4000 results about "Coal mining" patented technology

The invention discloses a multi-source heterogeneous data fusion method of a sky tower ground well carbon flux observation system, which comprises the following steps of: 1, preprocessing and automatically correcting data to solve the problems of noise, space-time inconsistency, format difference and systematic error in a multi-source heterogeneous data acquisition process; the multi-source heterogeneous data are respectively from satellite remote sensing, unmanned aerial vehicle remote sensing, an eddy covariance flux tower, ground observation and underground observation; step 2, multi-level data fusion: extracting multi-source heterogeneous data from original signals step by step to form unified high-dimensional feature expression, and finally outputting real-time observation data of carbon emission and carbon sink of the coal mining area through a decision model; and step 3, performing comprehensive observation and application output, performing real-time analysis according to the real-time observation data, and outputting a detection result and early warning information. According to the method, an advanced algorithm with adaptive correction and multi-level fusion capability is adopted, and data fusion of five observation modules of the sky tower surface well is realized, so that the key bottleneck in the prior art is broken.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

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 relates to the technical field of directional drilling track control, and discloses a coal mining underground directional drilling track control method and system, and the method comprises the steps: carrying out the fusion track analysis of a drilling machine based on sensor data, drilling parameters and pre-obtained geological hardness parameters, and obtaining a reference track of the drilling machine, a sensor, an azimuth angle sensor and a bit pressure sensor are used for synchronously collecting drill bit attitude data of a drilling machine to generate a real-time track of the drilling machine, then track offset calculation is carried out to obtain inclination angle deviation and azimuth angle deviation, and dynamic gain adjustment analysis is carried out on the drilling machine to obtain adjustment parameters of the drilling machine. And finally, the drill bit posture of the drilling machine is adjusted based on the adjusting parameters. According to the method, the directional control accuracy of the underground drilling track can be 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 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.

The invention relates to the technical field of coal mining safety monitoring, and discloses a coal mining subsidence space-time evolution intelligent prediction method, which comprises the following steps: acquiring multi-dimensional geological dynamic data such as surface displacement, underground water pressure and rock stratum stress of a coal mining area, extracting a space-time characteristic sequence by using a GCLSTM algorithm, and calculating a space-time fluctuation coefficient; extracting a principal component vector through spectral decomposition, mapping to obtain a space-time correlation weight, dynamically correcting an original prediction value to generate a fusion prediction value, and judging a subsidence space-time evolution trend; constructing a three-dimensional subsidence curved surface model to identify a high-risk area and generate graded early warning; and establishing a joint optimization model to solve an optimal mining path, and generating a subsidence risk minimization scheme. According to the method, intelligent analysis of multi-dimensional data fusion and spatial-temporal characteristics is realized, the prediction accuracy is improved, an effective means is provided for coal mining subsidence prevention and control and safe mining, and the method has a remarkable engineering application value.

The invention relates to a coal mining subsidence area plant foundation monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source monitoring data of a target coal mining subsidence area plant area, the multi-source monitoring data comprising satellite remote sensing data, ground measurement data and interference radar data; performing time synchronization and space unification on the multi-source monitoring data to obtain monitoring data with unified space; performing fusion processing on the monitoring data with uniform space to obtain comprehensive deformation data; constructing a three-dimensional settlement deformation field according to the comprehensive deformation data, and extracting deformation rate and acceleration information of each monitoring point through a gradient calculation method; and according to the deformation rate and the acceleration information, obtaining risk assessment of the plant foundation in the target coal mining subsidence area. The technical problems of multi-source heterogeneous data fusion and real-time settlement monitoring and early warning are effectively solved, and the precision and timeliness of foundation settlement monitoring are improved.

The invention relates to the technical field of knowledge maps and large models, and discloses a fully-mechanized coal mining equipment large model decision support method and system. The method comprises the following steps: defining entities in data information of fully-mechanized coal mining equipment and a relationship between the entities to form triple data; converting the triple data into question and answer pairs based on the question and answer template, and optimizing the question and answer pairs; establishing a large language model, training the large language model based on question and answer pairs, performing fine tuning on the large language model based on a LoRA technology, and performing quantification processing on the equipment state in combination with fuzzy comprehensive evaluation to obtain a quantification result; the system uses the fine-tuned large language model to carry out entity and intention recognition on a natural language input by a user, a Cypher query statement is constructed to carry out query in a graph database, sub-graph information is obtained, the sub-graph information and the quantization result serve as cue words and are transmitted to the large language model, and retrieval information supporting large model decision making is obtained. The operation and maintenance efficiency of the fully-mechanized coal mining equipment is improved.

The invention relates to production collaborative scheduling, in particular to a production collaborative scheduling method based on an intelligent mine comprehensive management and control platform. According to the intelligent linkage control system, a sensing-data-application three-layer framework is constructed, key links such as coal mining, transportation, storage, washing and selection and lifting are integrated, cross-equipment and cross-system intelligent linkage control is achieved, and the dispatching adaptive capacity and the cooperation efficiency under the complex working condition are remarkably improved. Comprising the following steps: S1, collecting equipment states, environmental parameters and production progress data of each production link of a mine, processing the data through an edge computing node, and uploading the data to a cloud platform; s2, constructing a digital twin mine model, and configuring an initial production scheduling rule and a resource constraint condition; s3, when an abnormal or disturbance event is detected, the system gives out an early warning and automatically responds, a scheduling instruction is issued to each equipment system, and linkage execution is realized; and S4, continuously returning a scheduling execution result and a running state to the platform for evaluation and analysis, and training and optimizing the scheduling model by using feedback data to realize self-adaptive capability enhancement.

The invention discloses a coal mining subsidence early warning system based on image processing, and relates to the technical field of mine environment monitoring, and the system comprises the steps: collecting image data of a coal mining area, carrying out the preprocessing of the image data, and generating standardized image data; performing continuous image frame processing on the standardized image data through an optical flow analysis method, calculating earth surface displacement and deformation information, and generating subsidence dynamic change data; combining the subsidence dynamic change data with the standardized image data, and inputting the combined data into an adaptive convolutional neural network to identify a subsidence area; according to the subsidence change trend and the coal mining progress, subsidence risks are evaluated, and real-time early warning signals are generated. According to the invention, by analyzing the motion trails of the pixel points in the image, the dynamic change of the coal mining area can be captured in real time, the sign of subsidence can be identified in time, early warning can be carried out in advance, and the timeliness and accuracy of monitoring are obviously improved.

The invention relates to the technical field of coal mining equipment, and discloses a mining roadway stability real-time monitoring and early warning system which comprises the following steps: S1, multi-source sensor network deployment, S2, data transmission and preprocessing, S3, stability index dynamic calculation, S4, fusion early warning model construction and S5, graded early warning triggering. Roadway surrounding rock deformation, stress, vibration and environmental parameters are collected in real time through a multi-source sensing network, efficient data transmission and preprocessing are achieved in combination with an industrial looped network, surrounding rock strain energy density, displacement convergence rate, support failure coefficient and other key indexes are dynamically calculated, a fusion early warning model is constructed based on machine learning, and the early warning accuracy is improved. According to the method, accurate evaluation of the stability level is achieved, grading alarm and emergency control are automatically triggered when a threshold value is reached, a'perception-analysis-decision-response 'closed loop is formed through the design, the real-time performance of roadway stability monitoring and the early warning reliability are remarkably improved, and accidents such as roof fall and wall caving are effectively prevented.

The invention provides a modified coal gangue-based large-flow-state grouting and water plugging material and a preparation method thereof. Comprising 80-90 parts of a coal gangue sample, 1-5 parts of Portland cement, 1-5 parts of sulphoaluminate cement, 0.51-3 parts of a polycarboxylic acid water reducing agent, 1-4 parts of cellulose ether with the viscosity of 200w, 0.1-5 parts of a calcium oxide expanding agent, 0.5-5 parts of a plastic expanding agent, 0.5-1 part of redispersible latex powder, 0.5-5 parts of polyacrylamide with the molecular weight of 1000 and water, and the water-material ratio is 0.4. According to the modified coal gangue-based large-flow-state grouting and water plugging material and the preparation method thereof, the cement consumption can be remarkably reduced, the cost is reduced, the utilization rate of solid wastes (such as coal gangue) is increased, and the material has good flow-state performance and adjustable setting time; the method is suitable for deep and large shaft surrounding rock water seepage treatment in coal mining and other mine engineering.

The invention discloses a real-time back break control method of a heading machine based on visual association and digital twinning, which relates to the technical field of intelligent control of coal mining equipment, and comprises the following four steps: processing binocular visual data through a double-domain double-branch Transformer model, extracting coal rock and roadway structure characteristics and outputting cutting head offset; a GCN-LSTM model is adopted to fuse visual, laser radar and fiber-optic gyroscope data, and dynamic calibration and output of a three-dimensional pose are carried out; a roadway three-dimensional model is constructed based on the SLAM technology, and real-time updating of a digital twin model is achieved through edge-cloud collaboration; and in combination with a reinforcement learning strategy, generating a cutting parameter adjustment instruction according to the deviation of the digital twin model, and correcting model parameters through error feedback. The method solves the problems of low visual recognition precision, sensor data drift, digital twinning update lag and the like in an underground complex environment, improves the back break control precision, tunneling efficiency and equipment stability, and is suitable for tunneling operation under complex geological conditions.

The invention relates to an MT-InSAR self-adaptive fusion measurement method and device for long-term surface deformation of a coal mining subsidence area, and the method comprises the steps: obtaining surface deformation monitoring data in a plurality of modes, and carrying out the at least one of time-space alignment, noise removal and error correction of the surface deformation monitoring data, so as to obtain surface deformation data; calculating coherence indexes of the earth surface deformation data; generating a space adjustment coefficient by using a double-peak Gaussian function model constructed based on a coal mining subsidence area deformation rule; constructing an adaptive weighting method based on the coherence index and the spatial adjustment coefficient of the earth surface deformation data to calculate the weight of the multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final surface deformation result. Therefore, the problems of insufficient monitoring precision, incomplete coverage, poor adaptability and the like due to the fact that a single InSAR technology is difficult to comprehensively reflect spatio-temporal evolution characteristics of long-term surface deformation of the wide-area coal mining subsidence area are solved.

The invention discloses an anti-jamming coal mining drilling machine with drilling and multidirectional impacting functions and a using method thereof.The anti-jamming coal mining drilling machine comprises a drilling machine body, a high-pressure driving mechanism is arranged on the drilling machine body through a multidirectional guide rail mechanism, and a drilling assembly is connected with the high-pressure driving mechanism; when the drilling machine works, the high-pressure driving mechanism drives the drill rod and the drill bit to conduct rotary drilling, the high-frequency transverse vibration mechanism synchronously generates transverse high-frequency vibration, the high-pressure driving mechanism drives the drill rod and the drill bit to conduct rotary drilling, and the high-frequency transverse vibration mechanism drives the drill rod and the drill bit to conduct rotary drilling. The friction force between the drill bit and rock is reduced, debris accumulation is prevented, efficient deslagging is achieved, when hard rock or drill jamming occurs, the longitudinal vibration impact mechanism triggers longitudinal vibration, impact and drill jamming prevention functions are achieved through cooperative work of transverse vibration and longitudinal vibration, the drill jamming problem under the complex geological condition is effectively solved, the drilling efficiency and equipment reliability are improved, and the drilling cost is reduced. The device is suitable for drilling operation of mining roofs and baseplates, sides and the like.

The invention relates to the field of mine geophysical exploration, and discloses a fully mechanized coal mining face geological abnormal area prediction method based on multi-source data, and the method comprises the steps: carrying out the detection of a fully mechanized coal mining face through a radio pit penetrating device and a slot wave seismograph, and obtaining a working face CT image and a slot wave energy attenuation coefficient image; analyzing a distribution rule of a plurality of abnormal prompt areas in the working face CT image, and delimiting a plurality of candidate abnormal areas to obtain an average abnormal amplitude value of each candidate abnormal area; analyzing a plurality of slot wave attenuation high value abnormal regions in the slot wave energy attenuation coefficient imaging, and carrying out overlapping region calculation on the plurality of candidate abnormal regions to obtain a plurality of overlapping regions; carrying out drilling point arrangement and drilling according to the plurality of overlapped areas, and measuring a plurality of drilling trajectories and drilling data corresponding to each drilling point through a trajectory instrument; and according to the plurality of drilling trajectories and the drilling data corresponding to each drilling point, abnormal region prediction data is generated, so that accurate prediction of the abnormal region is realized.

The invention relates to the technical field of coal mining, in particular to a grouting reinforcement anti-seepage method for an underground coal mine, which comprises the following steps: cleaning and temporarily supporting a target area; scanning the surrounding rock by adopting intrinsic safety type detection equipment to obtain fracture parameters and generate a three-dimensional distribution diagram; designing a construction grouting hole and cleaning the hole; double-liquid grouting equipment is adopted, reinforced grout containing aggregate is injected firstly to fill large cracks, after the reinforced grout is initially set, impermeable grout such as acrylate is injected to block micro cracks, and grouting pressure and flow are monitored and controlled in real time; finally, strength and impermeability detection is carried out, and hole patching and grouting are carried out if the standard is not met; according to the method, through accurate detection, partition design, staged grouting and strict quality inspection, the problem that in a traditional method, the reinforcing effect and the seepage-proofing effect are difficult to cooperate is solved, integrated construction of high-strength reinforcing and efficient seepage-proofing of the surrounding rock is achieved, and the engineering quality and efficiency are improved.

The vertical feeding coal mining machine comprises a rack, a traction part and a cutting assembly, the traction part is connected with the rack so as to be used for dragging the rack to move, the cutting assembly comprises a roller body and cutting teeth, the roller body comprises a cylinder body, an end disc and an end cover, the end disc is arranged on the cylinder body, and the cutting teeth are arranged on the end disc. The end disc is arranged on the outer circumferential wall of the roller body in a sleeving mode, the end disc and the first end of the roller body are arranged in a flush mode, the end cover is connected with the inner circumferential wall of the roller body, a groove is defined between the end cover and the roller body, the multiple cutting teeth are arranged in the circumferential direction of the center line of the roller body at intervals, and the cutting teeth are arranged in the groove. One part of the plurality of cutting teeth is connected with the end disc, and the other part of the plurality of cutting teeth is connected with the end cover. According to the vertical feeding coal mining machine, the arrangement positions of the cutting picks are optimized, the possibility of abrasion of the cutting picks is reduced, and the cutting efficiency is improved.

An integral frame-type machine body structure of a coal mining machine, and an integral frame-type intelligent coal mining machine. A structure in which an integral frame-type framework is welded to articulated frames is adopted, and the frame-type framework is configured to be of a transverse drawer-type structure, thereby achieving high interchangeability. The articulated frames are symmetrically distributed on a left side and a right side of the frame-type framework and are welded and connected to the frame-type framework; and a top plate (16) and a bottom plate (14) are fixedly arranged at an upper part and a lower part of the frame-type framework, respectively, external traction transverse plates (9) are fixedly arranged on the left side and the right side of the frame-type framework, and several intermediate vertical plates are fixedly arranged between the top plate (16) and the bottom plate (14), which are located an upper side and a lower side of the frame-type framework, to divide same, so as to obtain a plurality of cavities. The modularization rate of the coal mining machine is improved, thereby improving the interchangeability, facilitating underground maintenance and overhaul, and enhancing the matching adaptability; in addition, the force-bearing condition of the coal mining machine during the working process is improved, and the reliability of a transmission system of the coal mining machine is enhanced.

The invention belongs to the technical field of coal mine working face simulation, and particularly relates to a dynamic management and control method and system for a geological structure transparency model of a coal mine working face, and the system comprises a geological transparent modeling platform and a geological model driving dynamic management and control platform. In combination with geological acquisition data obtained in real time, a working face fine geological model is generated after modeling processing; the geologic model driving dynamic management and control platform is used for preprocessing a working face fine geologic model, digitalizing the working face fine geologic model to generate a working face fine geologic model digital mirror image by using a CT slicing technology, and receiving equipment operation condition data of the sensing coal mining system in real time through a data interface technology. And dynamically updating the working face digital mining geologic model and performing linkage updating on the mining virtual scene to realize iterative updating of the geologic model. The problem that an existing coal mine working face geologic model lacks a real-time dynamic updating means can be solved.

The invention discloses a coal mining machine positioning and visual analysis system under a multi-algorithm optimized fisheye camera, and relates to the field of coal mining machine positioning and visual analysis. The deployment acquisition module is installed according to an optimization algorithm, has an intelligent function and acquires images in a multispectral manner; the preprocessing module corrects distortion by using an improved model and enhances an image in combination with multiple algorithms; the identification calculation module integrates multiple technologies to identify features of the coal mining machine and accurately position the coal mining machine; the tracking and positioning module fuses multi-algorithm tracking, and deep learning is used for predicting a trajectory during shielding; the visual analysis module analyzes the state of the coal mining machine through multiple technologies; and the transmission interaction module transmits data by using a fusion technology and provides multiple interaction modes. The method is accurate in positioning, the fisheye camera and the correction technology improve the positioning accuracy, and accurate tracking can be achieved when shielding is carried out; visual analysis is deep, and the state of the coal mining machine and environmental hidden dangers are monitored; data transmission is safe and efficient, and interaction is convenient; the system has strong anti-interference and self-optimization capabilities, can adapt to an underground environment, and improves the coal mining operation efficiency and safety.

The embodiment of the invention provides a gas concentration prediction method based on multi-source information fusion. The method is applied to the technical field of coal mining. Performing time sequence decomposition processing on the historical gas concentration data, performing correlation analysis on the historical gas concentration data and the coal mining machine operation data, and determining influence factor data related to gas concentration change; forming a multi-dimensional data sequence by the historical gas concentration data subjected to the time sequence decomposition processing and the multiple pieces of influence factor data; and inputting the multi-dimensional data sequence into a gas concentration prediction model, and analyzing and processing the multi-dimensional data sequence through the gas concentration prediction model to obtain a gas concentration prediction result, thereby improving the accuracy and reliability of the gas concentration prediction result and gas over-limit prediction.

The invention provides a coal face mining auxiliary method based on visual and point cloud data fusion modeling, and the method comprises the steps: collecting the visual image data of a coal face, collecting the three-dimensional point cloud data through a point cloud sensor, and obtaining the motion state information through an inertial sensor; based on internal and external parameters and IMU data of joint calibration, mapping point cloud data to an image plane, establishing a geometric mapping relationship between three-dimensional points and pixels, generating an attribute point cloud model containing geometry, color and semantics, and generating a high-precision global three-dimensional digital twinning model; and identifying and tracking equipment instances in the digital twinborn model, and resolving motion parameters of key components based on a URDF kinematics model and a principal component analysis algorithm to support remote control decisions. According to the invention, high-precision real-time sensing and three-dimensional visualization of the working space size, the equipment connection relation and the movement position of the coal face can be realized, and the remote control precision and the response efficiency of unmanned mining are remarkably improved.

The invention provides a construction method of a self-adaptive cutting guide control system of a coal mining machine based on digital twinning, and relates to the technical field of self-adaptive cutting of coal mining machines. The method specifically comprises the following steps: designing a complex dirt band thin coal seam coal mining machine cutting state sensing method and a complex dirt band thin coal seam coal mining machine self-adaptive control strategy; the method comprises the following steps: based on a complex dirt band thin coal seam coal mining machine cutting state sensing method and a complex dirt band thin coal seam coal mining machine self-adaptive control strategy, establishing a complex dirt band thin coal seam coal mining machine self-adaptive cutting digital twinborn model for simulating the self-adaptive cutting process of the complex dirt band thin coal seam coal mining machine; and constructing a mechanism characteristic life prediction model driven by a digital model and a digital twinborn monitoring model for the adaptive cutting dynamic reliability of the coal mining machine for the complex dirt band thin coal seam, wherein the model is used for evaluating the reliability of the adaptive cutting process of the coal mining machine for the complex dirt band thin coal seam in real time. According to the invention, the adaptive capacity of the coal mining machine to the complex dirt band thin coal seam can be effectively improved.

The invention relates to the technical field of lubricating oil monitoring, and provides a multi-parameter intelligent monitoring method and system for coal mine equipment lubricating oil. The method comprises the following steps: establishing a monitoring point position set; extracting a first point location in the monitoring point location set, and dynamically monitoring the first point location through intelligent monitoring equipment to obtain a first initial monitoring result; introducing an error calibration mechanism to calibrate the first initial monitoring result to obtain a first target monitoring result; and performing normalized weighted calculation on the first target monitoring result in combination with preset weight distribution to obtain the real-time lubricating oil degradation degree of the coal mining machine. The technical problem that the service life of a coal mining machine is affected due to the fact that monitoring data is inaccurate due to environmental interference and equipment errors in the coal mining equipment lubricating oil monitoring process is solved, and the purpose of accurately evaluating the lubricating oil degradation condition through combination of multi-parameter intelligent monitoring and an error calibration mechanism is achieved. The monitoring accuracy of the deterioration degree of the lubricating oil is improved, and the service life of equipment is prolonged.

The crawler-type hydraulic support comprises a base, a supporting beam, a telescopic stand column, a crawler mechanism and a lifting oil cylinder, and the supporting beam is provided with a supporting top face arranged upwards; the telescopic stand column is provided with two ends capable of relatively stretching in the vertical direction, one end of the telescopic stand column is connected with the base, and the other end of the telescopic stand column is connected with the supporting beam. The crawler mechanism is slidably connected with the base in the vertical direction, and the lifting oil cylinder is used for driving the crawler mechanism to slide relative to the base. According to the crawler-type hydraulic support, in the moving mode, the hydraulic support can move in a roadway through the crawler mechanism, moving is convenient, and a roadway bottom plate cannot be damaged; in the supporting mode, the stability of the hydraulic support in the supporting process is guaranteed through contact between the base and a roadway bottom plate, and therefore the roadway supporting effect and normal coal mining work are guaranteed.

The invention discloses a sky tower ground well integrated carbon emission and carbon sink comprehensive observation system, which comprises a satellite remote sensing module and an unmanned aerial vehicle remote sensing module, which are used for acquiring mining area environmental parameters; the vorticity covariance flux tower is used for collecting wind speed, temperature, CO2 and other greenhouse gas concentration data in real time and calculating the carbon gas exchange flux in the atmosphere; the ground monitoring module is used for monitoring carbon emission and carbon absorption indexes in the surface environment in real time; the underground monitoring module is used for monitoring underground carbon flux data and underground environment parameters; the satellite remote sensing module, the unmanned aerial vehicle remote sensing module, the vortex covariance flux tower, the ground monitoring module and the underground monitoring module are all connected with the central data processing platform in a wired and / or wireless mode, and the central data processing platform is used for generating a unified and accurate mining area carbon emission and carbon sink dynamic map. According to the invention, synchronous observation and data fusion of five observation modules of the sky tower ground well are realized, and full-scale and real-time dynamic observation of carbon emission and carbon sink in a coal mining area is realized.

The invention discloses a coal mine fully mechanized coal mining face multi-mode prediction type intelligent straightening system and method.The system comprises a multi-mode sensing network, a data fusion prediction module and a control optimization module, and thermal imaging double cameras, pressure / dip angle / stroke / distance measuring sensors and laser scanners are arranged on hydraulic supports and inspection equipment; the comprehensive sensing of the working environment of the fully mechanized coal mining face is realized; fusing visible light, infrared, point cloud and state parameter data, and performing support attitude estimation and trend prediction by adopting Kalman filtering and an LSTM neural network; according to the intelligent support control system and the control method thereof, self-adaptive dynamic straightening control is executed based on a prediction result, remote monitoring and a closed-loop optimization mechanism are combined, the support control precision and the operation safety are improved, the automation level and the response capability of fully-mechanized coal mining operation are improved, and the intelligent support control system is suitable for complex mine pressure conditions.