7810 results about "Tunnel construction" patented technology

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention relates to the technical field of geological risk monitoring and early warning in tunnel construction, in particular to a karst tunnel risk intelligent early warning method, device and equipment based on deep learning, and the method comprises the steps: obtaining multi-source coupling data; performing high-dimensional fusion on the processed multi-source coupling data by using a generative adversarial network, and reconstructing a high-precision three-dimensional model of the detection area; performing geological risk grade classification on the detection area to generate a corresponding risk distribution map; generating a dynamic risk evaluation index, and triggering an early warning signal when the risk level exceeds a preset threshold value; the method comprises the steps of multi-source data acquisition, data preprocessing, high-precision three-dimensional model reconstruction, risk grade classification, dynamic risk monitoring and early warning and the like, and can solve the problems of single risk detection means, insufficient evaluation precision and poor real-time performance in existing karst tunnel construction.

The invention relates to a rockburst risk monitoring method and system in a tunnel construction period, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic monitoring data and geological parameters of a tunnel; calculating a rockburst risk index of the tunnel based on the micro-seismic monitoring data and the geological parameters; determining a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model; based on the rockburst risk index and the dynamic stress field analysis result, dangerous area distribution of the tunnel is obtained; evaluating the rockburst risk grade of the tunnel in the next construction period in combination with the rockburst risk index, the dangerous area distribution and the to-be-performed construction process, and dynamically adjusting the early warning threshold value of the rockburst risk index to obtain a dynamic early warning threshold value; and when the rockburst risk index of the tunnel exceeds a dynamic early warning threshold value, generating a risk assessment result, sending out early warning of a corresponding level, and giving a prevention and control suggestion of construction according to the risk assessment result. The tunnel rockburst risk monitoring accuracy can be improved.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

The present invention relates to a deformation dynamic prediction and early warning method for a tunnel under construction based on multi-source data fusion, and mainly solves the technical problem of large difficulty in evaluating tunnel safety states using macroscopic analysis. The present invention comprises: step 1, dividing dynamic prediction and early warning of an entire tunnel construction process into three stages; step 2, determining a safety level standard framework, and constructing an index system; step 3, constructing identification intervals of each index of an index layer in different safety states; step 4, specifying a relative weight distribution of each index; step 5, calculating distances from an interval to the identification intervals for different safety states on the basis of an interval distance formula, and generating interval distance vectors; step 6, converting the interval distance vectors into similarity vectors on the basis of a similarity formula, carrying out normalization processing, and constructing an analysis matrix; step 7, implementing same-level index data fusion by calculating a product of a relative weight vector of each index of the same layer and the analysis matrix layer by layer, and obtaining a safety level probability distribution of an entire section of a tunnel at a time point; and step 8, dynamically tracking a dominant state of a tunnel safety level, and performing dynamic prediction and early warning on the entire tunnel construction process.

The invention provides a tunnel construction monitoring analysis method and system based on AI video monitoring, and relates to the technical field of artificial intelligence. A tunnel construction original video stream data set is acquired through a multi-view video acquisition device, and a spatio-temporal feature sequence is generated through spatio-temporal feature fusion processing; the spatiotemporal feature unit comprises a pixel region movement track, a personnel wearing identifier and a construction equipment contour feature, then extracting dynamic features of the spatiotemporal feature sequence to obtain a dynamic spatiotemporal feature set, and executing construction personnel statistical processing based on the dynamic spatiotemporal feature set; generating a hole entering personnel quantity statistical result, a personnel distribution statistical result of each construction part and a personnel identity matching result, performing work efficiency comparative analysis according to the personnel statistical result and a preset construction progress plan, and generating a construction progress deviation index and resource allocation optimization strategy; and when the progress deviation index exceeds the first threshold value, early warning is triggered, and a strategy is output to the management terminal, so that effective monitoring and management of tunnel construction are realized.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

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

The invention provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are improved.

The present invention relates to the technical field of tunnel rockburst microseismic monitoring, and provides a strain-structural plane slip-type rockburst failure mechanism analysis method for a drill-and-blast method tunnel. The present invention collects strain-structural plane slip-type rockburst microseismic data during tunnel construction; in light of engineering geological survey data, performs microseismic monitoring and data analysis on strain-structural plane slip-type rockburst that has occurred in a tunnel, divides a strain-structural plane slip-type rockburst risk area, and determines a microseismic rockburst early warning area; and, by means of these data, deeply understands a microseismic activity law of strain-structural plane slip-type rockburst of deep-buried drill-and-blast method tunnels, discriminates respective functions of tensioning and shearing in rock failure processes, comprehensively analyzes the failure mechanism of strain-structural plane slip-type rockburst, and further obtains the failure mechanism of strain-structural plane slip-type rockburst of the deep-buried drill-and-blast method tunnels, providing an important theoretical basis for predicting and preventing rockburst disasters.

The invention discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention provides a tunnel surrounding rock blasting excavation parameter setting method and system based on a neural network, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining geological characteristic data, blasting excavation historical records and excavation contour data of surrounding rock; sparse subspace clustering processing is carried out according to blasting excavation historical records to obtain a feature data set; performing stress state analysis according to the geological characteristic data, the excavation contour data and the characteristic data set to obtain a dynamic stress state and topological change data of the surrounding rock; performing association rule mining according to the blasting excavation historical record and the feature data set to obtain an optimized decision rule; performing nonlinear finite element analysis according to the dynamic stress state and the topological change data to obtain an equivalent material model of the surrounding rock; and according to the equivalent material model and the optimization decision rule, a blasting excavation parameter setting scheme is obtained. According to the invention, dynamic response analysis is carried out by using the neural network, and real-time monitoring of the surrounding rock under different blasting conditions is realized.

The invention discloses a tunnel back break intelligent finishing and surrounding rock stability dynamic evaluation method and system, relates to the field of tunnel construction, and provides a tunneling-oriented back break intelligent finishing and surrounding rock stability dynamic evaluation method which integrates point cloud sensing, geological modeling, finite element analysis and deep learning prediction. And a closed-loop control process from identification, control, modeling, prediction and feedback is constructed. An over-excavation and under-excavation area is identified through a high-precision point cloud and a neural network, a trimming instruction is generated, a three-dimensional model and a support time sequence model are dynamically constructed in combination with geologic features, a variable parameter reduction method is introduced to realize more real stability analysis, and future displacement and a safety coefficient of surrounding rock are predicted by using deep learning. The method can improve the intelligent level of tunneling control and construction safety, and has remarkable engineering application value.

The invention discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention provides a tunnel construction structure health on-line monitoring method and system, and the method comprises the steps: obtaining stress data, crack images and seismic wave signals of tunnel construction, and carrying out the preprocessing; obtaining and constructing stress-crack joint representation mapping, generating a first joint feature vector, and predicting a lining health state of a future # imgabs0 # time step; generating a second joint feature vector, and determining a stratum stress field of the future # imgabs1 # time step according to the second joint feature vector; constructing a tunnel lining damage probability model according to the stress data and the crack image, and determining an overall construction risk score; and dividing the construction risks into a plurality of grades. According to the method, data multi-modal fusion, structure-stratum joint modeling and AI enhanced prediction and early warning are achieved, the defects in the prior art are effectively overcome, the method can be widely applied to engineering scenes such as tunnel construction, subway construction and underground space development, and the construction safety and the structure health monitoring capacity are remarkably improved.

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

The invention provides a mountainous area tunnel construction safety intelligent monitoring and early warning method and system, and relates to the technical field of construction safety monitoring, and the method comprises the steps: collecting visible light and depth images of tunnel surrounding rock, and carrying out the segmentation and extraction of crack features through a depth attention network after image preprocessing and data fusion; extracting parameter time sequence data based on the crack spatial position and the type feature; determining fracture evolution characteristics and critical state parameters by combining wavelet transform and stress-rate coupling analysis; and adopting deep reinforcement learning to calculate the instability probability and generate early warning information. According to the invention, intelligent identification, instability prediction and risk early warning of tunnel surrounding rock cracks are realized, and the safety monitoring accuracy and early warning timeliness are improved.

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

The invention provides a tunnel disaster source multi-attribute joint inversion method and system based on physical property constraint, and relates to the technical field of tunnel engineering and joint inversion, and the method comprises the steps: constructing a tunnel face three-dimensional geologic model, applying controlled disturbance to surrounding rock physical property parameters, analyzing the change characteristics of earthquake and electromagnetic observation response, and obtaining a tunnel face three-dimensional geologic model; establishing a quantitative mapping relation between the surrounding rock physical property parameter disturbance quantity and the multi-field response signal variable quantity; constructing a multi-dimensional sensitivity evaluation index, and generating a joint sensitivity result; the surrounding rock units are identified and divided, differentiated partition rock physical relationship constraints are constructed, and a physical multi-attribute inversion target of joint inversion is determined based on the partition rock physical relationship constraints; and on the basis of a physical multi-attribute inversion target, a multi-source joint inversion target function is constructed in combination with the joint sensitivity result, joint solving and iterative updating are carried out, and a tunnel face advanced inversion result is output. According to the invention, calamity source fine identification and risk quantitative evaluation in front of the tunnel face during the tunnel construction period are realized.

The invention belongs to the field of tunnel surrounding rock investigation, and relates to a tunnel face image and tunnel face front rock mass information are obtained by taking surrounding rock in front of a tunnel face in a tunnel construction process as a research object and adopting a mobile photographing device and a geological radar advanced geological forecast technology. And a deep learning method is adopted to perform feature extraction and recognition on the tunnel face image and the geological radar map, so that the tunnel surrounding rock dynamic grading method based on the geological radar and transfer learning is established, and rapid and accurate surrounding rock dynamic grading on the tunnel face and the surrounding rock in front in the tunnel construction excavation process is realized.

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

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides a marine facies soft soil tunnel construction disturbance induced surrounding rock energy dissipation grading evaluation method which comprises the following steps: step 1, establishing a marine facies soft soil energy dissipation constitutive model based on thermodynamics; 2, determining mechanical parameters of the constitutive model; 3, establishing a tunnel construction mechanical model, embedding the constitutive model in the step 1 and the mechanical parameters in the step 2 into the tunnel construction mechanical model, and outputting an energy subitem output quantity in real time; step 4, establishing a damage unit identification system; 5, arranging a monitoring system to obtain monitoring data and feeding the monitoring data back to the numerical model for verification; and step 6, performing graded early warning according to the closeness degree of the structural unit to the instability failure energy threshold value. According to the scheme, a surrounding rock damage grading system is constructed through an energetic perspective, rapid risk identification is realized in combination with multi-source monitoring data, and a new theoretical and engineering means is provided for safe construction of the marine soft soil tunnel.

The invention discloses an in-tunnel construction vehicle meeting scheduling system based on positioning equipment. The system comprises a basic service module which is used for storing basic data for business service analysis in an acquisition and collection mode; the business service module is used for carrying out detailed analysis of the corresponding service through the data acquired by the basic service, and providing an analysis result to the display end for display; and the display end module is used for displaying the analysis result of the business service module, wherein the display modes comprise a mobile phone APP, a webpage large screen, an LED panel, a tunnel loudspeaker and a tunnel traffic light. The system carries out weight grading on vehicle and personnel positioning labels by standardizing a tunnel construction process, and integrates data of avoidance points of a transverse tunnel. And in combination with an intelligent technology, efficient vehicle meeting scheduling, intelligent construction path planning and avoidance point path planning of the construction area are realized.

The invention discloses a BIM-based full-period management method and system for tunnel construction organization optimization, and belongs to the technical field of construction organization and cost management. The management method comprises the following steps: establishing a three-dimensional geometric model and a parameterized model of a tunnel based on a BIM technology; performing work decomposition on a construction task, and calculating a construction activity time parameter; calculating the cost of materials, machinery and manpower of each construction unit, and summarizing the total cost of the project; optimizing a construction period and resource allocation under a multi-constraint condition by adopting a genetic algorithm; predicting future process time consumption and construction progress based on an LSTM time sequence prediction model; and integrating dynamic data of the construction site, and updating the BIM model in real time. Through combination with dynamic data, efficient organization and scientific management of the whole tunnel construction process are realized, the construction efficiency can be improved, the engineering cost can be reduced, and the scientificity and rationality of project decision making are remarkably improved.

Disclosed in the present invention are a method and system for simulation based on identification of a rock mass structural plane and prediction of a mechanics parameter. The method comprises: obtaining rock mass multi-scale structural plane data and a rock mass physical mechanics parameter at a tunnel construction site; according to the rock mass multi-scale structural plane data, constructing a tunnel construction numerical calculation model; inputting the rock mass physical mechanics parameter into the tunnel construction numerical calculation model, and performing tunnel excavation numerical simulation; during simulation, obtaining a real-time tunneling parameter of construction machinery; according to the real-time tunneling parameter of the construction machinery and a correlation model between tunneling parameters and rock mass physical mechanics parameters, obtaining a predicted physical mechanics parameter of a rock mass in front of a tunneling point; and updating the tunnel construction numerical calculation model by means of the predicted physical mechanics parameter of the rock mass in front of the tunneling point. The accurate simulation of excavation of a tunnel is implemented.

The invention belongs to the technical field of tunnel construction monitoring, and provides a tunnel construction deformation monitoring system based on machine vision and a monitoring method thereof in order to solve the problem that in the prior art, machine vision equipment applied to tunnel deformation monitoring is insufficient in device protection and monitoring safety. The tunnel construction deformation monitoring system comprises a machine vision laying camera, a machine vision monitoring camera, a monitoring target, a data processing platform, a safety alarm, a protection device and a power supply device, through the design of the protection device, damage to the cameras caused by dust and moisture in a tunnel is avoided, and the influence of vibration interference on image acquisition is reduced; meanwhile, due to the design of the safety alarm, when the tunnel deforms, field staff can immediately know the abnormal section and the abnormal position and take supporting measures in time, and potential safety hazards are greatly reduced.

The invention relates to a fractured rock stratum water-rich tunnel excavation water inrush early warning method and system. The method comprises the steps that real-time monitoring data in the tunnel excavation process are obtained; based on the real-time monitoring data, predicting by using the water flow inrush prediction model, and generating a water flow inrush amount prediction result; analyzing crack propagation, deformation and instability conditions of the surrounding rock of the tunnel under different excavation parameters through the rock mass damage evaluation model in combination with the real-time monitoring data, and generating a rock mass damage evaluation result; generating a construction optimization scheme based on the water flow inflow prediction result and the rock mass damage evaluation result; comparing the real-time monitoring data with a preset safety threshold value, and judging whether a water inrush risk or a surrounding rock instability risk exists or not; if the real-time monitoring data exceed the preset safety threshold value, a safety early warning signal is triggered, and emergency measures are taken immediately. The method has the effect of improving the safety in tunnel construction.

The invention discloses a tunnel full-period construction feature information fusion and quality tracing method and system, and the method comprises the steps: converting the construction data of a tunnel full period into construction feature information through constructing a digital twinborn model of tunnel construction, and carrying out the dynamic weighting according to the importance of different construction feature information, performing fusion to generate high-dimensional construction feature data; representing the high-dimensional construction feature data as a graph structure; analyzing the graph structure by using a graph neural network algorithm GNN to obtain an anomaly detection result of the construction equipment; and constructing the construction data into a knowledge graph, tracing quality data in the construction process by using a knowledge graph reasoning technology, and analyzing potential quality risks in combination with the anomaly detection result, so as to realize risk early warning and full life cycle quality management of the construction process. According to the embodiment of the invention, more comprehensive and accurate data support can be provided for construction management, and the safety of the construction process and the efficiency of quality management are improved.