1580 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 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 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 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 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 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 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 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 belongs to the field of shield tunneling machines, and discloses an intelligent posture deviation correction control method and system for a continuous tunneling shield tunneling machine, and the method comprises the steps: carrying out a pushing and splicing synchronous construction experiment to collect data; integrating the BiLSTM and the KAN, and predicting the posture of the continuous tunneling shield tunneling machine; in combination with an MOGWO method, an optimal proportional-integral-differential (PID) parameter is determined, and attitude deviation is minimized. The invention provides a shield attitude intelligent control method integrating deep learning, physical modeling and an intelligent optimization algorithm. The key innovation point is that the KAN model and the BiLSTM model are combined to accurately predict the attitude of the continuous tunneling shield machine in various assembling hydraulic oil cylinder missing scenes. And then combining the improved BiLSTM model with an MOGWO algorithm to optimize the response performance of the propulsion hydraulic system, and finally improving the efficiency and quality of tunnel construction. Verification research is carried out by utilizing actual measurement data of a continuous tunneling shield tunneling machine test bed, and the effectiveness of the method is verified.

The invention discloses a muck treatment capacity monitoring method and a muck treatment capacity monitoring system, and relates to the technical field of shield construction and muck treatment capacity prediction treatment, and the method comprises the following steps: collecting operation parameters of a shield tunneling machine deslagging and conveying link, calculating a muck flow coupling index, evaluating a deslagging link coupling state, and carrying out optimization; the load coupling index of the shield tunneling machine is calculated, the energy distribution balance is judged, and dynamic adjustment is conducted according to the unbalance state; calculating the energy consumption deslagging propulsion efficiency, evaluating the matching degree, and optimizing the power and conveying parameters of each link; a CNN and LSTM combined multi-scale modeling method is adopted to predict the future muck handling capacity, and online correction is carried out through a sudden change adaptive network; and finally, fusing the standardized data set with the actual operation parameters to generate a unit time muck handling capacity sequence. By means of the method, the deslagging and propelling matching degree of the shield tunneling machine can be improved, the energy utilization efficiency is optimized, and high-precision prediction and real-time regulation and control of the muck handling capacity are achieved.

The invention relates to the technical field of shield tunnel construction intelligent control, in particular to a shield tunneling machine tunneling speed intelligent prediction method based on multi-algorithm collaborative optimization. The method comprises the following steps: firstly, acquiring operation parameters and geological and environmental parameters of the shield tunneling machine in real time through a multi-source sensor and an industrial bus; carrying out localized cleaning, normalization and feature extraction on the data by utilizing an edge computing device; further, integrating particle swarm optimization, a genetic algorithm, a sparrow search algorithm and a starvation game search algorithm, and realizing global automatic optimization of the hyper-parameters of the bidirectional long-short-term memory network through a parallel independent optimization and result aggregation strategy; and finally, predicting the tunneling speed in real time by using the optimized model. According to the method, the problems of low prediction precision, weak model generalization capability, dependence on manpower on hyper-parameter optimization and the like caused by insufficient multi-source heterogeneous data processing capability are effectively solved, the prediction accuracy, the adaptability and the engineering practical value are improved, and reliable support is provided for safe and efficient propulsion of shield construction.

The invention discloses a shield tunneling machine intelligent tunneling regulation and control system and method based on muck digital imaging and a knowledge graph, and the system comprises a muck image collection module, an image processing unit, a knowledge graph reasoning module, a parameter reference suggestion generation module, a visual interaction module, and a feedback and iterative optimization module. The method comprises the following steps: acquiring an image of residue soil on a residue discharge belt of a shield tunneling machine in real time, dynamically identifying a stratum type and a change trend in combination with a knowledge graph, generating a reference regulation and control suggestion of tunneling parameters of the shield tunneling machine, and displaying the reference regulation and control suggestion on a screen. According to the method, a shield tunneling machine driver can scientifically and systematically adjust construction operation, the real-time performance and accuracy of stratum recognition are improved, cutter head abrasion and shield attitude offset caused by uneven hardness or boulder strata are reduced, and construction safety and tunneling efficiency are improved; the system carries out iterative optimization on the knowledge graph and the image recognition model according to construction feedback, the adaptability and reliability of the system under different working conditions are enhanced, and the construction economy and the engineering application value are improved.

The invention discloses a slurry shield cutterhead nozzle arrangement method and system, and relates to the technical field of tunnel engineering and shield tunneling machines. The method comprises the steps that the total flow of nozzles and the number of the nozzles are determined, and the sediment critical starting flow speed is calculated; obtaining a flow velocity-target distance formula according to a water jet theoretical formula, boundary value conditions and a momentum flux conservation theorem; according to the total flow of the nozzles, the number of the nozzles, the sediment critical starting flow speed and the flow speed-target distance formula, the nozzle arrangement target distance is obtained; and arranging the nozzles according to the nozzle arrangement target distance. According to the method, the arrangement target distance of the nozzles of the slurry shield cutter head is integrally and effectively designed in combination with the water jet theory and the sediment starting mechanism.

The invention discloses a tunnel segment joint quality high-precision detection method and system based on an image stabilization mechanical arm. The device comprises a circular track patrol platform, the self-adaptive image stabilization mechanical arm and a macro-micro fusion detection module. The circular rail cruising platform comprises a cruising platform pedestal and a guide rail, the guide rail is installed on an H-shaped beam at the tail of the shield tunneling machine through a cushioning connecting piece, the guide rail is of a circular structure and is located in the shield tunneling machine, and the cruising platform pedestal is arranged on the guide rail and can move in the circumferential direction of the guide rail; one end of the self-adaptive image stabilization mechanical arm is mounted on the cruising platform pedestal, and the macro-micro fusion detection module is mounted at the other end of the self-adaptive image stabilization mechanical arm, so that the macro-micro fusion detection module synchronously moves along with the cruising platform pedestal and is arranged facing a joint of a segment to be mounted and a mounted segment. The invention relates to the technical field of tunnel construction, and can solve the problems of low efficiency, high risk and lagging of manual detection and large interference, low precision and poor flexibility of fixed equipment detection in the prior art.

The invention provides a karst cave group detection method and device in shield construction, and the method achieves the comprehensive detection of karst caves through a transient electromagnetic and sound wave array composite wave field detection mode, and improves the detection accuracy and detection efficiency of the karst cave group. And feature extraction is performed on the composite wave field detection data through a cross-physical coupling model, a karst cave probability model is established, and quantitative evaluation of a karst cave detection result is realized. When the karst cave possibly exists in the construction direction of the shield tunneling machine, the distribution prediction direction of the karst cave is further determined, and the shield tunneling machine is prevented from entering the karst cave area blindly in the tunneling process. Through the multi-source sensing array carried on the shield tunneling machine, equipment parameters during operation of the shield tunneling machine are collected in real time, potential construction anomalies can be found easily, and the accuracy and timeliness of karst cave detection are improved. Combined analysis is carried out on the composite wave field detection data and the operation parameters of the shield tunneling machine through the risk assessment model, a karst cave risk prediction value is determined, and the accuracy of karst cave risk assessment is improved.

The invention discloses a shield tunnel construction geology real-time prediction method and system based on a neural network, and the method comprises the steps: obtaining and preprocessing tunneling parameter data of a shield tunneling machine, inputting the preprocessed tunneling parameter data of the shield tunneling machine into a pre-trained geologic feature extraction model, and outputting geologic feature data; inputting the geologic feature data into a pre-trained geologic feature classification model, and outputting a geologic category; the geological feature extraction model is a neural network model formed by an integrated auto-encoder added with an attention mechanism, and the integrated auto-encoder comprises a plurality of parallel sub-auto-encoders; the geologic feature classification model is a residual CNN neural network model. According to the method, real-time accurate geological prediction can be realized by fully utilizing the tunneling parameters of the whole construction section, the total sample size of data required by training is reduced, the accuracy and timeliness of model prediction are greatly improved, and the geological change condition can be more effectively judged.

The invention discloses an intelligent control method for a cutter changing robot of a shield tunneling machine, belongs to the technical field of automatic control, and aims to solve the problems of low hob changing efficiency, inaccurate decision, large positioning error and the like of the shield tunneling machine under a complex stratum. Hob state parameters are collected through multiple sensors, a three-level decision threshold value is constructed, dynamic adjustment is conducted through fuzzy logic, and intelligent hierarchical control over abrasion pre-judgment, early warning and emergency response is achieved. A multi-dimensional weighting model is adopted to calculate the priority of the abraded hob, a cutterhead path is optimized in combination with a dual-objective function, and an efficient execution sequence is generated. In the positioning link, through reverse path planning, PID compensation inertial slippage and cooperation of multispectral imaging and feature matching technologies, hob mark points are accurately extracted, pose deviation is corrected, and it is ensured that the hob mark points enter a robot working space. A double-station conversion table is integrated, synchronous exchange of a new cutter and an old cutter is achieved through hydraulic and servo control, force-position hybrid control is adopted for an end effector, and the disassembly and assembly stability is improved by combining with a torque wrench for staged operation.

The invention discloses a shield tunneling stratum intelligent identification method and system based on strict single sample prototype network and verification set scale calibration, and the method comprises the steps: dividing samples collected from a shield tunneling machine in real time into a training set, a verification set and a test set according to a set time and an engineering interval; carrying out normalized preprocessing on the multi-dimensional mechanism parameters by adopting a training set, and then carrying out supervised pre-training on the lightweight MLP encoder by adopting a cosine interval classifier to obtain a discriminative embedding space; according to the method, a high-precision, reproducible and low-time-delay online recognition function on the shield tunneling stratum under the condition of extremely few labels is achieved, the overall precision and minority class recall can be considered on the premise that the labeling cost is not increased, so that the output probability is more calibrated, the expandability and rapid deployment capacity are achieved, and the method is suitable for popularization and application. And the real-time performance and the reliability of shield tunneling stratum identification are obviously improved.

The invention belongs to the technical field of shield tunnel construction, and particularly relates to a shield tunnel tunneling method under complex geological conditions. Comprising the steps that S1, reasonable shield tunneling machine model selection is conducted according to the specific stratum characteristics and the surrounding environment of an interval; s2, the boulders are detected, encryption and supplementary exploration are carried out in the boulder range, and the strength, size and distribution of the boulders are grasped in detail; s3, according to application conditions, the boulder treatment method comprises the steps that when good operation conditions exist in the ground range corresponding to the boulder area, the boulders are treated through ground reinforcement or deep hole blasting or punching pile crushing; if the boulder distribution range is concentrated and the development scale is not large, after a boulder area is confirmed, the stratum within a certain range around the boulder is reinforced through a sleeve valve pipe from the ground, and after slurry is solidified, the boulder is tightly wrapped; and S4, the shield tunneling machine conducts tunneling. By means of the shield tunneling method, safe and efficient tunneling of the shield under the complex geological condition of the boulder development stratum is achieved.

The invention discloses a posture-adjustable shield tunneling simulation device for a sandy soil stratum. The posture-adjustable shield tunneling simulation device comprises a soil box, a shield tunneling machine model, a pedestal and a monitoring system, the shield tunneling machine model comprises a shield body, a cutterhead, a transmission system, a propelling system and a dumping system. The soil box is filled with a test soil body; the shield body is slidably installed on a sliding rail on the top of the pedestal and faces the opening side of the soil box. The cutterhead is rotationally arranged at the front end of the shield body, and the aperture ratio is adjustable. A transmission shaft in the transmission system is driven by the first driving piece to drive the cutter head to rotate. The propelling system is arranged at the rear end of the shield body and comprises a power module and a posture adjusting module. The power module drives the shield body to axially advance; in the posture adjusting module, a hydraulic control unit controls a plurality of sets of posture adjusting oil cylinders abutting against the rear end of the shield body to generate differentiated expansion and contraction amounts so as to adjust the tunneling angle. The soil discharging system is used for discharging muck in the shield body, and the monitoring system is used for monitoring test data during tunneling simulation. The invention further discloses a shield tunneling simulation method based on the simulation device.

The invention discloses an intelligent monitoring method and system for a shield to pass through a bridge pile foundation, and belongs to the field of intelligent monitoring, and the method comprises the following steps: processing three-dimensional attitude data through a dynamic weighted Kalman filtering method, and obtaining a filtered attitude parameter sequence; estimating a dynamic deviation value of the shield tunneling machine based on the attitude parameter sequence, and outputting a quantitative evaluation result; performing space-time alignment on the inclination angle information and the attitude parameter sequence to obtain an aligned angle comparison data set; based on the angle comparison data set, a matching error vector between the posture of the shield tunneling machine and the inclination angle of the pile foundation is calculated, the error vector is optimized, and an optimized matching error index is obtained; fusing the attitude deviation value into the matching error index to generate a fused relative position relation matrix; based on the relative position relation matrix, the included angle deviation between the tunneling direction of the shield tunneling machine and the pile foundation axis is calculated, and a deviation adjusting signal is generated; and generating an attitude correction instruction sequence based on the deviation adjustment signal and the real-time construction parameters.

The invention provides a wear coefficient correction method for a shield cutter, and relates to the technical field of wear monitoring, and the method comprises the steps: collecting a time sequence magnetic field signal obtained by a magnetic sensor array disposed on a non-rotating part of a shield tunneling machine, and synchronously obtaining a cutter angle coding signal; performing time-frequency conversion and filtering on the magnetic field signal to obtain a noise reduction time sequence signal; determining a geometric position sequence of each shield cutter in combination with the angle coding signals; based on the magnetic sensor pose, the tool position and the magnetic dipole model, theoretical magnetic field mapping between the tool and the sensor is constructed, and a system response matrix is generated; reconstructing an independent magnetic feature vector of each cutter to obtain an optimal feature set; generating a vector difference through comparison with a reference vector, inputting the vector difference into an abrasion identification model, obtaining the abrasion loss and type identification of each cutter, and correcting an abrasion coefficient; the method improves the wear recognition precision and reliability, and is suitable for tunnel construction under complex geological conditions.

The invention discloses a binocular vision-based muck volume identification method suitable for muck volume measurement in shield tunneling machine operation. The method comprises the following steps of 1, acquiring image data of a muck area in an operation process through a binocular camera; 2, performing target detection on a detection head of the YOLOv8; step 3, target segmentation is carried out in the SAM model; 4, generating a point cloud; 5, calculating the volume; and step 6, performing real-time feedback and application. Dynamic convolution is introduced into a YOLOv8 backbone network, a target scale adaptive mechanism is integrated into a neck network, and three-dimensional point cloud data is generated in combination with binocular vision, so that variable shapes and sizes of muck can be better coped with, an efficient, accurate and real-time feedback muck volume measurement scheme is provided, and the muck volume measurement accuracy is improved. And the automation level and the operation efficiency of underground engineering construction are further improved.

The invention discloses a shield tunneling machine cutterhead water jet control method, and relates to the technical field of shield tunneling machine engineering, and the method comprises the steps: obtaining the current rotation state parameters of a cutterhead and the geological parameters of a tunneling face; determining the wear risk level of each area of the cutterhead according to the parameters; whether a high-risk area exists or not is judged, if yes, a directional water jet mode is started to conduct centralized scouring on the high-risk area, and if not, a uniform water jet mode is started to conduct comprehensive scouring on the cutterhead; cutting efficiency parameters are monitored in real time, whether the cutting efficiency parameters are lower than a preset efficiency threshold value or not is judged, and if yes, a pulse water jet mode is started to remove adherends on the surface of the cutterhead. Partition precise control and multi-mode intelligent switching of water jet are achieved, the cutter head protection effect and the water resource utilization efficiency are improved, and the service life of the cutter head is prolonged.

The invention relates to the field of shield tunneling machines, and discloses a fault diagnosis method for a quasi-rectangular shield tunneling machine based on artificial intelligence, and the method comprises the steps: collecting the operation state data of the shield tunneling machine through a sensor array, so as to obtain the operation data of the shield tunneling machine; performing component operation state difference analysis on the operation data of the shield tunneling machine to obtain shield component state difference data; performing abnormal feature distribution density analysis according to the shield part state difference data to obtain abnormal feature distribution density data; according to the method, sensor data collection, state difference analysis and abnormal feature analysis are combined, so that the operation state and the abrasion condition of each part of the shield tunneling machine can be accurately monitored, and the reliability of the shield tunneling machine is improved. A fault prediction model is established through a deep reinforcement learning method, potential faults can be recognized in advance, sudden faults are reduced, and the reliability and safety of equipment are improved.

The invention discloses a shield tunneling machine attitude prediction method and system, and relates to the field of shield tunneling machine construction.The shield tunneling machine attitude prediction method comprises the steps that firstly, shield tunneling machine construction parameters, geological environment parameters and tunnel planning parameters are obtained and preprocessed, and related characteristics are extracted; secondly, mapping the extracted features into nodes by using a heterogeneous graph model, and updating node features in real time through weighted processing of a graph attention mechanism; then, the node features and the connection relation are input into the trained hybrid drive prediction model, and time sequence, physical and space prediction values of the attitude angle of the shield tunneling machine are obtained; and finally, constructing a Bayesian causal graph to quantify the influence of each factor on the attitude angle, carrying out dynamic weighted fusion based on a quantization result, and outputting a final predicted value and a confidence interval. The problem that the prediction effect is poor under the complex geological condition due to the fact that prediction only depends on single shield tunneling machine parameters in the prior art is solved, and accurate prediction of the attitude angle of the shield tunneling machine can be achieved.

The invention discloses a synchronous propelling and assembling shield tunneling machine and a synchronous propelling and assembling construction method. The synchronous propelling and assembling shield tunneling machine comprises a cutterhead, a main drive, a front shield, a middle shield, a shield tail, a man gate, a propelling oil cylinder, a thrust transmission device, a segment erector and an excavation face balancing device. The thrust transmission device is composed of a first force transmission ring, an assembling oil cylinder, a force bearing block and the like, and the segment erector is located at the thrust transmission device. The excavation face balancing device comprises a second hydraulic oil cylinder, a third force transmission column, a third hydraulic oil cylinder and a maintaining mechanism. The front end of the thrust transmission device is connected with a thrust oil cylinder, the rear end of the thrust transmission device is connected with the front end face of the assembled duct piece, and thrust is transmitted to the duct piece through the thrust transmission device and borne by friction force between the duct piece and synchronous slurry. And when the shield is propelled, the segment erector completes one-ring assembly. And after one ring is propelled and assembled, the excavation face balancing device is used for achieving connection of the whole ring of pipe pieces and forward movement of the thrust transmission device, propelling and assembling are continued, synchronous propelling and assembling of the shield are achieved, and the construction progress is accelerated.