17622 results about "Mining engineering" patented technology

The invention discloses an image enhancement method and system in a complex coal mine environment, and relates to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a collected coal mine image of a target region, and dividing the coal mine image into different semantic regions, including a bright region, a dark region and a dust shielding region, through a deep learning semantic segmentation model; according to semantic region characteristics, a differentiation enhancement strategy is made; a traditional Retinex model is improved, non-local mean filtering is introduced, and an illumination component and a reflection component are decomposed through pixel similarity matching. According to the method, the image is divided into the bright area, the dark area and the dust shielding area through the deep learning semantic segmentation model, differential enhancement strategies are formulated according to different area characteristics, detail distortion caused by global adjustment is avoided, local contrast suppression is adopted in the bright area, illumination compensation is enhanced in the dark area, and the image quality is improved. Noise diffusion of the dust shielding area is inhibited through edge preservation smoothing, the image quality of each area is remarkably improved, and it is ensured that image details in a complex coal mine environment are clear and visible.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention provides a method for evaluating the grouting water control effect of an underground water-sealed cave depot project. The method comprises the steps that firstly, grouting and geological parameters and hydrological monitoring data of a project area are obtained; establishing a three-dimensional seepage-grouting coupling model, and simulating the dynamic state of a seepage field by using a finite element analysis algorithm; then calculating the equivalent permeability change rate of the grouting affected zone based on a simulation result; constructing an evaluation index system, and performing quantitative evaluation by using a fuzzy comprehensive evaluation algorithm; and finally, generating a grouting parameter dynamic adjustment scheme according to an evaluation result. According to the method, scientific and accurate evaluation and engineering optimization of the grouting water control effect can be achieved, and the safety and economical efficiency of engineering are improved.

A component includes a body having a coupon opening defined therein, and an additively manufactured (AM) metal coupon including a dense region, a porous region adjacent the dense region, and a coupon outer surface. The porous region has a first cross-sectional area at a first location at or near the coupon outer surface and a second cross-sectional area less than the first cross-sectional area at a second location distal from the coupon outer surface. A braze material couples the metal coupon in the coupon opening and infiltrates into the porous region, e.g., based at least on a characteristic of the porosity thereof. The dense region may have a third cross-sectional area at the first location and a fourth cross-sectional area greater than the third cross-sectional area at the second location so it can direct the braze material in the porous region toward an inner surface of the coupon opening.

The invention discloses an underground coal mine video monitoring method, which relates to the technical field of image communication, and comprises the following steps of: acquiring historical video data, extracting historical key feature data for analysis, establishing a feature baseline range of an underground coal mine normal operation state, constructing an anomaly rule base for different anomaly types, and establishing a feature baseline range of the underground coal mine normal operation state; establishing a mapping relation between historical key feature data and abnormal rules, acquiring real-time video data and audio data of the underground coal mine, extracting real-time key feature data, comparing the real-time key feature data with a feature baseline range, matching corresponding rules in an abnormal rule base based on the established mapping relation, outputting an abnormal analysis result, and if it is judged that an abnormality exists, outputting the abnormal analysis result. If yes, triggering a corresponding alarm according to the exception rule, associating an exception handling process, and realizing intelligent safety monitoring by collecting multi-period underground video extraction features, establishing baseline and differentiation judgment conditions, constructing an exception library and a triggering mechanism, comparing real-time data to judge exception and performing graded response handling.

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

The invention relates to the technical field of coal mine water disaster prevention and control, and particularly discloses a hidden structure detection system for mine water disaster intelligent monitoring, early warning and prevention, which is used for solving the problems of low mine hidden structure detection precision, mine water disaster early warning lag and large seismic wave propagation path simulation error. Comprising a multi-source data comprehensive detection module, a real-time imaging module, a micro-seismic multi-scale intelligent analysis module, a seismic wave signal identification module, a regional difference analysis module and an intelligent data processing module. According to the invention, through fusion of multi-source data, real-time imaging, micro-seismic analysis, seismic wave identification and intelligent data processing technologies, precise detection of a mine hidden structure, high-precision prediction of mine earthquakes and water disasters and real-time risk early warning are realized, and timeliness and accuracy of mine water disaster prevention and control are improved.

The present invention relates to a coal mine intelligent directional drill and its drilling method, belonging to the technical field of coal mine drills. It comprises a moving platform, machine frame, clamper, main manipulator, power head, drill pipe storage system, control system, and hydraulic system. The power head structure is improved by adding an angle adjuster with accurate angle adjustment, locking, and anti-rotation functions. The angle adjuster is positioned on the gearbox side opposite the main motor, connected via a driving shaft. The control system includes a toolface azimuth detection and initialization system that works with the angle adjuster. This solves prior issues such as the absence of specialized toolface azimuth adjustment devices, low precision, low track adjustment efficiency, and challenges in achieving automatic directional drilling in the prior art. The invention enhances precision and efficiency, enabling automated, intelligent directional drilling.

A mining, releasing and filling process flow collaboration method for a nearly-upright super-thick coal seam belongs to the technical field of coal mining and comprises the following steps: dividing the nearly-upright super-thick coal seam into a plurality of sections, respectively establishing a low-level mining and releasing system at the bottom of each section, and respectively establishing a high-level filling system at the horizontal position of the top interface of each section; comprising a coal mining period and a filling period, after the low-position mining and caving system is established, coal mining and caving are carried out through a coal mining machine and a support in the coal mining period, in the machine following and support moving process, a novel support synchronously lays a separation net on a goaf, and after a filling step pitch is pushed forwards, the novel support unfolds slurry blocking cloth; then filling slurry is injected into the goaf through a high-position filling process roadway, a filling chamber and a filling hole in sequence, the stability of nearly upright surrounding rock is maintained, and it is ensured that the mining depth is safely extended; the method effectively solves the problems of difficulty in prevention and control of surrounding rock instability, difficulty in control of cutting type subsidence and low recovery rate of coal resources in the existing horizontal section fully mechanized caving mining method of the steeply inclined nearly-upright extra-thick coal seam.

The invention discloses a deep water-rich weak-impact coal seam surrounding rock control method based on rapid tunneling, and relates to the technical field of surrounding rock control. The method comprises the steps that S1, a high-risk area is identified through address detection, a waterproof curtain is constructed, and the tunneling disturbance risk is reduced; s2, rapid tunneling and instant supporting are conducted, the surrounding rock exposure time is shortened, tunneling and supporting are completed synchronously, and deformation development is restrained; s3, sensing a surrounding rock-support response in real time, optimizing parameters and early warning risks, and realizing dynamic monitoring and feedback regulation and control; s4, later-stage continuous reinforcement maintenance is conducted, so that the roadway adapts to the rheological characteristics of deep surrounding rock, and the service cycle of the roadway is prolonged; according to the method, a high-risk area is identified through various detection technologies, and instant supporting is achieved through cutting process optimization and digging and supporting collaborative operation; real-time monitoring and dynamic feedback are achieved, and automatic optimization of supporting parameters is achieved; and later reinforcement and periodic maintenance are adopted to ensure that the surrounding rock supporting system can adapt to the rheological characteristics of deep surrounding rock for a long time.

The invention relates to the field of safety risk early warning, in particular to a BIM (Building Information Modeling)-based open-air goaf charging safety risk early warning method and a BIM-based open-air goaf charging safety risk early warning system. A three-dimensional BIM geologic model containing rock mass hardness data, joint density and fracture distribution data is constructed through unmanned aerial vehicle scanning and rock core sampling; determining a minimum effective explosive loading amount meeting a preset crushing lumpiness mean value in the three-dimensional BIM geological model, performing dynamic simulation through the three-dimensional BIM geological model to obtain a simulation result, and outputting a final explosive loading amount when the simulation result meets a preset safety constraint condition; and generating charge parameters according to the final charge amount, binding the charge parameters with space coordinates of the three-dimensional BIM geologic model, and outputting an early warning area through a visual interface. By determining the optimal explosive loading amount and optimizing the blast hole layout and the detonating sequence, the explosive loading safety risk is effectively reduced while the blasting effect is guaranteed.

The invention discloses a deep coal mine disaster early warning method and system based on a geological radar, and relates to the technical field of disaster early warning, and the method comprises the steps: carrying out the monitoring coverage analysis of a target monitoring region, laying geological radar equipment according to a result, and constructing a geological radar array; scanning a mine underground structure through an array to obtain a rock stratum data array; performing spatial hierarchical feature recognition on the rock stratum data array to obtain rock stratum risk feature distribution; calling historical data to obtain a plurality of historical risk feature distributions; and performing risk trend prediction on the rock stratum risk feature distribution according to the historical distribution, and outputting a predicted disaster critical working condition. The technical problem that the real-time performance and the accuracy of disaster risk identification are insufficient due to the fact that a traditional coal mine disaster early warning method depends on low-frequency and single-dimension monitoring data is solved, and the technical effect of improving the accuracy and the real-time performance of coal mine disaster early warning is achieved.

The invention discloses a coal fire area fracture active enhancement detection test system and method, and belongs to the technical field of mine safety and geophysical exploration, and the system comprises a fracture generation module which is used for simulating a heat-force-gas coupling condition of an underground coal fire area, and inducing a coal rock mass test piece to generate a fracture; the fracture detection module is used for synchronously acquiring multi-physics field data of the coal and rock mass test piece in real time; the fracture intervention module is used for injecting a natural medium simulant and an enhanced medium foam gel material into the fracture so as to simulate a real environment and enhance a detection signal; and the intelligent feedback and data fusion module is used for controlling operation parameters of each module, synchronously acquiring detection data, and performing fusion processing, intelligent inversion and four-dimensional visualization on the detection data to realize whole-process monitoring detection of fracture evolution. The invention provides a scientific method for precise delineation and targeted plugging treatment of hidden fractures in a coal fire area, and solves the problems of multiplicity of solution in traditional fracture detection and easy reburning in coal fire treatment.

The invention relates to the technical field of mine visualization, in particular to a three-dimensional visualization method and system for a mine. The method comprises the following steps: establishing a three-dimensional terrain model of a mine; designing exploration planning space distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operation through the exploration planning space distribution parameters; mine point rock stratum lithology data are collected according to exploration drilling operation; mapping the mine point location rock stratum lithology data to the mine three-dimensional terrain model to obtain optimized mine point location rock stratum lithology mapping data; analyzing mine rock stratum boundary structure data and rock stratum lithology fusion characteristic data according to the optimized mine point location rock stratum lithology mapping data, and performing mine rock stratum lithology structure analysis to generate mine rock stratum lithology structure data; and based on the lithologic structure data of the mine rock stratum, lithologic fusion differentiation rendering three-dimensional modeling of the mine rock stratum is carried out, and a three-dimensional visual rendering model of the mine rock stratum is generated. According to the invention, accurate visual analysis of the internal structure of the mine is realized.

The invention discloses a mobile tunnel detection method and system, and relates to the field of tunnel detection, and the method comprises the steps: dividing a tunnel into a shield tunnel and a mine tunnel; for a shield tunnel, obtaining a three-dimensional point cloud of the shield tunnel, and extracting bolt holes by adopting a CSF algorithm and a DBSCAN algorithm; adopting a mean-shift algorithm to obtain a central point of the bolt hole, and performing ring-dividing and block-dividing on the three-dimensional point cloud of the shield tunnel according to the central point of the bolt hole to obtain a shield tunnel point cloud after ring-dividing and block-dividing; the method comprises the following steps of: for a mine tunnel, arranging one target every preset distance along the axial direction of the tunnel by using mobile tunnel laser equipment for target arrangement to obtain a mine tunnel point cloud with target position information; generating a mine tunnel lining diagram according to the mine tunnel point cloud with the target position information; establishing an index relationship between the mine tunnel point cloud and the mine tunnel lining graph; and positioning the mine tunnel point cloud according to the index relationship to obtain the positioned mine tunnel point cloud and the like, so that the tunnel defect detection efficiency is improved.

The invention discloses a steeply-inclined nearly-upright extra-thick coal seam horizontal section fully-mechanized caving filling mining method, and relates to the technical field of coal mining. The method includes the following steps that the nearly-vertical super-thick coal seam is divided into different mining levels of 20-40 m, a low-position mining and releasing system is arranged at the horizontal position of the junction of a current section and a lower section, a high-position filling system is arranged in a top plate or bottom plate rock pillar at the horizontal position of the junction of the current section and an upper section, and the coal seam of the current section is mined through a fully-mechanized caving mining method; along with advancing of the coal face, a separation net and slurry blocking cloth are laid on the goaf through a net laying and slurry blocking support; a suspended cavity space detection characterization robot arranged in the high-position filling system is used for carrying out goaf space detection; the method comprises the following steps: dividing a nearly upright extra-thick coal seam into different sections for section mining, performing in-situ grouting filling on a subsequent mining space in the mining process, forming a stable filling interface in a goaf, fundamentally weakening stress concentration and reducing energy accumulation, and realizing collaborative proceeding of filling and coal mining.

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

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 belongs to the technical field of geological disaster monitoring, and particularly discloses an intelligent classification method for dangerous areas after instability of overhanging rocks. The method comprises the following steps: scanning the overhanging rock to generate a DEM and correcting the DEM; sampling and carrying out rock-soil mechanical test to obtain mechanical parameters, and calibrating the distribution range; importing the DEM into the PFC to generate a three-dimensional slope model, freely releasing the overhanging rock in the three-dimensional slope model to simulate falling, monitoring and recording key parameters, and associating the key parameters with risk levels; establishing a hierarchical structure model, a judgment matrix and an evaluation function, and checking the consistency of the judgment matrix to ensure that a weight result is credible; setting a risk grade division standard; calculating a risk score through an evaluation function and matching a risk level; constructing a data set and a CNN model, and training, verifying and testing the CNN model by adopting the data set; and the CNN model receives the key parameters, and calculates and outputs a corresponding risk level. The method has the characteristics of high timeliness, less human intervention and accurate judgment.

The invention relates to the technical field of coal mine geological safety monitoring and disaster early warning, in particular to a coal mine disaster early warning method and system based on a geological model and a storage medium, and the method comprises the steps: firstly obtaining high fractal fracture information and monitoring data such as gas, hydrology and stress through chaotic wave excitation and fractal analysis; and a three-dimensional fractal grid with multi-level fracture representation is constructed. And discretizing a mechanical field and a fluid field by using a fractal partial differential equation operator, and globally solving a large-scale nonlinear equation through a quantum annealing mode or a quantum and classical combined annealing mode to form a quantum fractal coupling simulation result. And finally, integrating the grid units and monitoring data in the fuzzy hypergraph structure, carrying out fuzzy membership analysis on a multi-disaster element coupling sign, and outputting early warning information once the risk is judged to reach a threshold value. According to the invention, the precision and timeliness of coal mine geological disaster monitoring can be obviously improved.

The invention discloses a filling disaster prevention and control green mining method for a nearly-upright extra-thick coal seam, and relates to the technical field of coal mining. The method includes the following steps that the nearly-vertical super-thick coal seam is divided into different sections according to the thickness of 20-40 m; all the sections are mined through a fully mechanized caving mining method, after the coal face is advanced by 8-10 m, namely a mining and filling step pitch, slurry blocking cloth is arranged on a top coal caving filling mining hydraulic support, and a separation net is laid in a goaf; grouting and filling are conducted on the goaf through the high-position process roadway; the nearly vertical coal seam is divided into a plurality of horizontal sections, the descending mining sequence is adopted, goaf timely filling is matched, rock stratum movement can be precisely and cooperatively controlled, steeply inclined rock stratum displacement and deformation caused by coal seam mining are effectively reduced, surrounding rock stress is remarkably weakened, earth surface deformation is controlled, and compared with a traditional mining mode, the mining efficiency is improved. And the risk of coal rock dynamic disasters is greatly reduced.

Disclosed are a filling curing agent for preparing mine superfine tailing slag from solid waste residues, which belongs to the field of preparation of solid waste residues. The filling curing agent comprises a cementing material component obtained by means of a wet milling process while grinding, and an exciting agent component obtained by means of a mixing and stirring process; the cementing material component comprises, in parts by mass, 45-50 parts of blast furnace slag powder, 35-40 parts of steel slag powder, 15-20 parts of coal ash and 53.8 parts of water, and the exciting agent component comprises 100 parts of desulfurized gypsum powder and 59.5-60.6 parts of an activating agent; and the weight of the exciting agent component accounts for 10-15% of the weight of the filling curing agent. The filling curing agent disclosed in the present invention has low filling cost, high filling body strength and good durability, which reduces the occupation of land resources, reduces the impact on the ecological environment and residents' lives, realizes a virtuous cycle of mining and filling, and has good industrial prospects and social benefits.

The invention provides an intelligent safety and stability assessment method, system and device for an underground cavern and a medium, and the method comprises the following steps: S1, building a three-dimensional geological model of the underground cavern according to initial geological exploration data; s2, multiple monitoring instruments are pre-buried in key parts of the underground cavern, and monitoring data are collected in real time; s3, constructing an underground cavern safety and stability evaluation model based on a deep learning algorithm; s4, inputting real-time monitoring data into an evaluation model, combining historical monitoring data and geological model information, analyzing and comparing with a three-dimensional numerical calculation result, adjusting geological parameters, performing reconstruction evaluation, and outputting a safety and stability level; and S5, according to an evaluation result, automatically generating early warning information and processing suggestions. According to the method, the problems of poor real-time performance and low data utilization rate of traditional evaluation under complex geological conditions are solved, and the intelligent level of construction safety monitoring of underground cavern groups such as hydropower stations and pumped storage projects can be effectively improved.

The present application belongs to the technical field of roller mills. Disclosed is a dual-transmission-type high-pressure roller mill for the mining industry, the roller mill comprising a housing supporting assembly, wherein circular feeding assemblies are mounted on two sides of the inner wall of the housing supporting assembly, respectively; a buffering and crushing assembly is mounted on the inner wall of the housing supporting assembly; a roller milling assembly is mounted on the inner wall of the housing supporting assembly, and the roller milling assembly is located below the buffering and crushing assembly; the housing supporting assembly is configured to support the circular feeding assembly, the buffering and crushing assembly and the roller milling assembly; the buffering and crushing assembly can buffer and crush ores; the circular feeding assembly can feed screened ores in a circular manner; and the roller rolling assembly is configured to mill the screened ores. Falling ores fall between two impact plates, and the two impact plates are pushed to move at the same time by means of two hydraulic rods, and can impact on the ores by means of impact needles on the two impact plates, such that the ores are crushed into small blocks, and thus the ore milling efficiency can be improved.

The invention belongs to the technical field of lithology identification, and discloses a coal cutter self-adaptive regulation and control method for coal rock identification based on cutting pick stress monitoring, so as to realize accurate and efficient identification of coal rock and self-adaptive regulation and control of a coal cutter based on cutting pick stress data and in combination with operation parameters of the coal cutter. According to the coal mining machine self-adaptive regulation and control method based on cutting pick stress monitoring and coal rock recognition, the pressure sensors arranged at the roots of all the cutting picks are used for obtaining cutting pick stress original data in real time, the coal rock interface is accurately and efficiently recognized by combining the operation parameters of the coal mining machine and utilizing a data processing and analysis algorithm, and the coal mining machine self-adaptive regulation and control method is high in practicability. And the cutting parameters of the coal mining machine are dynamically adjusted, so that safe, efficient and high-precision coal rock identification and self-adaptive regulation and control of the coal mining machine are realized, the intelligentization of coal mining operation is improved, the coal mining efficiency is improved, the abrasion of equipment is reduced, and the safety and stability of the mining operation are guaranteed.

The invention discloses a rapid method for determining the grade of target elements in concentrate based on mineral dissociation degree, and the grade of the target elements in the concentrate under a certain fineness condition is calculated according to the dissociation degree of target minerals in a raw ore sample under the certain fineness condition and the content of the target elements in the target minerals in the raw ore sample. By means of the method, the grade of the target element in the concentrate under the specified fineness condition can be calculated more quickly and more effectively, the beneficiation effect under the fineness condition can be predicted in advance when beneficiation operation is not conducted, and powerful data support is provided for formulation of the beneficiation technological process.

The invention discloses an efficient thin seam coaling roller for a coal mining machine and a using method, and relates to the field of coal mining equipment. A thin seam efficient coaling roller for a coal mining machine comprises a coal mining spiral roller which is fixedly installed at the power output end of a driving rocker arm of the coal mining machine through a fixing bolt; the mounting cover is fixedly mounted at the position, close to the coal mining spiral drum, of the coal mining machine driving rocker arm; while the coal mining spiral drum rotates to mine coal, water spraying at the rocker arm of the coal mining machine, water spraying at the cutting tooth seat and mist curtains sprayed by the wide-angle fan-shaped atomizing nozzles work cooperatively, the dust falling effect is enhanced, a water mist cover is rapidly formed under the assistance of the mounting cover, and the water mist cover can effectively isolate coal dust inside, so that the dust falling effect is improved. And the coal dust diffusion is greatly reduced, the body health of miners is guaranteed, the occupational health risk is reduced, the abrasion of the coal dust to equipment is reduced, the service life of the equipment is prolonged, the maintenance frequency and cost of the equipment are reduced, and the comprehensive benefits of coal mining operation are improved.

The invention provides a coal mine digital twin geologic model construction method and system based on precise exploration, and relates to the technical field of coal mine geologic modeling, and the method comprises the steps: respectively obtaining ground static data, roadway dynamic data and coal mining real-time data; preprocessing the ground static data, the roadway dynamic data and the coal mining real-time data to obtain preprocessed data; integrating the preprocessed data, and constructing a three-dimensional dynamic geologic model according to the integrated data; dynamically updating the three-dimensional dynamic geologic model according to the coal mining real-time data so as to ensure the real-time performance and the accuracy of the model; according to the method, the three-dimensional dynamic geologic model is displayed through a three-dimensional visualization technology. Through multi-source data fusion and dynamic updating, transparent management of coal mine geological information is realized, and a powerful guarantee is provided for safe and efficient mining of a coal mine.

The invention belongs to the field of convolutional neural networks, and particularly relates to a coal seam hole wall crack capturing method based on a YOLO algorithm. According to the method, a large number of high-precision coal seam hole wall crack pictures are manually collected, data are preprocessed, feature information is extracted and then put into a model for repeated training, and a loss function is continuously optimized through back propagation, so that the model achieves the effect of accurately recognizing the coal seam hole wall cracks; and finally applying the trained model to an actual coal seam hole wall image, and capturing a coal seam hole wall crack. According to the method, the high-precision automatic identification function of the hole wall cracks of the coal seam is realized, the manual workload is greatly reduced, meanwhile, a guarantee is provided for the personnel safety of a coal mine, the coal mining efficiency is improved, and a scheme is provided for underground route arrangement of the coal mine.