2127 results about "Shield machine" patented technology

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention discloses an intelligent shield muck improvement method and system based on machine learning, and the method and system achieve the intellectualization and precision of muck improvement in the tunneling process of a shield tunneling machine through integrating muck image classification, improvement parameter prediction and model optimization. Firstly, a YOLOV8 network is utilized to classify muck images in real time, classification randomness is corrected in combination with an XGBOOST model, and classification accuracy and stability are improved. Secondly, performing time sequence prediction on the muck improvement parameters based on an LSTM network, and introducing a linear regression and genetic algorithm to improve the prediction precision; and finally, optimal solution is carried out on the muck improvement parameters through a particle swarm optimization (PSO) algorithm, and the stability and efficiency of shield tunneling are remarkably improved. The method solves the problems of randomness and hysteresis caused by dependence on manual adjustment in the prior art, has the advantages of high efficiency and accuracy, and is suitable for automatic application scenes of shield engineering.

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 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 tunneling machine real-time data monitoring and operation efficiency improving method and system, and relates to the field of industrial equipment intelligent monitoring and optimal control. Multifunctional sensor modules are arranged on a cutter head system, a propelling system, a soil bin system and a shield tail system of a shield tunneling machine to collect operation parameters; and the local data processing unit is used for filtering and checking the data, so that the accuracy and reliability of the data are ensured. Based on a geological survey report, geological conditions are divided into multiple stratums, differential monitoring parameter combinations for different stratums are constructed, and dynamic monitoring and adjustment are achieved. And a tunneling speed optimization model based on the stratum discrimination function is further established, and the optimal tunneling speed is calculated in real time according to the dynamic change of the stratum characteristics, so that the tunneling efficiency of the shield tunneling machine is remarkably improved, the construction uncertainty is reduced, and the construction safety is 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 tunneling machine or TBM hob associated characteristic abrasion analysis method and system, and belongs to the technical field of associated characteristic analys.The method comprises the steps that parameter data related to cutter abrasion are collected in real time, and a parameter time sequence is constructed; calculating a change curve of the statistical characteristics, the derivative characteristics and the rock-soil layer, and updating a parameter sequential sequence; analyzing a dynamic change condition of a parameter time sequence, calculating a feature importance score by using a random forest algorithm, avoiding interference by unimportant information in an analysis process, and constructing an associated feature model; according to the method, three-dimensional point cloud data of a rock-soil layer is constructed, fused point cloud data containing a tool wear label is generated, rich dimension data is provided, a point cloud model used for judging the state of the tool is constructed, the risk of misjudgment and missed judgment is reduced, and the state of the tool is judged; once it is judged that the cutter is damaged, a cutter damage signal is generated, and a powerful guarantee is provided for safe and efficient operation of the shield tunneling machine or the TBM.

The invention belongs to the technical field of underground engineering, and provides a slurry shield tunneling machine carrying a high-pressure jet system and a working method thereof.The slurry shield tunneling machine comprises a cutterhead mechanism, a slurry supply mechanism and a high-pressure jet mechanism, and the slurry supply mechanism and the high-pressure jet mechanism are arranged on the cutterhead mechanism through a rotation center; the high-pressure water supply assembly comprises a first water supply pipe and a second water supply pipe which are connected through a pipeline dynamic and static conversion end. The grinding material supply assembly comprises a first supply pipe connected with the fixed end and a second supply pipe connected with the rotating end; the second water supply pipe and the second material supply pipe are jointly connected with a spray head, the spray head is arranged on the cutter head mechanism, in the soft soil layer construction process, when the cutter head mechanism suddenly encounters hard substances such as stones, high-pressure water and abrasive materials are provided for the spray head through the second water supply pipe and the second material supply pipe, and the hard substances such as the stones are cut in a water jet mode; abrasion and damage to the hob are reduced, and construction efficiency and safety are guaranteed.

The invention discloses an intelligent control method and system for muddy water treatment of a muddy water shield tunnel, and relates to the technical field of muddy water treatment.The method comprises the steps that physical characteristic data and chemical characteristic data of muddy water are collected in real time, preliminary treatment is conducted through edge computing nodes, and standardized muddy water characteristic data flow is output; carrying out preliminary screening operation on muddy water according to an intelligent separation strategy, and screening out medium-particle impurities; through self-adaptive dynamic vortex regulation and control, the muddy water rotational flow speed and the muddy water pressure are regulated, and medium particle impurities are subjected to rotational flow separation; carrying out deep separation treatment on medium-particle impurities subjected to cyclone separation, and outputting clear water and deposited mud cakes; clear water flows back to the shield tunneling machine through pumping equipment to be recycled, and deposited mud cakes are subjected to dehydration treatment. According to the invention, medium particle impurities are accurately separated according to the change of muddy water characteristics by utilizing a regulation and control method of dynamically adjusting the rotational flow speed and pressure, so that the muddy water separation efficiency is effectively improved, and the energy consumption is reduced.

The invention relates to the technical field of tunnel engineering and intelligent construction, in particular to a shield tunneling real-time control method based on a random forest and a particle swarm optimization algorithm. The method comprises the steps that initial tunneling parameters are generated through a parameter recommendation random forest model according to geology and tunnel geometric parameters, and model hyper-parameters are optimized through a sparrow optimization algorithm; carrying out settlement prediction by utilizing the settlement prediction random forest model; if the predicted value exceeds the limit, carrying out iterative optimization by adopting a particle swarm optimization algorithm and taking the initial parameter as a starting point, and searching a global optimal tunneling parameter combination meeting the settlement requirement; finally, the optimized parameters are issued to the shield tunneling machine to be executed, the model is continuously updated based on real-time construction data, and closed-loop control is formed. According to the method, intelligent recommendation and real-time optimization of tunneling parameters can be realized, the ground surface settlement control precision and the system response speed are improved, the dependence on artificial experience is effectively reduced, and the self-adaptive capability and the intelligent level of shield construction under complex geological conditions are enhanced.

The invention discloses a shield tunneling machine tunneling key parameter real-time multi-step prediction method based on ST-GCN-LSTM, and the method comprises the following steps: obtaining the operation data of a shield tunneling machine under a normal working condition, and carrying out the preprocessing of the operation data; analyzing the preprocessed operation data, and determining related variables of total thrust, cutterhead torque, cutterhead rotating speed, penetration and propelling speed average value tunneling key variables; respectively calculating the control limit of each tunneling key variable for different soil layers, and training a multi-step prediction model based on an ST-GCN and LSTM combined model; acquiring operation data of the shield tunneling machine in real time, inputting the operation data into the trained model for preprocessing, dynamically predicting tunneling key parameter values of multiple steps in the future, and judging whether predicted values continuously exceed a control limit or not; according to the method, real-time multi-step prediction of the tunneling key parameters can be carried out in combination with soil layer classification, and prediction inaccuracy caused by changes of external factors such as soil layers is reduced.

The invention relates to the technical field of shield tunnel construction. The invention discloses a method, device and equipment for measuring the volume of muck on a horizontal conveying belt of a shield tunneling machine and a medium. The accuracy of muck volume measurement can be improved. The method comprises the steps that first point cloud data of a horizontal conveying belt in the shield tunneling machine under the no-load condition are obtained, the first point cloud data are preprocessed, and a first curve and a first coordinate set at a horizontal supporting roller are obtained; acquiring second point cloud data of at least one group of horizontal conveyor belts under the condition of carrying the muck based on the scanning frequency; the target second point cloud data is preprocessed, a second coordinate set, a third coordinate set and a fourth coordinate set are obtained, and the target second point cloud data is any group of data of all the second point cloud data; fitting the first coordinate set, the third coordinate set and the fourth coordinate set into a second curve; selecting an optimal curve based on a preset rule; and calculating the muck volume based on the preset time, the scanning frequency, the optimal curve and the second coordinate set.

The invention discloses a shield tunnel muck improvement parameter prediction method and system, and relates to the technical field of tunnel engineering construction.The method comprises the steps that a multi-modal sensor is arranged at a key part of a shield tunneling machine, and tunneling parameters, stratum parameters, multi-modal sensor signals, muck physical properties and modifier injection parameters are collected; then, extracting change characteristics, calculating mutation sensitive factors and carrying out working condition judgment; constructing a normal prediction sub-model library and a sudden change quick response model, calculating a stratum model adaptation index and performing model suitability judgment; establishing a working condition coupling prediction model by using a graph neural network, extracting nonlinear coupling characteristics, calculating a working condition coupling index and judging prediction stability; actual construction performance data are collected and compared with a model prediction result, a prediction correction index is calculated, deviation analysis is carried out, and a corresponding correction strategy is triggered. According to the method, intelligent prediction and dynamic correction of shield tunnel muck improvement parameters can be achieved, and the safety and stability of the construction process are improved.

The invention discloses an intelligent adjusting system and method for soil layer penetrating power of a shield tunneling machine. The intelligent adjusting system comprises a sensor array, wherein the sensor array is annularly and uniformly distributed around a cutter head of the shield tunneling machine; the risk level module is used for calculating a similarity matrix between section rings by adopting a Gaussian kernel function, dividing performance communities through a Louvain algorithm and outputting geological risk levels and corresponding coordinates; and the intelligent decision module integrates a multilayer convolutional neural network, fuses sensing data and a network analysis result in real time, and generates dynamic instructions of the cutterhead rotating speed, the propelling speed and the grouting pressure. The shield tunneling machine soil layer penetrating power intelligent adjusting system achieves real-time monitoring and intelligent analysis of multi-dimensional information such as soil layer hardness, pore water pressure, ground stress and temperature by integrating a high-precision multi-dimensional sensing module, a risk level module and an intelligent decision module. The system can automatically adjust the cutterhead rotating speed, the propelling speed and the grouting pressure of the shield tunneling machine according to the geological risk grade.

The invention relates to the technical field of shield tunneling machine man cabin monitoring, in particular to a man cabin monitoring system based on an optic neural network, which is characterized in that light scattering prior is constructed based on dust particle size, dust concentration and relative positions of a camera and light by collecting original images in a shield tunneling machine man cabin and data of dust particle size, concentration, temperature and smoke concentration; the transmissivity is inversed in combination with a monocular depth estimation network, and the image is further corrected in a cross-scale manner through a residual pyramid compensation network, so that a dust removal image sequence suitable for a complex light scattering environment is obtained; and then accurate identification of abnormal staying of personnel and tool missing is realized based on a three-dimensional space-time diagram convolutional network and a causal attention mechanism, and a Transform model is utilized to fuse vision, temperature and smoke concentration data to realize rapid and accurate prediction of fire and in-cabin leakage risks.

The invention belongs to the field of tunnel engineering construction, and provides an anomaly detection method and system before shutdown of a shield tunneling machine, and the method comprises the steps: collecting and preprocessing multi-system sensor data of the shield tunneling machine; performing grouping, feature extraction and dimension reduction processing on the data by adopting a hierarchical hybrid dimension reduction strategy; a multi-scale space-time attention residual error neural network model is constructed and trained; and real-time monitoring and shutdown early warning are carried out on the operation state of the shield tunneling machine. Experiments prove that the accuracy rates of the method on four test sets all exceed 95%, the F1 score is 0.90 or above, and the abnormal state of the shield tunneling machine before shutdown can be accurately predicted. According to the method, engineers can take measures in advance, construction period delay and economic loss caused by sudden shutdown are avoided, the safety and efficiency of shield construction are improved, and the method has important significance in promoting shield tunneling machine construction intelligentization.

The invention relates to the technical field of shield tunneling machine cutterhead evaluation, and discloses an intelligent evaluation system and method for the state of a shield tunneling machine cutterhead, and the system obtains rich operation state statistical characteristics through the collection of multi-source data of torque, current and vibration sensors and the utilization of a sliding window feature extraction technology. Further, time sequence context coding is carried out on the time queue of the cutterhead operation state statistical characteristics to obtain cutterhead operation state time sequence context coding characteristics, and deep information of the cutterhead operation state is captured. And finally, inputting the coded cutter head operation state time sequence context coding features into the trained GBRT model to realize accurate prediction of the cutter wear degree. According to the method, the accuracy and comprehensiveness of state monitoring are improved, the early warning capability of potential faults is enhanced, and a guarantee is provided for safe and stable operation of the shield tunneling machine.

The invention provides a layered freezing and layered excavation method for butt joint of large-diameter shield tunnels, and relates to the technical field of shield tunnel engineering.During butt joint of the large-diameter shield tunnels, the butt joint faces are layered in sequence from top to bottom, and the procedures of coring and drilling, freezing pipe driving, positive freezing, cutterhead disassembling and excavation are conducted on each layer; after the coring and drilling of the upper layer are finished, the coring and drilling of the next layer are immediately started, and the butt joint surfaces are sequentially layered from top to bottom through the layered budget model, so that the working procedures of coring and drilling, freezing pipe driving and positive freezing of the next layer are simultaneously completed before all the working procedures of the upper layer are completed; the time for coring, drilling, freezing pipe driving and equipment mounting and debugging of other layers except the top layer is saved, the construction period for taking more time for drilling and freezing pipe driving on the shield tunneling machine is saved, and therefore the whole construction period is shortened.

The invention belongs to the technical field of shield construction, and provides a shield synchronous grouting control method and system fusing tunneling parameters and geological information, and the method comprises the steps: obtaining the tunneling parameters of a shield tunneling machine and the geological information of a front tunnel in real time; the obtained tunneling parameters and geological information are analyzed and processed, the mapping relation among the tunneling parameters, the geological information and the grouting parameters is determined, and grouting parameter predicted values are obtained; and according to the obtained grouting parameter prediction value, grouting parameters are automatically adjusted, and synchronous grouting of the shield is controlled.

The invention discloses a layered settlement prediction method and device based on multi-source data fusion and a medium, and relates to the technical field of shield tunnel construction monitoring. The method comprises the following steps: acquiring multi-source monitoring data, and preprocessing the multi-source monitoring data to obtain standard monitoring data; wherein the multi-source monitoring data comprises layered settlement monitoring data, shield construction parameters and geological survey data; inputting the standard monitoring data into a preset layered settlement prediction model to obtain a layered settlement prediction result; wherein the layered settlement prediction result comprises the layered settlement amount and the settlement rate in the unit distance before and after the shield tunneling machine in the unit time in the future; and based on a layered settlement prediction result, triggering graded early warning, and generating a visual decision report. By constructing the layered settlement prediction model, the coupling relationship among the shield construction parameters, the address survey data and the settlement of different depths is excavated, and the settlement of the shield tunneling machine in the unit time in the future and the specific distance before and after the shield tunneling machine is accurately predicted.

The invention provides a heterocentric type mother-child shield tunneling machine and a construction method thereof, and belongs to the technical field of shield tunneling machines. Comprising mother-son cutterheads, mother-son shield bodies, a driving mechanism and a propelling mechanism, the mother-son cutter head comprises a mother machine cutter head and a son machine cutter head, and the son machine cutter head is eccentrically nested in the mother machine cutter head; the mother-son shield body comprises a mother machine shield body and a son machine shield body, and the son machine shield body is eccentrically nested in the mother machine shield body; the driving mechanism is connected with the sub-machine cutterhead through a torque beam; the propelling mechanism comprises a primary machine propelling mechanism and a secondary machine propelling mechanism, and the primary machine propelling mechanism is mounted in a primary machine shield body and provides propelling force for a primary machine cutterhead during tunneling; the sub-machine propelling mechanism is installed in the sub-machine shield body and provides propelling force for the sub-machine cutterhead in the tunneling process. According to the invention, one shield tunneling machine can be adopted to carry out tunnel construction with different diameters, and tunnels with two diameters are not concentric, so that the equipment can be used for constructing an underground platform and a tunnel at one time, station-tunnel integrated construction is realized, and the space utilization rate of the platform is greatly improved.

The invention belongs to the field of shield tunneling machine deviation rectification track planning, and discloses a shield tunneling machine deviation rectification track high-precision autonomous planning method and system, and the method comprises the steps: determining the minimum turning radius of a shield tunneling machine; carrying out coordinate calculation on the DTA to obtain the coordinate, the direction and the curvature of each point; projecting the starting point state and the end point state of the shield tunneling machine under the Cartesian coordinate system into the established Frenet coordinate system, establishing a correction curve parameterization equation based on a quintic polynomial line type, and converting the state of the shield tunneling machine into the Cartesian coordinate system; through segment pre-typesetting constraint, determining a deviation correction curve end point feasible region; the deviation degree of the deviation correction curve and the DTA and the length and smoothness of the deviation correction curve serve as optimization parameters, normalization processing is carried out according to the threshold value of each target, a comprehensive cost function is established, an improved particle swarm optimization algorithm is used for solving, and a global optimal deviation correction track is output. According to the method, real-time autonomous high-precision deviation rectification track planning of the shield tunneling machine can be realized, the track feasibility is improved, the pose deviation degree is reduced, and the tunnel quality and the construction efficiency are improved.

The invention belongs to the technical field of engineering construction equipment, and discloses a trolley variable-diameter launching method which comprises the following steps: firstly, reducing the width of a trolley from 6m to 4.9 m through a trolley narrowing mechanism so as to transport and hoist in a limited space and complete assembly; when tunneling to 80m, expanding the width of the trolley to 6m, and recovering the bearing capacity of the trolley; continuously tunneling to 110m, hoisting a subsequent trolley, and connecting to form a complete machine; and after the shield body passes through the starting tunnel portal, the diameters of the cutter head, the shield body and the shield tail are synchronously expanded to the size required by construction. According to the method, through the flow design of narrowing transportation, reducing assembly and step-by-step expansion, the problems that a traditional shield tunneling machine cannot be started in a limited space and the construction efficiency is low are solved, and the construction efficiency and feasibility under the complex working condition are improved.

The invention discloses a tunnel stratum recognition method based on shield tunneling machine tunneling parameter machine learning, and relates to the technical field of monitoring analysis, and the method comprises the steps: data collection: constructing a project data set, collecting rock core samples for drilling and coring from the project data set, and determining a learning sample set based on the rock core samples; data feature mining, which is used for obtaining tunneling parameters corresponding to various types of stratums from the learning sample set, mining a statistical feature set responding to stratum changes based on the tunneling parameters corresponding to various types of stratums, and forming a first feature parameter matrix; data feature screening, which is used for carrying out sensitivity analysis on the first feature parameter matrix so as to screen out a second feature parameter matrix; the model construction is used for constructing a stratum recognition model by adopting a K nearest neighbor algorithm based on the learning sample set and the second characteristic parameter matrix; and stratum identification, which is used for identifying the target stratum according to the stratum identification model. The method has the effect of improving the stratum recognition efficiency.

The invention discloses an intelligent positioning method for a TBM disassembly supporting tool, and the method comprises the steps: installing laser ranging sensors at four corners of a supporting tool frame, forming space projection mapping with a shield body four-quadrant reflection target, collecting target distance data, comparing the target distance data with a pre-stored shield body theoretical axis model, and calculating a deviation value; the controller converts the axial deviation into a hydraulic cylinder stroke instruction to drive the four supporting hydraulic cylinders to conduct cooperative adjustment, when the pressure sensor monitors that the pressure difference exceeds 0.8 MPa, the compensation amount is recalculated, and historical ovality data is called to calculate an inclination angle correction value; the double-inclination-angle adjusting oil cylinder is controlled for compensation and secondary verification, and if the radial deviation range is exceeded, adjustment is returned; after three times of cross validation are completed, a hydraulic cylinder self-locking mechanism and a positioning pin are locked, positioning parameters are encrypted and stored, and a completion signal is triggered. The method is used for improving the positioning precision and efficiency of shield tunneling machine disassembling operation, effectively reduces the contact damage risk between the supporting tool and the shield body, and is particularly suitable for large shield tunneling machine disassembling engineering with the diameter larger than 8 m.

The invention belongs to the technical field of tunnel engineering geophysical prospecting, and particularly relates to a full-wave-field tunnel advanced prospecting method based on rock physical constraint, which comprises the following steps of: constructing an initial geologic model, sampling on an upper interface of a layer section where a tunnel is located and carrying out longitudinal and transverse wave velocity measurement to obtain a longitudinal wave velocity Vp and a transverse wave velocity Vs; combining all sampled longitudinal wave velocities Vp and transverse wave velocities Vs, calculating longitudinal wave velocities and transverse wave velocities of a target layer section, obtaining elastic parameters of the target layer section, importing the obtained elastic parameters into an initial geologic model, obtaining a parameter inversion initial model, constructing a forward wave field and a simulated seismic record, and making a difference between an actual observation record and a simulation record, so as to obtain an initial geologic model; and conjugate gradient inversion is carried out. According to the method, all wave field information excited by the shield tunneling machine is fully utilized, and high-precision parameter inversion is carried out under rock physical constraints, so that more accurate tunnel front geological prediction can be provided, and a scientific basis is provided for safe and efficient construction of the shield tunneling machine.