters to each participant; and each participant updates the model of the participant according to the average value of the parameters, and realizes shield construction risk early warning based on the updated model.

The invention discloses a shield tunneling machine driving attitude adjusting method and system under multi-sensor monitoring, and relates to the technical field of shield tunneling machine control. Interactive engineering planning is carried out, a tunnel center line is identified, a driving reference axis is determined, a tunnel section is subjected to triangular grid division, and an attitude adjusting module is trained by setting the attitude position relation of a shield tunneling machine; and collecting measurement data, carrying out shield attitude misalignment measurement and adjustment decision of each shield single ring in the tunneling operation of the shield tunneling machine, determining an attitude adjustment strategy, and responding to a control center of the shield tunneling machine to carry out tunneling attitude adjustment management and control. The method and the device are used for solving the technical problems that in the prior art, decision consideration is not comprehensive enough, the technical support degree is insufficient, instantaneity and adaptivity of tunneling posture adjustment cannot be guaranteed, and consequently accuracy and reliability of an adjustment strategy are insufficient. High-precision real-time adjustment and control of the posture of the shield tunneling machine are achieved, and construction safety and efficiency are effectively improved.