Intelligent detection tunnel karst cave roof stability evaluation method and system

Abstract

The invention provides an intelligent-detection tunnel karst cave roof stability evaluation method and system, and the method comprises the steps: exploring the geological state of a tunnel karst cave roof through deep drilling, carrying out the distance measurement through a surface leveling laser range finder, receiving a signal reflected by the surface of a natural object, carrying out the distance measurement, and constructing a three-dimensional solid model of a tunnel karst cave; performing 3D analysis processing, filtering and interpretation on the original point cloud data of the karst cave, and establishing a numerical analysis model of the karst cave based on a finite element simulation program; according to the actual tunnel external load and the serialized virtual tunnel external load, tunnel karst cave roof mechanical calculation and finite element simulation calculation analysis are carried out, and karst cave roof stability analysis sequence data are generated; and according to the mechanical calculation sequence data of the tunnel karst cave roof, statistically analyzing the maximum principal stress data of the tunnel karst cave roof, and judging whether the stability of the karst cave roof meets the maximum load requirement outside the tunnel.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic induction detection and laser detection of tunnel geological analysis, and more specifically, to an intelligent detection system and method for evaluating the stability of a tunnel cave roof. Background Art

[0002] In tunnel engineering, the stability of the tunnel cave roof is very important. Due to the complex and diverse factors such as the location, size, and geology of the cave, the location, type, and filling degree of the tunnel are different, which makes the construction safety risk of the tunnel cave section high. The cave and the tunnel affect each other. How to analyze the thickness stability of the tunnel under the cave roof is very critical; how to explore the geological state of the tunnel cave roof and accurately measure the distance to construct a three-dimensional solid model of the tunnel cave, how to process and filter the data, interpret and establish a cave numerical analysis model, how to accurately perform mechanical calculations and finite element simulation analysis of the tunnel cave roof, how to statistically analyze the stress data of the tunnel cave roof and judge the stability of the cave roof. These problems remain to be solved. Therefore, it is necessary to propose an intelligent detection tunnel cave roof stability evaluation system and method to at least partially solve the problems existing in the existing technology. Summary of the Invention

[0003] A series of simplified concepts are introduced in the summary of the invention, which will be further explained in detail in the specific implementation method. The summary of the invention does not mean to attempt to limit the key features and necessary technical features of the technical solution for protection, nor does it mean to attempt to determine the scope of protection of the technical solution for protection.

[0004] To at least partially solve the above problems, the present invention provides a method for evaluating the stability of a tunnel cave roof using intelligent detection, comprising: S10, deep drilling is used to explore the geological conditions of the tunnel cave roof, and distance measurement is performed using a surface-leveling laser rangefinder. Simultaneously, distance measurement is performed using signals reflected from natural surfaces, thereby constructing a three-dimensional solid model of the tunnel cave. S20, the original point cloud data of the cave is processed, filtered and interpreted through 3D analysis, and a numerical analysis model of the cave is established based on the finite element simulation program; S30, performing mechanical calculation and finite element simulation analysis of the tunnel cave roof based on the actual tunnel external load and the serialized virtual tunnel external load, and generating sequence data for stability analysis of the cave roof; S40, based on the mechanical calculation sequence data of the tunnel cave roof, statistically analyzing the maximum principal stress data of the tunnel cave roof, and judging whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

[0005] Preferably, S10 includes: S101, exploring the geological conditions of the tunnel cave roof through deep drilling; S102: A multi-directional acoustic and magnetic resonance probe set is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; S103, measuring the distance by using a surface-leveling laser rangefinder. The surface-leveling laser rangefinder actively emits laser light and simultaneously receives signals reflected from the surface of natural objects to measure the distance, thereby constructing a three-dimensional solid model of the tunnel cave.

[0006] Preferably, S20 includes: S201, the original point cloud data of the cave is processed by 3D analysis to obtain 3D analysis-processed point cloud data of the cave; S202, filtering the 3D analysis point cloud data of the cave, and interpreting the filtered data to obtain 3D point cloud interpretation information of the cave; S203 , establishing a numerical analysis model of the cave based on the finite element simulation program according to the interpreted information of the cave 3D point cloud.

[0007] Preferably, S30 includes: S301, based on the actual tunnel external load, serialize the virtual extension according to the load upper and lower limit ranges to generate a serialized virtual tunnel external load; S302, performing a tunnel cave roof mechanics calculation based on the actual tunnel external load and the sequenced virtual tunnel external load, and obtaining tunnel cave roof mechanics calculation sequence data; S303: Perform finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

[0008] Preferably, S40 includes: S401, statistically analyzing the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; S402: Based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof, it is determined whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

[0009] The present invention provides an intelligent detection system for evaluating the stability of a tunnel cave roof, comprising: The roof geological surface leveling monitoring subsystem uses deep drilling to explore the geological status of the tunnel cave roof, uses a surface leveling laser rangefinder to measure distance, and simultaneously receives signals reflected from the surface of natural objects to measure distance, thereby constructing a three-dimensional solid model of the tunnel cave; The filtering and interpretation numerical model subsystem is used to process the original point cloud data of the cave through 3D analysis, filtering and interpretation, and to establish a numerical analysis model of the cave based on the finite element simulation program; The karst cave roof mechanical analysis subsystem performs mechanical calculations and finite element simulation analysis of the tunnel karst cave roof based on the actual tunnel external load and the sequenced virtual tunnel external load, generating sequence data for karst cave roof stability analysis. The cave roof stress evaluation subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof to determine whether the stability of the cave roof meets the maximum external load requirements of the tunnel.

[0010] Preferably, the roof geological surface leveling monitoring subsystem includes: Deep drilling exploration subsystem, which explores the geological status of the tunnel cave roof through deep drilling; Multi-directional acoustic, magnetic and resonant detection subsystem: a multi-directional acoustic, magnetic and resonant probe group is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; The surface leveling laser ranging modeling subsystem measures distance through a surface leveling laser rangefinder. The surface leveling laser rangefinder actively emits lasers and simultaneously receives signals reflected from the surface of natural objects to measure distance and construct a three-dimensional solid model of the tunnel cave.

[0011] Preferably, the filtering interpretation numerical model subsystem includes: The original point cloud 3D analysis subsystem obtains the cave 3D analysis processed point cloud data after the original point cloud data of the cave is processed by 3D analysis; The filtering data interpretation subsystem filters the cave 3D analysis point cloud data and interprets the filtered data to obtain the cave 3D point cloud interpretation information; The cave numerical analysis model subsystem establishes a cave numerical analysis model based on the finite element simulation program according to the interpretation information of the cave 3D point cloud.

[0012] Preferably, the cave roof mechanical analysis subsystem includes: The tunnel external load sequence subsystem sequences virtual extensions according to the actual tunnel external load and the load upper and lower limit ranges to generate sequenced virtual tunnel external loads; The tunnel karst cave mechanics calculation subsystem performs tunnel karst cave roof mechanics calculation based on the actual tunnel external load and the serialized virtual tunnel external load, and obtains tunnel karst cave roof mechanics calculation sequence data; The cave roof stability analysis subsystem performs finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

[0013] Preferably, the cave roof stress evaluation subsystem includes: The principal stress data statistics subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; The cave roof stability determination subsystem determines whether the stability of the cave roof meets the maximum external load requirement of the tunnel based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof.

[0014] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention provides a method and system for evaluating the stability of a tunnel karst cave roof with intelligent detection. The method and system explore the geological state of the tunnel karst cave roof through deep drilling, measure the distance through a surface-leveling laser rangefinder, and simultaneously receive the signal reflected by the surface of a natural object for distance measurement, so as to construct a three-dimensional solid model of the tunnel karst cave; the original point cloud data of the karst cave is processed, filtered and interpreted through 3D analysis, and a karst cave numerical analysis model is established based on a finite element simulation program; the mechanical calculation and finite element simulation analysis of the tunnel karst cave roof are performed according to the actual tunnel external load and the serialized virtual tunnel external load, and a karst cave roof stability analysis sequence data is generated; based on the tunnel karst cave roof mechanical calculation sequence data, the maximum principal stress data of the tunnel karst cave roof is statistically analyzed to determine whether the stability of the karst cave roof meets the tunnel external maximum load requirement; the method can analyze the complex and diverse factors such as the karst cave location, size, geology, and the tunnel karst cave status under different tunnel location, type, and filling degree, and significantly reduce the risk of karst cave. Low construction safety risk; able to conduct thickness stability analysis of tunnel cave roof; deep drilling exploration of tunnel cave roof geological conditions, surface-leveling laser rangefinder for distance measurement and simultaneous reception of signals reflected from the surface of natural objects for distance measurement, making the three-dimensional solid model of tunnel cave more accurate; original point cloud data of cave is processed, filtered and interpreted through 3D analysis, and a numerical analysis model of cave is established based on finite element simulation program, which significantly improves the accuracy of the model; based on actual tunnel external load and serialized virtual tunnel external load, mechanical calculation and finite element simulation analysis of tunnel cave roof are performed, and sequence data for cave roof stability analysis is generated, with significantly increased comprehensiveness of data expansion; able to statistically analyze the maximum principal stress data of tunnel cave roof, determine whether the stability of cave roof meets the maximum external load requirement of tunnel and perform vibration protection, significantly improving the safety of tunnel and cave construction and operation process; has important technical significance and significant effects.

[0015] The present invention describes an intelligent detection method and system for evaluating the stability of a tunnel cave roof. Other advantages, objectives, and features of the present invention will be partially reflected in the following description and will also be understood by those skilled in the art through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1This is a diagram showing an embodiment of the intelligent detection method for evaluating the stability of a tunnel cave roof according to the present invention.

[0017] Figure 2 This is a diagram of an embodiment of an intelligent detection tunnel cave roof stability evaluation system described in the present invention.

[0018] Figure 3 This is another embodiment diagram of the intelligent detection tunnel cave roof stability evaluation system described in the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments so that those skilled in the art can implement the invention with reference to the description. Figure 1 As shown, the present invention provides a method for evaluating the stability of a tunnel cave roof with intelligent detection, comprising: S10, deep drilling is used to explore the geological conditions of the tunnel cave roof, and distance measurement is performed using a surface-leveling laser rangefinder. Simultaneously, distance measurement is performed using signals reflected from natural surfaces, thereby constructing a three-dimensional solid model of the tunnel cave. S20, the original point cloud data of the cave is processed, filtered and interpreted through 3D analysis, and a numerical analysis model of the cave is established based on the finite element simulation program; S30, performing mechanical calculation and finite element simulation analysis of the tunnel cave roof based on the actual tunnel external load and the serialized virtual tunnel external load, and generating sequence data for stability analysis of the cave roof; S40, based on the mechanical calculation sequence data of the tunnel cave roof, statistically analyzing the maximum principal stress data of the tunnel cave roof, and judging whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

[0020] The principle and effect of the above technical solution are as follows: the present invention provides a method for evaluating the stability of tunnel cave roof with intelligent detection, which explores the geological status of tunnel cave roof through deep drilling, measures distance through a surface-leveling laser rangefinder, and simultaneously receives signals reflected from the surface of natural objects for distance measurement, thereby constructing a three-dimensional entity model of the tunnel cave; the original point cloud data of the cave is processed, filtered, and interpreted through 3D analysis, and a numerical analysis model of the cave is established based on a finite element simulation program; according to the actual tunnel external load and the serialized virtual tunnel external load, the mechanical calculation and finite element simulation analysis of the tunnel cave roof are performed to generate sequence data for analysis of the stability of the cave cave roof; according to the mechanical calculation sequence data of the tunnel cave roof, the maximum principal stress data of the tunnel cave roof are statistically analyzed to determine whether the stability of the cave cave roof meets the maximum external load requirement of the tunnel; it can analyze the complex and diverse factors such as the location, size, and geology of the cave and the tunnel cave under different conditions of the location, type, and filling degree of the tunnel The 3D model of the tunnel and cave is very simple and easy to use, and the 3D model of the tunnel and cave is very simple and easy to use.

[0021] In one embodiment, S10 includes: S101, exploring the geological conditions of the tunnel cave roof through deep drilling; S102: A multi-directional acoustic and magnetic resonance probe set is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; S103, measuring the distance by using a surface-leveling laser rangefinder. The surface-leveling laser rangefinder actively emits laser light and simultaneously receives signals reflected from the surface of natural objects to measure the distance, thereby constructing a three-dimensional solid model of the tunnel cave.

[0022] The principle and effect of the above technical solution are: exploring the geological status of the top plate of the tunnel cave through deep drilling; setting a multi-directional acoustic magnetic resonance probe group at the end of the drilling exploration to detect the local vibration strength of the top plate of the tunnel cave; measuring the distance through a surface-leveling laser rangefinder, which actively emits lasers and receives signals reflected from the surface of natural objects to measure the distance, and constructs a three-dimensional solid model of the tunnel cave; measuring the distance through a surface-leveling laser rangefinder, which actively emits lasers and receives signals reflected from the surface of natural objects to measure the distance, and constructs a three-dimensional solid model of the tunnel cave, including: measuring the distance through a surface-leveling laser rangefinder; the surface-leveling laser rangefinder includes: a tunnel surface-fitting extension substrate 1031, a precision leveling gear seat 1032, a laser rangefinder probe group 1033, and a laser distance measurement monitoring device 1034; the tunnel surface-fitting extension substrate fits the shape of the tunnel surface and fits at any position on the tunnel surface; precision adjustment The flat gear seat precisely levels the laser rangefinder probe group to maintain the consistency of all laser probes in the laser rangefinder probe group with the horizontal plane reference angle; the laser ranging monitoring device controls automatic leveling, laser rangefinder probe laser emission reflection sensing detection and laser rangefinder ranging; the laser rangefinder probe group of the surface leveling laser rangefinder has multiple laser probes that actively emit lasers, and synchronously measures the distance of multiple scanning points to measure the slant distance from the coordinate center of the precision leveling gear seat to the scanning point, and obtains the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling gear seat in coordination with the horizontal and vertical angles of the scan. At the same time, it accepts the signal reflected by the surface of the natural object for ranging, and mutually verifies the accuracy of the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling gear seat, selects the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling gear seat closest to the mean, and obtains the precise three-dimensional measured data of the multi-point detection mean mutual verification, and then constructs a three-dimensional solid model of the tunnel cave.

[0023] In one embodiment, S20 includes: S201, the original point cloud data of the cave is processed by 3D analysis to obtain 3D analysis-processed point cloud data of the cave; S202, filtering the 3D analysis point cloud data of the cave, and interpreting the filtered data to obtain 3D point cloud interpretation information of the cave; S203 , establishing a numerical analysis model of the cave based on the finite element simulation program according to the interpreted information of the cave 3D point cloud.

[0024] The principle and effect of the above technical solution are as follows: after the original point cloud data of the cave is processed by 3D analysis, the 3D analysis and processing point cloud data of the cave is obtained; the 3D analysis and processing point cloud data of the cave is filtered, and the filtered processing data is interpreted to obtain the 3D point cloud interpretation information of the cave; according to the 3D point cloud interpretation information of the cave, a numerical analysis model of the cave is established based on the finite element simulation program.

[0025] In one embodiment, S30 includes: S301, based on the actual tunnel external load, serialize the virtual extension according to the load upper and lower limit ranges to generate a serialized virtual tunnel external load; S302, performing a tunnel cave roof mechanics calculation based on the actual tunnel external load and the sequenced virtual tunnel external load, and obtaining tunnel cave roof mechanics calculation sequence data; S303: Perform finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

[0026] The principle and effect of the above technical solution are as follows: according to the actual tunnel external load, the virtual extension is serialized according to the upper and lower limit ranges of the load to generate a serialized virtual tunnel external load; according to the actual tunnel external load and the serialized virtual tunnel external load, the mechanical calculation of the tunnel cave roof is performed to obtain the mechanical calculation sequence data of the tunnel cave roof; according to the serialized virtual tunnel external load, the finite element simulation calculation and analysis are performed to generate the stability analysis sequence data of the cave roof.

[0027] In one embodiment, S40 includes: S401, statistically analyzing the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; S402: Based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof, it is determined whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

[0028] The principle and effect of the above technical solution are as follows: according to the mechanical calculation sequence data of the tunnel cave roof, the maximum principal stress data of the tunnel cave roof is statistically analyzed; according to the stability analysis sequence data of the cave roof, the maximum principal stress data of the cave roof is used to judge whether the stability of the cave roof meets the maximum load requirement of the tunnel outside; the maximum principal stress data of the cave roof include: the maximum tensile stress data of the cave roof and the maximum compressive stress data of the cave roof; judge whether the stability of the cave roof meets the maximum tensile stress data of the cave roof or the maximum compressive stress data of the cave roof; and withstand the maximum load outside the tunnel. Simulate vibration levels; simulate multiple vibration frequencies under the maximum tensile stress of the cave roof, monitor the resonance frequency of the cave roof, continue the vibration frequency at the resonance frequency of the cave roof until the simulated cave roof falls off, and calculate the resonance time of the simulated cave roof falling off; simulate multiple vibration frequencies under the maximum compressive stress of the cave roof, monitor the resonance frequency of the cave roof, continue the vibration frequency at the resonance frequency of the cave roof until the simulated cave roof collapses, and calculate the resonance time of the simulated cave roof collapse; set a resonance frequency damper to prevent the cave roof from falling off at the resonance frequency of the cave roof.

[0029] like Figure 2As shown, the present invention provides an intelligent detection system for evaluating the stability of a tunnel cave roof, comprising: The roof geological surface leveling monitoring subsystem uses deep drilling to explore the geological status of the tunnel cave roof, uses a surface leveling laser rangefinder to measure distance, and simultaneously receives signals reflected from the surface of natural objects to measure distance, thereby constructing a three-dimensional solid model of the tunnel cave; The filtering and interpretation numerical model subsystem is used to process the original point cloud data of the cave through 3D analysis, filtering and interpretation, and to establish a numerical analysis model of the cave based on the finite element simulation program; The karst cave roof mechanical analysis subsystem performs mechanical calculations and finite element simulation analysis of the tunnel karst cave roof based on the actual tunnel external load and the sequenced virtual tunnel external load, generating sequence data for karst cave roof stability analysis. The cave roof stress evaluation subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof to determine whether the stability of the cave roof meets the maximum external load requirements of the tunnel.

[0030] The principle and effect of the above technical solution are as follows: the present invention provides an intelligent detection system for evaluating the stability of tunnel cave roof, including: a roof geological surface leveling monitoring subsystem, which explores the geological status of the tunnel cave roof through deep drilling, measures the distance through a surface leveling laser rangefinder, and simultaneously receives the signal reflected by the surface of the natural object for distance measurement, and constructs a three-dimensional entity model of the tunnel cave; a filtering and interpretation numerical model subsystem, which processes the original point cloud data of the cave through 3D analysis, filtering, and interpretation, and establishes a cave numerical analysis model based on a finite element simulation program; a cave roof mechanical analysis subsystem, which performs mechanical calculations and finite element simulation analysis of the tunnel cave roof according to the actual tunnel external load and the serialized virtual tunnel external load, and generates sequence data for analyzing the stability of the cave roof; a cave roof stress evaluation subsystem, which statistically analyzes the maximum principal stress data of the tunnel cave roof according to the sequence data of the tunnel cave roof mechanical calculation, and determines whether the stability of the cave roof meets the maximum external load requirement of the tunnel; it can analyze the complex conditions such as the location, size, and geology of the cave. The various factors and the tunnel karst state under different conditions of tunnel location, type and filling degree can significantly reduce the construction safety risk; it can conduct thickness stability analysis of tunnel karst roof; it can conduct deep drilling exploration of the geological state of tunnel karst roof, and can use surface-leveling laser rangefinder to measure distance while receiving signals reflected from the surface of natural objects for distance measurement, so that the three-dimensional solid model of tunnel karst is more accurate; the original point cloud data of the karst cave is processed, filtered and interpreted through 3D analysis, and a karst cave numerical analysis model is established based on the finite element simulation program, and the accuracy of the model is significantly improved; according to the actual tunnel external load and the serialized virtual tunnel external load, the mechanical calculation and finite element simulation analysis of the tunnel karst roof are carried out to generate karst cave roof stability analysis sequence data, and the comprehensiveness of data expansion is significantly increased; it can statistically analyze the maximum principal stress data of the tunnel karst roof, judge whether the stability of the karst cave roof meets the maximum external load requirements of the tunnel and can perform vibration protection, significantly improving the safety of the tunnel and karst cave construction and operation process; it has important technical significance and significant effects.

[0031] In one embodiment, the roof geological surface leveling monitoring subsystem includes: Deep drilling exploration subsystem, which explores the geological status of the tunnel cave roof through deep drilling; Multi-directional acoustic, magnetic and resonant detection subsystem: a multi-directional acoustic, magnetic and resonant probe group is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; The surface leveling laser ranging modeling subsystem measures distance through a surface leveling laser rangefinder. The surface leveling laser rangefinder actively emits lasers and simultaneously receives signals reflected from the surface of natural objects to measure distance and construct a three-dimensional solid model of the tunnel cave.

[0032] The principle and effect of the above technical solution are as follows: Figure 3As shown in the figure, the roof geological surface leveling monitoring subsystem includes: a deep drilling exploration subsystem, which explores the geological status of the tunnel cave roof through deep drilling; a multi-directional acoustic magnetic resonance detection subsystem, which sets a multi-directional acoustic magnetic resonance probe group at the end of the drilling exploration to detect the local vibration resistance of the tunnel cave roof; a surface leveling laser ranging modeling subsystem, which measures the distance through a surface leveling laser rangefinder. The surface leveling laser rangefinder actively emits lasers and receives signals reflected from the surface of natural objects to measure the distance, thereby constructing a tunnel cave model. Three-dimensional entity model; measuring distance by surface leveling laser rangefinder, which actively emits laser and receives signals reflected from the surface of natural objects to measure distance, and constructing a three-dimensional entity model of tunnel cave includes: measuring distance by surface leveling laser rangefinder; the surface leveling laser rangefinder includes: tunnel surface bonding extension substrate 1031, precision leveling gear seat 1032, laser rangefinder probe group 1033, laser distance measurement monitoring device 1034; tunnel surface bonding extension substrate bonding tunnel The road surface shape fits any position on the tunnel surface; the precision leveling tooth seat precisely levels the laser rangefinder probe group to maintain the consistency of the reference angle of all laser probes in the laser rangefinder probe group with the horizontal plane; the laser ranging monitoring device controls automatic leveling, laser rangefinder probe laser emission reflection sensing detection and laser rangefinder distance measurement; the laser rangefinder probe group of the surface leveling laser rangefinder has multiple laser probes that actively emit lasers, and synchronously measures the distance from the coordinate center of the precision leveling tooth seat to the scanning point to measure the slant distance. In coordination with the horizontal and vertical angles of the scan, the spatial relative coordinates of the multiple scanning points and the coordinate center of the precision leveling tooth seat are obtained. At the same time, the signal reflected by the surface of the natural object is received for distance measurement, and the accuracy of the spatial relative coordinates of the multiple scanning points and the coordinate center of the precision leveling tooth seat is mutually verified. The spatial relative coordinates of the multiple scanning points and the coordinate center of the precision leveling tooth seat are selected to be closest to the mean, and the precise three-dimensional measured data of the multi-point detection mean mutual verification is obtained, and then a three-dimensional solid model of the tunnel cave is constructed.

[0033] In one embodiment, the filtering interpretation numerical model subsystem includes: The original point cloud 3D analysis subsystem obtains the cave 3D analysis processed point cloud data after the original point cloud data of the cave is processed by 3D analysis; The filtering data interpretation subsystem filters the cave 3D analysis point cloud data and interprets the filtered data to obtain the cave 3D point cloud interpretation information; The cave numerical analysis model subsystem establishes a cave numerical analysis model based on the finite element simulation program according to the interpretation information of the cave 3D point cloud.

[0034] The principle and effect of the above technical solution are as follows: the filtering interpretation numerical model subsystem includes: the original point cloud 3D analysis subsystem, which obtains the cave 3D analysis processing point cloud data after the original point cloud data of the cave is processed by 3D analysis; the filtering processing data interpretation subsystem, which filters the cave 3D analysis processing point cloud data and interprets the filtered processing data to obtain the cave 3D point cloud interpretation information; the cave numerical analysis model subsystem, which establishes the cave numerical analysis model based on the finite element simulation program according to the cave 3D point cloud interpretation information.

[0035] In one embodiment, the cave roof mechanical analysis subsystem includes: The tunnel external load sequence subsystem sequences virtual extensions according to the actual tunnel external load and the load upper and lower limit ranges to generate sequenced virtual tunnel external loads; The tunnel karst cave mechanics calculation subsystem performs tunnel karst cave roof mechanics calculation based on the actual tunnel external load and the serialized virtual tunnel external load, and obtains tunnel karst cave roof mechanics calculation sequence data; The cave roof stability analysis subsystem performs finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

[0036] The principles and effects of the above technical solution are as follows: the karst cave roof mechanical analysis subsystem includes: the tunnel external load sequence subsystem, which sequences the virtual extension according to the upper and lower limit ranges of the load based on the actual tunnel external load to generate a serialized virtual tunnel external load; the tunnel karst cave mechanical calculation subsystem, which performs tunnel karst cave roof mechanical calculation based on the actual tunnel external load and the serialized virtual tunnel external load to obtain tunnel karst cave roof mechanical calculation sequence data; the karst cave roof stability analysis subsystem, which performs finite element simulation calculation and analysis based on the serialized virtual tunnel external load to generate a karst cave roof stability analysis sequence data.

[0037] In one embodiment, the cave roof stress assessment subsystem includes: The principal stress data statistics subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; The cave roof stability determination subsystem determines whether the stability of the cave roof meets the maximum external load requirement of the tunnel based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof.

[0038] The principle and effect of the above technical solution are as follows: the cave roof stress evaluation subsystem includes: the principal stress data statistics subsystem, which statistically analyzes the maximum principal stress data of the tunnel cave roof according to the mechanical calculation sequence data of the tunnel cave roof; the cave roof stability judgment subsystem, which judges whether the cave roof stability meets the maximum external load requirement of the tunnel through the maximum principal stress data of the cave roof according to the stability analysis sequence data of the cave roof; the maximum principal stress data of the cave roof include: the maximum tensile stress data of the cave roof and the maximum compressive stress data of the cave roof; the judgment subsystem judges whether the cave roof stability meets the maximum tensile stress data of the cave roof or the maximum compressive stress data of the cave roof stress data; and withstand vibration level simulation under the maximum load outside the tunnel; simulate multiple vibration frequencies under the maximum tensile stress of the cave roof, monitor the resonance frequency of the cave roof, continue the vibration frequency at the resonance frequency of the cave roof until the simulated cave roof falls off, and calculate the resonance time of the simulated cave roof falling off; simulate multiple vibration frequencies under the maximum compressive stress of the cave roof, monitor the resonance frequency of the cave roof, continue the vibration frequency at the resonance frequency of the cave roof until the simulated cave roof collapses, and calculate the resonance time of the simulated cave roof collapse; set a resonance frequency damper to prevent the cave roof from falling off at the resonance frequency of the cave roof.

[0039] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for evaluating the stability of a tunnel cave roof using intelligent detection, characterized in that: include: S10, deep drilling is used to explore the geological conditions of the tunnel cave roof, and distance measurement is performed using a surface-leveling laser rangefinder. Simultaneously, distance measurement is performed using signals reflected from natural surfaces, thereby constructing a three-dimensional solid model of the tunnel cave. S20, the original point cloud data of the cave is processed, filtered and interpreted through 3D analysis, and a numerical analysis model of the cave is established based on the finite element simulation program; S30, performing mechanical calculation and finite element simulation analysis of the tunnel cave roof based on the actual tunnel external load and the serialized virtual tunnel external load, and generating sequence data for stability analysis of the cave roof; S40, based on the mechanical calculation sequence data of the tunnel cave roof, statistically analyzing the maximum principal stress data of the tunnel cave roof, and judging whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

2. The method for evaluating the stability of a tunnel cave roof by intelligent detection according to claim 1, characterized in that: The S10 includes: S101, exploring the geological conditions of the tunnel cave roof through deep drilling; S102: A multi-directional acoustic and magnetic resonance probe set is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; S103, measuring the distance by using a surface-leveling laser rangefinder. The surface-leveling laser rangefinder actively emits laser light and simultaneously receives signals reflected from the surface of natural objects to measure the distance, thereby constructing a three-dimensional solid model of the tunnel cave.

3. The method for evaluating the stability of a tunnel cave roof by intelligent detection according to claim 1, characterized in that: The S20 includes: S201, the original point cloud data of the cave is processed by 3D analysis to obtain 3D analysis-processed point cloud data of the cave; S202, filtering the 3D analysis point cloud data of the cave, and interpreting the filtered data to obtain 3D point cloud interpretation information of the cave; S203 , establishing a numerical analysis model of the cave based on the finite element simulation program according to the interpreted information of the cave 3D point cloud.

4. The method for evaluating the stability of a tunnel cave roof by intelligent detection according to claim 1, characterized in that: The S30 includes: S301, based on the actual tunnel external load, serialize the virtual extension according to the load upper and lower limit ranges to generate a serialized virtual tunnel external load; S302, performing a tunnel cave roof mechanics calculation based on the actual tunnel external load and the sequenced virtual tunnel external load, and obtaining tunnel cave roof mechanics calculation sequence data; S303: Perform finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

5. The method for evaluating the stability of a tunnel cave roof by intelligent detection according to claim 1, characterized in that: S40 includes: S401, statistically analyzing the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; S402: Based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof, it is determined whether the stability of the cave roof meets the maximum external load requirement of the tunnel.

6. An intelligent detection system for evaluating the stability of a tunnel cave roof, characterized in that: include: The roof geological surface leveling monitoring subsystem uses deep drilling to explore the geological status of the tunnel cave roof, uses a surface leveling laser rangefinder to measure distance, and simultaneously receives signals reflected from the surface of natural objects to measure distance, thereby constructing a three-dimensional solid model of the tunnel cave; The filtering and interpretation numerical model subsystem is used to process the original point cloud data of the cave through 3D analysis, filtering and interpretation, and to establish a numerical analysis model of the cave based on the finite element simulation program; The karst cave roof mechanical analysis subsystem performs mechanical calculations and finite element simulation analysis of the tunnel karst cave roof based on the actual tunnel external load and the sequenced virtual tunnel external load, generating sequence data for karst cave roof stability analysis. The cave roof stress evaluation subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof to determine whether the stability of the cave roof meets the maximum external load requirements of the tunnel.

7. The intelligent detection system for evaluating the stability of a tunnel cave roof according to claim 6, characterized in that: Roof geological surface leveling monitoring subsystem, including: Deep drilling exploration subsystem, which explores the geological status of the tunnel cave roof through deep drilling; Multi-directional acoustic, magnetic and resonant detection subsystem: a multi-directional acoustic, magnetic and resonant probe group is set at the end of the drilling exploration to detect the local vibration strength of the tunnel cave roof; The surface leveling laser ranging modeling subsystem measures distance through a surface leveling laser rangefinder. The surface leveling laser rangefinder actively emits lasers and simultaneously receives signals reflected from the surface of natural objects to measure distance and construct a three-dimensional solid model of the tunnel cave.

8. The intelligent detection system for evaluating the stability of a tunnel cave roof according to claim 6, characterized in that: The filtering interpretation numerical model subsystem includes: The original point cloud 3D analysis subsystem obtains the cave 3D analysis processed point cloud data after the original point cloud data of the cave is processed by 3D analysis; The filtering data interpretation subsystem filters the cave 3D analysis point cloud data and interprets the filtered data to obtain the cave 3D point cloud interpretation information; The cave numerical analysis model subsystem establishes a cave numerical analysis model based on the finite element simulation program according to the interpretation information of the cave 3D point cloud.

9. The intelligent detection system for evaluating the stability of a tunnel cave roof according to claim 6, characterized in that: The cave roof mechanical analysis subsystem includes: The tunnel external load sequence subsystem sequences virtual extensions according to the actual tunnel external load and the load upper and lower limit ranges to generate sequenced virtual tunnel external loads; The tunnel karst cave mechanics calculation subsystem performs tunnel karst cave roof mechanics calculation based on the actual tunnel external load and the serialized virtual tunnel external load, and obtains tunnel karst cave roof mechanics calculation sequence data; The cave roof stability analysis subsystem performs finite element simulation and analysis based on the external load of the serialized virtual tunnel to generate sequence data for cave roof stability analysis.

10. The intelligent detection system for evaluating the stability of a tunnel cave roof according to claim 6, characterized in that: The cave roof stress evaluation subsystem includes: The principal stress data statistics subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof based on the mechanical calculation sequence data of the tunnel cave roof; The cave roof stability determination subsystem determines whether the stability of the cave roof meets the maximum external load requirement of the tunnel based on the cave roof stability analysis sequence data and the maximum principal stress data of the cave roof.

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