1238 results about "Shield machine" patented technology

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 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 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 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 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 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 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 cross-sea shield construction method under a long-distance complex stratum. The cross-sea shield construction method comprises the steps of construction preparation and site arrangement, end reinforcement, shield machine type selection and debugging, trial tunneling, formal tunneling, muddy water system management, waterproof and reinforcement measures, shield arrival receiving and the like. According to the cross-sea shield construction method under the long-distance and complex stratum, all construction links are systematically optimized, safe and efficient construction of a cross-sea shield tunnel under the long-distance and complex stratum is achieved, and the cross-sea shield construction method can be widely applied to similar projects.

The invention relates to the technical field of industrial automation, and particularly discloses a fault prevention and diagnosis control system based on a red-mine intelligent mining shield tunneling machine, which is characterized in that time-space synchronous acquisition and fusion of multi-source heterogeneous sensor data are realized through a red-mine operating system, and a multi-dimensional state sequence containing equipment operating parameters and health indexes is constructed; establishing a probability world model based on historical data, predicting state transition probability distribution and quantifying uncertainty; dividing a safety area according to uncertainty, and verifying action safety through multi-step rolling prediction; and iteratively updating a decision strategy by adopting a security constraint strategy optimization algorithm, and dynamically updating model parameters in combination with an online learning mechanism, so that the problems of insufficient data fusion, lagging fault prediction and insufficient exploration action security in the prior art are solved; accurate sensing of the operation state of the shield tunneling machine, early warning of faults and safe autonomous decision making are achieved, and the operation reliability and the maintenance efficiency of equipment are improved.

The invention relates to a geological radar advanced forecasting system for TBM shield construction and a detection method, which are characterized in that a transmitting antenna and a receiving antenna are arranged in a tool bit gap of a TBM shield machine, rotation of a tool bit is not affected, measurement can be carried out when the tool bit rotates, and a three-dimensional structure section right in front of the tool bit is further acquired; a method of forming obvious reflection based on geological body physical property dielectric constant difference is adopted, the TBM shield tunneling advancing direction is detected and imaged, a geological structure image is obtained, then geological information such as faults, rich water, cavities and boulders is provided, the influence of geological difference is small, and testing is more accurate compared with a seismic wave method; the receiving antenna and the transmitting antenna are arranged in the tool bit gap of the TBM, the receiving antenna and the transmitting antenna are not affected by the blade, rotation of the tool bit is not affected, and stability and accuracy of rotation measurement of the whole set of forecasting system can be guaranteed.

The invention discloses a shield tunnel ground penetrating radar one-transmitting-multi-receiving antenna array advanced detection system, which comprises a terminal processor and a one-transmitting-multi-receiving antenna array, the terminal processor is connected with the one-transmitting-multi-receiving antenna array through a coaxial cable, the one-transmitting-multi-receiving antenna array comprises a transmitting antenna and a plurality of receiving antennas, and the transmitting antenna is connected with the receiving antennas. The shield cutter heads are arranged in the radial direction of the shield cutter head, are fixed on the same radius of the shield cutter head and cover the radius of the whole shield cutter head; the shield cutterhead rotates to cut a front soil body, the terminal processor controls the one-transmitting-multiple-receiving antenna array to synchronously carry out advanced detection, the one-transmitting-multiple-receiving antenna array rotates along with the shield cutterhead to detect a circle, the transmitting antennas radiate electromagnetic waves once at fixed angle intervals, and the multiple receiving antennas record reflected electromagnetic echo signals of a front stratum once at the same time. And the one-transmitting multi-receiving antenna array follows the shield tunneling machine to gradually tunnel so as to detect obstacles in the whole tunnel excavation section. The method has the advantages of large detection range, high detection efficiency and clear detection result.

To provide a device for aligning subsequent equipment segments of a shield machine during tunnel construction using the shield machine. [Solution] An adjustable lifting mechanism 2 is provided on the outer wall of the device body 1, a fixed block 3 is provided inside the device body, a connecting plate 4 is fixedly connected to the bottom of the fixed block, a slide rail is opened inside the connecting plate, a clamping assembly is provided at the bottom of the connecting plate, and a buffer assembly is provided inside the clamping assembly, which includes a movable block 5, a clamping block, and a rubber pad, and the outer wall of the movable block is slidably connected inside the slide rail. The output end of the motor rotates the rotating rod, which drives the movable block along the slide rail via the two connecting rods, further achieving stable clamping of different segment grooves, and the buffer assembly is used to effectively reduce the instantaneous clamping force on the segment grooves.

The invention discloses a muck conveying pipeline crushing device for a large-diameter slurry balance shield tunneling machine, which comprises a muck conveying pipeline, a rotary extrusion type crushing device, a crushing power device and an HARDOX wear-resistant muck blocking grating, and the left end of the conveying pipeline is welded with the HARDOX wear-resistant muck blocking grating; a mounting hole is formed in the portion, below the HARDOX wear-resisting muck blocking grating, of the slurry conveying pipeline, the rotary extrusion type crushing device is located at the flange connecting position below the muck conveying pipeline and connected with the muck conveying pipeline in a fastened mode, and the crushing power device is located outside the muck conveying pipeline and connected with the rotary extrusion type crushing device. The rock ballast blocked by the HARDOX wear-resistant muck blocking grating falls into the rotary extrusion type crushing device, the rotary extrusion type crushing device conducts rotary extrusion crushing on the rock ballast, and the rock ballast is discharged through a slag discharging opening in the lower portion of the rotary extrusion type crushing device. Secondary muck crushing can be achieved, muck fluidity is improved, and the construction efficiency of the shield tunneling machine is improved.

The invention relates to the technical field of tunnel and underground engineering construction, and particularly discloses a complex variable stratum shield tunnel dynamic regulation and control tunneling method which comprises the following steps: S1, performing multi-dimensional data acquisition by utilizing a sensor array; s2, carrying out preprocessing operation on the collected multi-dimensional data; s3, constructing a shield tunneling-based mechanical model, a stratum three-dimensional geologic model and a tunneling parameter optimization model; s4, utilizing a CNN-LSTM machine learning network to predict the tunneling posture of the shield tunneling machine, the cutter abrasion loss and the stratum response; s5, dynamically regulating and controlling tunneling parameters in different scenes according to a prediction result; and S6, updating model parameters by using a gradient descent method, and optimizing machine learning network performance by using RMSprop. By the adoption of the shield tunnel dynamic regulation and control tunneling method for the complex and changeable stratums, the construction efficiency can be effectively improved, the construction safety is enhanced, and the shield tunnel tunneling problem in the complex and changeable stratums is solved.

The invention relates to a shield tunneling machine tunneling construction method for stratums with different sand contents, which comprises the following steps of: S1, investigating characteristic parameters, including types, sand contents and mechanical parameters, of stratums to be tunneled; s2, installing an ultrasonic-assisted hob system integrated with an ultrasonic excitation module; s3, ultrasonic parameters and shield tunneling parameters are set according to the characteristic parameters of the stratum; and S4, shield tunneling is started, ultrasonic parameters and shield tunneling parameters are adjusted in real time through the cooperative control module, vibration, hob abrasion and mud cake forming conditions in the construction process are monitored, and in the tunneling process, the operation mode of the cooperative control module is that on the basis of multi-dimensional monitoring data collected by a vibration sensor, an abrasion detection device and a slag sample analysis sensor, the ultrasonic parameters and the shield tunneling parameters are adjusted in real time. Parameters are dynamically adjusted according to stratum types and sensitive area requirements, the single adjustment amplitude and adjacent adjustment intervals of the parameters are controlled, the adjustment effect is verified in real time through a sensor, and closed-loop control over parameter adjustment and monitoring is formed.

The invention discloses a shield tunneling machine cutter wear prediction method and device and a medium, and relates to the field of shield tunnel construction equipment monitoring, and the method comprises the steps: obtaining multi-source monitoring data in a shield tunneling machine construction process, carrying out the fusion of the multi-source monitoring data, extracting a core feature vector, and constructing a feature data set; based on the feature data set, through a pre-trained time sequence prediction model, generating a tool wear prediction result sequence corresponding to continuous time points in a future preset construction period; the prediction result sequence comprises a predicted abrasion loss sequence and a predicted abrasion rate sequence; matching the prediction result sequence with a preset multi-level early warning rule, determining a current early warning level, and generating a corresponding early warning signal; and outputting a construction parameter adjustment instruction and a tool changing planning suggestion through a parameter adjustment engine corresponding to the early warning level based on the prediction result sequence. By fusing the multi-source monitoring data and constructing the time sequence prediction model, the accuracy and foresight of tool wear prediction are remarkably improved.

The invention relates to the technical field of shield tunneling machine construction, in particular to a shield tunneling machine settlement control method which comprises the steps that the maximum pressure and the minimum pressure of a shield tunneling machine in an undercrossing subway tunnel section are calculated according to the burial depth and geological data, and when the shield tunneling machine conducts tunneling, the notch pressure of an excavation face is monitored according to the maximum pressure and the minimum pressure; in the tunneling process of the underpass subway tunnel section, the tunneling speed and the unearthing amount of a shield tunneling machine are monitored, meanwhile, grouting is conducted through radial holes in a shield shell by adopting a plastic material, and a gap, caused by excavation, between a soil body and the shield shell is filled; and when the shield tunneling machine tunnels to the granite layer, slurry injection parameters are controlled. According to the scheme, the safety and reliability of the construction process can be effectively controlled, the construction settlement condition is effectively controlled, it is guaranteed that the safety of the subway tunnel and the surrounding ground is effectively guaranteed, and related potential safety hazards are eliminated.

The invention belongs to the technical field of intelligent shield construction, and particularly discloses a shield tunneling machine posture multi-step prediction and deviation correction control method based on latent variable enhanced Free Transform. Firstly, shield construction process data are obtained, and a control quantity set is established and preprocessed; error features, change rate features and unit mileage features are constructed based on the preprocessed data, and a feature sequence is formed after normalization; organizing the sequence into a token sequence with a single time step, and inputting the token sequence into a pre-trained Free Transform model after position and mileage coding; the model aggregates complete sequence information through a non-causal encoder dedicated block, calculates latent variable distribution parameters based on a hidden state sequence, samples to obtain latent variables, maps the latent variables into conditional vectors and broadcasts the conditional vectors to a decoding path; and finally, outputting a future shield attitude vector, an over-threshold probability and a recommended control quantity in parallel by a multi-task output head. According to the method, working condition self-adaptive multi-step prediction and deviation correction are realized, and the safety and intelligent level of shield construction can be improved.

The invention discloses a shield mud-water separation comprehensive processing system and method in a complex geological environment, and relates to the technical field of shield construction, and the method comprises the steps: collecting geological parameters and mud parameters of a construction area in real time, and synchronizing the geological parameters and mud parameters to a data processing unit through an Internet of Things transmission protocol; the propelling speed of hydraulic equipment of the shield tunneling machine and operation parameters of mud-water separation equipment are synchronously adjusted, and the mud separation efficiency and the shield tunneling speed are dynamically matched; monitoring performance indexes of the separated mud in real time, feeding back the performance indexes to a geological data fusion algorithm and a reinforcement learning algorithm, and updating a dynamic correction coefficient and a reward function weight; and carrying out filter-pressing dehydration on the solid muck after mud-water separation, detecting the heavy metal content, and uploading the heavy metal content to a block chain distributed account book in real time for evidence storage. On the basis of a feedback closed loop of real-time monitoring data, processing parameters and algorithm weights are continuously adjusted, the adaptive capacity to sudden change working conditions of a complex geological environment is enhanced, and efficient matching of the slurry separation efficiency and the tunneling speed is achieved.