3017 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 discloses a shield machine operation and maintenance management system based on data analysis, which relates to the technical field of shield equipment monitoring and comprises a data acquisition unit, a data transmission unit, a data processing and analysis unit, an anomaly detection unit, a verification unit and an early warning unit. The data acquisition unit is used for installing various sensors at the cutter part of the shield tunneling machine, acquiring cutting force, vibration, temperature and displacement data and providing basic information for subsequent analysis; according to the invention, through integration of various sensors and a deep learning algorithm, real-time monitoring and anomaly detection of the state of the shield tunneling machine cutter are realized, compared with a traditional manual inspection method, the system can provide more continuous and accurate monitoring, and errors and omission caused by manual inspection are reduced; through automatic data acquisition, transmission, processing and analysis, the efficiency and accuracy of operation and maintenance management of the shield tunneling machine are remarkably improved, and the construction risk and economic loss caused by tool abnormity are reduced.

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 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 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 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 embodiment of the invention discloses a shield tunneling machine cutter abrasion loss prediction method and device and a medium. Belongs to the technical field of shield tunneling machines, and solves the problems of poor prediction precision and robustness of the abrasion loss of a shield tunneling machine tool. Generating sample data from the historical working data of the shield tunneling machine; performing feature extraction on the wavelet transform image and the geological cross-section diagram of the main shaft vibration signal in the sample data through a dual-channel fusion shield tunneling machine lightweight feature extraction network to obtain a feature vector; constructing a multi-view information fusion feature vector based on the feature vector corresponding to the sample data; constructing a time sequence feature vector based on the multi-view information fusion feature vector, so as to construct an improved VIT shield machine cutter wear loss prediction model through the time sequence feature vector; and performing parameter optimization on the improved VIT shield tunneling machine cutter abrasion loss prediction model, and predicting the shield tunneling machine cutter abrasion loss under the condition that a parameter optimization result meets a preset error condition.

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 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.

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 shield tunneling machine tunneling axis deviation early warning, and discloses a shield tunneling machine tunneling axis deviation early warning method based on data analysis. According to the method, related data of the shield tunneling machine are collected through a sensing monitoring system, noise is removed from the collected data, abnormal data are deleted, the data format is unified, the deviation value of a shield tunneling machine theoretical tunneling route and the shield tunneling axis is calculated according to the data with the unified format, and the deviation value of the shield tunneling axis is analyzed, researched and judged. Judging whether the deviation exceeds a preset deviation range or not, establishing an early warning rule, determining an early warning level and outputting early warning information and suggestions according to a real-time deviation judgment result, adjusting operation parameters of the shield tunneling machine in real time according to an early warning result, calculating deviation of the tunneling axis of the shield tunneling machine in a data analysis mode, and performing deviation early warning. And errors possibly caused by manual parameter setting are avoided, an effective processing scheme and real-time adjustment suggestions are provided according to the early warning level of the deviation, and the construction safety of the shield tunneling machine is improved.

The invention discloses a shield tunneling machine cutterhead abrasion monitoring and analyzing method and system in tunneling, and mainly relates to the technical field of shield tunneling machine engineering. Comprising the following steps: interactively arranging monitoring sensor arrays on the surface of a shield tunneling machine cutterhead and a cutter, extracting monitoring data of a preset monitoring window, and obtaining a monitoring data array sequence; obtaining a cutterhead operation feature set; working soil layer state characteristics are obtained; acquiring a tool surface acquisition image; and performing tool edge comparison on the tool standard image and the tool surface acquisition image to obtain a tool comparison result, and taking the tool comparison result as a cutterhead wear analysis result. The method has the beneficial effects that the technical problems that in the prior art, when the abrasion state of the tunneling shield tunneling machine cutterhead is analyzed, the mastering degree of the cutterhead stability condition implied in the monitoring data is low, and the monitoring and analysis reliability is poor are solved, and the technical effect of improving the abrasion monitoring and analysis accuracy of the shield tunneling machine cutterhead is achieved.

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 tunneling machine attitude control method and system based on axis deviation, relates to the technical field of shield tunneling machine control, and solves the problem that an existing shield tunneling machine control method cannot accurately control a shield tunneling machine to advance along a tunnel design axis. The method comprises the steps that in a posture control ring, a guiding system compares current posture data of a shield tunneling machine with target posture data determined by a tunnel design axis to obtain the axis deviation of the shield tunneling machine relative to the tunnel design axis through calculation, and the axis deviation is input into a posture controller to obtain the pressure difference between upper and lower partition hydraulic cylinders or left and right partition hydraulic cylinders; therefore, the pose deviation is continuously reduced, and the shield tunneling machine advances along the tunnel design axis; according to the method, a specific mathematical model is not needed, accurate tracking of the notch horizontal / vertical deviation reference trajectory can be achieved only through I / O data in the tunneling process of the shield tunneling machine, and the problem that attitude control is difficult due to the complexity of a shield tunneling machine system is solved.

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 assembly type bearing structure of a shield tunneling machine thrust hydraulic cylinder and an operation method.The assembly type bearing structure comprises a middle shield shell, a tail shield shell, a first bearing structure and a second bearing structure, the first bearing structure is arranged on the middle shield shell, and the second bearing structure is arranged on the tail shield shell; the first bearing structure and the second bearing structure are correspondingly arranged front and back, and the propelling hydraulic cylinders are arranged on the first bearing structure and the second bearing structure and detachably connected with the first bearing structure and the second bearing structure. The assembly type bearing structure is adopted for bearing the propelling hydraulic cylinder, stable and safe bearing of the propelling hydraulic cylinder is guaranteed, lifting and translation of the propelling hydraulic cylinder are directly conducted during disassembly and assembly, the operation that the propelling hydraulic cylinder is axially moved firstly and then lifted is not needed, the disassembly and assembly difficulty of the propelling hydraulic cylinder is reduced, and the disassembly and assembly efficiency is improved. And the disassembly and assembly efficiency of the propelling hydraulic cylinder is greatly 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 provides an in-hole large-diameter main drive disassembling device and disassembling method, and relates to the field of shield tunneling machines, the in-hole large-diameter main drive disassembling device comprises a base platform with an upper platform and a lower platform, the lower platform is provided with a sliding block, the sliding block slides in the length direction of the lower platform, and the upper platform is provided with two bearing holes; the connecting assembly comprises a first connecting element, a second connecting element and third connecting elements, the first connecting element and the second connecting element are used for being fixed to the main drive, the two third connecting elements are fixed to the upper platform in the width direction, and the third connecting elements are hinged to the second connecting element; the disassembling assembly comprises a first telescopic element and a second telescopic element, the first telescopic element is connected with the first connecting element and the sliding block, and the second telescopic element is hinged to the sliding block; the supporting assembly is located below the bearing hole. According to the disassembling device, the first telescopic element and the second telescopic element are matched, so that the disassembling device can select elements with smaller lengths and strokes, the size of the disassembling device is smaller, and the disassembling device is more suitable for disassembling the main drive in a hole.

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.