Tunnel surrounding rock dynamic classification and blasting parameter optimization method and system

Abstract

The application provides a tunnel surrounding rock dynamic grading and blasting parameter optimization method and system, and relates to the technical field of data processing.The method comprises the following steps: collecting geological parameters of a current tunnel face in real time through a multi-source sensing system arranged on the tunnel face; identifying a plurality of characteristic sampling points with geological representation in the tunnel face space domain based on the collected geological parameters; constructing an initial tetrahedron by selecting four non-coplanar characteristic sampling points, and sequentially adding the points farthest from the current convex hull surface in the remaining points into the tetrahedron by using an iterative expansion method, and recalculating the convex hull surface until all the characteristic sampling points are enveloped to form a convex polyhedron evaluation area boundary.The application can realize data full-process connection and function cooperation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock dynamic classification and blasting parameter optimization method and system. BACKGROUND

[0002] With the extension of China's high-speed rail, highway, water conservancy and other infrastructure construction to complex geological areas, the geological conditions faced by tunnel engineering are increasingly severe, and the traditional static assessment and experience construction mode has been difficult to meet the safety construction demand.

[0003] From the perspective of surrounding rock classification, the existing technology relies on advanced geological drilling or geological radar detection to obtain discrete data, which has limitations: poor data timeliness, drilling data can only reflect the geological conditions of the detection section, and geological mutations in the construction process, such as sudden gushing and fault exposure, cannot be included in the classification in real time.

[0004] In addition, the monitoring, evaluation and optimization closed loop of the traditional technology is broken: the surrounding rock classification, blasting parameter design and effect monitoring belong to independent links, and multi-source data such as drilling data, vibration data and section scanning data are not analyzed by fusion, leading to a vicious cycle of geological changes, parameter mismatch and effect exceeding the standard, for example, in the construction of a mountainous highway tunnel, due to the failure to timely include the sudden increase of water content in the working face into the classification correction, the original designed charge amount is still used, which eventually leads to local collapse of the working face. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a tunnel surrounding rock dynamic classification and blasting parameter optimization method and system to realize data full-process connection and function cooperation.

[0006] To solve the above technical problems, the technical scheme of the present application is as follows:

[0007] In the first aspect, the tunnel surrounding rock dynamic classification and blasting parameter optimization method comprises:

[0008] A multi-source sensing system deployed in the tunnel working face is used to collect geological parameters of the current working face in real time;

[0009] Based on the collected geological parameters, a plurality of characteristic sampling points with geological representation are identified in the spatial domain of the working face; an initial tetrahedron is constructed by selecting four non-coplanar characteristic sampling points, and the farthest point in the remaining points from the current convex hull surface is sequentially included by using an iterative expansion method, and the convex hull surface is recalculated until all characteristic sampling points are included, forming a convex polyhedron evaluation region boundary;

[0010] According to the convex polyhedron evaluation region boundary, the sub-region is discretely divided, forming a continuous analysis unit, and the distribution characteristics of each geological parameter in each unit are counted; based on the variation characteristics of the geological parameters of each unit, a comprehensive geological data correction coefficient is generated, and the original collected uniaxial compressive strength, joint development degree and water content parameters are corrected according to the correction coefficient to obtain the corrected geological parameters;

[0011] Based on the corrected geological parameters, multi-index weighted fusion analysis is carried out according to the preset surrounding rock classification evaluation system to dynamically determine the surrounding rock grade of the current tunnel face;

[0012] According to the surrounding rock grade, a corresponding set of optimized blasting parameters is matched and generated from the pre-constructed blasting parameter knowledge base; based on the set of optimized blasting parameters, the blasting operation is implemented, the overbreak and underbreak rate is calculated by collecting the cross-section shape data, and the surrounding rock vibration response data is collected by the distributed vibration monitoring system;

[0013] Through real-time comparison of the overbreak and underbreak rate and the vibration response data with the preset specification safety threshold, the comparison result is obtained, and a closed-loop optimization is finally formed.

[0014] Further, based on the collected geological parameters, a plurality of characteristic sampling points with geological representation are identified in the tunnel face spatial domain; an initial tetrahedron is constructed by selecting four non-coplanar characteristic sampling points, and the farthest point in the remaining points from the current convex hull surface is sequentially included by using an iterative expansion method, and the convex hull surface is recalculated until all characteristic sampling points are enveloped, forming the convex polyhedron evaluation region boundary, including:

[0015] Based on the geological parameter data, a spatial clustering analysis method is used to identify characteristic sampling points with geological representation, and spatial coordinate information is obtained; four non-coplanar points in space are selected as initial vertices to construct an initial tetrahedron structure;

[0016] Based on the initial tetrahedron structure as a starting point, the remaining characteristic sampling points are processed by using an iterative expansion algorithm; in each iteration process, the spatial distance between the remaining points and the current convex hull surface is calculated, and the farthest point is selected to be included in the convex hull vertex set;

[0017] According to the convex hull vertex set, the convex hull surface geometry is recalculated, and all characteristic sampling points are enveloped in the convex hull or on the surface, forming a final three-dimensional convex polyhedron;

[0018] Based on the outer surface of the three-dimensional convex polyhedron as a limit, the convex polyhedron evaluation region boundary is determined.

[0019] Further, according to the convex polyhedron evaluation region boundary, sub-region discrete division is carried out to form continuous analysis units, and the distribution characteristics of each geological parameter in each unit are counted; based on the variation characteristics of the geological parameters of each unit, a comprehensive geological data correction coefficient is generated, and the parameter value correction is carried out according to the uniaxial compressive strength, joint development degree and water content correction coefficient, including:

[0020] The convex polyhedron evaluation region boundary is divided into a plurality of continuous analysis units by using a regular voxel grid division method;

[0021] For each analysis unit, the values of each geological parameter of the characteristic sampling points are calculated; according to the values of each geological parameter of the characteristic sampling points, the statistical characteristics thereof in the analysis unit are obtained, and the statistical characteristics include the mean value, variance and spatial distribution characteristics;

[0022] Based on the statistical characteristics, the spatial variation characteristics are analyzed, and the geological parameter variation coefficient of each analysis unit is calculated;

[0023] According to the geological parameter variation coefficient, a comprehensive geological data correction coefficient is obtained by using a weighted average algorithm;

[0024] Through the comprehensive geological data correction coefficient, the original collected uniaxial compressive strength, joint development degree and water content parameters are corrected and calculated to obtain the corrected geological parameter values;

[0025] Further, based on the corrected geological parameters, multi-index weighted fusion analysis is carried out according to the preset surrounding rock classification evaluation system to dynamically determine the surrounding rock grade of the current working face, including:

[0026] The corrected geological parameter values are received, and the geological parameter values include the corrected uniaxial compressive strength, rock mass integrity coefficient and water content index;

[0027] Based on the corrected geological parameter values, a preset surrounding rock classification evaluation system is called, and the surrounding rock classification evaluation system includes each parameter threshold range corresponding to different surrounding rock grades and weight coefficients distributed based on the influence degree of surrounding rock stability;

[0028] The corrected geological parameter values are input into the surrounding rock classification evaluation system, and a multi-index weighted fusion algorithm is used for calculation, each parameter is weighted and fused according to the corresponding weight coefficient, and a comprehensive index value is obtained;

[0029] According to the comprehensive index value, based on the preset surrounding rock grade determination rule, the corresponding surrounding rock grade of the current working face is dynamically determined;

[0030] Further, according to the surrounding rock grade, a corresponding set of optimized blasting parameters is matched and generated from the pre-constructed blasting parameter knowledge base, including:

[0031] receiving the current face surrounding rock grade determination result; based on the surrounding rock grade determination result, retrieving a corresponding reference blasting parameter set from a pre-constructed blasting parameter knowledge base, the reference blasting parameter set including a borehole arrangement scheme, a single-hole charge amount, a total charge amount, and a micro-delay time sequence;

[0032] In combination with the specific engineering condition characteristics of the current face, a parameter optimization algorithm is used to adaptively adjust the reference blasting parameter set to generate a blasting parameter optimization set suitable for the current geological conditions;

[0033] Further, based on the blasting parameter optimization set, a blasting operation is implemented, the overbreak and underbreak rates are calculated by collecting cross-section shape data, and the surrounding rock vibration response data are collected by a distributed vibration monitoring system, including:

[0034] The blasting parameter optimization set is received and transmitted to an intelligent blasting control terminal to perform the blasting operation;

[0035] Based on the completion of the blasting operation, three-dimensional shape data of the post-blasting tunnel section are collected by a three-dimensional laser scanning system deployed in the tunnel;

[0036] The three-dimensional shape data are fitted and compared with the tunnel design section data to calculate the cross-section overbreak and underbreak areas and the overbreak and underbreak rates; the surrounding rock vibration response data generated during the blasting process are collected by a distributed vibration monitoring sensor network;

[0037] Further, the overbreak and underbreak rates and the vibration response data are compared in real time with preset specification safety thresholds to obtain comparison results, and finally a closed-loop optimization is formed, including:

[0038] The collected actual overbreak and underbreak rates and surrounding rock vibration response data are compared in real time with the safety thresholds in the preset specification to obtain parameter adjustment instructions and adjustment amount suggestions;

[0039] Based on the parameter adjustment instructions and adjustment amount suggestions, the blasting parameter optimization scheme is dynamically corrected;

[0040] The corrected blasting parameter optimization scheme is fed back to the blasting operation system to realize continuous iterative optimization and closed-loop control of the blasting parameters.

[0041] In a second aspect, a tunnel surrounding rock dynamic classification and blasting parameter optimization system includes:

[0042] The acquisition module is used for collecting the geological parameters of the current tunnel face in real time through a multi-source sensing system arranged on the tunnel face; based on the collected geological parameters, a plurality of characteristic sampling points with geological representation are identified in the spatial domain of the tunnel face; an initial tetrahedron is constructed by selecting four non-coplanar characteristic sampling points, and the farthest point in the remaining points from the current convex hull surface is sequentially included by using an iterative expansion method, and the convex hull surface is recalculated until all the characteristic sampling points are enveloped, forming a convex polyhedron evaluation area boundary;

[0043] The reasoning module is used for discretely dividing the sub-regions according to the convex polyhedron evaluation area boundary, forming continuous analysis units, and counting the distribution characteristics of the geological parameters in each unit; based on the variation characteristics of the geological parameters in each unit, a comprehensive geological data correction coefficient is generated, and the uniaxial compressive strength, joint development degree and water content parameters originally collected are corrected according to the correction coefficient to obtain corrected geological parameters;

[0044] The judgment module is used for dynamically determining the surrounding rock grade of the current tunnel face according to a preset surrounding rock classification evaluation system based on the corrected geological parameters and multi-index weighted fusion analysis;

[0045] The calculation module is used for matching and generating a corresponding blasting parameter optimization set from a pre-constructed blasting parameter knowledge base according to the surrounding rock grade; the blasting operation is implemented based on the blasting parameter optimization set, the overbreak and underbreak rates are calculated by collecting the section shape data, and the surrounding rock vibration response data are collected by a distributed vibration monitoring system;

[0046] The processing module is used for obtaining a comparison result by comparing the overbreak and underbreak rates and the vibration response data with preset specification safety threshold values in real time, and finally forming a closed-loop optimization.

[0047] The above scheme of the present application at least has the following beneficial effects:

[0048] The application adopts a multi-source sensing system deployed on the tunnel face to collect geological parameters in real time, combines spatial clustering analysis and an iterative extended convex hull algorithm to construct a convex polyhedron evaluation area boundary, then discretizes the convex polyhedron evaluation area boundary through a regular voxel grid and generates a comprehensive correction coefficient based on a variation coefficient weighting to correct core geological parameters, dynamically determines the surrounding rock grade based on a pre-set evaluation system through multi-index weighted fusion, matches the baseline parameters from the pre-constructed blasting parameter knowledge base and optimizes adaptively combined with the engineering conditions, collects post-blasting data through three-dimensional laser scanning and distributed vibration monitoring, and compares the standard threshold in real time to form a closed-loop optimization integrated technical means, which effectively overcomes the technical problems in traditional tunnel construction that the surrounding rock classification relies on discrete drilling data, resulting in poor timeliness and inability to capture geological mutations on the face, the experience-based blasting parameter design does not match the real-time geology, and the surrounding rock classification, parameter design and effect monitoring are broken in the closed loop, and multi-source data is not fused, and further realizes the connection of the whole process data and the functional synergy of the tunnel construction, so that the surrounding rock classification can dynamically adapt to the real-time geological conditions, and the blasting parameters match the current surrounding rock characteristics, thereby reducing the overbreak and underbreak rates and the risk of excessive surrounding rock vibration. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of the tunnel surrounding rock dynamic classification and blasting parameter optimization method provided by the embodiment of the application.

[0050] Figure 2 is a schematic diagram of the tunnel surrounding rock dynamic classification and blasting parameter optimization system provided by the embodiment of the application. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0052] As shown in Figure 1 , the embodiment of the application proposes a tunnel surrounding rock dynamic classification and blasting parameter optimization method, which comprises the following steps:

[0053] Step 1, through a multi-source sensing system deployed on the tunnel face, real-time collection of the geological parameters of the current face;

[0054] Step 2, based on the collected geological parameters, a plurality of characteristic sampling points with geological representation are identified in the spatial domain of the working face; an initial tetrahedron is constructed by selecting four non-coplanar characteristic sampling points, and an iterative expansion method is used to sequentially include the points farthest from the current convex hull surface in the remaining points, and the convex hull surface is recalculated until all characteristic sampling points are enveloped, forming a convex polyhedron evaluation region boundary;

[0055] Step 3, according to the convex polyhedron evaluation region boundary, sub-regional discrete division is carried out to form continuous analysis units, and the distribution characteristics of each geological parameter in each unit are counted; based on the variation characteristics of the geological parameters of each unit, a comprehensive geological data correction coefficient is generated, and the uniaxial compressive strength, joint development degree and water content parameters originally collected are corrected according to the correction coefficient to obtain the corrected geological parameters;

[0056] Step 4, based on the corrected geological parameters, multi-index weighted fusion analysis is carried out according to the pre-set surrounding rock classification evaluation system to dynamically determine the surrounding rock grade of the current working face;

[0057] Step 5, according to the surrounding rock grade, a corresponding set of optimized blasting parameters is matched and generated from the pre-constructed blasting parameter knowledge base; based on the optimized set of blasting parameters, blasting operations are carried out, the overbreak and underbreak rates are calculated by collecting the section shape data, and the surrounding rock vibration response data is collected by the distributed vibration monitoring system;

[0058] Step 6, the overbreak and underbreak rates and the vibration response data are compared with the pre-set specification safety threshold in real time to obtain the comparison results, and finally a closed-loop optimization is formed.

[0059] In the embodiment of the present application, a multi-source sensing system deployed at the tunnel face is used to collect geological parameters in real time, a convex polyhedron evaluation region boundary is constructed in the spatial domain of the tunnel face by identifying characteristic sampling points and an iterative expansion method, the convex polyhedron evaluation region boundary is discretely divided into sub-regions, a correction coefficient is generated based on the variation characteristics of the geological parameters to correct the core parameter of the uniaxial compressive strength, a multi-index weighted fusion analysis is performed in combination with a preset surrounding rock classification evaluation system to determine the surrounding rock grade, parameters are matched from a pre-constructed blasting parameter knowledge base according to the surrounding rock grade and an optimized set is generated through self-adaptive adjustment, the overbreak and underbreak rates are calculated by collecting the cross-section shape data, and the vibration response data are collected by a distributed vibration monitoring system, the data are compared with the preset specification safety threshold to form a closed-loop optimization technical means, so the technical problems of poor timeliness caused by relying on discrete detection data in the traditional technology, inaccurate classification caused by not considering the parameter spatial variation characteristics and static single in the surrounding rock classification, and the mismatch between parameters and geological conditions and construction safety risks caused by the broken links of monitoring, evaluation and optimization in the blasting parameter design experience are overcome, and real-time perception of the geological conditions of the tunnel face, dynamic determination of the surrounding rock grade, intelligent optimization of the blasting parameters and closed-loop control of the tunnel construction are achieved, which effectively reduces the safety hidden dangers of the tunnel face collapse and improves the blasting operation and tunnel construction efficiency.

[0060] In a preferred embodiment of the present application, step 1 can include:

[0061] Step 1.1, acquiring the surface morphology data of the tunnel face by a three-dimensional laser scanner in the multi-source sensing system, acquiring the internal structure data of the rock mass in front of the tunnel face by a geological radar probe, acquiring the rock composition data by an infrared spectrometer, and acquiring the joint fissure development data by a borehole imaging instrument, specifically including: fixing and calibrating the three-dimensional laser scanner to face the tunnel face in the safe area of the tunnel face, starting the equipment to scan the surface of the tunnel face, and generating three-dimensional point cloud data containing surface undulations and contours to acquire the surface morphology data; arranging the geological radar antenna on the flat rock surface below the tunnel face, adjusting the frequency according to the detection depth, moving the antenna along the preset line, receiving the electromagnetic wave signals reflected by the internal structure of the rock mass, and forming a radar profile to acquire the internal structure data of the rock mass in front; detecting the representative rock sample or in-situ by the infrared spectrometer, analyzing the reflected infrared spectrum signal, matching the database to determine the rock composition information, and acquiring the rock composition data; inserting the camera of the borehole imaging instrument into the pre-detection hole of the tunnel face, moving at a constant speed to shoot the hole wall image and record the depth, and identifying the joint fissure characteristics by splicing the images to acquire the joint fissure development data.

[0062] Step 1.2, based on surface morphology data, internal structure data, rock mass composition data and joint fracture development data, the multi-source data are fused and preprocessed to generate a comprehensive geological parameter set containing rock mass strength, joint distribution, water content and rock mass integrity, specifically including: based on multi-source data preprocessing, remove noise points, radar clutter, correct spectral baseline, enhance the contrast of borehole wall image; then taking the working face spatial coordinate system as the reference, the preprocessed data are fused and aligned in space coordinates; parameters are extracted from the fused data, combined with rock composition and surface morphology to deduce rock mass strength, according to joint fracture and internal structure to count joint distribution, judge water content through spectral and radar signals, and calculate rock mass integrity combined with structural integrity and joint development; these parameters are arranged in a unified format to generate a comprehensive geological parameter set containing rock mass strength, joint distribution, water content and rock mass integrity.

[0063] In the embodiments of the present application, the three-dimensional laser scanner, the geological radar, the infrared spectrometer and the borehole imaging instrument in the multi-source sensing system are used to respectively acquire the working face surface morphology data, the front rock mass internal structure data, the rock mass composition data and the joint fracture development data, and the multi-source data are fused and preprocessed to obtain the technical means of the comprehensive geological parameter set containing rock mass strength, joint distribution, water content and rock mass integrity, so that the technical problem that the single detection device in the traditional technology can only acquire one-sided geological information and cannot fully reflect the overall condition of the working face rock mass is overcome, and the current working face geological conditions are completely mastered, thereby providing comprehensive and reliable basic data support for feature sampling point identification, convex polyhedron evaluation region boundary construction and surrounding rock dynamic classification.

[0064] In a preferred embodiment of the present application, the above step 2 can include:

[0065] Step 2.1, based on the geological parameter data, a spatial clustering analysis method is used to identify the characteristic sampling points with geological representation, and the spatial coordinate information is obtained; four non-coplanar points in space are selected as initial vertices from the characteristic sampling points to construct an initial tetrahedral structure, which specifically includes: based on the generated comprehensive geological parameter data including rock mass strength, joint distribution, water content and rock mass integrity, a spatial clustering analysis method is used, which is an analysis method according to the similarity and difference of each geological parameter in the spatial domain of the working face, the dispersed geological parameter data is classified into different groups, the data in each group has consistency in key indicators such as rock mass strength, joint distribution density, water content condition and rock mass integrity level, and the data between groups has difference, according to the joint distribution density, the dense and sparse areas are distinguished, according to the water content condition, the dry and wet partitions are defined, according to the rock mass integrity level, the complete and broken levels are divided, through the coordinated matching of multi-dimensional indicators, the geological parameter data in the spatial domain of the working face is accurately grouped, and the sampling points in each group that can reflect the core attributes of the geological characteristics of the group are selected as the characteristic sampling points with geological representation, and the specific spatial coordinate information of each characteristic sampling point in the spatial domain of the working face is recorded through a three-dimensional coordinate measuring device; four sampling points are randomly selected from all the characteristic sampling points, the volume of the tetrahedron formed by the spatial coordinates of the four sampling points is calculated, if the volume is not zero, it is determined that the spatial positions of the four points are non-coplanar, and the four points are determined as initial vertices, and the initial tetrahedral structure is constructed by connecting the four initial vertices according to the geometric construction rules of the tetrahedron.

[0066] Step 2.2, based on the initial tetrahedral structure as the starting point, the remaining characteristic sampling points are processed by using the iterative expansion algorithm, in each iteration process, the spatial distance between the remaining points and the current convex hull surface is calculated, and the point with the farthest distance is selected into the convex hull vertex set, which specifically includes: based on the initial tetrahedral structure as the starting point of iterative expansion, the four vertices of the initial tetrahedron are included in the convex hull vertex set, and the remaining characteristic sampling points are taken as the point set to be processed; in each iteration process, all triangular facets of the convex hull surface are constructed according to the current convex hull vertex set, the vertical distance of each point in the processing point set to each triangular facet is calculated respectively, and the smallest vertical distance is selected as the spatial distance between the current convex hull surface; compare the spatial distances of all points in the processing point set, find the point with the maximum distance value, and include it in the convex hull vertex set, and remove it from the processing point set to enter the next iteration process, until all points in the processing point set complete distance calculation and selection.

[0067] Step 2.3, recalculating the convex hull surface geometry according to the convex hull vertex set, all feature sampling points are enveloped in the convex hull interior or surface, forming the final three-dimensional convex polyhedron, specifically including: according to the updated convex hull vertex set of each iteration, using a three-dimensional convex hull construction algorithm to determine the spatial connection relationship between each vertex and other vertices, judging the orientation of the facet by calculating the vector cross product of adjacent vertices, screening out the triangular facets facing the outside of the convex hull, and sequentially connecting these triangular facets to form a new convex hull surface geometry; checking the position relationship between all feature sampling points and the new convex hull surface, if there are feature sampling points not enveloped in the convex hull interior or surface, readjusting the connection mode of the convex hull vertex, adjusting and calculating through the calculation of the convex hull surface geometry, based on all feature sampling points being completely enveloped in the convex hull interior or surface, finally forming a three-dimensional convex polyhedron that can cover all feature sampling points.

[0068] Step 2.4, determining the convex polyhedron evaluation area boundary based on the outer surface of the three-dimensional convex polyhedron as the limit, specifically including: reading all the spatial coordinates of the outer surface vertices of the final three-dimensional convex polyhedron through a three-dimensional modeling software, sequentially connecting these vertices to generate a complete contour line of the outer surface of the three-dimensional convex polyhedron; taking the three-dimensional convex polyhedron outer surface formed by the complete contour line as the spatial limit to define the enclosed space region, and determining the enclosed space region as the convex polyhedron evaluation area boundary that can cover the key geological features of the working face.

[0069] In the embodiment of the present application, the spatial clustering analysis method based on geological parameter data identifies feature sampling points with geological representativeness to obtain spatial coordinate information, selects four non-coplanar points from the feature sampling points to construct an initial tetrahedron structure, processes the remaining feature sampling points based on the initial tetrahedron as the starting point using an iterative expansion algorithm, selects the point farthest from the current convex hull surface in each iteration to include in the convex hull vertex set, recalculates the convex hull surface geometry according to the convex hull vertex set to form a three-dimensional convex polyhedron that envelops all feature sampling points, and determines the dynamic geological convex polyhedron evaluation area boundary space boundary based on the outer surface of the three-dimensional convex polyhedron as the limit, which overcomes the technical problems in the prior art that the surrounding rock classification relies on discrete detection data and cannot identify key geological feature sampling points of the working face, the convex polyhedron evaluation area boundary is fuzzy and cannot cover the geological changes of the working face, resulting in that the surrounding rock classification lacks spatial range and representative data support, and further achieves the purpose of locking the core area of the working face with geological representativeness and ensuring that the dynamic geological convex polyhedron evaluation area boundary can cover the key geological features of the working face.

[0070] In a preferred embodiment of the present application, the above step 3 can include:

[0071] Step 3.1, the convex polyhedron evaluation region boundary is divided into a plurality of continuous analysis units by using a regular voxel grid division method, specifically including: according to the three-dimensional coordinate data of the convex polyhedron evaluation region boundary, the maximum and minimum ranges of the convex polyhedron evaluation region boundary on the x, y and z three spatial axes are determined, the size of the regular voxel grid is set according to the spatial variation degree of the working face geological parameters, so as to ensure that the grid size can reflect the slight changes of the local geological parameters, and the calculation amount will not be too large due to too small grid size, and the entire dynamic geological convex polyhedron evaluation region boundary is divided into a plurality of cubic analysis units which are uniformly sized, continuously arranged and non-overlapping, and the position of each unit in the convex polyhedron evaluation region boundary is determined by its spatial coordinate range.

[0072] Step 3.2, for each analysis unit, the values of each geological parameter of the characteristic sampling points are calculated; and the statistical characteristics of the characteristic sampling points in the analysis unit are obtained according to the values of each geological parameter of the characteristic sampling points, including the average value, the variance and the spatial distribution characteristics, specifically including: based on each divided analysis unit, the characteristic sampling points located inside or on the boundary of the analysis unit are filtered out by spatial coordinate comparison, the rock mass strength, joint development degree, water content and rock mass integrity geological parameter values of these sampling points are extracted, all values of the same parameter are added and then divided by the number of sampling points of the parameter in the unit to obtain the average value of each parameter in the unit, the square of the difference between each parameter value and the average value is calculated and then averaged to obtain the variance of each parameter, and the distribution of the parameter values at different positions in the unit is observed to determine whether they are concentrated in a certain area or show a gradient change trend, so as to determine the spatial distribution characteristics of each parameter.

[0073] Step 3.3, based on the statistical characteristics, the spatial variation characteristics are analyzed, and the geological parameter variation coefficient of each analysis unit is calculated, specifically including: based on the average value and the variance of the geological parameters in each analysis unit, the standard deviation of each parameter, i.e. the arithmetic square root of the variance, is calculated, and the standard deviation is divided by the average value of the parameter to obtain the variation coefficient of each parameter, which reflects the dispersion degree of the parameter in the unit, the variation coefficients of all geological parameters in the unit are integrated to analyze the spatial variation characteristics of the geological parameters in the unit as a whole, if the variation coefficient of a unit is large, it indicates that the geological parameters in the unit are unevenly distributed and have significant spatial variation, otherwise it indicates that the parameters are relatively evenly distributed, and finally the comprehensive geological parameter variation coefficient of each analysis unit is recorded.

[0074] Step 3.4, according to the geological parameter variation coefficient, a comprehensive geological data correction coefficient is obtained by using a weighted average algorithm, which specifically includes: according to the geological parameter variation coefficient of each analysis unit, the weight is determined, the unit with larger variation coefficient indicates that the reliability of the geological parameter is lower, and higher weight is given to enhance the correction effect, the unit with smaller variation coefficient is given lower weight, the variation coefficient of each geological parameter is multiplied by the corresponding weight and then summed, and finally divided by the sum of the weights of all units, to obtain a comprehensive geological data correction coefficient reflecting the overall variation of the boundary geological parameters of the entire dynamic geological convex polyhedron evaluation area.

[0075] Step 3.5, by using the comprehensive geological data correction coefficient, the original collected uniaxial compressive strength, joint development degree and water content parameters are corrected and calculated to obtain the corrected geological parameter values, which specifically includes: based on the obtained comprehensive geological data correction coefficient, the original collected uniaxial compressive strength, joint development degree and water content parameters are applied respectively, for uniaxial compressive strength, if the correction coefficient is greater than 1, the original value is appropriately increased, if it is less than 1, the original value is appropriately reduced, for joint development degree and water content, according to the correlation between the correction coefficient and the parameter characteristics, the corresponding adjustment is made, so that the adjusted parameter value can reflect the parameter size and distribution characteristics under the actual geological conditions, and finally the corrected uniaxial compressive strength, joint development degree and water content geological parameter values are obtained.

[0076] In the embodiment of the present application, the boundary of the convex polyhedron evaluation area is discretized into a plurality of continuous analysis units, the average value, variance and spatial distribution characteristics of the geological parameters are counted for each unit, the spatial variation characteristics are analyzed and the variation coefficient is calculated based on the statistical characteristics, the comprehensive geological data correction coefficient is obtained by using the weighted average algorithm, and then the original uniaxial compressive strength, joint development degree and water content parameters are corrected, so that the technical problems in the prior art that the spatial variation characteristics of the geological parameters are not considered, the analysis of the geological parameters in the boundary of the convex polyhedron evaluation area is rough, and the direct use of the original parameters leads to insufficient accuracy and affects the accuracy of the surrounding rock classification are overcome, and the analysis and correction of the geological parameters are realized.

[0077] In a preferred embodiment of the present application, the above step 4 can include:

[0078] Step 4.1, receiving the corrected geological parameter values, the geological parameter values including the corrected uniaxial compressive strength, rock mass integrity coefficient and water content index, specifically including: based on the output corrected geological parameter values, the geological parameter values including the corrected uniaxial compressive strength, rock mass integrity coefficient and water content index, the received parameters are subjected to integrity check to ensure that the necessary parameters have been obtained and the data format meets the analysis requirements.

[0079] Step 4.2, based on the corrected geological parameter value, a preset surrounding rock classification evaluation system is called, the surrounding rock classification evaluation system includes parameter threshold range corresponding to different surrounding rock grades and weight coefficient distributed based on the influence degree of surrounding rock stability, specifically including: based on the received corrected geological parameter value, the surrounding rock classification evaluation system preset in the database is called through system instruction, the surrounding rock classification evaluation system is preset with surrounding rock grade division from grade 1 to grade 5, including uniaxial compressive strength range, rock mass integrity coefficient interval and water content threshold corresponding to each grade, weight coefficients are distributed according to the influence degree of each parameter on the stability of surrounding rock, for example, uniaxial compressive strength has the highest weight because it has the greatest influence on rock mass stability, and the weight of water content is appropriately increased in the area prone to water gushing.

[0080] Step 4.3, input the corrected geological parameter value into the surrounding rock classification evaluation system, and calculate using a multi-index weighted fusion algorithm, each parameter is weighted and fused according to the corresponding weight coefficient to obtain a comprehensive index value, specifically including: input the corrected uniaxial compressive strength, rock mass integrity coefficient and water content index into the called surrounding rock classification evaluation system one by one, multiply each parameter value by the corresponding weight according to the preset parameter weight coefficient in the surrounding rock classification evaluation system, and then add the product, finally calculate a comprehensive index value which can comprehensively reflect the stability of surrounding rock.

[0081] Step 4.4, according to the comprehensive index value, based on the preset surrounding rock grade determination rule, dynamically determine the surrounding rock grade corresponding to the current working face, specifically including: according to the obtained comprehensive index value, the preset surrounding rock grade determination rule in the surrounding rock classification evaluation system is matched, the surrounding rock grade determination rule clearly defines the specific surrounding rock grade corresponding to different comprehensive index value range, for example, the comprehensive index value greater than 90 corresponds to grade 1 surrounding rock, and less than 30 corresponds to grade 5 surrounding rock, when the geological of the working face changes suddenly resulting in the change of the corrected parameter, the comprehensive index value is recalculated and the corresponding surrounding rock grade is matched, and finally the surrounding rock grade of the current working face is dynamically determined.

[0082] In the embodiment of the application, based on the received corrected uniaxial compressive strength, rock mass integrity coefficient and water content geological parameter value, a preset surrounding rock classification evaluation system containing parameter threshold range corresponding to different surrounding rock grades and weight coefficient distributed based on the influence degree of surrounding rock stability is called, the corrected parameters are input into the system and a multi-index weighted fusion algorithm is used to calculate a comprehensive index value, and the preset rule is used to dynamically determine the surrounding rock grade of the current working face, so that the technical problems in the prior art that the surrounding rock classification is not based on the corrected geological parameters, lacks system parameter threshold and weight setting, and cannot dynamically adapt to the geological changes of the working face through multi-index fusion analysis are overcome, and the stability of the rock mass of the current working face is reflected, and the dynamic and scientific determination of the surrounding rock grade is realized.

[0083] In a preferred embodiment of the present application, step 5 above can include:

[0084] Step 5.1, receiving the current face surrounding rock grade determination result; based on the surrounding rock grade determination result, retrieving the corresponding reference blasting parameter set from the pre-constructed blasting parameter knowledge base, the reference blasting parameter set including the borehole arrangement scheme, single-hole charge amount, total charge amount, and millisecond delay time sequence, specifically including: based on the current face surrounding rock grade determination result, such as grade 1, grade 2, or grade 5, which reflects the key geological conditions of the face rock mass strength, integrity, and water content; in the pre-constructed blasting parameter knowledge base, the corresponding reference blasting parameter set is pre-stored according to different surrounding rock grade classification, and the parameter set is formed based on a large number of tunnel blasting engineering cases under similar geological conditions, industry construction specifications, and safety standards, the borehole arrangement scheme specifies the borehole depth, borehole spacing, borehole row spacing, and borehole angle corresponding to different surrounding rock grades, the single-hole charge amount is set according to the rock mass compressive strength, such as higher single-hole charge amount for hard rock grade 1 surrounding rock than for soft rock grade 5, the total charge amount is calculated and determined in combination with the tunnel design cross-sectional area and surrounding rock grade, and the millisecond delay time sequence is set according to the principle of avoiding adjacent blasthole blasting vibration superposition; according to the received surrounding rock grade determination result, the completely matched or closest reference blasting parameter item is found in the knowledge base, and the corresponding borehole arrangement scheme, single-hole charge amount, total charge amount, and millisecond delay time sequence are extracted to form the initial reference blasting parameter set.

[0085] Step 5.2, combining the specific engineering condition characteristics of the current face, using a parameter optimization algorithm to adaptively adjust the reference blasting parameter set to generate a blasting parameter optimization set suitable for the current geological conditions, specifically including: based on the specific engineering condition characteristics of the current face, including the actual size of the tunnel design cross-section, whether there are sensitive structures such as nearby mountain slopes, underground pipelines, or existing buildings around the face, the distance to these structures, whether there is sudden gushing water at the face, fault fracture zone geological mutation, tunnel construction progress requirements, and safety control indicators; based on the engineering condition characteristics, calling the parameter optimization algorithm, which adaptively adjusts each parameter in the reference blasting parameter set, for example, if there is sudden gushing water at the face causing an increase in water content, the single-hole charge amount and total charge amount can be appropriately reduced to avoid rock mass instability, if there are sensitive structures around, the millisecond delay time interval can be shortened to reduce the peak value of blasting vibration, and if the tunnel cross-sectional size is larger than the conventional size corresponding to the reference parameter, the borehole row spacing and total charge amount can be adjusted proportionally; through the algorithm, the adjustment of each parameter is completed, and the complete parameter scheme including the optimized borehole arrangement, charge amount, and millisecond time is obtained, and the blasting parameter optimization set suitable for the current face geological conditions and engineering requirements is obtained.

[0086] In the embodiment of the present application, by receiving the current working face surrounding rock grade determination result, the matching reference blasting parameter set containing the borehole arrangement scheme, single-hole charge weight, total charge weight, and millisecond delay time sequence is retrieved from the pre-constructed blasting parameter knowledge base, combined with the specific engineering condition characteristics of the current working face, such as the tunnel cross-section size, the distance to the surrounding sensitive structure, and the working face geological mutation, the parameter optimization algorithm is used to adaptively adjust the reference blasting parameter set to obtain the blasting parameter optimization set suitable for the current geological conditions, so that the technical problems in the prior art that the blasting parameter design relies on experience and does not combine real-time surrounding rock grade and specific engineering conditions to cause the parameters to be mismatched with the actual geological conditions, such as the sudden increase of the water content in the working face still using the original designed charge weight, which easily causes construction safety risks or poor blasting effect, are overcome, and the blasting parameters are adapted to the current working face geological conditions and engineering requirements.

[0087] In a preferred embodiment of the present application, step 5 can include:

[0088] Step 5.3, receiving the blasting parameter optimization set and transmitting the blasting parameter optimization set scheme to the intelligent blasting control terminal to perform the blasting operation, specifically including: according to the generated blasting parameter optimization set, checking the integrity and rationality of the parameter set, confirming that there is no parameter missing and each value meets the tunnel construction safety specification; the intelligent blasting control terminal is a special control device composed of a central processing module, a data receiving module, an instruction output module, a man-machine interface, and a safety verification unit, and its core function is to realize the receiving, analysis, and execution control of the blasting parameters; the complete blasting parameter optimization set scheme is transmitted to the intelligent blasting control terminal, the data receiving module of the terminal first verifies the integrity of the transmission data, and then the central processing module analyzes the parameter set into electrical signal control instructions that can directly drive the initiation device, the borehole arrangement scheme corresponds to the initiation point position calibration instruction, the single-hole charge weight and the total charge weight correspond to the initiation energy adjustment instruction, and the millisecond delay time sequence corresponds to the time interval control instruction of each initiation point; before performing the blasting operation, the man-machine interface of the terminal clearly displays all parameter information in the form of text and charts, such as the charge weight of each blast hole, the initiation sequence, and the delay time; the instruction output module of the terminal sends precise control signals to the initiation device of each blast hole through a special line according to the preset millisecond delay time sequence, controls the initiation time and charge weight release rate of each blast hole, and the safety verification unit monitors the key indicators such as current and voltage in the initiation process to finally complete the blasting operation.

[0089] Step 5.4, based on the completion of the blasting operation, the three-dimensional morphological data of the post-blasting tunnel section is collected by the three-dimensional laser scanning system deployed in the tunnel, specifically including: based on the completion of the blasting operation, the environmental conditions meet the safety operation requirements, start the three-dimensional laser scanning system deployed in the tunnel, the three-dimensional laser scanning system is composed of a laser transmitter, a high-precision receiver, a real-time data processing unit and a spatial positioning device, which can emit a laser beam of a specific wavelength and receive the reflected signal, calculate the laser propagation time or phase difference to determine the distance between the scanning point and the device, and then generate the three-dimensional coordinates of the scanning point combined with the spatial position information of the device itself; the scanning device of the three-dimensional laser scanning system is pre-installed on the tunnel side wall or a movable support, and the calibration before scanning includes laser emission intensity calibration, distance measurement calibration and alignment calibration with the tunnel coordinate system; the scanning range is set before scanning to determine the complete area of the post-blasting tunnel section, covering the top to the bottom and the left to the right of the section, after starting the scanning, the device continuously emits a laser beam to scan the surface of the section rock point by point, the receiver synchronously receives the laser signal reflected by the rock surface, the real-time data processing unit converts the signal into the three-dimensional spatial coordinates and reflection intensity information of each scanning point, and the reflection intensity information can assist in judging the material difference of the rock surface; the device is kept stable by a stable support during scanning to avoid data deviation caused by vibration, and finally the three-dimensional morphological data of the surface relief, contour details and local damage of the post-blasting tunnel section is obtained.

[0090] Step 5.5, fitting and comparing the three-dimensional morphological data with the tunnel design section data, calculating the overbreak and underbreak area and overbreak and underbreak rate; collecting the surrounding rock vibration response data generated in the blasting process through the distributed vibration monitoring sensor network, specifically including: based on the three-dimensional morphological data of the post-blasting tunnel section, including the three-dimensional space coordinates of each scanning point on the section, determining the preset three-dimensional coordinate range of the tunnel design section, and clearly specifying the coordinate values of the top, bottom, left side, right side and key positions inside the section; comparing each scanning point coordinate in the post-blasting three-dimensional morphological data with the preset coordinate range of the design section one by one, identifying the scanning points whose coordinates exceed the preset range of the design section, and the area formed by the connection of the scanning points is the overbreak area, identifying the scanning points whose coordinates do not reach the preset range of the design section, and the area formed by the connection of the scanning points that do not reach the preset range of the design section is the underbreak area; dividing the overbreak area and the underbreak area into several regular geometric sub-areas, such as triangles and rectangles, calculating the area of each sub-area through the geometric area calculation formula, then adding up the areas of all overbreak sub-areas to obtain the total overbreak area, and adding up the areas of all underbreak sub-areas to obtain the total underbreak area, taking the sum of the total overbreak area and the total underbreak area as the total overbreak and underbreak area, and dividing the total overbreak and underbreak area by the preset area of the tunnel design section to obtain the overbreak and underbreak rate of the section; before the blasting operation starts, the vibration monitoring sensor network is distributed in different depths and different directions of the surrounding rock of the tunnel face, each sensor is directly connected to the data acquisition terminal through a data line, and during the blasting process, the sensor captures the vibration acceleration and vibration speed response signals generated by the blasting impact of the surrounding rock through the internal sensing element, converts these signals into recordable electrical signals, and transmits them to the data acquisition terminal in real time through the data line. The terminal stores the received electrical signals in real time and records the whole process of the surrounding rock vibration response from the start of blasting to the complete dissipation of vibration.

[0091] In the embodiment of the present application, based on receiving the optimized set of blasting parameters and transmitting it to the intelligent blasting control terminal to perform the blasting operation, after the blasting is completed, the three-dimensional laser scanning system deployed in the tunnel is used to collect the three-dimensional morphological data of the post-blasting tunnel section, the three-dimensional morphological data is fitted and compared with the tunnel design section data to calculate the overbreak and underbreak area and overbreak and underbreak rate, and the vibration monitoring sensor network is distributed to collect the surrounding rock vibration response data during the blasting process, so as to overcome the technical problems in the prior art that the blasting operation relies on manual operation and is prone to parameter execution deviation, the post-blasting section overbreak and underbreak detection is low and inefficient, and the surrounding rock vibration response during the blasting process lacks effective monitoring, which leads to the inability to timely grasp the impact of blasting on the stability of the surrounding rock, and further may cause construction safety risks such as the collapse of the tunnel face caused by improper charge amount, and further realizes the execution of the blasting operation parameters, the detection of the post-blasting section morphology and the real-time grasp of the impact of blasting vibration.

[0092] In a preferred embodiment of the present application, step 6 above can include:

[0093] Step 6.1, the collected actual overbreak / underbreak rate and the surrounding rock vibration response data are respectively compared in real time with the safety threshold in the preset specification to obtain parameter adjustment instructions and adjustment amount suggestions, specifically including: the safety threshold in the preset specification is determined, the overbreak / underbreak rate safety threshold is set according to the tunnel engineering construction quality acceptance specification, for example, the overbreak / underbreak rate of grade 1 surrounding rock is not more than 5%, and the overbreak / underbreak rate of grade 5 surrounding rock is not more than 8%, and the safety threshold of the surrounding rock vibration response data is set according to the blasting safety regulations, for example, the peripheral surrounding rock vibration speed is not more than 15 cm / s; the collected actual overbreak / underbreak rate data and the surrounding rock vibration response data including vibration acceleration and vibration speed peak value are called, the actual overbreak / underbreak rate is compared with the overbreak / underbreak rate safety threshold corresponding to the surrounding rock grade, if the actual overbreak / underbreak rate is higher than the threshold, it is determined that the blasting parameters affecting the section shape need to be adjusted, and if it is lower than the threshold, it is determined that the index meets the requirements; the actual surrounding rock vibration response data is compared with the vibration safety threshold, if the actual vibration speed or acceleration exceeds the threshold, it is determined that the parameters of the blasting impact need to be adjusted and reduced, and if it does not exceed, it is determined that the index meets the requirements; parameter adjustment instructions are generated according to the comparison results, for example, the instructions for adjusting the drilling angle and the single-hole charge amount are generated when the overbreak / underbreak rate exceeds the threshold, and the instructions for reducing the total charge amount and optimizing the millisecond delay time are generated when the vibration data exceeds the threshold, and the specific adjustment amount suggestions are given in combination with the overbreak / underbreak rate, for example, the overbreak / underbreak rate exceeds the threshold by 2%, and the vibration speed exceeds the threshold by 3 cm / s.

[0094] Step 6.2, based on the parameter adjustment instructions and the adjustment amount suggestions, the blasting parameter optimization scheme is dynamically corrected, specifically including: according to the parameter adjustment instructions and the adjustment amount suggestions, the currently used blasting parameter optimization scheme is called, including the drilling arrangement scheme, the single-hole charge amount, the total charge amount, and the millisecond delay time sequence; if the adjustment instruction is to adjust the drilling angle and the single-hole charge amount, the drilling angle in the original scheme is corrected in the direction of reducing overbreak / underbreak according to the adjustment amount suggestion, and the single-hole charge amount is reduced by the suggested proportion; if the adjustment instruction is to reduce the total charge amount and optimize the millisecond delay time, the total charge amount is reduced by the suggested amount, and the original millisecond delay time interval is appropriately extended; after the correction is completed, the safety of the new blasting parameter optimization scheme is verified, and it is confirmed that the corrected parameters meet the construction safety specification corresponding to the current surrounding rock grade.

[0095] Step 6.3, the modified blasting parameter optimization scheme is fed back to the blasting operation system to realize continuous iterative optimization and closed-loop control of the blasting parameters, specifically including: based on the modified blasting parameter optimization scheme, the data transmission module is fed back to the core components of the blasting operation system, including the intelligent blasting control terminal and the blasting parameter knowledge base; when fed back to the intelligent blasting control terminal, the terminal automatically updates the stored parameter data after receiving the new scheme to replace the original old scheme; when fed back to the blasting parameter knowledge base, the system stores the modified scheme and the corresponding surrounding rock grade, overbreak and underbreak rate and vibration response data, enriches the parameter cases corresponding to different geological conditions and construction effects in the knowledge base; when the next tunnel face blasting operation is carried out, the blasting operation system will preferentially call the modified parameter scheme to execute the operation, and after the operation is completed, the overbreak and underbreak rate and vibration response data are collected again for comparison and correction, forming a continuous iterative process of parameter execution, effect monitoring, comparison and correction, scheme feedback and re-execution, and realizing closed-loop control of the blasting parameters.

[0096] In the embodiment of the present application, the actual overbreak and underbreak rate and the surrounding rock vibration response data collected are respectively compared and analyzed in real time with the safety threshold in the preset specification to obtain parameter adjustment instructions and adjustment amount suggestions, and the blasting parameter optimization scheme is dynamically modified based on the parameter adjustment instructions and adjustment amount suggestions. The modified blasting parameter optimization scheme is fed back to the blasting operation system to realize continuous iterative optimization and closed-loop control of the blasting parameters. Therefore, the technical problems of broken control loop, inability to dynamically adjust parameters according to the actual blasting effect, and repeated mismatch between parameters and geological conditions and construction safety risks in the prior art are overcome, and the blasting parameters can be dynamically adapted to the geological changes of the tunnel face and the blasting effect in real time, forming a complete control chain of monitoring, comparison, correction and feedback.

[0097] As shown in Figure 2 The embodiment of the present application also provides a tunnel surrounding rock dynamic grading and blasting parameter optimization system, which comprises:

[0098] The acquisition module is used for acquiring the geological parameters of the current tunnel face in real time through the multi-source sensing system deployed on the tunnel face; based on the acquired geological parameters, a plurality of characteristic sampling points with geological representation are identified in the spatial domain of the tunnel face; an initial tetrahedron is constructed by selecting four non-coplanar characteristic sampling points, and the farthest point from the current convex hull surface in the remaining points is sequentially included by using an iterative expansion method, and the convex hull surface is recalculated until all the characteristic sampling points are enveloped to form a convex polyhedron evaluation region boundary;

[0099] The reasoning module is used for carrying out sub-region discrete division according to the convex polyhedron evaluation region boundary, forming continuous analysis units, and counting the distribution characteristics of each geological parameter in each unit; based on the variation characteristics of the geological parameters of each unit, a comprehensive geological data correction coefficient is generated, and the original collected uniaxial compressive strength, joint development degree and water content parameters are corrected according to the correction coefficient to obtain corrected geological parameters;

[0100] The judgment module is used for carrying out multi-index weighted fusion analysis according to a preset surrounding rock grading evaluation system based on the corrected geological parameters, and dynamically determining the surrounding rock grade of the current working face.

[0101] The calculation module is used for matching and generating a corresponding blasting parameter optimization set from a pre-constructed blasting parameter knowledge base according to the surrounding rock grade; the blasting operation is implemented based on the blasting parameter optimization set, the overbreak and underbreak rate is calculated by collecting the section shape data, and the surrounding rock vibration response data is collected by a distributed vibration monitoring system.

[0102] The processing module is used for carrying out real-time comparison of the overbreak and underbreak rate and the vibration response data with a preset specification safety threshold to obtain a comparison result, and finally forming a closed-loop optimization.

[0103] The above is the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled persons in the technical field, a number of improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for dynamic classification of surrounding rock and optimization of blasting parameters in tunnels, characterized in that, The method includes: The geological parameters of the tunnel face are collected in real time through a multi-source sensing system deployed at the tunnel face. Based on the collected geological parameters, multiple geologically representative feature sampling points are identified in the spatial domain of the tunnel face. An initial tetrahedron is constructed by selecting four non-coplanar feature sampling points, and an iterative expansion method is used to sequentially include the remaining points that are farthest from the current convex hull surface, recalculate the convex hull surface, until all feature sampling points are enveloped, and form the boundary of the convex polyhedron evaluation area. The evaluation area is divided into sub-regions based on the boundary of the convex polyhedron to form continuous analysis units. The distribution characteristics of geological parameters in each unit are statistically analyzed. Based on the variation characteristics of geological parameters in each unit, a comprehensive geological data correction coefficient is generated. The original uniaxial compressive strength, joint development degree and water content parameters are corrected according to the correction coefficient to obtain the corrected geological parameters. Based on the corrected geological parameters, a multi-index weighted fusion analysis is performed according to the preset surrounding rock classification evaluation system to dynamically determine the surrounding rock grade of the current working face. Based on the surrounding rock grade, a corresponding optimized set of blasting parameters is generated by matching from a pre-constructed blasting parameter knowledge base; blasting operations are carried out based on the optimized set of blasting parameters, the over-excavation and under-excavation rates are calculated by collecting cross-sectional morphology data, and the vibration response data of the surrounding rock is collected by a distributed vibration monitoring system; By comparing the over-excavation and under-excavation rates and vibration response data with the preset safety thresholds in real time, the comparison results are obtained, and a closed-loop optimization is finally formed.

2. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 1, characterized in that, Based on the collected geological parameters, multiple geologically representative feature sampling points are identified within the working face spatial domain. An initial tetrahedron is constructed by selecting four non-coplanar feature sampling points. An iterative expansion method is then used to sequentially include the remaining points furthest from the current convex hull surface, recalculating the convex hull surface until it encompasses all feature sampling points, forming the boundary of the convex polyhedron evaluation region, including: Based on geological parameter data, spatial clustering analysis was used to identify geologically representative feature sampling points and obtain spatial coordinate information. Four non-coplanar points were selected from the feature sampling points as initial vertices to construct an initial tetrahedral structure. Starting from the initial tetrahedral structure, an iterative expansion algorithm is used to process the remaining feature sampling points. In each iteration, the spatial distance between each remaining point and the current convex hull surface is calculated, and the point with the farthest distance is selected and included in the convex hull vertex set. The geometry of the convex hull surface is recalculated based on the convex hull vertex set, and all feature sampling points are enclosed inside or on the surface of the convex hull to form the final three-dimensional convex polyhedron. The boundary of the evaluation region of the convex polyhedron is determined based on the outer surface of the three-dimensional convex polyhedron.

3. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 2, characterized in that, Based on the boundary of the convex polyhedron assessment area, sub-regions are discretized to form continuous analysis units. The distribution characteristics of geological parameters within each unit are statistically analyzed. Based on the variability of geological parameters in each unit, comprehensive geological data correction coefficients are generated. Parameter values ​​are then corrected based on uniaxial compressive strength, joint development, and water content according to these correction coefficients, including: The boundary of the convex polyhedron evaluation region is divided into several continuous analysis units using a regular voxel mesh generation method. For each analysis unit, calculate the geological parameter values ​​of each feature sampling point; based on the geological parameter values ​​of each feature sampling point, obtain its statistical characteristics within the analysis unit, including the mean, variance, and spatial distribution characteristics; Based on statistical characteristics, we analyze its spatial variability and calculate the coefficient of variation of geological parameters for each analysis unit. Based on the coefficient of variation of geological parameters, a weighted average algorithm is used to obtain the comprehensive geological data correction coefficient; By using comprehensive geological data correction coefficients, the original collected uniaxial compressive strength, joint development degree, and water content parameters are corrected and calculated to obtain the corrected geological parameter values.

4. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 3, characterized in that, Based on the corrected geological parameters, a multi-index weighted fusion analysis is performed according to the preset surrounding rock classification evaluation system to dynamically determine the current surrounding rock grade of the working face, including: Receive the corrected geological parameter values, which include the corrected uniaxial compressive strength, rock mass integrity coefficient, and water content index; Based on the corrected geological parameter values, a pre-set surrounding rock classification evaluation system is invoked. The surrounding rock classification evaluation system includes the threshold range of each parameter corresponding to different surrounding rock grades and the weight coefficients allocated based on the degree of influence on surrounding rock stability. The corrected geological parameter values ​​are input into the surrounding rock classification and evaluation system, and a multi-index weighted fusion algorithm is used for calculation. Each parameter is weighted and fused according to its corresponding weight coefficient to obtain a comprehensive index value. Based on the comprehensive index value and the preset surrounding rock grade determination rules, the surrounding rock grade corresponding to the current working face is dynamically determined.

5. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 4, characterized in that, Based on the surrounding rock grade, a corresponding optimized set of blasting parameters is generated by matching from a pre-built blasting parameter knowledge base, including: Receive the current rock mass level determination result at the working face; based on the rock mass level determination result, retrieve the corresponding benchmark blasting parameter set from the pre-constructed blasting parameter knowledge base. The benchmark blasting parameter set includes the borehole layout scheme, single hole charge amount, total charge amount, and micro-delay time series. Based on the specific engineering conditions of the current tunnel face, a parameter optimization algorithm is used to adaptively adjust the benchmark blasting parameter set to generate an optimized blasting parameter set suitable for the current geological conditions.

6. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 5, characterized in that, Blasting operations are implemented based on an optimized set of blasting parameters. Over-excavation and under-excavation rates are calculated by collecting cross-sectional morphology data. Vibration response data of the surrounding rock is collected through a distributed vibration monitoring system, including: Receive the optimized set of blasting parameters and transmit the optimized blasting parameter set scheme to the intelligent blasting control terminal to execute the blasting operation; Based on the completion of the blasting operation, three-dimensional morphological data of the tunnel cross section after the blasting was collected by a three-dimensional laser scanning system deployed inside the tunnel. The three-dimensional morphological data is fitted and compared with the tunnel design cross-section data to calculate the over- and under-excavation area and over- and under-excavation rate of the cross-section; the vibration response data of the surrounding rock generated during blasting is collected through a distributed vibration monitoring sensor network.

7. The method for dynamic classification of tunnel surrounding rock and optimization of blasting parameters according to claim 6, characterized in that, By comparing over-excavation and under-excavation rates and vibration response data with preset safety thresholds in real time, the comparison results are obtained, ultimately forming a closed-loop optimization, including: The actual over-excavation and under-excavation rates and the surrounding rock vibration response data are compared and analyzed in real time with the safety thresholds in the preset specifications to obtain parameter adjustment instructions and adjustment suggestions. The blasting parameter optimization scheme is dynamically corrected based on parameter adjustment instructions and adjustment amount suggestions; The revised blasting parameter optimization scheme is fed back to the blasting operation system to achieve continuous iterative optimization and closed-loop control of the blasting parameters.

8. A dynamic grading and blasting parameter optimization system for tunnel surrounding rock, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to collect the geological parameters of the tunnel face in real time through a multi-source sensing system deployed at the tunnel face; Based on the collected geological parameters, multiple geologically representative feature sampling points were identified in the spatial domain of the working face. An initial tetrahedron is constructed by selecting four non-coplanar feature sampling points, and an iterative expansion method is used to sequentially include the remaining points that are farthest from the current convex hull surface, recalculate the convex hull surface, until all feature sampling points are enveloped, and form the boundary of the convex polyhedron evaluation region. The inference module is used to discretize sub-regions based on the boundary of the evaluation region of the convex polyhedron, forming continuous analysis units, and statistically analyzing the distribution characteristics of various geological parameters within each unit. Based on the variation characteristics of geological parameters of each unit, a comprehensive geological data correction coefficient is generated, and the original collected uniaxial compressive strength, joint development degree and water content parameters are corrected according to the correction coefficient to obtain the corrected geological parameters. The judgment module is used to dynamically determine the surrounding rock grade of the current working face by performing multi-index weighted fusion analysis based on the corrected geological parameters and according to the preset surrounding rock classification evaluation system. The calculation module is used to generate a corresponding optimized set of blasting parameters by matching them from a pre-built blasting parameter knowledge base based on the surrounding rock grade. Blasting operations are carried out based on an optimized set of blasting parameters. Over-excavation and under-excavation rates are calculated by collecting cross-sectional morphology data, and surrounding rock vibration response data are collected by a distributed vibration monitoring system. The processing module is used to compare the over-excavation and under-excavation rates and vibration response data with preset safety thresholds in real time, obtain the comparison results, and finally form a closed-loop optimization.

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