Rapid detection and assessment method and system for shield tunnel disasters based on wall-climbing robot

Abstract

The present application relates to a method and system for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot, which solves the problem that the internal environment and structural conditions of the shield tunnel undergo great changes after a disaster, and if the relevant tunnel detection machine is started without prior judgment, the internal detection and assessment results cannot be obtained smoothly. The method includes: starting the wall-climbing robot to enter the disaster area according to the planned route, identifying and processing to obtain disaster-related image information, and synchronously starting the electromagnetic signal acquisition and processing device preset on the wall-climbing robot to perform disaster electromagnetic signal identification and processing and determine the location of the disaster center through a preset disaster center determination algorithm; analyzing and determining the disaster action level. The present application has the following effects: using a wall-climbing robot to increase the tunnel disaster detection range and reduce the disaster detection time, indirectly improving the efficiency of the comprehensive assessment of the post-disaster structural safety of the shield tunnel, and providing a scientific basis for the repair and reconstruction of the tunnel.

Description

Technical Field

[0001] The present invention relates to the field of tunnel engineering technical equipment, and in particular to a method and system for rapid detection and assessment of shield tunnel disasters based on a wall climbing robot. Background Art

[0002] With the continuous development of urban underground space, shield tunnels, as important underground transportation facilities, are of vital importance for their safety and stability. However, tunnels may suffer from various disasters (such as floods, fires, earthquakes, etc.) during operation, resulting in structural damage and safety hazards. Therefore, rapid and accurate assessment of tunnel damage status after a disaster is of great significance for ensuring tunnel safety and guiding post-disaster repair.

[0003] At present, the structural safety inspection of shield tunnels after disasters (such as fires, floods, etc.) mainly relies on manual inspections, which is inefficient, risky, and difficult to fully cover all areas of the tunnel. With the development of robotics technology, the use of robots for tunnel inspection has gradually become a research hotspot. Existing tunnel inspection robots mostly use wheeled or tracked mobile mechanisms.

[0004] On the one hand, tunnel inspection machines with wheeled or crawler mobile mechanisms have good mobility on flat roads, but are difficult to stably attach and move on the curved walls of shield tunnels. On the other hand, the internal environment and structural conditions of shield tunnels undergo tremendous changes after a disaster. If relevant tunnel inspection machines are started for internal inspection without prior judgment, it is easy for the relevant tunnel inspection machines to malfunction due to the internal environment or structure, making it impossible to successfully obtain internal inspection and evaluation results. Summary of the invention

[0005] In order to use wall-climbing robots to increase the scope of tunnel disaster detection, reduce the effect of disaster detection time, indirectly improve the efficiency of comprehensive assessment of the post-disaster structural safety of shield tunnels, and provide a scientific basis for tunnel repair and reconstruction, the present application provides a shield tunnel disaster rapid detection and assessment method and system based on a wall-climbing robot.

[0006] In the first aspect, the present application provides a method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot, which adopts the following technical solutions:

[0007] A method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot, comprising:

[0008] Obtain tunnel-related and disaster-related information;

[0009] Analyze and determine the tunnel's bad geological area and bad construction area based on the geological survey parameter data contained in the tunnel related information, the relevant parameter data of the construction record, and the preset survey parameter data range of bad geological conditions and the parameter data range of the associated parameters of the bad construction area;

[0010] Based on the pre-compiled 3D finite element rapid modeling method, using automated scripts and parametric design methods, a finite element model containing information on stratum distribution, shield tunnel structure and disaster effects is established for tunnel poor geological areas and poor construction areas;

[0011] Through the preset finite element analysis software, the established finite element model is subjected to structural static analysis, dynamic analysis, and stability analysis to obtain stress concentration areas, areas where deformation exceeds the preset range, and areas where dynamic stability is lower than the preset reasonable data range, as the areas where the tunnel is severely affected;

[0012] Finite element analysis software is used to analyze the disaster results of the severely affected areas of the tunnel, including crack width analysis, lining status assessment, collapse simulation and prevention, and joint opening width analysis;

[0013] According to the mapping relationship between the disaster results and the judgment criteria for the wall-climbing robot's admission and the disaster results analysis of the location of the tunnel's severely affected area, the admission results of the wall-climbing robot are analyzed and determined. The admission results of the wall-climbing robot include admission and non-admission;

[0014] If the admission result of the wall-climbing robot is not allowed, the detection ends;

[0015] If the admission result of the wall-climbing robot is admission, the route of the wall-climbing robot to enter the disaster area is planned according to the preset joint opening width range that the wall-climbing robot needs to avoid;

[0016] The wall-climbing robot is started to enter the disaster area according to the planned route, and the image acquisition and processing device preset on the wall-climbing robot is started to perform disaster image recognition processing to obtain disaster-related image information, and the electromagnetic signal acquisition and processing device preset on the wall-climbing robot is simultaneously started to perform disaster electromagnetic signal recognition processing and determine the disaster center location area through a preset disaster center determination algorithm;

[0017] Based on the acquired disaster-related image information and the disaster center location area, the specific status data of the disaster-related results in the disaster center location area are analyzed and obtained, and based on the correspondence between the results of the specific status data of the disaster-related results falling into different status assessment data ranges and the disaster effect level, the disaster effect level is analyzed and determined, and the disaster effect level is output.

[0018] By adopting the above technical scheme, the post-disaster results of the shield tunnel are effectively analyzed. According to the post-disaster results and the access rules of the wall-climbing robot, it is possible to effectively determine whether the wall-climbing robot is suitable for entry, thereby reducing the possibility that the wall-climbing robot will be damaged after entering and unable to detect the corresponding results. On the premise that the wall-climbing robot can enter, the collection of image information and electromagnetic signals can be used to analyze the disaster center location area and the disaster action level, thereby improving the efficiency of the comprehensive assessment of the post-disaster structural safety of the shield tunnel and providing a scientific basis for the repair and reconstruction of the tunnel.

[0019] Optionally, if the disaster result analysis is a joint opening width analysis, the disaster result analysis of the severely affected area of ​​the tunnel using finite element analysis software includes:

[0020] Collect key information of the segments, including but not limited to stress, strain, deformation, material properties, and actual usage scenarios;

[0021] According to the mapping relationship between the preset numerical range of the segment damage index and the damage index value and the weight value of each damage index, the damage parameters of the segment are analyzed and obtained. The segment damage index includes but is not limited to stress, strain and deformation index;

[0022] If the damage parameter of the segment does not exceed the preset damage threshold, the opening width of the joint is detected and obtained;

[0023] If the damage parameters of the segment exceed the preset damage threshold, the finite element analysis software is used to construct a refined model of the segment based on the collected key segment information;

[0024] According to the actual use scenario of the segment, apply the load conditions corresponding to the actual use scenario of the segment, run the finite element analysis to solve, and obtain the response results of the model under the load;

[0025] The opening width of the joint is extracted from the response results.

[0026] By adopting the above technical solutions, the key information of the pipe segments is collected in a refined manner, the damage of the pipe segments is comprehensively evaluated, and the accuracy of the data is ensured. The preset damage indicators and weight values ​​are used to intelligently analyze the damage parameters of the pipe segments to quickly determine the degree of damage. Once the damage exceeds the standard, a refined model is immediately constructed, and the load is applied in combination with the actual scenario, and the joint opening width is accurately obtained through finite element analysis. This method not only improves the accuracy and efficiency of disaster assessment, but also provides strong data support for subsequent repair work.

[0027] Optionally, if the disaster result is crack width analysis, the criteria for the admission of the wall-climbing robot are as follows:

[0028] If the crack width obtained by the crack width analysis is smaller than the second crack width, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot needs to support the conventional operation mode, which includes path planning, wall attachment, movement and navigation, and task execution;

[0029] If the crack width obtained by the crack width analysis is greater than or equal to the second crack width and less than the first crack width, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot must not only support the conventional operation mode, but also be equipped with an enhanced sealing ring;

[0030] If the crack width obtained by the crack width analysis exceeds the first crack width, the admission result of the wall-climbing robot is no.

[0031] By adopting the above technical solutions, the access judgment standard provides a scientific basis for the access of wall-climbing robots to disaster sites by accurately analyzing the crack width. According to different crack widths, the robot configuration can be flexibly adjusted to ensure its safe and efficient execution of tasks. The conventional operation mode meets basic needs, while the enhanced sealing ring improves the robot's adaptability in harsh environments. This standard not only ensures the smooth progress of rescue work, but also extends the service life of the robot.

[0032] Optionally, according to a preset joint opening width range that the wall-climbing robot needs to avoid, planning a route for the wall-climbing robot to enter the disaster area includes:

[0033] According to the task requirements and tunnel map information, and based on the path planning algorithm, analyze and obtain the global optimal path. The task requirements include the specific goals of rapid detection of shield tunnel disasters and the urgency ranking of different detection tasks;

[0034] Analyze whether the opening widths of all joints are smaller than the preset first opening width;

[0035] If yes, the joints with the smallest opening width are connected and the relevant part of the path in the global optimal path is replaced as the new global optimal path;

[0036] If not, then analyze whether there is a tunnel disaster result with a negative access result caused by the joint opening width being greater than the first opening width in the global optimal path;

[0037] If yes, the preset third joint width is used as the upper limit of the joint width, and the acceptable joint width is gradually increased until the path no longer contains tunnel disaster results with a negative access result due to the joint opening width being greater than the first opening width, and the analyzed and determined path is used as the route for the wall-climbing robot to enter the disaster area;

[0038] If not, the global optimal path obtained through analysis is used as the route for the wall-climbing robot to enter the disaster area.

[0039] By adopting the above technical solutions, the route planning scheme comprehensively considers factors such as task requirements, tunnel map information and joint opening width, and through intelligent analysis of the global optimal path and flexible adjustment according to the joint opening, it ensures that the wall-climbing robot can enter the disaster area safely and efficiently. By gradually increasing the acceptable joint width, the access problem caused by excessive joint opening is effectively avoided, and at the same time, the robot's route is optimized, improving the efficiency and accuracy of disaster detection and response.

[0040] Optionally, starting the wall-climbing robot to enter the disaster area according to the planned route includes:

[0041] Obtain the opening width and distribution density of the joints within a preset distance range from the wall-climbing robot on the planned route;

[0042] According to the corresponding relationship between the opening width and distribution density of the joints and the traveling speed of the wall-climbing robot, the traveling speed of the wall-climbing robot is analyzed and determined;

[0043] The travel speed of the wall-climbing robot is determined based on the analysis, and the wall-climbing robot is controlled to enter the disaster area according to the planned route.

[0044] By adopting the above technical solution, the opening width and distribution density of the joints are accurately obtained, and the travel speed of the wall-climbing robot is determined by intelligent analysis, so that the robot can enter the disaster area safely and efficiently according to the planned route. This solution not only improves the robot's ability to adapt to complex environments, but also ensures the efficient execution of rescue and detection tasks. By scientifically controlling the travel speed, the risk of damage to the robot in the disaster area is effectively reduced, while the accuracy of rescue and detection is improved.

[0045] Optionally, the preset disaster center determination algorithm includes:

[0046] Obtain disaster electromagnetic signals, process them using static correction, gain and background noise reduction, construct a three-dimensional data volume, and perform slice analysis on the three-dimensional data volume;

[0047] Binarization is performed on the three-dimensional electromagnetic wave data volume of the slice analysis to convert it into a black and white image, where black represents low electric field intensity and white represents high electric field intensity;

[0048] Extracting electric field intensity data from the center of the image at fixed intervals;

[0049] Monitor the extracted electric field strength data. When the increase in the electric field strength data within the preset time range exceeds the preset increase, the interval will be reduced successively until the interval reaches the preset value. The extracted electric field strength data will be compared with the electric field strength data within the preset distance range to find the area with the highest electric field strength, which is the disaster center location area.

[0050] By adopting the above technical solutions, through fine processing of disaster electromagnetic signals, combined with slice analysis and binary conversion, efficient monitoring and positioning of electric field strength are achieved. By dynamically adjusting the data extraction interval, it is ensured that when the electric field strength increases sharply, the disaster center location area can be quickly locked. This algorithm not only improves the timeliness of disaster response, but also ensures the safety of rescue personnel and equipment. Its precise positioning capability provides strong support for subsequent rescue and disaster control work, and fully demonstrates the huge potential of scientific and technological innovation in the field of disaster management.

[0051] In the second aspect, the present application provides a shield tunnel disaster rapid detection and assessment system based on a wall climbing robot, which adopts the following technical solutions:

[0052] A shield tunnel disaster rapid detection and assessment system based on a wall-climbing robot comprises a memory, a processor and a program stored in the memory and executable on the processor. The program can be loaded and executed by the processor to implement a shield tunnel disaster rapid detection and assessment method based on a wall-climbing robot as described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to an embodiment of the present application.

[0054] Figure 2 It is a specific flow chart of a disaster center determination algorithm according to another embodiment of the present application.

[0055] Figure 3 It is a flowchart of another embodiment of the present application for planning a route for a wall-climbing robot to enter a disaster area based on a preset joint opening width range that the wall-climbing robot needs to avoid.

[0056] Figure 4 This is a flowchart of another embodiment of the present application for starting a wall-climbing robot to enter a disaster area along a planned route. DETAILED DESCRIPTION

[0057] The present application is further described in detail below in conjunction with the accompanying drawings.

[0058] Reference Figure 1 , which is a shield tunnel disaster rapid detection and assessment method based on a wall climbing robot disclosed in this application, comprising:

[0059] Step S100, obtaining tunnel related information and disaster related information.

[0060] Among them, tunnel-related information includes basic data of the tunnel, such as the structural form, structural dimensions, material properties, etc. of the shield tunnel, as well as comprehensive information from design planning to construction acceptance, such as geological survey parameter data, construction records, and relevant parameter data of stratum distribution. Comprehensive information from design planning to construction acceptance can be obtained from tunnel design planning data, tunnel structure design data, tunnel engineering geological survey data, tunnel engineering construction records, and tunnel engineering quality acceptance records.

[0061] Specifically, by querying the tunnel design and planning data, one can understand the direction of the tunnel project, tunnel functions, and disaster prevention requirements; by querying the tunnel structure design data, one can understand the mechanical parameters, seismic resistance, and waterproof performance of the important lining structures of the tunnel; by querying the tunnel engineering geological survey data, one can understand the distribution conditions of adverse geology, with a focus on the distribution of fault fracture zones and soft strata, the elevation of groundwater levels, and the distribution of high water pressure areas; by querying the tunnel engineering construction records, one can understand the technology used in the construction process, the construction process, and clarify whether secondary disasters have occurred during the construction process; by querying the tunnel engineering quality acceptance records, one can understand the construction quality of the project and identify areas with poor construction quality.

[0062] Disaster-related information can be obtained from disaster record information, such as potential disaster action information (such as groundwater seepage, seismic waves, etc.), earthquake-related information and debris flow-related information. Earthquake-related information can be an earthquake acceleration time-history curve; debris flow-related information can be the impact angle and duration of the debris flow.

[0063] Step S200, analyzing and determining the tunnel's bad geological area and bad construction area based on the geological survey parameter data contained in the tunnel related information, the relevant parameter data of the construction record, and the preset survey parameter data range of bad geological conditions and the parameter data range of the associated parameters of the bad construction area.

[0064] Among them, the analysis of the tunnel's bad geological area is determined as follows: by comparing the geological survey parameter data with the preset bad geological survey parameter data range, the geological areas that may have stability problems or geological disaster risks can be located. These areas often have specific physical or chemical characteristics, such as broken rocks, soft soil, abnormal groundwater levels, etc., which are important bases for judging bad geology.

[0065] One of the implementation methods for analyzing and determining the poor construction area of ​​a tunnel is to set a parameter data range of parameters associated with the poor construction area, extract data of parameters associated with the poor construction area from geological survey parameters and relevant parameters of construction records, and compare them with the preset parameter data range of parameters associated with the poor construction area, so as to determine the poor construction area of ​​the tunnel.

[0066] Another implementation method for analyzing and determining poor construction areas of tunnels is to input the acquired geological survey parameter data and relevant parameter data of construction records into an intelligent data model for identifying the characteristics of poor construction areas, and output the poor construction areas. The construction of the intelligent data model for identifying the characteristics of poor construction areas is as follows: 1. Perform meticulous preprocessing of historical construction data and geological survey data, including steps such as data cleaning, standardization and normalization to ensure data quality and consistency. 2. Use feature engineering technology to extract features closely related to poor construction areas, such as geological structural anomalies, abnormal indicators during construction, etc. 3. Select a neural network as a deep learning model, and compare the predicted results with the actual results to determine the accuracy. When the accuracy reaches the preset value for multiple consecutive times, such as 95%, the training is determined to be completed.

[0067] Step S300, based on the pre-compiled three-dimensional finite element rapid modeling method, using automated scripts and parametric design methods, a finite element model containing stratum distribution, shield tunnel structure form and disaster action information is established for the tunnel's poor geological areas and poor construction areas.

[0068] Specifically, the process involved in step S300 is as follows: 1. Data preparation and preprocessing: Collect and organize tunnel geological survey data, construction records, and disaster history data. 2. Automated script writing: Use programming languages ​​(such as Python) to write automated scripts to realize automatic data import, processing, and model generation. The script can recognize geological and construction data in different formats and convert them into the format required by the finite element model. 3. Parametric design method: According to the geological characteristics and construction conditions of the tunnel, a series of key parameters such as stratum thickness, shield diameter, lining thickness, etc. are determined. Using parametric design tools, these parameters are input into the finite element model to achieve rapid generation and adjustment of the model. 4. Finite element model generation: Use professional finite element analysis software (such as ANSYS, Abaqus, etc.) to construct a three-dimensional finite element model based on the data output by the automated script and the parametric design results. The model should include stratum distribution, shield tunnel structure (such as shield shell, lining, etc.) and potential disaster information (such as groundwater seepage, seismic waves, etc.).

[0069] Step S400, using the preset finite element analysis software, the established finite element model is subjected to structural static analysis, dynamic analysis, and stability analysis, and the stress concentration area, the area where the deformation degree exceeds the preset range, and the area where the dynamic stability is lower than the preset reasonable data range are analyzed and obtained as the area where the tunnel is severely affected.

[0070] Among them, structural static analysis is an important analysis process of the tunnel finite element model through the preset finite element analysis software, which mainly focuses on the stress distribution and deformation of the tunnel structure under the action of static loads. In the process of structural static analysis, the tunnel model is first imported into the finite element analysis software, and various static loads are applied to it. These loads may include gravity, formation pressure, etc. These loads simulate the various static forces that the tunnel is subjected to in actual use. Then, the software will start running the analysis, simulating the response of the tunnel structure under these static loads through complex calculations. After the analysis is completed, we can obtain the stress distribution diagram and deformation of the tunnel structure in various parts.

[0071] The specific analysis process of stress concentration areas can be quantified, as follows: 1. Stress value quantification: In the finite element analysis results, the stress values ​​of each area are directly extracted. These values ​​can directly reflect the force magnitude of each point, thereby quantifying the degree of stress concentration. Set a stress threshold and mark the area exceeding a certain threshold as a high-risk area to facilitate subsequent focus and processing. 2. Deformation quantification: In addition to stress values, the deformation of the model is also an important quantitative indicator. By measuring the deformation of each area (such as displacement, deformation rate, etc.), its stress condition can be indirectly reflected. 3. Comprehensive index quantification: Combined with stress values ​​and deformation, a weighted average is used to construct a comprehensive evaluation index. Here, the weights set for stress values ​​and deformations can be 0.5. The tunnel areas are ranked according to the size of the comprehensive index. 4. Based on the above quantitative indicators, the tunnel area is divided into different risk levels (such as low, medium, and high) according to the interval range of the indicator data, and the area with a high risk level is regarded as a stress concentration area.

[0072] Dynamic analysis is an important part of the analysis of the tunnel finite element model through the preset finite element analysis software, focusing on the response and behavior of the tunnel structure under dynamic loads. In the dynamic analysis, various dynamic loads are first applied to the tunnel model. These loads may include seismic waves, traffic vibrations, wind loads, etc., to simulate the various dynamic effects that the tunnel may encounter during actual operation. Then the software will use advanced numerical calculation methods, such as finite element method and boundary element method, to simulate and analyze the response of the tunnel model under dynamic loads. Through dynamic analysis, the vibration characteristics of the tunnel structure under various dynamic loads can be obtained, including key parameters such as amplitude, frequency, and vibration mode.

[0073] The process of analyzing the locations of areas where the deformation exceeds the preset range can be quantified as follows: First, based on the vibration data obtained from the dynamic analysis, the vibration amplitude and frequency spectra of each area of ​​the tunnel are drawn. These spectra can intuitively show the response of the tunnel under different dynamic loads, and then combined with the preset deformation range, we can set the corresponding threshold in the spectrum. Once the vibration amplitude or frequency of a certain area exceeds this threshold, it means that the deformation of the area has exceeded the safe range.

[0074] Stability analysis is mainly dynamic stability analysis, which evaluates the stability performance of tunnels under dynamic loads. By simulating the impact of dynamic loads such as earthquakes and traffic vibrations on tunnel structures, the vibration response and stability changes of the structure are observed. The data acquisition of specific dynamic stability mainly uses finite element analysis software to simulate the dynamic response of the tunnel and obtain vibration data and stability indicators of each area under different dynamic loads. Numerical simulation can fully consider the impact of various dynamic loads and provide comprehensive data support for dynamic stability evaluation.

[0075] The process of specifically analyzing the locations of areas where the dynamic stability is lower than the preset reasonable data range can be quantified as follows: by simulating the tunnel response under different dynamic loads, after obtaining the dynamic stability data of each area, a reasonable dynamic stability data range is established as the preset standard. This range can be set based on the design requirements, historical operation data and industry specifications of the tunnel to ensure the safety and stability of the tunnel in a dynamic environment. Then, using data analysis technology, the dynamic stability data of each area is compared with the preset standard one by one. By calculating the deviation value between the stability index and the preset range, it is possible to accurately identify which areas have dynamic stability lower than the preset reasonable data range.

[0076] Step S500, using finite element analysis software to analyze the disaster results of the severely affected area of ​​the tunnel.

[0077] Disaster result analysis includes crack width analysis, lining status assessment, collapse simulation and prevention, and joint opening width analysis. The first is crack width analysis. Finite element analysis software is used to simulate the deformation of the tunnel under different stress states, and crack width data in various regions can be collected. The second is lining status assessment. As an important structural part of the tunnel, the status of the lining is directly related to the safety and stability of the tunnel. First, an accurate model of the tunnel lining is established through finite element analysis software, taking into account factors such as material properties and construction technology. Then, a stress analysis is performed to analyze the deformation and stress distribution of the lining under various stresses. The experimental data and simulation results can also be combined to evaluate the degree of damage to the lining, including cracks and spalling. Let's look at collapse simulation and prevention. Finite element analysis software is used to simulate the collapse process of the tunnel under different geological conditions and dynamic loads.

[0078] The last step is joint opening width analysis. The joints in the tunnel are important connection points of the structure, and their status has a significant impact on the overall performance of the tunnel. Specifically, if the disaster result analysis is joint opening width analysis, the disaster result analysis of the severely affected area of ​​the tunnel through finite element analysis software includes:

[0079] Step Sa00, collecting key information of the pipe segment, the key information of the pipe segment includes but is not limited to stress, strain, deformation, material properties, and actual usage scenarios.

[0080] The collection and acquisition of stress can be achieved by using stress sensors attached to the surface of the pipe segment to monitor and record stress data in real time. These sensors can accurately capture the stress changes of the pipe segment under stress. The collection and acquisition of strain can use strain gauges or strain gauges to measure the deformation degree of the pipe segment during stress. These devices can accurately detect the tiny deformation of the pipe segment and convert it into an electrical signal for output. The collection and acquisition of deformation can be achieved by using laser rangefinders, displacement sensors and other devices to monitor the actual deformation of the pipe segment. These devices can accurately measure the position changes of the pipe segment at different time points to obtain deformation data. Material properties can be obtained through laboratory testing or by consulting relevant literature to obtain material property information of the pipe segment, such as elastic modulus, tensile strength, etc. This information is crucial for the subsequent establishment of an accurate finite element model. The actual use scenario of the pipe segment can be queried from a preset professional database that stores pipe segment use scenario information, using key information such as the type, purpose, and geographical location of the pipe segment as limited query conditions.

[0081] Step Sb00, analyzing and obtaining the damage parameters of the segment according to the mapping relationship between the preset numerical range of the segment damage index and the damage index value and the weight value of each damage index, the segment damage index includes but is not limited to stress, strain and deformation index.

[0082] Among them, first, according to the preset numerical range of segment damage indicators, such as the safety range, warning range, danger range, etc. of indicators such as stress, strain and deformation, the actually collected data are compared with these ranges to determine the damage index value of each indicator, and the damage index value of each indicator is multiplied by the corresponding weight value and added separately. The overall sum obtained is the damage parameter of the segment.

[0083] Step Sc00: If the damage parameter of the pipe segment does not exceed the preset damage threshold, the opening width of the joint is detected and obtained.

[0084] The detection and acquisition of the opening width of the joint portion can be performed by using an opening width sensor installed at the joint portion.

[0085] Step Sd00: If the damage parameter of the segment exceeds the preset damage threshold, a finite element analysis software is used to construct a refined model of the segment based on the collected key information of the segment.

[0086] The preset damage threshold may need to be set, for example, it may be set to 0.92. When the damage parameter of the pipe segment is 0.92, it may be considered that the pipe segment is cracked.

[0087] Among them, the process of building a refined model of the segment is as follows: 1. Model establishment and meshing: Create a geometric model of the segment in the software, and perform fine meshing according to actual conditions. The density and quality of the mesh directly affect the accuracy of the analysis results, so it is necessary to set it reasonably according to the shape, size and force characteristics of the segment. 2. Material property assignment: Assign the collected segment material property information, such as elastic modulus, Poisson's ratio, density, etc., to the corresponding parts of the model. These material properties are the key factors that determine how the model responds when subjected to force. 3. Boundary conditions and load application:

[0088] According to the actual installation and use of the segments, the boundary conditions of the model are set, such as fixed supports, free ends, etc. At the same time, the corresponding loads are applied according to the analysis requirements, such as gravity loads, seismic loads, etc. 4. Model verification and optimization: After completing the preliminary model construction, the accuracy of the model is verified by comparing it with the actual measurement data. According to the verification results, the model is adjusted and optimized as necessary to improve its prediction accuracy and reliability.

[0089] Step Se00, according to the actual use scenario of the segment, applies load conditions corresponding to the actual use scenario of the segment, runs finite element analysis to solve, and obtains the response results of the model under the load.

[0090] Among them, the load conditions can be queried and obtained from a preset database that stores the correspondence between the actual use scenarios of the pipe segments and the load conditions. The method for obtaining the response results of the model under load includes the following steps: 1. Load condition setting: According to the actual use scenarios of the pipe segments, such as subway tunnels, mountain tunnels, etc., determine the types of loads that may be encountered, such as static loads (including deadweight, earth pressure, water pressure, etc.) and dynamic loads (such as vibration loads generated by train running). 2. Run finite element analysis: Use the constructed refined model of the pipe segment to perform finite element analysis under the set load conditions. Through the solution function of the software, various responses of the model under load are calculated, such as stress distribution, deformation, opening width of the joint, etc.

[0091] Step Sf00, extracting the opening width of the joint from the response result.

[0092] Step S600, analyzing and determining the admission result of the wall-climbing robot according to the mapping relationship between the disaster result and the judgment standard of the wall-climbing robot admission and the disaster result analysis of the location of the tunnel's severely affected area.

[0093] Among them, the admission results of the wall-climbing robot include admission and non-admission. Regarding the admission situation, after the disaster results analysis of the disaster-stricken area of ​​the tunnel, if all indicators (such as crack width, lining status, collapse risk, etc.) are within the safe range, the wall-climbing robot will be allowed to enter.

[0094] In addition, the criteria for the entry of wall-climbing robots are explained as follows based on crack width, lining status, and collapse risk.

[0095] If the disaster result is a crack width analysis, the criteria for the wall-climbing robot's access are as follows: if the crack width obtained by the crack width analysis is less than the second crack width, the second crack width can be 1 cm or other widths, then the result of the wall-climbing robot's access is yes, and the configuration of the wall-climbing robot must support the conventional operation mode, which includes path planning, wall attachment, movement and navigation, and task execution; if the crack width obtained by the crack width analysis is greater than or equal to the second crack width and less than the first crack width, the first crack width can be 2 cm or other widths,

[0096] The result of the wall-climbing robot's access is yes, and the configuration of the wall-climbing robot must not only support the normal operation mode, but also be equipped with an enhanced sealing ring; if the crack width obtained by the crack width analysis exceeds the first crack width, the result of the wall-climbing robot's access is no.

[0097] If the disaster result is a lining status assessment, the criteria for the access of the wall-climbing robot are as follows: if the lining status assessment shows that there is no path on the tunnel surface that can avoid the block falling area, then the result of the wall-climbing robot access is no; if the lining status assessment is local block falling, then the result of the wall-climbing robot access is yes, and the configuration of the wall-climbing robot needs to support the normal operation mode; if the lining status assessment is continuous block falling, and the thickness of the block falling area does not exceed the preset thickness, which can be 3cm or its width, and there is a path on the tunnel surface that can avoid the block falling area, then the result of the wall-climbing robot access is yes, and the configuration of the wall-climbing robot needs to not only support the normal operation mode, but also be equipped with an enhanced sealing ring.

[0098] Among them, the analysis of local block loss in the lining status assessment is as follows: Use finite element analysis software to establish a detailed model of the tunnel lining, and import relevant geological, material, load and other parameters, and then observe the stress distribution and deformation of the tunnel lining by applying different loads and constraints. Pay special attention to the local stress concentration areas that may appear on the lining surface, which are often the precursors of local block loss. During the analysis process, the simulation results can be automatically processed by combining image processing and recognition technology. By setting a specific threshold, the software can automatically identify and mark the local block loss areas that may exist on the lining surface.

[0099] The analysis process of evaluating the lining status as continuous block loss is as follows: First, use finite element software to build a detailed model of the tunnel lining to ensure that the model can accurately reflect the geometric characteristics and material properties of the actual lining structure. Introduce key parameters such as geology, materials, and loads to provide a data basis for subsequent simulation analysis. Then, according to the actual stress conditions of the tunnel, apply corresponding loads to the model, such as gravity, formation pressure, temperature stress, etc. Run the simulation analysis to observe the stress distribution and deformation behavior of the lining under different loads. Finally, analyze the simulation results, paying special attention to areas of stress concentration and abnormal deformation, which are often precursors to continuous block loss. By setting a reasonable threshold, the software is used to automatically detect and mark potential continuous block loss areas.

[0100] The analysis of the thickness of the block area not exceeding the preset thickness is as follows: First, in the simulation results, the thickness of the marked continuous block area is measured, and the average thickness and maximum thickness of each area are recorded. Then the measured block thickness is compared with the preset thickness (such as 3cm) to determine which areas have a thickness exceeding the preset value.

[0101] The analysis of whether there is a path on the tunnel surface that can avoid the block drop area is as follows: First, a high-precision tunnel lining model is constructed using finite element analysis software. By inputting detailed information such as the geometric dimensions, material properties, and boundary conditions of the tunnel, it is ensured that the model can truly reflect the actual situation of the tunnel. Then, a comprehensive lining state analysis is carried out. By applying appropriate loads and constraints, the stress distribution and deformation of the tunnel under various possible working conditions are simulated. Focus on the stress concentration area and potential block drop area on the tunnel surface. Then, with the help of advanced image processing and data analysis technology, the finite element analysis results are deeply mined. By setting a reasonable threshold, the possible block drop area on the tunnel surface is automatically identified, and a detailed block drop distribution map is generated. On this basis, the path planning algorithm is used to search the path on the tunnel surface. Based on the block drop distribution map and combined with the actual traffic needs of the tunnel, an attempt is made to find a safe path that can avoid all block drop areas. Here, optimization algorithms such as graph theory and dynamic programming can be used to ensure the accuracy and efficiency of the search results. Finally, based on the results of the path search, it is determined whether there is a path on the tunnel surface that can avoid the block drop area.

[0102] If the disaster result is the joint opening width, the judgment criteria for the wall-climbing robot's access are as follows: if the joint opening width analysis shows that there are joints with an opening width less than or equal to the first opening width, then the result of the wall-climbing robot's access is yes, and the configuration of the wall-climbing robot must not only support the normal operation mode, but also be equipped with an enhanced sealing ring; if the joint opening width analysis shows that the opening width of all joints is greater than the first opening width, then the result of the wall-climbing robot's access is no.

[0103] If the disaster result is collapse, the judgment criteria for the access of the wall-climbing robot are as follows: If the disaster result analysis is confirmed collapse, the result of the access of the wall-climbing robot is no.

[0104] The collapse analysis process is as follows: First, a simplified model of the tunnel structure is constructed using finite element software to highlight key structural features while reducing computational complexity. Ensure that the model can reflect the basic geometry, material properties and support conditions of the tunnel. Then, reasonable loads and boundary conditions are set according to the actual operating environment of the tunnel and the extreme conditions that may be encountered. Consider key factors such as gravity, formation pressure, seismic force, and constraints around the tunnel. In the simulation analysis, key collapse risk assessment indicators are set, such as stress concentration, deformation, structural stability, etc. These indicators will serve as an important basis for judging whether the tunnel collapses. Run the simulation analysis to observe the response of the tunnel structure under the set load. Focus on the stress distribution, deformation trend and stability status of high-risk areas. Through the post-processing function provided by the software, extract key data and compare them with the safety threshold. According to the simulation results, combined with the set collapse risk assessment indicators, the collapse risk of the tunnel structure is determined.

[0105] Step SA00: If the admission result of the wall-climbing robot is not allowed, the detection is ended.

[0106] Step S700, if the admission result of the wall-climbing robot is admission, a route for the wall-climbing robot to enter the disaster area is planned according to a preset joint opening width range that the wall-climbing robot needs to avoid.

[0107] The preset joint opening width range that the wall-climbing robot needs to avoid may be greater than 1 cm.

[0108] The process of planning the route for the wall-climbing robot to enter the disaster area based on the preset joint opening width range that the wall-climbing robot needs to avoid is as follows: Use mapping technology to mark every joint and its opening width inside the tunnel in detail. In this way, we can clearly see which areas are safe and which areas need to be avoided. On this basis, we can use intelligent algorithms to plan a safe and efficient route for the wall-climbing robot.

[0109] Step S800, start the wall-climbing robot to enter the disaster area according to the planned route, start the image acquisition and processing device preset on the wall-climbing robot to perform disaster image recognition processing to obtain disaster-related image information, and simultaneously start the electromagnetic signal acquisition and processing device preset on the wall-climbing robot to perform disaster electromagnetic signal recognition processing and determine the disaster center location area through a preset disaster center determination algorithm.

[0110] Among them, the image acquisition and processing device can be a high-definition camera.

[0111] The process of obtaining disaster-related image information is as follows: When the wall-climbing robot enters the disaster area, the image acquisition and processing device on it will be immediately started to identify and process the disaster image. The device is equipped with a high-definition camera that can capture subtle changes at the disaster site. Through the built-in image processing algorithm, the robot can quickly identify different types of disaster features such as cracks, collapses, and fires, and save these features in the form of image information. The built-in image processing algorithm can use feature extraction and matching algorithms. This type of algorithm can extract key features in the image, such as edges, corners, etc., and match them with pre-defined disaster features to achieve disaster identification.

[0112] The process of determining the disaster center location area is as follows: the electromagnetic signal acquisition and processing device can receive the electromagnetic signals emitted by the disaster area, and accurately locate the center of the disaster through a preset disaster center determination algorithm.

[0113] The preset disaster center determination algorithm has the following steps:

[0114] Step S8a0, obtaining disaster electromagnetic signals, processing them using static correction, gain and background noise reduction, constructing a three-dimensional data volume, and performing slice analysis on the three-dimensional data volume.

[0115] Static correction is to calibrate the signal using static correction technology for static interference that may exist in disaster electromagnetic signals. By identifying and removing these static offsets, the baseline of the signal is ensured to be stable, providing an accurate basis for subsequent data processing. Gain processing is to dynamically adjust the gain according to changes in signal strength. When the signal is weak, the signal is amplified by increasing the gain to improve the signal-to-noise ratio; when the signal is strong, the gain is appropriately reduced to avoid signal saturation. In this way, the dynamic range of the data can be fully utilized while ensuring signal quality. Background noise reduction is to use advanced noise reduction algorithms, such as wavelet transform and adaptive filtering, to remove background noise from disaster electromagnetic signals. By separating the useful components and noise components in the signal, pure disaster electromagnetic signals are extracted to provide a high-quality data source for the subsequent construction of three-dimensional data volumes.

[0116] The construction of the three-dimensional data volume is as follows: 1. Data interpolation and gridding: The pre-processed disaster electromagnetic signals are interpolated according to the spatial position to form a regular three-dimensional grid. By selecting appropriate interpolation methods, such as Kriging interpolation and spline interpolation, the continuity and smoothness of the data are ensured; 2. Data volume visualization: Using three-dimensional visualization technology, the interpolated data is presented in an intuitive three-dimensional graphical form. By adjusting parameters such as color mapping and transparency, disaster electromagnetic signals of different intensities are highlighted to facilitate subsequent analysis and interpretation.

[0117] The process of slicing a 3D data volume is as follows: the 3D data volume is sliced ​​in a specific direction and layer thickness to generate a series of 2D slices. These slices can show disaster information at different depths and angles, such as the distribution of cracks, abnormal electromagnetic fields, etc.

[0118] Step S8b0, binarizing the three-dimensional electromagnetic wave data volume obtained by slice analysis to convert it into a black-and-white image, where black represents low electric field intensity and white represents high electric field intensity.

[0119] Specifically, the binarization process will first set a threshold value for the electric field strength. The binarization parameter can be 180. Areas below this threshold will be represented as black in the generated black-and-white image, representing low electric field strength; and areas above this threshold will be represented as white, representing high electric field strength.

[0120] For example, suppose we have a set of three-dimensional data about underground electromagnetic waves. After slice analysis, we get multiple two-dimensional slices. In order to more intuitively observe the distribution of electric field intensity in these slices, we can perform binarization. After setting a suitable threshold, those areas with low electric field intensity (such as rocks, soil, etc.) will appear black in black and white images, while areas with high electric field intensity (such as metal deposits, groundwater, etc.) will appear white. In this way, we can quickly identify the electric field intensity characteristics of different underground areas through simple black and white images, providing strong support for subsequent resource exploration or environmental monitoring.

[0121] Step S8c0, extracting electric field strength data from the center of the image at fixed intervals.

[0122] Specifically, the system extracts electric field strength data from the center of the image at fixed intervals, which may be 0.5 m.

[0123] Step S8d0, monitor the extracted electric field strength data. When the increase in the electric field strength data within the preset time range exceeds the preset increase, the interval will be reduced successively until the interval reaches the preset value. The extracted electric field strength data will be compared with the electric field strength data within the preset distance range to find the area with the highest electric field strength, which is the disaster center location area.

[0124] Among them, the preset time range can be analyzed by reviewing the electric field strength change data of similar disaster events in the past and analyzing the typical change patterns of electric field strength before and after the disaster. Based on these patterns, a reasonable time range can be determined to capture the increase in electric field strength when the disaster occurs. Suppose through historical data analysis, it is found that the electric field strength usually increases significantly within 10 minutes before a certain type of disaster occurs. Therefore, the preset time range can be set to 10 minutes to capture this key change.

[0125] The preset increase can be calculated based on the electric field strength data of a large number of historical disaster events, and the average increase in electric field strength when a disaster occurs can be calculated, and an appropriate threshold can be set as the preset increase. When the increase in electric field strength monitored in real time exceeds this threshold, it can be considered that the location of the disaster center may have changed. Suppose that the average increase in electric field strength when a certain type of disaster occurs is determined to be 100 units through the statistical threshold method. Therefore, the preset increase can be set to 100 units. When the real-time monitored electric field strength increases by more than 100 units within a preset time range, the algorithm will trigger further processing to determine the location of the disaster center.

[0126] Specifically, according to the above setting rules, a preset increase in electric field strength and time range can be set first. The sensors deployed in the tunnel will continuously extract electric field strength data. When the increase in the monitored electric field strength data exceeds the preset threshold within a preset time range, the system will immediately initiate a response mechanism.

[0127] The core of this response mechanism is to gradually reduce the interval of data extraction. Initially, the system may extract data at a larger interval to save resources and maintain overall efficiency. However, once the electric field strength increases abnormally, the system will begin to gradually reduce this interval to obtain data more frequently. The purpose of this is to more accurately capture the dynamic changes in electric field strength and provide a rich information basis for subsequent data analysis.

[0128] As the data extraction interval continues to decrease, the system can obtain more and more dense data points. When this interval reaches a preset minimum value, the system will proceed to the next step: compare and analyze these dense electric field strength data with other data within a preset distance range. This preset distance range is set based on multiple factors such as disaster type, geographical environment and historical data.

[0129] Through comprehensive data comparison, the system can accurately identify the area with the highest electric field intensity. This area is regarded as the center of the disaster because it is usually directly related to the source of the disaster and exhibits the strongest electromagnetic signal characteristics. This area is likely to be the center of the disaster, such as potential electrical fault points, rock fracture areas, etc.

[0130] Step S900, based on the acquired disaster-related image information and the disaster center location area, analyze and obtain the specific status data of the disaster-related results in the disaster center location area, and analyze and determine the disaster effect level based on the correspondence between the results of the specific status data of the disaster-related results falling into different status assessment data ranges and the disaster effect level, and output the disaster effect level.

[0131] Specifically, firstly, based on the acquired disaster-related image information, the system can accurately extract key feature information such as disaster type, scale, and impact range through image segmentation, feature extraction and other algorithms. Specifically, it can be key features such as flame range and crack length. These key features are the disaster-related results.

[0132] In addition, the collected electromagnetic signal data can also be processed through time-frequency analysis, pattern recognition, etc. The system can extract characteristic parameters that are closely related to the disaster level, and these characteristic parameters can also be used as part of the disaster-related results.

[0133] Then, combined with the information of the disaster center location area, the system will conduct an in-depth analysis of the specific situation data in the area, including but not limited to key indicators such as the intensity, speed, and impact range of the disaster.

[0134] After obtaining the specific status data of the disaster-related results, the system will compare these data with the preset different status assessment data ranges. Each data range corresponds to a specific disaster action level, which helps to quickly determine the severity and urgency of the disaster.

[0135] Taking a fire disaster in a tunnel as an example, suppose the system determines the center of the fire through image information and extracts specific fire status data, such as the size of the fire and the speed of spread. After analysis, these data will be compared with the preset fire status assessment data range. Based on the comparison results, the system can quickly determine the level of fire, such as "primary fire", "intermediate fire" or "major fire".

[0136] The final output of the disaster level may include charts, reports or real-time updated data interfaces, etc., to ensure the timeliness and accuracy of the information.

[0137] exist Figure 1 In step S700, further consideration is given to how to plan the wall-climbing robot to enter the disaster area safely and efficiently, as shown in FIG. Figure 3 The illustrated embodiment is described in detail.

[0138] Reference Figure 3 , based on the preset joint opening width range that the wall-climbing robot needs to avoid, the route of the wall-climbing robot to enter the disaster area includes:

[0139] Step S710: Analyze and obtain the global optimal path according to the task requirements and tunnel map information and the path planning algorithm.

[0140] Among them, the task requirements include the specific goals of rapid detection of shield tunnel disasters and the urgency ranking of different detection tasks. The specific goals include disaster type identification, disaster scope definition, and key data collection. Disaster type identification is to determine the type of disaster that occurs in the tunnel, such as cracks, water leakage, and block falling. Disaster scope definition is to estimate the size and severity of the area affected by the disaster. Key data collection is to collect key data for subsequent analysis, such as images, temperature, and humidity of the disaster area.

[0141] The urgency ranking can be divided into emergency, high priority, and general. Emergency can be a disaster that directly affects the safety of the tunnel structure or personnel, such as large cracks and serious water leakage. High priority can be a disaster that may limit the tunnel function or affect the operation, such as medium-scale block falling and local leakage. General can be a disaster with less impact on the tunnel operation, such as slight cracks and surface peeling. The disasters involved in the above emergency tasks can be obtained by analyzing the disaster results of the severely affected areas of the tunnel through finite element analysis software or by checking and obtaining through relevant detection devices. Whether the specific cracks are large and whether the water leakage is serious can be determined by setting a specific data range.

[0142] For example, for disasters that directly affect the safety of tunnel structures or personnel in emergency missions, such as large cracks and severe water leakage, the crack width analysis function of finite element analysis can quickly identify the precise location and width of the cracks, providing rescuers with intuitive disaster scene images. At the same time, the lining status assessment function can monitor the stability and safety of the lining in real time, ensuring that effective measures can be taken quickly to protect personnel safety in emergency situations. In high-priority missions, facing disasters that may limit tunnel functions or affect operations, such as medium-scale block falls and local leakage, the collapse simulation and prevention function of finite element analysis is particularly important. By simulating the collapse of tunnels under different disaster scenarios, we can predict potential danger areas and formulate corresponding preventive measures in advance. In addition, the joint opening width analysis function can monitor the status of the joints in real time to ensure that the overall structure of the tunnel can still maintain a certain stability when a disaster occurs.

[0143] The path planning algorithm can be a greedy path planning algorithm based on the "disaster index". The specific analysis to obtain the global optimal path can be as follows:

[0144] First, we set a "disaster index" for each disaster type, which comprehensively considers the severity and urgency of the disaster. For example, large cracks and severe water leakage, which directly affect the safety of tunnel structures or personnel, have a disaster index set as high; medium-scale block falling, local leakage, and other disasters that may limit tunnel functions have a disaster index set as medium; and minor cracks, surface peeling, and other disasters that have little impact on tunnel operations have a disaster index set as low.

[0145] Next, we calculated a "comprehensive disaster index" for each area based on the tunnel map information and disaster distribution. This index is the sum of all disaster indices in the area. In this way, areas with high comprehensive disaster indexes indicate that the disaster in the area is serious and urgent, and needs to be tested first.

[0146] During the path planning process, we adopt a greedy strategy, selecting the adjacent area with the highest comprehensive disaster index as the next target area each time until the end of the global optimal path is reached. This strategy ensures that the robot prioritizes areas with severe and urgent disasters, thereby meeting the urgency ranking in the task requirements.

[0147] Specifically, the algorithm steps are as follows: 1. Initialization: Calculate the comprehensive disaster index of each area based on the tunnel map information and disaster distribution. 2. Select the starting point: Set the current position of the robot as the starting point. 3. Greedy selection: Starting from the current position, select the adjacent area with the highest comprehensive disaster index as the next target area. 4. Update position: Move the robot to the selected target area.

[0148] Repeat steps 3 and 4 until you reach the end or have traversed all areas.

[0149] Step S720, analyzing whether the opening widths of all joints are smaller than the preset first opening width. If yes, executing step S730; if no, executing step S740.

[0150] The preset first opening width may be 1 cm, and other widths may be set as required.

[0151] Step S730: Connect the joints with the smallest opening width and replace the relevant part of the path in the global optimal path to form a new global optimal path.

[0152] The process of connecting the joints with the smallest opening width and replacing the relevant parts of the path in the global optimal path is as follows: First, the system will identify the opening widths of all joints and sort them from small to large according to the opening width. In this way, we can quickly find the joint with the smallest opening width; next, the system will analyze the global optimal path, especially those parts that pass through the joints. This includes determining which joints are on the optimal path, as well as the specific location and opening state of these joints. After determining the joint with the smallest opening width, the system will connect them to form a new path. This new path will replace the relevant parts of the original global optimal path, thereby optimizing the path. This replacement not only takes into account the opening width of the joint, but also integrates multiple factors such as the length of the path, the difficulty of passage, and safety to ensure that the new path is globally optimal.

[0153] Assume that in a shield tunnel disaster detection task, the global optimal path originally includes three joints A, B, and C. However, during the real-time monitoring process, the system found that although the opening width of joint B did not exceed the safety range, it was still larger than other joints. At the same time, the system found another joint D with a smaller opening width.

[0154] According to the above strategy, the system will perform the following operations: first, confirm that the stability and safety of joint D meet the requirements; then, connect joint D with A and C in the original path to form a new path A, D, C. This new path further optimizes the opening width of the joint while retaining the global optimality, thereby improving the safety and efficiency of the robot's passage.

[0155] Step S740, analyzing whether there is a tunnel disaster result with a negative access result caused by the joint opening width being greater than the first opening width in the global optimal path. If yes, execute step S750; if no, execute step S760.

[0156] Step S750, using the preset third joint width as the upper limit of the joint width, and gradually increasing the acceptable joint width until the path no longer contains tunnel disaster results with a negative access result due to the joint opening width being greater than the first opening width, and the analyzed and determined path is used as the route for the wall-climbing robot to enter the disaster area.

[0157] The third joint width may be 1.4 cm or 1.8 cm. This third joint width is usually a relatively safe maximum width that the robot can pass through.

[0158] The process of step S750 is as follows: the system begins to gradually increase the acceptable joint width. After each step of relaxation, the system will re-evaluate the global optimal path to check whether there are still inaccessible areas due to excessive joint width. The purpose of this process is to find a balance point, that is, to relax the joint width restriction as much as possible to ensure that the robot can pass, but not to the extent that it poses a threat to the robot's safety. When the path no longer contains tunnel disaster results with a negative access result caused by the joint opening width being greater than the first opening width, the gradual relaxation process will terminate. At this point, the system believes that a new, optimized global optimal path has been found.

[0159] Step S760: Use the global optimal path obtained through analysis as the route for the wall-climbing robot to enter the disaster area.

[0160] exist Figure 1 In step S800, it should be further considered that the speed setting when starting the wall-climbing robot to enter the disaster area according to the planned route needs to be adjusted according to the opening width and distribution density of the joints, as shown in FIG. Figure 4 The illustrated embodiment is described in detail.

[0161] Step S810, obtaining the opening width and distribution density of the joints within a preset distance range from the wall-climbing robot on the planned route.

[0162] First, the opening width of the joints. Inside the tunnel, the joints refer to the connecting parts between the tunnel walls or top structural segments. Due to various reasons such as construction and use, these joints may open to varying degrees. When the wall-climbing robot moves on the planned route, it needs to use the sensors it carries to detect the opening width of these joints in real time. This data is not only related to the robot's travel safety, but also an important indicator for evaluating the stability of the tunnel structure.

[0163] Secondly, regarding the distribution density of joints. In addition to the status of individual joints, the distribution density of joints in the tunnel is also an important parameter worthy of attention. The distribution density directly affects the path planning and maintenance strategy formulation of the wall-climbing robot. When planning a route, the robot will combine the distribution density data of the joints to select a path that is both safe and efficient.

[0164] Take a section of a route in a tunnel as an example. Suppose the density of joints on this section is high. This means that when the robot travels on this section of the route, it needs to deal with the challenge of joints more frequently. Based on this data, the robot can optimize its path planning, such as choosing to avoid high-density areas or adjusting the travel speed to ensure safety.

[0165] Step S820, analyzing and determining the travel speed of the wall-climbing robot according to the corresponding relationship between the opening width and distribution density of the joints and the travel speed of the wall-climbing robot.

[0166] The analysis and determination of the travel speed of the wall-climbing robot is as follows: taking the opening width and distribution density of the joint as common query objects, the travel speed of the wall-climbing robot is queried from a preset database that stores the corresponding relationship between the opening width and distribution density of the joint and the travel speed of the wall-climbing robot.

[0167] Taking a tunnel as an example, suppose that the joints in a certain section of the tunnel have a large opening width and are densely distributed. In this case, the wall-climbing robot will automatically reduce its travel speed according to the preset corresponding relationship, and the initial operating speed of the wall-climbing robot can be 3m / min.

[0168] Step S830, controlling the wall-climbing robot to enter the disaster area along the planned route according to the analyzed moving speed of the wall-climbing robot.

[0169] exist Figure 1 In step S700, it is further considered that the inspection order of different location areas should be considered when planning the route of the wall-climbing robot to enter the disaster area. The inspection order should comprehensively consider the situation of people in the tunnel and the situation of lost sensors. For details, please refer to the embodiment shown below.

[0170] Planning the route of the wall-climbing robot into the disaster area includes:

[0171] Step S7A0, obtaining result information of whether there are people in the tunnel when the disaster occurs. If yes, execute step S7B0; if no, execute step S7D0.

[0172] Specifically, the result information of whether there are people in the tunnel when the disaster occurs can be obtained through monitoring cameras, personnel positioning systems or emergency communication equipment deployed in the tunnel.

[0173] For example, after a fire breaks out in a tunnel, the images captured by the surveillance camera can be used to quickly determine whether there are people trapped in the tunnel. If the images show that there are people moving, the next step S7B0 is executed; if the images show that there are no people moving, the process jumps to step S7D0.

[0174] Step S7B0, obtaining the distribution ratio of people in the tunnel at different locations in the disaster area before the disaster occurs.

[0175] Specifically, the distribution ratio of people in different locations in the disaster area in the tunnel before the disaster occurs requires the help of the personnel positioning system or historical data records in the tunnel. Through the positioning system, the movement trajectory and location information of people can be tracked in real time, and then the distribution ratio of people in each area before the disaster occurs can be analyzed. Historical data records can provide the distribution of people in a certain period of time in the past.

[0176] Step S7C0, sorting the inspection order of the location areas from high to low according to the distribution ratio of personnel, and planning the route of the wall-climbing robot entering the disaster area according to the location areas in the inspection order.

[0177] Specifically, after obtaining the data on the distribution of personnel, it is necessary to process and analyze these data to determine the inspection order. With the help of data analysis software or algorithms, each area can be sorted from high to low according to the distribution of personnel. Then, the route of the wall-climbing robot is planned according to the sorting results to ensure that the areas with dense personnel are inspected first.

[0178] For example, in a tunnel fire scenario, we set the area near the tunnel entrance as a priority inspection area based on the personnel distribution data. Therefore, when planning the route of the wall-climbing robot, we will first arrange the robot to inspect these areas to ensure that the trapped people are found and rescued as soon as possible.

[0179] Step S7D0, obtaining the distribution ratio of the lost sensors in the tunnel after the disaster occurs in different locations of the disaster area.

[0180] Step S7E0, sorting the inspection order of the location areas from high to low according to the sensor distribution ratio, and planning the route of the wall-climbing robot entering the disaster area according to the location areas in the inspection order.

[0181] After step S7D0, an analysis should be performed to determine whether the sensors that lost connection in the tunnel after the disaster are of the same type. For details, please refer to the following embodiment.

[0182] A method for rapid detection and assessment of shield tunnel disasters based on a wall climbing robot also includes steps after obtaining the distribution ratio of disconnected sensors in different positions of the disaster area in the tunnel after the disaster occurs, which are as follows:

[0183] Step S7F0, analyzing whether the sensors that lost connection in the tunnel after the disaster are of the same type. If yes, execute step S7G0; if no, execute step S7H0.

[0184] Specifically, the analysis of whether the sensors that lost connection in the tunnel after the disaster were of the same type was as follows: The type of the sensor was determined by its unique identifier, transmission protocol or data characteristics. The collected information of the lost sensors was automatically screened and compared using data processing software to determine whether they were of the same type.

[0185] For example, after a tunnel fire, the system detected multiple disconnected sensors. By comparing their identifiers or data features, it was found that these sensors were all smoke sensors, thus determining that they were of the same type.

[0186] Step S7G0, continue with the subsequent steps.

[0187] Step S7H0, obtaining sensor categories and the number of sensor categories within different location ranges in the disaster area.

[0188] Specifically, the sensor categories and the number of sensor categories within different locations in the disaster area are obtained as follows: the sensor categories and the number information of the sensor categories within different locations in the disaster area are obtained by using the sensor management system or database query function.

[0189] For example: In the tunnel fire case, by querying the sensor management system, we learned that in the fire-affected area, in addition to the lost smoke sensors, there were also temperature sensors, humidity sensors, etc., and their numbers and locations were recorded separately.

[0190] Step S7I0, based on the mapping relationship between tunnel shield disasters and sensor importance, the sensor categories and the number of sensor categories in different locations in the disaster area, and the distribution ratio of lost sensors, analyze and obtain the overall importance distribution ratio of lost sensors in different locations in the disaster area that were lost in the tunnel after the disaster.

[0191] Specifically, it is necessary to establish a mapping model between tunnel shield disasters and sensor importance, which defines the importance of different sensors in responding to specific disasters. Secondly, based on the type, quantity and loss distribution ratio of sensors in the disaster area, data analysis algorithms (such as weighted average, hierarchical analysis, etc.) are used to calculate the overall importance distribution ratio of lost sensors at each location.

[0192] For example, in a tunnel fire scenario, according to the mapping model, smoke sensors are crucial for fire monitoring. Combining the loss of connection of smoke sensors in the fire area and the distribution and status of other types of sensors, through data analysis algorithms, we can derive the overall importance distribution of lost sensors at different locations. For example, the importance of locations close to the fire source increases significantly due to the loss of connection of smoke sensors.

[0193] Step S7J0, sorting the inspection order of the location areas from high to low according to the overall importance distribution ratio, and planning the route of the wall-climbing robot entering the disaster area according to the location areas in the inspection order.

[0194] Planning the route of the wall-climbing robot entering the disaster area according to the location area of ​​the inspection order includes the following steps: Step S7J1, according to the mapping relationship between the tunnel shield disaster and the importance of the sensor and the location of the sensor, the inspection order of the wall-climbing robot in each location area is planned from high to low according to the importance of the sensor. Step S7J2, according to the inspection order of the wall-climbing robot in each location area, the route of the wall-climbing robot in different location areas is formed.

[0195] Based on the same inventive concept, an embodiment of the present invention provides a shield tunnel disaster rapid detection and assessment system based on a wall climbing robot, including a memory and a processor, wherein the memory stores a program that can be executed on the processor to implement the following Figures 1 to 4 Procedure for either method.

[0196] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot, characterized in that: include: Obtain tunnel-related and disaster-related information; Analyze and determine the tunnel's bad geological area and bad construction area based on the geological survey parameter data contained in the tunnel related information, the relevant parameter data of the construction record, and the preset survey parameter data range of bad geological conditions and the parameter data range of the associated parameters of the bad construction area; Based on the pre-compiled 3D finite element rapid modeling method, using automated scripts and parametric design methods, a finite element model containing information on stratum distribution, shield tunnel structure and disaster effects is established for tunnel poor geological areas and poor construction areas; Through the preset finite element analysis software, the established finite element model is subjected to structural static analysis, dynamic analysis, and stability analysis to obtain stress concentration areas, areas where deformation exceeds the preset range, and areas where dynamic stability is lower than the preset reasonable data range, as the areas where the tunnel is severely affected; Finite element analysis software is used to analyze the disaster results of the severely affected areas of the tunnel, including crack width analysis, lining status assessment, collapse simulation and prevention, and joint opening width analysis; According to the mapping relationship between the disaster results and the judgment criteria for the wall-climbing robot's admission and the disaster results analysis of the location of the tunnel's severely affected area, the admission results of the wall-climbing robot are analyzed and determined. The admission results of the wall-climbing robot include admission and non-admission; If the admission result of the wall-climbing robot is not allowed, the detection ends; If the admission result of the wall-climbing robot is admission, the route of the wall-climbing robot to enter the disaster area is planned according to the preset joint opening width range that the wall-climbing robot needs to avoid; The wall-climbing robot is started to enter the disaster area according to the planned route, and the image acquisition and processing device preset on the wall-climbing robot is started to perform disaster image recognition processing to obtain disaster-related image information, and the electromagnetic signal acquisition and processing device preset on the wall-climbing robot is simultaneously started to perform disaster electromagnetic signal recognition processing and determine the disaster center location area through a preset disaster center determination algorithm; The preset disaster center determination algorithms include: Obtain disaster electromagnetic signals, process them using static correction, gain and background noise reduction, construct a three-dimensional data volume, and perform slice analysis on the three-dimensional data volume; Binarization is performed on the three-dimensional electromagnetic wave data volume of the slice analysis to convert it into a black and white image, where black represents low electric field intensity and white represents high electric field intensity; Extracting electric field intensity data from the center of the image at fixed intervals; Monitor the extracted electric field strength data. When the increase in the electric field strength data within a preset time range exceeds a preset increase, the interval is reduced in sequence until the interval reaches a preset value. The extracted electric field strength data is compared with the electric field strength data within a preset distance range to find the area with the highest electric field strength, which is the disaster center location area. Based on the acquired disaster-related image information and the disaster center location area, the specific status data of the disaster-related results in the disaster center location area are analyzed and obtained, and based on the correspondence between the results of the specific status data of the disaster-related results falling into different status assessment data ranges and the disaster effect level, the disaster effect level is analyzed and determined, and the disaster effect level is output.

2. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 1 is characterized in that: If the disaster result analysis is the joint opening width analysis, the disaster result analysis of the severely affected area of ​​the tunnel using finite element analysis software includes: Collect key information of the segments, including but not limited to stress, strain, deformation, material properties, and actual usage scenarios; According to the mapping relationship between the preset numerical range of the segment damage index and the damage index value and the weight value of each damage index, the damage parameters of the segment are analyzed and obtained. The segment damage index includes but is not limited to stress, strain and deformation index; If the damage parameter of the segment does not exceed the preset damage threshold, the opening width of the joint is detected and obtained; If the damage parameters of the segment exceed the preset damage threshold, the finite element analysis software is used to construct a refined model of the segment based on the collected key segment information; According to the actual use scenario of the segment, apply the load conditions corresponding to the actual use scenario of the segment, run the finite element analysis to solve, and obtain the response results of the model under the load; The opening width of the joint is extracted from the response results.

3. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 1 is characterized in that: If the disaster result is crack width analysis, the criteria for the wall-climbing robot to enter are as follows: If the crack width obtained by the crack width analysis is smaller than the second crack width, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot needs to support the conventional operation mode, which includes path planning, wall attachment, movement and navigation, and task execution; If the crack width obtained by the crack width analysis is greater than or equal to the second crack width and less than the first crack width, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot must not only support the conventional operation mode, but also be equipped with an enhanced sealing ring; If the crack width obtained by the crack width analysis exceeds the first crack width, the admission result of the wall-climbing robot is no.

4. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 1 is characterized in that: If the disaster result is a lining condition assessment, the criteria for the admission of the wall-climbing robot are as follows: If the lining status assessment shows that there is no path on the tunnel surface that can avoid the block area, the result of the wall-climbing robot's admission is no; If the lining status is assessed as partial block fall, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot needs to support the normal operation mode; If the lining status is assessed as continuous block falling, the thickness of the block falling area does not exceed the preset thickness, and there is a path on the tunnel surface that can avoid the block falling area, then the result of the wall-climbing robot's access is yes, and the configuration of the wall-climbing robot must not only support the normal operation mode, but also be equipped with an enhanced sealing ring.

5. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 2 is characterized in that: If the disaster result is the joint opening width, the criteria for the wall-climbing robot to enter are as follows: If the joint opening width analysis shows that there is a joint with an opening width less than or equal to the first opening width, the result of the wall-climbing robot admission is yes, and the configuration of the wall-climbing robot must not only support the conventional operation mode, but also be equipped with an enhanced sealing ring; If the joint opening width analysis shows that the opening widths of all joints are greater than the first opening width, the result of the wall-climbing robot's admission is no.

6. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 1 is characterized in that: If the disaster result is collapse, the criteria for judging the access of the wall-climbing robot are as follows: If the disaster analysis result is confirmed collapse, the result of the wall-climbing robot's access is no.

7. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 2 is characterized in that: According to the preset joint opening width range that the wall-climbing robot needs to avoid, the route of the wall-climbing robot to enter the disaster area includes: According to the task requirements and tunnel map information, and based on the path planning algorithm, analyze and obtain the global optimal path. The task requirements include the specific goals of rapid detection of shield tunnel disasters and the urgency ranking of different detection tasks; Analyze whether the opening widths of all joints are smaller than the preset first opening width; If yes, the joints with the smallest opening width are connected and the relevant part of the path in the global optimal path is replaced as the new global optimal path; If not, then analyze whether there is a tunnel disaster result with a negative access result caused by the joint opening width being greater than the first opening width in the global optimal path; If yes, the preset third joint width is used as the upper limit of the joint width, and the acceptable joint width is gradually increased until the path no longer contains tunnel disaster results with a negative access result due to the joint opening width being greater than the first opening width, and the analyzed and determined path is used as the route for the wall-climbing robot to enter the disaster area; If not, the global optimal path obtained through analysis is used as the route for the wall-climbing robot to enter the disaster area.

8. The method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot according to claim 7 is characterized in that: Starting the wall-climbing robot to enter the disaster area according to the planned route includes: Obtain the opening width and distribution density of the joints within a preset distance range from the wall-climbing robot on the planned route; According to the corresponding relationship between the opening width and distribution density of the joints and the traveling speed of the wall-climbing robot, the traveling speed of the wall-climbing robot is analyzed and determined; The travel speed of the wall-climbing robot is determined based on the analysis, and the wall-climbing robot is controlled to enter the disaster area according to the planned route.

9. A shield tunnel disaster rapid detection and assessment system based on a wall-climbing robot, characterized in that: It includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can be loaded and executed by the processor to implement a method for rapid detection and assessment of shield tunnel disasters based on a wall-climbing robot as described in any one of claims 1 to 8.

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