131 results about "Tunnel safety" patented technology

The invention relates to the technical field of tunnel safety monitoring, in particular to a strong noise tunnel point cloud monitoring method based on improved RANSAC and dynamic residual, which comprises the following steps: firstly, collecting two-dimensional point cloud data of a cross section of a tunnel, and performing attitude correction through ground baseline fitting to unify a coordinate system; afterwards, de-noising is carried out by adopting a vault-dominated improved RANSAC algorithm, and the algorithm effectively filters out strong noise and retains key lining points through constraint sampling, model geometric constraint and dynamically adjusted residual error threshold values; and performing nonlinear least square complete circle fitting on the denoised point cloud, and accurately extracting circle center and radius parameters. And finally, based on the absolute deviation, the relative deviation, the abrupt change increment and other multi-dimensional indexes of the radius, safety evaluation and early warning are carried out in combination with a standard threshold system. According to the invention, the robustness and precision of tunnel contour monitoring under complex working conditions of strong noise, equipment installation deviation and the like are obviously improved.

The utility model provides a tunnel inverted arch structure which comprises a corrugated steel plate layer, a secondary lining layer, a frame column layer and a pavement layer which are sequentially arranged from bottom to top, and the corrugated steel plate layer is provided with a grouting hole and an anchor rod hole for a system anchor rod to penetrate through. Foam concrete is injected into an interlayer space between the corrugated steel plate layer and the surrounding rock through the grouting holes to form a foam concrete layer, the system anchor rod comprises an anchoring end and a locking end which are oppositely arranged in the extending direction of the system anchor rod, the anchoring end penetrates through the anchor rod hole to be anchored into the surrounding rock, and the locking end is connected with the corrugated steel plate layer. The secondary lining layer is arranged on the inner wall of the corrugated steel plate layer, the bottom of the frame column layer is pre-buried in the secondary lining layer, and the pavement layer is laid on the top of the frame column layer. According to the novel inverted arch structure, particularly, a traditional inverted arch backfilling layer is arranged to be of a frame layer structure, the use amount of concrete can be greatly reduced, the safety and stability of a tunnel are guaranteed, meanwhile, the material cost and the construction cost are reduced, and economic benefits are improved.

The invention discloses a design method for anti-erosion tunnel parameters. The design method comprises the steps that tunnel environment information and a tunnel safety index range are acquired; determining a catastrophe risk of the tunnel based on the tunnel environment information; determining a tunnel structure parameter range based on the catastrophe risk of the tunnel; determining tunnel structure parameters based on the tunnel structure parameter range; determining tunnel safety indexes based on the tunnel structure parameters; and determining or adjusting tunnel structure parameters based on a comparison result of the tunnel safety index and the tunnel safety index range. According to the design method of the anti-erosion tunnel parameters, blindness and randomness of composite corrosion and erosion surrounding rock tunnel structure parameter values are avoided, the tunnel rigidity and durability are effectively guaranteed, and the later tunnel structure degradation risk is reduced.

The invention provides a tunnel blasting vibration monitoring method and system based on image recognition, and relates to the technical field of tunnel safety monitoring, and the method comprises the steps: obtaining a geological parameter set of a tunnel target region and an image sequence of a blasting moment; carrying out fluctuation analysis on geological parameters to obtain vibration characteristic data reflecting rock mass non-uniform characteristics, synchronously carrying out optical flow analysis and region identification processing on an image sequence, respectively generating a displacement vector field and an identification deformation region, carrying out spatial association on the vector field and a micro-deformation region to form vibration field data with rock mass structure characteristics, and carrying out vibration field identification processing on the vibration field data; and coupling with the vibration characteristic data to reconstruct a three-dimensional propagation path, and finally identifying a damage risk area of the tunnel target area based on an energy distribution pattern on the path. According to the method, the damage risk identification precision of the tunnel rock mass under blasting vibration is improved.

The invention provides a tunnel activity measurement method with a bidirectional loading demand, and belongs to the technical field of tunnel activity measurement, and the method comprises the steps: collecting a multi-dimensional force signal and a displacement signal, carrying out the digital processing, building a mechanical coupling tensor matrix, and obtaining a preliminary decoupling force component through a condition number adaptive inversion strategy; tensor network high-order correlation modeling and density matrix reforming swarm optimization are utilized for optimization to obtain an accurate decoupling force component, a simulated annealing particle swarm optimization algorithm is combined for solving a non-convex optimization objective function to obtain real mechanical response parameters, defects are identified through ultrasonic detection, and an influence coefficient matrix is established; a Bayesian inversion algorithm is adopted to remove defect influence to obtain corrected tunnel activity parameters, tunnel safety monitoring is finally realized through time sequence analysis and stress redistribution evaluation, and the technical problem of insufficient measurement precision caused by multi-dimensional force signal coupling interference is solved.

The invention discloses a tunnel harmful gas monitoring and early warning method and system based on adit exploration, and relates to the technical field of tunnel safety monitoring, and the method comprises the steps: building a physical three-dimensional model of a tunnel based on tunnel static parameter data of adit exploration as a basic model, and dividing a plurality of voxel grids for the basic model; deploying a monitoring network composed of head nodes and monitoring nodes in the tunnel, establishing a spatial registration relationship between the monitoring network and the basic model, calculating distribution of harmful gas concentration in all voxel grids according to data of the monitoring nodes based on the spatial registration relationship, and generating a regional map data set; and mapping the regional map data set into a basic model, and organizing the plurality of regional maps into a plurality of levels to trigger space early warning in the basic model based on a preset concentration threshold. The tunnel harmful gas monitoring accuracy, the visualization degree and the early warning response timeliness are remarkably improved, and a more reliable technical support is provided for tunnel safety guarantee.

The invention discloses a tunnel event-driven multi-mode induction warning control method and system, and relates to the technical field of tunnel safety management. The method comprises the following steps: generating a tunnel global dynamic risk field based on monitoring data acquired by a multi-source sensor in a tunnel; according to the intensity distribution characteristic and the spatial change characteristic of the dynamic risk field, calculating optical flow driving potential energy, and generating the working mode, the basic brightness and the flicker frequency of each induction sign based on the optical flow driving potential energy; when the emergency mode is determined, calculating an optical flow steering vector according to the distribution characteristics of the dynamic risk field and preset emergency exit information, and generating a dynamic optical flow evacuation path pointing to the nearest emergency exit; and calculating a tunnel body efficiency factor according to the tunnel traffic state and the dynamic risk field situation change, and when the tunnel body efficiency factor is smaller than a preset efficiency threshold value, triggering a parameter adjustment mechanism. The tunnel safety and the emergency response efficiency are improved by adjusting an induction sign and an optical flow guide evacuation path and optimizing system parameters in real time.

The invention relates to a tunnel scene-oriented vehicle-tunnel collaborative innovation mode and a safety operation and maintenance management and control system and method thereof. The system comprises a vehicle-tunnel collaborative subsystem, a tunnel safety operation and maintenance and intelligent disaster prevention subsystem and a vehicle-tunnel digital base. The vehicle-tunnel cooperation subsystem is composed of an intelligent vehicle and an intelligent tunnel side subsystem, multi-source sensing fusion, road condition prediction and cooperative decision making are achieved, and a low-delay vehicle-tunnel integrated data flow is provided for the vehicle-tunnel digital base. The tunnel safety operation and maintenance and intelligent disaster prevention subsystem collects the operation state and environment data of electromechanical equipment and uploads the operation state and environment data to the vehicle tunnel digital base. The vehicle and tunnel digital base carries out unified processing, analysis and decision making on vehicle and tunnel data based on a full space-time digital twinborn model and an artificial intelligence algorithm, distributes road resources for an automatic driving vehicle and issues an electromechanical equipment control instruction. According to the system, tunnel traffic safety is improved, operation and maintenance difficulty and cost are reduced, and safe operation and intelligent management of the whole life cycle of the tunnel are supported.

The invention provides a tunnel safety monitoring and early warning management method and system, and relates to the technical field of tunnel safety, and the method comprises the steps: carrying out the multi-mode data collection of a tunnel environment through a plurality of intelligent monitoring terminals connected to an RS485 bus, and carrying out the environment abnormality alarm triggering analysis; when any intelligent monitoring terminal detects an alarm event, the alarm event is broadcasted to other terminals through an RS485 bus, and other terminals receiving the alarm event autonomously analyze a diffusion probability map, calculate an emergency evacuation route and control an escape indicating device to display an evacuation direction; communication authority differentiation scheduling based on the RS485 bus is executed, and alarm event situation awareness based on multi-modal data dynamic updating is completed. The technical problems that in the prior art, tunnel safety monitoring and early warning management often adopts a centralized control mode, information transmission and processing are lagged, the alarm response time is long, and effective emergency disposal cannot be carried out in time are solved.

The invention discloses a heavy haul railway tunnel disease inversion evaluation method, and relates to the technical field of tunnel safety. Comprising the following steps: generating 216 working conditions by adopting an L27 (38) orthogonal table according to 8 types of key variables; establishing a tunnel three-dimensional elastic-plastic damage model based on an ABAQUS platform, wherein the tunnel three-dimensional elastic-plastic damage model is coupled with a dynamic load, a temperature field and a seepage field; inputting 216 working conditions into the three-dimensional elastic-plastic damage model of the tunnel to obtain structural response data; analyzing the structure response data through a Python script, and extracting key features; constructing a disease label data set based on the key features; and constructing a deep learning forward model and a deep learning inversion model containing physical constraints. The technical problems that in the prior art, data sets under the influence of multiple factors are lacked, and an inversion model lacks physical constraints are solved.

The application provides a long-distance crossing river and lake pipe jacking construction method, which comprises the following steps: when a long-distance crossing river and lake encounters a special stratum, injecting grouting material into the special stratum to reinforce the stratum; then driving a pipe jacking device to drive a pipe jacking tunnel and a pipe jacking machine to push into the special stratum, and simultaneously starting a grouting mechanism to pour resistance-reducing thixotropic mud outside the pipe jacking tunnel through a grouting hole; a hydraulic and grouting pressure sensor collects grouting pressure and hydraulic pressure in real time, and transmits the pressure values to a control system; the control system compares the collected pressure values with a pressure threshold value; when the pressure values are greater than the pressure threshold value, the grouting amount, grouting pressure, pushing force and tunneling parameters are increased, and the control system continues to compare the collected pressure values with the pressure threshold value until the collected pressure values are less than the pressure threshold value. The application can realize safe tunneling construction of the long-distance crossing river and lake pipe jacking tunnel without replacing cutters, reduce cutter wear and improve construction efficiency.

The invention discloses an internet-of-things multi-device intelligent tunnel security penetration interconnection system and an internet-of-things multi-device intelligent tunnel security penetration interconnection method. The system comprises a management end, an equipment end and a cloud end. The management end integrates an intelligent connection management layer, an efficient data plane engine, a unified business operation layer and a dynamic strategy and control center. The intelligent connection management layer dynamically selects an optimal tunnel link through a pluggable protocol framework; the efficient data plane engine intelligently switches multiple modes such as an original socket, libpcap and the like to perform high-performance data capture and filtration according to authority and requirements; the unified service operation layer deeply integrates operation and maintenance tools such as an embedded Web browser and an SSH terminal into a single interface; and the dynamic policy center can issue network rules in real time. According to the method, high-reliability and low-delay tunnel connection in a complex network environment is realized, refined flow control is driven based on service intention, and functions of network penetration, state monitoring, configuration maintenance and the like are seamlessly fused.

This invention discloses a comprehensive parameter design method for the cross-section of long-span highway tunnels. Using tunnel flatness ratio and equivalent initial support strength as two variables, and with tunnel safety and economy as dual objectives, it employs numerical simulation and regression analysis to establish evaluation indicators and objective functions for flatness ratio and initial support. Based on survey results, the range of variables for flatness ratio and equivalent initial support strength is determined. Safety constraints are defined according to safety requirements. A non-dominated sorting genetic algorithm is used to perform optimization calculations within the specified variable range and constraints to obtain mutually suitable tunnel cross-section flatness ratio and equivalent initial support strength. Furthermore, the equivalent initial support strength is transformed into support form and parameters, ultimately forming the optimal initial support parameters for long-span tunnels. This method overcomes the shortcomings of traditional long-span highway tunnel design methods that only consider safety or economy as a single objective, and solves the problem of relying on manual experience to determine the optimal flatness ratio and initial support parameters.

The invention discloses a tunnel photoelectric sign system and an intelligent photoelectric guiding method, and relates to the field of tunnel photoelectric signs, and the method comprises the steps: inputting the data of a multi-modal sensor into a pre-trained multi-modal fusion accident recognition model, recognizing the type of an accident, and positioning the position of the accident, and according to the identified accident type, the accident occurrence position and the current tunnel parameters, utilizing a physical model and a diffusion model pre-trained for a corresponding target tunnel to predict an accident sweep range, and based on the predicted accident sweep range, planning a safe evacuation path. And mapping the safe evacuation path to a preset photoelectric sign network in the tunnel, and generating a control instruction for each sign in the photoelectric sign network. The tunnel safety management level can be significantly improved, rapid, accurate and efficient emergency evacuation can be realized when an accident occurs, the life and property safety of personnel can be effectively guaranteed, and the safety of the tunnel is improved. And meanwhile, the energy-saving effect and the system reliability are excellent.

The application relates to a tunnel full-life safety monitoring system based on an impedance strain effect, which comprises a monitoring system and terminal monitoring equipment; the monitoring system comprises an anchor rod piezoelectric monitoring module, a lining piezoelectric monitoring module, a ring piezoelectric monitoring module, a longitudinal Z-shaped piezoelectric axial force monitoring module and a longitudinal piezoelectric temperature monitoring module; the anchor rod piezoelectric monitoring modules are uniformly distributed in the surrounding rock; the lining piezoelectric monitoring module is laid on a lining reinforcement cage; the ring piezoelectric monitoring module is uniformly laid in a ring shape on the inner wall of the tunnel; the longitudinal Z-shaped piezoelectric axial force monitoring module is laid in a Z shape along the surface of the inner wall of the tunnel; the longitudinal piezoelectric temperature monitoring module is laid on the surface of the inner wall of the tunnel and is spaced apart from the longitudinal Z-shaped piezoelectric axial force monitoring module; and the above-mentioned five modules are electrically connected with the terminal monitoring equipment. The system comprehensively monitors the safety of the tunnel and realizes continuous monitoring of the whole tunnel.

The application provides a high ground stress tunnel surrounding rock stability analysis method and device, relates to the technical field of tunnel safety, and comprises the following steps: acquiring tunnel information, wherein the tunnel information comprises section information, historical monitoring data and real-time monitoring data; performing numerical simulation according to multi-dimensional characteristics in the tunnel information and extracting characteristic parameters; optimizing a random forest regression model based on a chaotic sparrow search algorithm, inputting the characteristic parameters into the optimized regression model for prediction, and obtaining surrounding rock prediction results; inputting parameters of the surrounding rock prediction results into a preset surrounding rock physical model to calculate a potential energy function, performing cusp catastrophe analysis on the potential energy function, and constructing a bifurcation set equation; performing state discrimination based on the bifurcation set equation and a preset surrounding rock instability constraint condition, and outputting a stability result. The safety problems of tunnel face instability convergence deformation, collapse and peeling caused by surrounding rock instability are solved.

The application discloses a mobile fire detection system and method, and belongs to the technical field of fire detection. The system realizes over-distance accurate positioning and sub-pixel level precision. Through a unique "polynomial fitting + extreme value calculation" algorithm, the system can perform three times curve fitting on the signals of 2-3 detection units covered by the fire source imaging, and can analyze the sub-pixel level center coordinates by derivation. In combination with a preset coordinate mapping table, the fire source positioning precision can be improved to within 0.3 meters at a distance of 150 meters, and the specific tunnel post number and lane position can be accurately output, so that the limitation of the traditional detector that can only give an area alarm is completely changed. In terms of adaptability to complex environments, the application not only solves three major pain points of the traditional flame detector, i.e., positioning loss, weak anti-vibration and high false alarm rate, but also provides a complete set of high-reliability, intelligent and practical fire monitoring and emergency command tools for tunnel safety.

The application discloses a kind of comprehensive intelligent monitoring decision system and method for overlapping shield tunnel system, system includes information acquisition system, iterative optimization state real-time evaluation system and intelligent maintenance decision system;First, preset model is trained by numerical calculation and other data;Operational measured data trains feedback model, and the output result of the weighted two models obtains tunnel deformation prediction value;Again, experience overlapping tunnel safety evaluation system and iterative optimization evaluation system are established, and the depth correlation of tunnel state, damage, maintenance decision is carried out by correlation degree intelligent real-time analysis, realize tunnel state real-time monitoring-tunnel damage real-time capture-tunnel maintenance decision real-time update, and the intelligent collocation of maintenance measure is carried out, then according to the operation effect feedback iterative optimization prediction model and safety evaluation system.The application realizes the real-time perception, intelligent diagnosis and maintenance of whole life cycle overall service performance of overlapping shield system, and improves the long-term service quality of overlapping shield tunnel structure.

The invention relates to a road tunnel middle section brightness demand calculation method based on traffic density, and belongs to the technical field of tunnel illumination, and the method comprises the following steps: 1, building a calculation model of tunnel middle section brightness and driver response time; 2, establishing a calculation model of traffic density and driver response time; 3, establishing a road tunnel middle section brightness demand calculation model based on traffic density; and 4, calculating the brightness of the middle section of the road tunnel by using the road tunnel middle section brightness demand calculation model based on the traffic density. The tunnel middle section brightness demand calculation method is provided from the perspective of traffic density, the accuracy of tunnel middle section brightness demand calculation is improved, the understanding of industry personnel on the tunnel middle section brightness demand is deepened, and safe operation and green illumination of the tunnel are assisted with scientific and reasonable brightness.

The invention discloses a shallow tunnel full-life-cycle safety analysis method under vehicle dynamic load, which relates to the technical field of tunnel life analysis, and comprises the following steps: arranging a vehicle dynamic load adaptive acquisition device, dynamically adjusting sampling density according to traffic flow change rhythm, continuously acquiring frequency characteristics, amplitude characteristics and duration characteristics of vehicle dynamic load, and calculating the safety of the shallow tunnel full-life-cycle safety of the shallow tunnel under the vehicle dynamic load. And generating a dynamic load fingerprint sequence. According to the method, dynamic tracking and risk identification of the safety state of the shallow-buried tunnel are achieved through vehicle dynamic load self-adaptive acquisition and full-life-cycle correlation analysis, and the structural degradation rule of the vehicle under the long-term action of the dynamic load is revealed. Through reverse-phase rhythm regulation and control based on a risk rhythm list, adaptive closing of monitoring, evaluation and control is realized, stable operation of the tunnel is maintained, and continuity and intelligent level of safety management are improved.

The invention relates to the technical field of tunnel safety control, in particular to a tunnel fan control and disaster prevention equipment linkage system and a control method thereof.The system comprises a data acquisition module, an execution module and a comprehensive control module; the jet fan is controlled to operate according to a preset standard smoke exhaust strategy; calculating a hydraulic pressing efficiency index; when it is judged that the fire-fighting water system has the risk that the fire-fighting water system is about to fail or the pressing capacity is insufficient, the jet fan is controlled to be switched to a heat extraction compensation mode from a standard smoke extraction strategy; according to the method, the hydraulic pressing efficiency index containing the pressure trend term and the vibration penalty term is calculated in real time, when the hydraulic pressing efficiency index is decreased to be below the threshold value, the jet fan is forcibly controlled to operate at the rated highest frequency, high-temperature flue gas accumulated on the tunnel vault can be rapidly taken away, and the tunnel vault is protected. Therefore, the tunnel concrete structure is effectively prevented from bursting, peeling and even collapsing due to bearing ultrahigh temperature for a long time.

The utility model provides an environment monitoring device for tunnel safety, which comprises a shell, an air inlet flow channel, a semiconductor detection unit, a control panel and a power supply module, the outer wall of the air inlet flow channel is in a circular truncated cone shape and can gather and accelerate air flow, so that the semiconductor detection unit can obtain gas data more efficiently and accurately, and the detection efficiency and sensitivity are improved; in the filter piece connected with the air inlet flow channel, the filter screen can intercept impurities, pollution to the detection unit, influence on result accuracy and reduction of element loss are avoided, and the threaded connection of the connecting cylinder facilitates maintenance; the anti-vibration pad at the bottom of the shell supports the air inlet channel, external vibration influence is reduced, looseness of internal components is reduced, and stable work of the device is guaranteed; the warning lamp is electrically connected with the controller and the like, and warning signals can be sent out in time when abnormity is detected, so that rapid processing is facilitated, and the early warning capacity is improved. According to the device, efficient and accurate detection, stable operation and timely early warning are realized, and the tunnel safety monitoring level is improved.

The invention relates to a tunnel traffic control method and device, and belongs to the technical field of traffic. The method comprises the steps of firstly obtaining vehicle real-time behavior data and environment data of a controlled road section in a tunnel; judging whether an accident vehicle or a moving bottleneck exists or not and a management and control space exists, and if yes, starting management and control; after starting management and control, calling a management and control unit in an influence range, and starting management and control semantic pushing; and when the traffic accident treatment is finished, the mobile bottleneck is dissipated or the mobile bottleneck is driven out of the tunnel, the management and control are stopped. The device can be arranged on a congested road section in a tunnel, and more efficient and safer tunnel traffic control is realized, so that the tunnel passing efficiency is improved, and the tunnel safety guarantee is enhanced.

The invention relates to a dynamic risk assessment and syndrome early warning method and device for a tunnel exit section, and belongs to the technical field of tunnel safety. According to the method, vehicle behavior data are collected, a rear-end collision risk assessment model of an exit section is constructed, the dynamic risk level is output, and the early warning area and the warning mode of the lamp strip are dynamically adjusted according to the dynamic risk level and the vehicle driving track, so that accompanying early warning of vehicle driving lamp accompanying is realized. According to the method, the potential operation risk of the accident high-frequency road section can be measured and controlled, the operation risk of the tunnel exit road section is effectively reduced, the risk active prevention and control level of the tunnel exit road section is improved, and the driving safety level of the tunnel exit road section is improved.

This invention discloses an AI-based traffic incident detection and analysis system and method for tunnels, belonging to the field of tunnel safety technology. The system includes a perception layer, an edge computing layer, and a cloud platform layer. The perception layer collects multi-source heterogeneous data within the tunnel. The edge computing layer deploys multiple edge nodes to perform parallel feature extraction, dynamic weighted fusion based on an attention mechanism to detect events, and utilizes a lightweight prediction model for short-term risk prediction and local early warning. The cloud platform layer performs spatiotemporal correlation verification of events reported by multiple nodes, global risk prediction, and strategy generation. This method is implemented based on the above system. This invention significantly improves the accuracy and real-time performance of traffic incident detection within tunnels through multi-source data fusion and real-time edge processing, reduces response latency, and enhances the system's environmental adaptability and overall reliability.

The application discloses a kind of based on space-time decoupling network's foundation pit adjacent operating tunnel state prediction method, comprising: obtaining historical tunnel change data and foundation pit real-time monitoring data, corresponding foundation pit feature data is extracted for historical tunnel change time, and the key foundation pit feature set with significant causal relationship with tunnel state change is screened out.Based on the key foundation pit features screened, historical tunnel change data and the space relationship and stress transmission relationship between foundation pit-tunnel, construct time series graph network set.Establish space-time decoupling network model, train using time series graph network set, learn the complex space-time coupling characteristics between foundation pit and tunnel, obtain the trained prediction model.Input the key foundation pit feature data of foundation pit current time, realize the prediction of adjacent operating tunnel future several time deformations, convergences and displacements.The application can effectively capture the dynamic influence of foundation pit construction disturbance on tunnel, improve the accuracy and real-time performance of tunnel safety state prediction.

The invention provides a vehicle-mounted highway tunnel brightness real-time monitoring and early warning system, and belongs to the technical field of vehicle driving, and the system comprises a data obtaining module which obtains tunnel brightness test data through a real vehicle driving experiment; the image preprocessing module obtains a tunnel driving image and preprocesses the tunnel driving image to obtain a seven-dimensional spatial-temporal feature vector; the brightness identification monitoring module establishes a tunnel brightness identification model according to the test data and the seven-dimensional spatial-temporal feature vector, and extracts a tunnel brightness value; the threshold generation and early warning module classifies the brightness values by using an improved clustering algorithm to obtain a tunnel dangerous brightness threshold and performs early warning; and the vehicle-mounted integrated module connects the tunnel brightness identification model and the tunnel dangerous brightness threshold to the vehicle to realize real-time monitoring and early warning. The problems that tunnel safety protection is high in deployment threshold and high in cost due to the fact that a professional brightness detection instrument is needed to obtain accurate brightness detection capacity when a vehicle enters and exits a tunnel, and an existing early warning mechanism lacks dynamic environment continuous quantitative evaluation are solved.

This invention relates to the field of intelligent safety and tunnel engineering safety technology, specifically to an intelligent tunnel safety response system, comprising a multimodal data acquisition module, a data feature extraction module, a risk prediction module, and a response module. The multimodal data acquisition module collects multi-dimensional data such as temperature, humidity, and harmful gas concentration within the tunnel; the data feature extraction module uses an improved CNN to extract multi-scale features from images and combines Fourier transform to extract frequency domain features of structural strain; the risk prediction module constructs a model based on GRU combined with an attention mechanism and outputs the risk probability; the response module automatically initiates emergency measures such as ventilation adjustment based on the risk probability. The purpose of this invention is to solve the problems of traditional tunnel safety monitoring systems, such as limited data, coarse feature extraction, delayed risk prediction, and passive emergency response.

1. The name of the design product: tunnel safety warning board. 2. The use of the design product: the design product is used for warning the safety condition and marking the construction level in the tunnel. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective view 1.

The application discloses a tunnel crack detection method and a tunnel crack detection system and belongs to the field of tunnel safety detection. The method comprises the following steps: a tunnel inspection robot is used to inspect in a tunnel to obtain complete three-dimensional data inside the tunnel, to construct a three-dimensional map inside the tunnel, and to determine the positions of crack regions in the three-dimensional map; the corresponding inspection regions of the crack regions are determined and path planning is performed to generate an optimal inspection path of a UAV; the optimal inspection path enables the UAV to enter the tunnel, to pass through each inspection region in turn under the condition of meeting the obstacle avoidance condition, and to leave the tunnel with the shortest total time; the UAV moves according to the optimal inspection path, and a depth camera is used to scan the crack region each time the UAV moves to an inspection region to obtain fine three-dimensional data of the crack region; the fine three-dimensional data of the crack region is analyzed and processed to complete the tunnel crack detection. The application can improve the efficiency and precision of long and large tunnel crack detection.