48 results about "Rock classification" patented technology

The invention belongs to the technical field of tunnel and underground engineering intelligent construction and geotechnical engineering informatization, and discloses a high-energy geological environment surrounding rock classification and decision-making method based on digital twinning and multi-source feedback. The problems caused by difficulty in realizing surrounding rock state dynamic sensing, multi-source data fusion classification and construction decision closed-loop linkage in the prior art in a high-energy geological environment are solved. The method comprises the following steps: firstly, constructing a tunnel three-dimensional geology-structure digital twinborn body based on initial survey data; in the construction process, multi-source data such as geology, construction disturbance and surrounding rock response are collected in real time through the Internet of Things technology and mapped to the digital twinborn body, and virtual-real synchronous updating is achieved. And then, constructing a deep learning-parameter inversion hybrid model on the basis of the multi-source fusion data, outputting a dynamic surrounding rock classification index DRCI and key mechanical parameters, inputting the DRCI and the key mechanical parameters into a multi-objective optimization module, and giving a self-adaptive drilling and blasting scheme. And finally, reversely correcting the model through a construction feedback result to realize closed-loop self-learning.

The invention mainly relates to the technical field of tunnel engineering, and provides a tunnel surrounding rock grading dynamic correction method based on image texture features in order to improve the accuracy and real-time performance of surrounding rock grading under complex geological conditions. An improved deep residual network model integrated with an attention mechanism is input and introduced, the model improves the precision of single-point surrounding rock grade identification based on key textures, an initial prediction value of the surrounding rock grade is obtained, and meanwhile a time sequence prediction model is introduced; the whole excavation process is regarded as a continuous geological sequence based on the multi-source surrounding rock feature vectors of the current excavation cycle and at least one previous historical cycle, a time sequence trend value of the surrounding rock grade is obtained, a self-adaptive weight is set, the surrounding rock grade initial prediction value and the time sequence trend value of the surrounding rock grade are smoothed and corrected, and the surrounding rock grade is obtained. Therefore, the accuracy and the stability of the surrounding rock grading result are improved.

The invention, which belongs to the technical field of tunnel engineering, discloses a tunnel surrounding rock pressure arch calculation system comprising a multi-source data fusion acquisition module, a surrounding rock grade intelligent identification module, a self-adaptive pressure arch parameter calculation module and a dynamic load prediction and optimization module. The multi-source data fusion acquisition module acquires geological parameters, drilling monitoring data and case data and generates fusion feature vectors; the surrounding rock grade intelligent identification module outputs a surrounding rock classification result based on a deep residual network and an attention mechanism; the self-adaptive pressure arch parameter calculation module calculates pressure arch parameters by adopting an improved Prscherski theory and a stress release time-varying function; the dynamic load prediction and optimization module predicts the load through the space-time collaborative network and feeds the deviation back to the calculation module to form closed-loop optimization, precise dynamic calculation of the surrounding rock pressure arch parameters is achieved, and the calculation precision is improved by 20% or above compared with a traditional method.

The invention relates to a tunnel surrounding rock grading method and system based on multi-source data fusion, and the method comprises the following steps: obtaining image data and point cloud data of a tunnel face, carrying out the preprocessing and data enhancement, obtaining image features and point cloud features reflecting the shape, structure, texture and roughness of the tunnel face, and carrying out the classification of the image features and the point cloud features; carrying out fusion processing on the obtained features; during feature fusion, according to the joint density in the image, determining the image block granularity, and projecting the image block granularity to the image features; determining a geological weight according to geological information acquired in advance, and embedding the geological weight into the image features; fusing the surrounding rock roughness and the rock quality index in the point cloud features with the image features through a cross-modal attention mechanism to obtain fused features; and the fused features learn geological structure laws through a pre-trained main network and a pre-trained slave network to obtain a comprehensive score, and a surrounding rock grading result is determined in combination with a threshold value of a set geological parameter.

The invention discloses a tunnel slotting blasting parameter intelligent design method and system based on while-drilling sensing and dynamic optimization, and the method comprises the steps: S1, carrying out the real-time inversion of geological parameters based on a while-drilling signal frequency spectrum, and evaluating the rock mass quality; s2, determining slotting hole arrangement through a slotting mode decision tree according to the evaluated rock mass quality and a dynamic surrounding rock classification result; s3, based on the rock mechanics parameter library, the explosive loading amount of a single slotting hole is calculated through an explosive loading amount dynamic equation; and S4, through vibration propagation numerical simulation and three-dimensional scanning, the slotting hole arrangement and the explosive loading amount of a single slotting hole are verified, blasting parameters are corrected according to the verification result, and design is completed. According to the method, through a collaborative mechanism of rock mass parameter real-time sensing, blasting energy dynamic distribution and safety effect closed-loop verification, the core purposes of reducing the over-excavation condition, improving the construction efficiency and reducing the construction cost are achieved. According to the method, the blasting precision and the construction efficiency are remarkably improved, the over-excavation and under-excavation phenomena are reduced, and the safety risk is reduced.

The invention discloses an intelligent grading method and system for tunnel face surrounding rock. According to the method, a two-dimensional image, a three-dimensional point cloud and environmental mechanics data of a tunnel face are synchronously collected through a sensor array integrated on tunneling equipment; after space-time registration fusion is carried out on the multi-source data, pixel-level identification and quantitative extraction of geological features are carried out by using a deep learning model, and three-dimensional reconstruction is carried out based on a motion recovery structure algorithm to obtain rock mass structural plane occurrence parameters; visual, geometric and environmental characteristics are fused, and accurate prediction of the surrounding rock grade is realized through an integrated learning model; and finally, dynamic excavation numerical simulation is carried out by combining the three-dimensional geologic model and a prediction result, the surrounding rock stability is analyzed, and safety early warning is generated. According to the method, automation and quantification of the whole process of surrounding rock grading are achieved, and the accuracy and timeliness of tunnel construction safety decision making are effectively improved.

The invention provides a tunnel surrounding rock identification method and system, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining a mixed frequency sound wave reflection signal collected by a sensor array disposed on a tunnel face, synchronously acquiring the hyperspectral image data of the tunnel face rock mass from the visible light to the short wave infrared band through a portable spectrometer; topological mode construction is carried out according to the mixed frequency sound wave reflection signals, and topological invariant features are obtained; performing cross-modal fusion according to the topology invariant features and the hyperspectral image data to obtain a fused topology-spectrum association graph; hidden manifold structure learning is carried out according to the fusion topology-spectrum correlation graph, and a low-dimensional feature vector is obtained; performing dynamic surrounding rock category division according to the low-dimensional feature vector to obtain a surrounding rock dynamic classification label; and performing identification result decision according to the surrounding rock dynamic classification label to obtain a tunnel surrounding rock identification result. According to the invention, high-precision and high-robustness dynamic identification of the tunnel surrounding rock state is realized.

The invention discloses an underground engineering surrounding rock while-drilling intelligent sensing and in-situ classification method, and relates to the technical field of underground engineering, and the method comprises the steps: building a rock mass mechanical parameter while-drilling inversion model, carrying out the dynamic prediction of the equivalent compressive strength of a rock mass, and generating a strength curve changing with the drilling depth; according to the sudden change characteristics of the strength curve, recognizing the position of the structural plane, constructing a rock mass structural plane response-while-drilling model, obtaining structural plane parameters according to the position of the structural plane, and further calculating rock mass quality indexes; after the testing while drilling is completed, an underground water state testing while drilling model is constructed, and underground water state characterization parameters are obtained; based on a surrounding rock classification method, performing underground engineering surrounding rock intelligent while-drilling in-situ classification evaluation by integrating rock mass equivalent compressive strength, structural plane width, structural plane spacing, rock mass quality indexes and underground water state characterization parameters; according to the invention, real-time detection and in-situ while-drilling classification of underground engineering surrounding rock properties can be realized, and the test accuracy and timeliness are improved.

The invention discloses a zone division method, device and equipment for multi-type coal rock gas in a complex structure deformation zone, a storage medium and a program product. The method comprises the following steps: determining a coal rock classification result according to sedimentary environment description information and geochemical index information; generating a geological model of the target exploration area in a coal rock development geological period, and determining a coal rock distribution range in the geological model according to a coal rock classification result; according to the core data, the logging data and the seismic data, establishing a sequence stratigraphic framework in the coal rock distribution range; determining a coal rock seismic image according to the seismic data; according to the coal rock seismic imaging, determining coal rock space-time distribution information of the target exploration area; and determining a zone division result of the coal rock gas according to the spatial and temporal distribution information of the coal rock. According to the technical scheme, the problem that a coal rock gas zone of a complex structure deformation zone is difficult to reliably divide is solved, multiple types of coal rocks can be effectively identified through layer-by-layer coal rock distribution depiction, and the coal rock gas exploration efficiency is improved.

The invention provides a real-time TBM (Tunnel Boring Machine) cross-tunnel-diameter surrounding rock category distinguishing method and system based on a rock breaking mechanism. The method comprises the following steps: acquiring tunneling data of different TBM construction tunnels, forming a sample data set, extracting tunneling parameters and equipment parameters of each engineering TBM, and forming a refining data set; extracting stable tunneling section data from the refined data set by adopting an MMD method, calculating DFPI and DTPI rock breaking characteristic indexes, and enabling the rock breaking characteristic indexes to correspond to surrounding rock category labels through pile numbers and tunneling time to obtain a surrounding rock grade data set; taking two standardized indexes of DFPI and DTPI as two-dimensional input variables, and constructing a two-dimensional Bayesian surrounding rock classification model; carrying out weighted fusion on [DFPI, DTPI] to obtain an index R, and constructing a one-dimensional surrounding rock discrimination model and a threshold identification method; and inputting the acquired TBM tunneling data into the one-dimensional surrounding rock discrimination model to obtain the surrounding rock grade of the current TBM tunnel face. According to the method, the model generalization ability is improved, and the problem that a current surrounding rock classification method is insufficient in physical interpretation is solved.

The present application mainly relates to the technical field of tunnel engineering, in order to improve the accuracy and real-time of surrounding rock classification under complex geological conditions, the present application provides a kind of dynamic correction method of surrounding rock classification of tunnel based on image texture feature, the texture feature of surrounding rock image is fused with geological parameters, form multi-source feature vector, input improved deep residual network model with integrated attention mechanism is introduced, model is based on key texture improves the precision of single-point surrounding rock grade identification, obtains the initial prediction value of surrounding rock grade, simultaneously introduces time series prediction model, based on the multi-source surrounding rock feature vector of current excavation cycle and at least one historical cycle, the entire excavation process is regarded as a continuous geological sequence, the time trend value of surrounding rock grade is obtained, and the adaptive weight is set, the initial prediction value of surrounding rock grade and the time trend value of surrounding rock grade are smoothed and corrected, and the dynamic classification result of surrounding rock is obtained, the accuracy and stability of surrounding rock classification result are improved.

The invention discloses a rock slice image classification method based on domain self-adaption, and the method comprises the steps: collecting rock slice image data, the method comprises the following steps: acquiring a rock slice image, performing multi-scale data preprocessing and geological field data enhancement, extracting multi-level visual features of the rock slice image by utilizing a pre-trained DINOv3 model, performing field specialized adaptation on general visual features through a geological field adaptive Adapter module, and enhancing rock slice discriminative feature representation by adopting a double-path attention mechanism. Constructing a progressive hierarchical classification head to realize coarse-to-fine rock classification; and designing a multi-stage progressive training strategy to optimize the overall performance of the model. According to the method, mineral composition and structural features of the rock slices under different scales can be accurately captured, and multi-scale features and an attention mechanism are fully utilized, so that accurate classification of the rock slices is realized, and the accuracy and reliability of rock slice identification are remarkably improved.

The present application belongs to the technical field of deep hole blasting parameter optimization, and discloses a hole site parameter optimization analysis method based on open pit deep hole blasting. The present application determines rock types through multi-index coordination, improves the accuracy of rock classification by combining a dynamic threshold adjustment mechanism, and ensures the accurate matching of hole site parameters and geological conditions. The present application determines through three-level correlation of geological parameters-rock types-mapping relationship, avoids the limitations of empirical formulas, and makes the parameter range more in line with actual geological characteristics. At the same time, historical blasting data sets are introduced for similar case matching, potential risk parameter sets are removed through experience screening, the deviation between theoretical simulation and actual engineering is reduced, and the parameter reliability is improved. The present application adopts a multi-objective optimization model, simultaneously considers fragmentation quality, safety control and economy, outputs a parameter set balancing each target through a Pareto optimal algorithm, and realizes the coordinated optimization of high quality, low risk and low cost.

This invention proposes a method and system for determining the classification of tunnel surrounding rock based on a dual-modal fusion model, relating to the field of tunnel engineering technology. Addressing the problems of poor accuracy and applicability in existing surrounding rock classification technologies, this invention collects drilling parameters, tunnel face images, and level labels, and performs preprocessing. Based on the drilling parameters, a multi-dimensional initial feature set is constructed and key drilling parameter feature sets are obtained through screening. Dimensionality reduction processing of the tunnel face images yields key image feature sets, which are then used to determine the fusion feature set. Using the fusion feature set as input and the level labels as output, a feature-level fusion classification model is trained. Using the key drilling parameter feature set and key image feature set as input, combined with the level labels, two single-modal classification models are trained and then co-validated and fused to obtain a decision-level fusion classification model. The tunnel data to be classified is then input into either the feature-level or decision-level fusion classification model to determine the classification result. This invention offers high classification accuracy and applicability.

This invention relates to an optimized processing method for monitoring surrounding rock displacement during the excavation of a cavern complex. The method includes: determining the rock mass strength parameters and establishing a three-dimensional geological model by combining topographic information of the target area and on-site geological survey data; calculating the corresponding three-dimensional geostress field; determining the approximate range of surrounding rock strength and deformation parameters according to specifications and rock mechanics handbooks based on the surrounding rock category determined by surrounding rock classification and surrounding rock parameters obtained from laboratory tests; calculating the relative deformation of different measurement sections and inverting the optimized surrounding rock parameters using the relative deformation of different measurement sections monitored by multi-point displacement gauges; and verifying the reliability of the inverted surrounding rock parameters by comparing them with the measured relative deformation characteristics of the surrounding rock. The beneficial effects of this invention are: by optimizing the surrounding rock parameters to obtain the excavation response characteristics of the surrounding rock, the on-site monitoring data is corrected and analyzed to correct the multi-point displacement gauge monitoring data, making the monitoring data obtained by the multi-point displacement gauges more accurate.

The invention relates to the technical field of tunnel face surrounding rock grading in railway construction, and provides a drilling and blasting method tunnel face surrounding rock three-dimensional refined grading method based on a seismic wave reflection method, which comprises the following steps: collecting three-dimensional inversion dot matrix data of an area in front of a tunnel face; the data coordinate system is converted into a tunnel design coordinate system, the surrounding rock basic quality index BQ value of each point is calculated according to the wave velocity, and a space BQ value dot matrix database is generated; performing spatial region division on the BQ value dot matrix by adopting a three-dimensional region self-growth method to obtain a connected region with similar BQ value characteristics; after the representative value of the region boundary is determined, using a Marking Cubes algorithm to extract contour surfaces to construct a surrounding rock three-dimensional grid model; and finally, mapping the model vertex BQ value with a preset grading standard, and outputting a three-dimensional refined grading model with a surrounding rock grading label. Automatic processing and three-dimensional visualization of surrounding rock classification are achieved, and classification accuracy and construction decision reliability are improved.

The invention relates to the technical field of tunnel engineering intelligent perception and surrounding rock grading, and discloses a mountain tunnel surrounding rock grade automatic identification method, system and device based on image-point cloud fusion, and a storage medium. The automatic mountain tunnel surrounding rock grade identification method based on image-point cloud fusion comprises the following steps of multi-source surrounding rock perception data acquisition, surrounding rock image apparent semantic feature extraction, surrounding rock point cloud geometric state feature extraction, surrounding rock evaluation unit construction and image-point cloud feature fusion, surrounding rock engineering state comprehensive evaluation and grade identification. And surrounding rock grade zoning and result output. According to the invention, through image-point cloud fusion, a continuous evaluation unit is constructed along the axial direction of the tunnel, and the surrounding rock grade is automatically identified and output; according to the method, the objectivity and consistency of surrounding rock grade identification are improved, continuous sensing and grading of surrounding rock states are realized, the real-time performance of acquiring surrounding rock grade information is improved, deep fusion and engineering output of multi-source information are realized, and the method has good engineering applicability.

The application discloses a kind of asymmetric decoupling tunnel smooth blasting method, comprising: obtaining the surrounding rock classification data of target area and initial ground stress distribution and optimizing low-damage test blasting scheme;Based on the low-damage test blasting scheme, drilling operation is carried out, and asymmetric decoupling fine charging casing device is assembled by selecting specific pre-charging site and assembling peripheral hole pre-charging casing device;The surrounding hole is filled by the peripheral hole pre-charging casing device, and the remaining blast hole is filled by using continuous charging method, and is blocked after filling is completed;Build initiation network, ventilate after initiation and check blasting effect, and iteratively optimize test blasting scheme according to damage determination result.The application can accurately control the working direction and range of energy of explosives in the surrounding hole during blasting, thereby reducing the disturbance of blasting effect on surrounding rock, reducing the damage degree of surrounding rock blasting, ensuring construction safety, reducing the number of overbreak and underbreak, and greatly improving construction efficiency.

The invention relates to the technical field of rock structure roughness surveying and mapping, in particular to a rock structure surface roughness high-precision surveying and mapping method and system. Comprising the steps that firstly, a rock classification model is provided, the model classifies 3D point cloud data of a rock structural surface, and a rock feature weighting layer is added to perform weighting operation on geometric features, shape features and color features of rocks; secondly, a rock roughness surveying and mapping model is provided, and the model achieves surveying and mapping of local roughness and global roughness of a rock structural surface through a structural surface point cloud clustering algorithm; and finally, providing a rock structural surface roughness analysis method, according to the method, establishing a three-dimensional rock structural surface model according to the obtained 3D point cloud data of the rock structural surface, performing meshing division on the three-dimensional rock structural surface model, and performing reason analysis on the rock structural surface roughness obtained by surveying and mapping in combination with rock types and various related data.

The application discloses a large-scale underground cavern surrounding rock grading correction method and device for complex layered rock mass, which comprises the following steps: step S1: for the tunnel section where the tunnel body is located in the same type of tilted stratum, basic surrounding rock grading is carried out to obtain the initial surrounding rock grade of the tunnel body located in Q1-Q4 stratum; step S2: judging the complex layered rock mass structure combination type and layer thickness proportion, the initial surrounding rock grade of the tunnel body located in Q1-Q4 stratum is respectively graded and corrected to obtain the corrected surrounding rock grade. According to the grading correction of the initial surrounding rock grade according to the complex layered rock mass structure combination type and the layer thickness proportion, the subjective influence is reduced, and through the surrounding rock grade quantization, the tunnel surrounding rock classification effect is remarkable.

This invention discloses a method for classifying surrounding rock in coal mine TBM engineering based on a modified BQ index, belonging to the field of coal mine engineering technology. This method includes: obtaining the saturated uniaxial compressive strength and rock integrity coefficient of the rock in the roadway to be excavated; calculating and obtaining basic rock mass quality indicators; obtaining geological survey data and engineering design parameters of the roadway to be excavated, determining a first correction coefficient for quantifying the reduction in excavation efficiency, a second correction coefficient for quantifying the reduction in equipment operating efficiency, and a third correction coefficient for quantifying the reduction in construction continuity efficiency; calculating and obtaining rock mass quality indicators for coal mine TBM engineering; and determining the level of the surrounding rock by comparing it with a preset surrounding rock classification standard. This invention can quantify the reduction effect of dynamic engineering disturbances on excavation efficiency, improve the correlation between evaluation indicators and actual efficiency, and provide a scientific decision-making basis for construction organization and risk control.

The invention provides a surrounding rock grade identification method and system based on a convolutional neural network, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the feature extraction of an input image through a BIFM module, and generating a boundary fusion feature image; inputting the boundary fusion feature image into an MFFM module for feature extraction and fusion, and generating a multi-scale fusion feature image; wherein the MFFM module comprises a plurality of stages of feature extraction modules with different scales, and each stage of feature extraction module comprises a maximum pooling layer and a plurality of cascaded and stacked improved Inception modules; performing global average pooling operation and linear normalization operation on the multi-scale fusion feature image to map the image category into a surrounding rock grade; a group of continuous rock slag images to be recognized are collected at set intervals and sent to the server side for surrounding rock grade judgment, and the surrounding rock grade with the maximum in-group proportion serves as the surrounding rock grade judgment result of the current group. And the accuracy, the stability and the identification efficiency of the surrounding rock grade identification are improved.

The invention provides a deep well coal mine roadway surrounding rock grading support construction method which comprises the following steps: carrying out geological survey and ground exploration on a to-be-tunneled area, and determining the distribution position of a joint stratum in the to-be-tunneled area; performing advanced detection on the to-be-tunneled area according to the distribution position of the joint stratum to obtain a rock mass of the to-be-tunneled area, and determining geological characteristics and structural integrity characteristics of the rock mass; according to the geological characteristics and the structural integrity characteristics of the rock mass, roadway surrounding rock grades are determined, the roadway surrounding rock grades comprise a plurality of grades, a supporting scheme matched with the roadway surrounding rock grades is determined, and supporting operation is carried out on the excavated deep well coal mine roadway; and carrying out displacement monitoring on the supported roadway surrounding rock until the displacement of the supported roadway surrounding rock converges, and completing the support. The roadway surrounding rock can be graded, the supporting scheme can be correspondingly adjusted, the supporting efficiency is improved, and the stability and safety of the roadway are effectively improved.

The application discloses a tunnel surrounding rock grading method and device. The method comprises the following steps: shooting a first tunnel image of a first tunnel face at at least one angle; adopting vibration intervention to the first tunnel face to obtain a second tunnel face; shooting a second tunnel image of the second tunnel face at the angle; obtaining a tunnel face change according to the first tunnel image and the second tunnel image; and grading the tunnel face according to the tunnel face change by using a neural network model. The application firstly adopts active vibration intervention to the tunnel face to obtain geological information change of the tunnel face before and after the intervention; and then evaluates the tunnel face change by using the neural network model, so that different grades of the changeable degree of the current tunnel face of the tunnel surrounding rock are obtained.

The invention provides a tunnel surrounding rock classification method and system based on multi-dimensional data. The method comprises the steps of obtaining a candidate feature set of a tunnel surrounding rock sample, an initial tunnel surrounding rock level classifier and an initial model hyper-parameter range; initializing a traveler population by adopting a random sampling mode according to the candidate feature set and the initial model hyper-parameter range to obtain an initial traveler population; according to the traveler population, an improved traveler optimization algorithm is adopted to execute a feature screening stage and a parameter fine optimization stage, a candidate feature set of the tunnel surrounding rock sample and an initial model hyper-parameter range are screened, and an optimal surrounding rock feature subset and a corresponding optimal model hyper-parameter are obtained; and according to the optimal surrounding rock feature subset and the optimal model hyper-parameters, a final surrounding rock grade identification model is constructed, and tunnel surrounding rock classification is performed by using the final surrounding rock grade identification model, so that dual requirements of rapid preliminary judgment and accurate identification of complex working conditions can be adaptively balanced, and the practicability and reliability of the tunnel surrounding rock identification method are improved.

The present application relates to the technical field of geochemical quality migration calculation, in particular to a method for calculating the quality migration of geological evolution, and mainly comprises the following characteristics: an initial material-product geological evolution conceptual box model is established, the mass concentration of each element in different subsystems is determined, the system evolution degree epsilon is calculated to measure the degree of system mass change, the element activity ability is measured by calculating the partition coefficient D i , and finally the quality migration is calculated by using a formula to objectively reflect the mass change process of the element; compared with the prior art, the present application does not need to assume inactive elements, avoids subjective bias, can effectively overcome the influence of the closed effect, and provides more reliable calculation results of the element quality migration; the method is widely applicable to the field of geological research, and has significant application value in aspects of structural background discrimination, magmatic rock classification, and sediment source area analysis.

The application provides a tunnel surrounding rock identification method and system, and relates to the technical field of tunnel construction, comprising: acquiring mixed frequency acoustic wave reflection signals collected by a sensor array arranged on a tunnel face, and hyperspectral image data of the tunnel face rock mass in the visible light to short wave infrared band synchronously acquired by a portable spectrometer; constructing a topological pattern according to the mixed frequency acoustic wave reflection signals to obtain topological invariant features; performing cross-modal fusion according to the topological invariant features and the hyperspectral image data to obtain a fused topological-spectral correlation graph; performing implicit manifold structure learning according to the fused topological-spectral correlation graph to obtain a low-dimensional feature vector; performing dynamic surrounding rock classification according to the low-dimensional feature vector to obtain a surrounding rock dynamic classification label; and performing identification result decision according to the surrounding rock dynamic classification label to obtain a tunnel surrounding rock identification result. The application realizes high-precision and high-robustness dynamic identification of the tunnel surrounding rock state.

The present application relates to the field of tunnel engineering and geological exploration technology, and discloses a surrounding rock grade discrimination method and system based on TBM vibration and tunneling information, which solves the problems of low efficiency, discontinuous monitoring and insufficient discrimination accuracy existing in the prior art surrounding rock grade discrimination scheme. The present application scheme comprises: collecting the cutterhead vibration signal and tunneling parameters in the TBM tunneling process in real time; performing modal decomposition on the cutterhead vibration signal, screening effective modal components and extracting key characteristic parameters representing rock mass characteristics; then, inputting the key characteristic parameters and tunneling parameters into a pre-established mapping relationship model to output the identification result of the current surrounding rock grade. The present application realizes accurate real-time identification of surrounding rock under the non-stop state of TBM by fusing signal processing technology, rock mechanics mechanism and deep learning algorithm.

The invention relates to a high-integrity hard rock ore body collapsibility grading method and system, and the method comprises the steps: collecting a rock mass quality grading core parameter group and a crustal stress field actual measurement data set of a target ore body, inputting a three-dimensional caving model, and generating an adaptive trial explosion scheme, synchronously outputting the simulated collapsibility grade and a dynamic response threshold value of a preset grading standard; collecting real-time dynamic data in the trial explosion process; if the real-time dynamic data exceed the dynamic response threshold value range, generating a new adaptive trial explosion scheme and an adjusted collapsibility grade, and repeating field trial explosion and data acquisition according to the new adaptive trial explosion scheme until the real-time dynamic data meet the threshold value requirement; and the disintegration grade distribution in the three-dimensional space is updated through a preset spatial interpolation algorithm, and the disintegration grade is dynamically updated. The method has the following effects that the problems of high cost, incomplete coverage and disjunction with stope working conditions of metal mine high-integrity hard rock classification are solved, and accurate classification and dynamic adaptation are achieved.

The invention relates to a shale lithofacies integration discrimination method and device based on machine learning, equipment, a storage medium and a computer program.The method comprises the steps that rock classification is carried out according to logging data of a research well, a rock classification result is obtained, lithofacies classification is carried out according to the logging data and the rock classification result, and a shale lithofacies integration discrimination result is obtained; determining a sensitive element ratio of each lithofacies and correlation of the element ratios according to the logging data, obtaining a brittleness index, a rock mechanical parameter and a wave impedance curve corresponding to each lithofacies, and constructing a lithofacies classification model according to the correlation of the element ratio of each lithofacies, the brittleness index, the rock mechanical parameter and the wave impedance curve. And constructing a lithofacies quantitative discrimination index calculation model according to the mineral composition, the sensitive element ratio, the brittleness index, the rock mechanical parameters and the wave impedance curve of each lithofacies, and constructing a shale lithofacies comprehensive division scheme according to the lithofacies classification model and the lithofacies quantitative discrimination index calculation model.