455 results about "Mine site" patented technology

The invention belongs to the technical field of coal mine disaster early warning, and discloses a coal mine fire and gas disaster risk prediction method and system, and the method comprises the steps: collecting multi-source heterogeneous data collected at a mine site, and carrying out the data preprocessing and conversion; for the preprocessed image and video data, a Video Swin Transform model is adopted to extract spatial and temporal features, and a cross-modal association fusion Transform model is adopted to realize multi-modal feature unified fusion; mapping the fused multi-modal feature representation to a disaster-causing factor index space, calculating a risk assessment index containing a secondary interaction item, and dividing risk levels; and generating a disaster early warning strategy according to risk level analysis, issuing an early warning notice, performing regular maintenance and updating, and outputting a decision report. According to the method, unified processing and fusion representation of multi-source heterogeneous data can be realized, complex association among disaster-causing factors is accurately captured, the risk index with physical significance and engineering reference value is calculated, and high-quality decision support is provided for prevention and control of coal mine fire and gas disasters.

The invention relates to the technical field of ore conveying, and discloses an intelligent mine ore conveying management system, which comprises a deep reinforcement learning scheduling module used for realizing optimal scheduling through a deep reinforcement learning algorithm based on an ore conveying equipment state, a conveying path condition and ore queue information; the digital twinning system is used for creating a digital twinning model of the conveying equipment and carrying out multi-physics field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment; and the edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on a mine site so as to realize equipment anomaly detection and early warning. By introducing deep reinforcement learning scheduling, digital twinning, edge computing and block chain data management technologies, adaptive optimization scheduling, accurate state monitoring and fault prediction of the ore conveying system are realized, an efficient safety protection and intelligent user interface is provided, and the operation efficiency, reliability and safety of the system are improved.

A method and system for early prediction and warning of mine disasters through on-site monitoring is provided. [Solution] The method involves installing sensors at the mine site to monitor surface water, groundwater data and underground rock data in real time, so that mine safety management personnel can obtain timely and comprehensive data to better understand the state of the mine environment. The predictive analysis module uses deep drilling and computational learning technology to analyze the surface water, groundwater data and underground rock data to predict the risk assessment index Fpzs, which is helpful in predicting potential disaster risks, and comprehensively takes into account various factors such as the number of past disasters Lzs, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs, which is helpful in quantifying the risk level, providing a more accurate disaster risk assessment, and reducing the casualties caused by mine disasters.

The invention provides a multi-construction-method collaborative dust fall intelligent control method based on deep learning. The method comprises the following steps: collecting mine field data of a mine field in real time; wherein the mine field data comprises mine field environment data, equipment operation data and topographic data; constructing a digital mine field model based on the mine field data; wherein the digital mine field model comprises a terrain model, an equipment model and a dust generation and diffusion model; based on the mine field data and the digital mine field model, constructing a reinforcement learning agent; based on the reinforcement learning agent, performing dust fall control on the mine field; the dust fall control is monitored on the basis of the pre-created human-computer interaction interface, intelligent and efficient control over the mine field dust fall process is achieved, the dust fall effect and the resource utilization efficiency are improved, and meanwhile the flexibility and reliability of the system are improved through human-computer cooperative control.

The invention relates to the technical field of side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention discloses a method for calculating the migration of a proppant in a horizontal well fracturing crack and the global flow conductivity of the crack, and relates to the field of unconventional oil and gas reservoir yield increase renovation, and the method comprises the following steps: (1) establishing a reservoir geologic model according to on-site logging exploration data; (2) establishing a hydraulic fracturing crack propagation fluid-solid coupling model; (3) establishing a horizontal well fracturing inter-cluster flow distribution model; (4) constructing a propping agent migration liquid-solid two-phase flow model in the hydraulic fracture; (5) solving the fluid-solid coupling model and the proppant migration model through loop iteration, and predicting the concentration distribution of the proppant in the hydraulic fracture; and (6) on the basis of the crack width and the proppant distribution result, calculating the hydraulic fracturing crack global flow conductivity. According to the method, the proppant migration model is solved by adopting a high-order discrete format, dynamic coupling simulation of hydraulic fracture propagation and proppant migration under the mine field scale is realized, the global flow conductivity of the hydraulic fracture is calculated, and reference is provided for evaluation and optimization of unconventional reservoir fracturing transformation.

A device for detecting a composite rock stratum structure while drilling, and an intelligent identification method. The device comprises an apparatus base (8), wherein a vertically arranged supporting rod is fixedly connected to the middle portion of the apparatus base (8), a drilling hydraulic oil cylinder (4) is fixedly connected on one side of the bottom of the supporting rod, and a piston end of the drilling hydraulic oil cylinder (4) is fixedly connected to an adaptive while-drilling data acquisition module (5) and is in sliding connection with the supporting rod; a sample table (1) is arranged above the adaptive while-drilling data acquisition module (5) and is fixedly connected to the supporting rod, and a top limiting system (3) is fixedly connected to the top end of the supporting rod; and a rock gripper (2) is arranged between the sample table (1) and the top limiting system (3), and the adaptive while-drilling data acquisition module (5), a data acquisition device (9) and a data analysis terminal (10) are all electrically connected to each other. A real mechanical environment of a coal mine site is reproduced, multiple parameters are acquired by means of the adaptive while-drilling data acquisition module (5), and the property parameters of a composite rock stratum are represented by using a machine learning method, so as to achieve the intelligent identification of rock lithology, such that support can be provided for intelligent control of surrounding rock of a coal mine roadway.

The invention discloses a double-flywheel system and an intelligent mine field system and method.The double-flywheel system comprises a sensing system and a planning system, the sensing system fuses acquired multi-dimensional environment data of a mine field through the bird's-eye view angle technology and a deep learning algorithm, a mine field 3D environment model is established, and environment sensing data is output; the planning system performs planning path prediction on each mine car by using a deep learning algorithm and a Monte Carlo tree search algorithm based on vehicle management information, task scheduling data and environment perception data fed back by the motorcade management platform, and generates an optimal driving path. The vehicle management information comprises vehicle posture and health information, vehicle position state information, vehicle operation task priority information and mine field safety standard information. According to the invention, through deep cooperation of sensing and planning and integration of motorcade management information and safety standards, the vehicles can work according to the optimal path more accurately, the working yield of the mine field is improved, and the development of the intelligent mine field is promoted.

The invention provides an open pit coal mine safety detection and production evaluation method based on unmanned aerial vehicle surveying and mapping. The method comprises the steps of obtaining surveying and mapping data of a target mine field through unmanned aerial vehicle oblique photography surveying and mapping; obtaining geographic information data including a digital orthoimage, a digital elevation model, an oblique photography model and digital vector data based on the surveying and mapping data; constructing a three-dimensional digital twinborn scene of the target mine field based on the geographic information data; performing profile detection on the three-dimensional digital twinborn scene, constructing a vertical profile, and performing safety detection on the slope angle, the step height and the width based on the vertical profile; and performing filling or excavation measurement on the coal mining area, the coal piling area or the soil piling area of the target mining area, and calculating the volume to obtain a production evaluation result. The problems that in the prior art, a geographic surveying and mapping method is low in efficiency and disordered in data, and a safety detection and production evaluation method depends on detection equipment, so that safety detection and production evaluation of open pit coal mining are poor in accuracy and low in efficiency are solved.

The invention provides an ultra-deep high-pressure gas well variable-flow-state sand-carrying capacity evaluation method, and relates to the technical field of oil-gas exploration, and the method comprises the steps: collecting well logging data of a target gas well, constructing a gas-liquid two-phase flow parameter expression, and carrying out the quantitative analysis of key flow characteristics such as the gas content and the speed; a mathematical model is established by applying an H-K flow state judgment criterion, and numerical simulation is performed in combination with fluid dynamics simulation software, so that the flow state in the shaft is accurately identified and distinguished; then, according to high-temperature and high-pressure characteristics under actual working conditions and sand shape information monitored in a mine field, carrying out secondary correction on the resistance coefficient CD so as to improve a sand-carrying capacity prediction model; and finally, the critical sand-carrying flow velocity and yield are calculated based on the corrected model, and a scientific basis is provided for setting the sand-carrying capacity and reasonable production parameters of the gas well in different flow states. Safe and efficient development of a gas reservoir is guided, production parameter setting is optimized, and long-term stable and efficient operation of a gas well is ensured.

The utility model discloses a dust falling structure for a concentrating machine, which relates to the field of mine machinery, and comprises a box body, a feed hopper is fixedly arranged at the right part of the upper end of the box body, and screening components are fixedly arranged at the bottom positions of the left part and the right part of the inner wall of the box body. According to the dust falling structure for the concentrating machine, ore particles of different sizes can be classified and collected through the screening assembly, the screening effect can be obvious due to the vibration effect of the screening plate, and the possibility that the three screening net areas are blocked can be reduced. And when the sieve plate vibrates, the plurality of shifting plates drive the ore materials to quickly move on the guide plate, so that the area between the lower part of the feeding hopper and the guide plate can be prevented from being blocked. Dust generated in the box body can be absorbed through the dust falling assembly, dust on the two filter plates can be scraped off through the cleaning assembly, and the dust enters the collecting box through the two inclined grooves.

The invention belongs to the technical field of trajectory planning, and provides an electric shovel unmanned mining method and system based on digital twinning in order to solve the problem that the design of an electric shovel unmanned mining trajectory is difficult to deal with a complex heap surface, and the method comprises the following steps: obtaining sensing data, a historical mining trajectory and historical heap surface point cloud data; an electric shovel unmanned mining digital twin system is constructed based on the sensing data; uploading the historical mining trajectory and the historical heap surface point cloud data to a central database, and training by using generative artificial intelligence to obtain prediction trajectories suitable for different mine fields; constructing an agent model by using a neural network, calculating to obtain a predicted fullness rate, and performing simulated excavation according to the predicted fullness rate and the predicted trajectory to obtain electric shovel excavation control data; according to the method, deep reinforcement learning is utilized, the electric shovel unmanned excavation track is generated based on the electric shovel excavation control data, the electric shovel is controlled to conduct unmanned excavation in real time, the safety of the excavation process is guaranteed, and the track control precision of the electric shovel is also improved.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention relates to the field of mine intelligent management, and discloses an artificial intelligence autonomous identification model, which comprises a data acquisition module used for acquiring visual data, infrared data, audio data and environmental data from a coal mine site; the data preprocessing module is used for carrying out scale normalization, data enhancement, short-time Fourier transform, Mel frequency cepstrum coefficient extraction and normalization processing on the collected data; and the feature extraction module is used for extracting features from the visual data, the infrared data, the audio data and the environment data through a deep neural network. And through a multi-modal information fusion technology, the adaptability to a complex environment in mine operation is improved, the complementarity of different modal data is fully mined, and the recognition precision and robustness are remarkably improved. And an introduced feedback and self-learning optimization mechanism enables the model to be automatically adjusted according to real-time data, so that the stability and adaptability in a mine environment are enhanced.

The invention discloses a strip mine blasting numerical simulation analysis method based on an RHT constitutive model, and the method comprises the following steps: S1, building a strip mine blasting model: building a three-dimensional model through ANSYS software according to the actual geological conditions and blasting design requirements of a strip mine, and determining the geometric dimension of the model, the arrangement of blast holes, and the parameters of a charging structure; s2, determining material parameters: performing sampling and physical and mechanical property testing on field rocks of the strip mine to obtain rock parameters, determining rock material parameters in combination with an RHT constitutive model, determining explosive material parameters according to types of explosives used on site, and determining parameters of other related materials such as air and filling materials. According to the method, the RHT constitutive model is adopted, so that the accuracy of numerical simulation is improved; a more reliable basis is provided for blasting engineering design; the method has obvious economic benefits and social benefits; and powerful technical support is provided for the development of the strip mine blasting technology.

The invention discloses a manned transport vehicle scheduling method and device, and relates to the manned transport vehicle scheduling technology, and the method comprises the steps: obtaining the vehicle information and map data of a target mining area, and generating a global scheduling scheme through the vehicle information and map data, the global scheduling scheme comprises the step of carrying out vehicle scheduling according to the determined number of vehicles to be allocated at each loading point and the determined number of vehicles to be allocated at each heavy-load transportation path; according to the method, the manned transport vehicles in the target mining area are scheduled according to the global scheduling scheme, the mining area vehicles can be accurately scheduled, accurate and balanced scheduling of the manned transport vehicles is realized, the overall efficiency of mining area operation is improved, and the transportation cost is reduced.

A facility for predicting on site electric power requirements for maintaining a fully electric fleet of vehicles in a mining operation to ensure sustainable electric operations, comprising a server comprising communications module configured to receive data and to transmit load management instructions; plurality of electrical charging stations; plurality of electric vehicles configured to be recharged by a selected electrical charging station; wherein the server comprises processor coupled with non-volatile memory, the non-volatile memory storing processing instructions that, when executed by the processor, causes the processor to simulate a total electrical power demand of the mine site due to the plurality of cabled mine equipment and the plurality of electric vehicles for each electric vehicle and each charging station within the facility over a prescribed period thereby to simulate and determine an energy storage capacity requirement of the charging stations of the facility through the prescribed period; thereby to forecast the electrical load management needs of the facility through the prescribed period.

The invention relates to the technical field of thallium element analysis and detection, in particular to an on-site analysis and detection device for thallium element in mine water. The water sample storage assembly is mounted on one side of the interior of the frame and is used for storing a mine water sample; the water sample collecting, storing, transferring and detecting functions are integrated, real-time detection can be conducted on a mine site, complexity and professional requirements of manual operation are reduced through automatic and integrated design, thallium elements in the water sample can be detected, sludge samples can be treated at the same time, and the water sample collecting, storing, transferring and detecting functions are achieved. More data support is provided for comprehensive evaluation of mine water, an organic matter adsorption plate, a molecular sieve and an aluminum oxide sieve treatment assembly are arranged to effectively remove organic matters and peculiar smell in a water sample, and meanwhile, the detection accuracy is improved.

The invention relates to the technical field of image processing, and discloses a mine personnel behavior recognition method and system based on YOLOv5. The method comprises the following steps: acquiring a video frame sequence of a mine site, extracting an environment interference vector from the video frame sequence, and optimizing the video frame sequence based on the environment interference vector to obtain a clear frame sequence; human body key point coordinates and posture vectors are extracted from the clear frame sequence, and time sequence changes of the key point coordinates are analyzed to obtain preliminary action features; calculating a similarity score between the preliminary action feature and a preset standard template, and refining the preliminary action feature based on the similarity score to obtain a refined action component vector; and matching the refined action component vector from a preset action library to obtain an action recognition result, and generating corresponding action response output based on the action recognition result. According to the invention, the accuracy of personnel action recognition in a complex mine environment is improved.

The invention relates to the technical field of slope monitoring, in particular to a mine field slope instability trend-based radar monitoring and early warning method and system, and the method comprises the following steps: recognizing a slope region with an instability risk on a geological image through a pre-trained Fast R CNN model, and taking the slope region as an instability risk region; scanning the instability risk area in real time through a slope radar to obtain a radar image sequence of the instability risk area; and acquiring a geological image sequence of the instability risk area with the same time sequence as the radar image sequence, determining an instability trend of the instability risk area based on the radar image sequence and the geological image sequence, and then allocating an early warning level according to the instability trend. According to the method, locking of instability factors in advance is realized on the basis of the Fast R CNN model, falling rock and soil loosening sliding track trends are quickly analyzed in real time on the actual falling rock and soil loosening track obtained through multi-modal data solution on the basis of the LSTM network, and thus integrated display, focusing monitoring and graded early warning of mining area risk points are realized.

The invention discloses a shale gas gathering and transportation station yard blockage and high-low pressure gathering and transportation interference diagnosis optimization method, and belongs to the field of natural gas mine field gathering and transportation. According to the method, the judgment method of the blockage site of the pipeline in the station and the calculation method of the blockage degree are determined by checking the friction resistance of the whole-station pipe fitting of the shale gas station and calculating the pressure drop of the pipeline in the station, and a solution is provided for the problem of gathering and transportation interference in the high-pressure well mine field and the low-pressure well mine field. The method is suitable for shale gas gathering and transportation stations in the middle and later periods of development, can assist in making of equipment overhaul and production parameter optimization schemes, further improves the operation efficiency of a pipe network, and has wide application prospects.

The invention belongs to the technical field of bag type dust collection, and discloses an ore processing pulse bag type dust collector for a mine field, the ore processing pulse bag type dust collector comprises a support, a cleaning bin and a dust hopper, the left bottom and the right top of the cleaning bin are sequentially provided with an air inlet pipe and an exhaust pipe, and the inner wall of the cleaning bin is provided with a partition plate; a plurality of groups of filter bags which are distributed in a matrix are fixedly mounted at the top of the partition plate, a transfer bin is fixedly mounted at the bottom of the inner wall of the cleaning bin, and an air inlet which communicates with and coincides with the air inlet pipe is formed in the left side of the transfer bin. According to the device, high-pressure dust-containing air flow from the air inlet pipe is converged through the inner cavity of the transfer bin and finally is obliquely and upwards sprayed out through the air outlet pipe, spiral ascending air flow from bottom to top is formed in the inner cavity of the cleaning bin, and the tip movement direction of the dust-containing air flow is spirally changed, so that any part of the outer surface of the filter bag can be uniformly covered; therefore, the uniformity of the filter bag during filtering is ensured, and the maintenance period of the device is effectively prolonged.

The invention relates to an unconventional oil reservoir volume fractured horizontal well yield prediction method and device and a storage medium. The method comprises the steps that horizontal well parameters of a horizontal well to be predicted are obtained; obtaining the peripheral vertical well yield of the horizontal well to be predicted; constructing a horizontal well after-pressure yield prediction formula of the horizontal well to be predicted; determining an undetermined coefficient of the horizontal well after-pressure yield prediction formula; and calculating the yield of the horizontal well to be predicted. According to the method, the actual mine field data regression analysis method is adopted to calculate the horizontal well yield after volume fracturing, the influence of the oil and gas enrichment degree and the fracturing transformation degree on the yield increasing effect is considered, meanwhile, by introducing the peripheral vertical well yield, the difference of the yield increasing effects of different blocks and different parts is reduced, and the yield increasing efficiency is improved. Practice proves that the calculation method has certain accuracy; the method has high operability and is well applied to exploration and development practice of low-grade oil reservoirs.

The invention discloses a mineral resource prediction model construction method based on a complex geological background, and relates to the technical field of mineral resource prediction, a three-dimensional ideal geological background field without mineralization interference is constructed based on the geological background of an unknown mining area, and the three-dimensional ideal geological background field is used for displaying the mining field of a non-mineralization area; constructing a three-dimensional real geological field based on the exploration data, and comparing the three-dimensional real geological field with the three-dimensional ideal geological background field to obtain first difference distribution data; extracting geologic features of the known mineralization area, and generating interference factors based on the geologic features; introducing the interference factor into the ideal geological background field to generate an interference coupling background field, and extracting the difference between the interference coupling background field and the ideal geological background field as second difference distribution data; the mineral resource prediction model is constructed based on the first difference distribution data and the second difference distribution data, the method can adapt to prediction requirements of different geological backgrounds, and the intelligent level and application flexibility of a mineral resource prediction system are remarkably improved.

The invention belongs to the technical field of mine ecological damage analysis, and discloses a mine ecological damage data integration and ecological damage analysis method. According to the method, when the ecological environment condition of the target mine field is analyzed, the target mine field is subjected to monitoring area division, then the soil conservation function loss and groundwater pollution exposure of each monitoring area are analyzed, specific analysis can be carried out according to the ecological environment characteristics of different areas, the analysis accuracy and comprehensiveness are improved, and the analysis efficiency is improved. And targeted treatment measures can be formulated. According to the method, the water quality evaluation condition is comprehensively analyzed on the basis of the groundwater body fluidity and the groundwater body influence during exposure analysis of the groundwater pollution line of the target mine field, the diffusion range and speed of pollutants can be more accurately predicted, and thus the degree and range of groundwater pollution are more accurately evaluated. The complexity and diversity of groundwater pollution can be known more deeply, and a scientific basis is provided for formulating comprehensive treatment measures.

The invention relates to the technical field of coal seam fully-mechanized caving mining, and discloses an extra-thick coal seam fully-mechanized caving mining hard roof-gas cooperative control method which comprises the following specific steps: collecting coal mine site stratum composition data; a numerical simulation physical model of the coal mine working face is established based on FLAC3D software, and the horizon of the hydraulic fracturing hard roof is obtained through simulation; mining-induced stress field inversion is carried out, and the fracturing implementation time and the drilling position are determined; according to the super-thick coal seam fully-mechanized caving mining hard roof-gas cooperative control method, the layer position of the hydraulic fracturing hard roof, the fracturing implementation time and the drilling position are obtained through simulation analysis, the position of a drilling end hole point is determined according to the goaf gas migration rule, and the gas migration rule of a mining field is determined by combining the expansion and evolution rule of hydraulic cracks and the mining field gas migration rule. The strong mine pressure weakening mechanism and the gas seepage path and gas pressure distribution characteristics in the gas extraction process are mastered, and effective control over the hard roof and efficient gas treatment are achieved.

The invention relates to an intelligent identification method for a roadway surrounding rock loosening circle, and belongs to the technical field of intelligent identification of geotechnical engineering. The method comprises the following steps: S1, acquiring a borehole peeping image which is a picture intercepted from video data measured by a borehole imager; s2, preprocessing and crack identification are carried out on the borehole peeping image; s3, measuring the elastic modulus of the rock through an elastic wave method; s4, calibrating the range of the loose circle through the elastic modulus and the identified fracture, and carrying out category balance improvement through a weighted loss function; s5, training a YoLov5 deep learning model through the calibrated loose circle information, and improving the YoLov5 deep learning model; s6, evaluating and verifying the model; and S7, inputting a borehole peeping image obtained on site and the measured elastic modulus into the model to predict the loose circle. According to the method, the loosening circle can be quickly and accurately identified on an actual mine site, and a scientific basis is provided for roadway support design and mine safety management.

The invention relates to the technical field of vehicle automatic driving, and discloses a multi-vehicle joint trajectory prediction method based on self-vehicle fusion and related equipment. The method comprises the steps of collecting multi-vehicle state data and mine field environment data in a target mine field, and performing preprocessing and lane changing behavior calibration to generate a standard multi-vehicle motion data set; carrying out intention extraction on own vehicle planning intention information through own vehicle planning tracks and operation task information, and carrying out mine field environment feature extraction and vehicle dynamic distribution on a standard multi-vehicle motion data set to construct mine field scene context representation; attention weight calculation and feature fusion are carried out through operation task features in the self-vehicle planning intention information and vehicle interaction features in the mine scene context representation, intention enhancement scene representation is generated, and a multi-modal query result is constructed; and through multi-vehicle collaborative prediction, path conflict detection and collaborative optimization scheduling, a multi-vehicle collaborative operation scheme is generated. The accuracy of mine field multi-vehicle trajectory prediction and the safety of collaborative operation are improved.

The invention belongs to the technical field of trackless mining preparation drift type filling mining, and discloses a steeply inclined thin ore body segmentation-free roadway mining method, which comprises the following steps of space partitioning and foundation engineering construction; stoping and filling in layers; a slope coping channel for trackless mining and transportation equipment to pass through is formed on an upward layered bottom plate on the top of the connection roadway of the lower stope through the blasting technology, the slope coping channel communicates with the segmented winze through the short connection roadway, and ore removal is conducted through the segmented winze; and entering the previous layer through the coping channel, and repeating the steps of stoping and filling the layer and constructing the coping channel until the mining is finished. A slope channel is formed by blasting at the top of a layered stope joint roadway, an upper layer of mine field is communicated with a draw shaft and a ventilation shaft of an upper section, and the sectional draw shafts are combined with the collaborative layout of top and bottom stage haulage drifts and ore loading transverse drifts, so that the beneficial effect that the whole accurate mining work amount is obviously reduced is achieved.

The invention relates to the technical field of coal rock failure early warning, in particular to a multi-parameter combined impact coal rock instability failure early warning method, which comprises the following steps of: firstly, carrying out engineering geology investigation on a mine site, collecting a coal rock sample and testing ground stress distribution; secondly, preparing a standard coal rock test piece in a laboratory, and performing nondestructive testing; then, determining a loading and unloading path according to numerical simulation, and designing a graded loading and unloading path of the coal rock test pieces of the roof group and the laneway side group; then, loading and unloading the test piece by using a true triaxial loading testing machine, and synchronously acquiring electromagnetic radiation and acoustic emission data; and finally, by calculating the acoustic emission energy, the fractal dimension and the joint fractal dimension, dividing the grading early warning critical value of each index, and constructing a three-level early warning model for the coal rock impact instability. According to the method, the coal rock impact instability risk can be effectively identified, graded early warning is provided, corresponding safety measures are guided to be taken on site, and safe production of a mine is guaranteed.