WO2021184614A1 - 一种用于复杂条件工作面的综采装备智能决策控制方法及系统 - Google Patents
一种用于复杂条件工作面的综采装备智能决策控制方法及系统 Download PDFInfo
- Publication number
- WO2021184614A1 WO2021184614A1 PCT/CN2020/102159 CN2020102159W WO2021184614A1 WO 2021184614 A1 WO2021184614 A1 WO 2021184614A1 CN 2020102159 W CN2020102159 W CN 2020102159W WO 2021184614 A1 WO2021184614 A1 WO 2021184614A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- equipment
- data
- control
- mechanized mining
- fully mechanized
- Prior art date
Links
- 238000005065 mining Methods 0.000 title claims abstract description 128
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000005259 measurement Methods 0.000 claims abstract description 25
- 239000011435 rock Substances 0.000 claims abstract description 25
- 230000008569 process Effects 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 238000005457 optimization Methods 0.000 claims abstract description 10
- 238000004088 simulation Methods 0.000 claims description 44
- 230000033001 locomotion Effects 0.000 claims description 37
- 239000003245 coal Substances 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 10
- 230000008054 signal transmission Effects 0.000 claims description 6
- 238000005452 bending Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 5
- 238000006073 displacement reaction Methods 0.000 claims description 5
- 238000003703 image analysis method Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000011217 control strategy Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 230000008030 elimination Effects 0.000 abstract 1
- 238000003379 elimination reaction Methods 0.000 abstract 1
- 230000036544 posture Effects 0.000 description 8
- 230000004927 fusion Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000007790 scraping Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000033772 system development Effects 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21C—MINING OR QUARRYING
- E21C35/00—Details of, or accessories for, machines for slitting or completely freeing the mineral from the seam, not provided for in groups E21C25/00 - E21C33/00, E21C37/00 or E21C39/00
- E21C35/24—Remote control specially adapted for machines for slitting or completely freeing the mineral
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D23/00—Mine roof supports for step- by- step movement, e.g. in combination with provisions for shifting of conveyors, mining machines, or guides therefor
- E21D23/12—Control, e.g. using remote control
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F13/00—Transport specially adapted to underground conditions
- E21F13/06—Transport of mined material at or adjacent to the working face
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- the invention relates to the field of equipment control for fully mechanized coal mining faces, in particular to an intelligent decision-making control method and system for fully mechanized mining face equipment.
- Deep mining faces the constraints of multiple factors such as high ground pressure, high ground temperature and complex geological conditions.
- a complete set of fully mechanized mining equipment hydroaulic support, coal mining machine, scraper conveyor, transfer machine and advanced support
- the originally neatly aligned and coordinated equipment group randomly tilts and moves with the roof and floor and coal seam conditions, and cannot maintain the normal spatial posture and mechanical state.
- the scope of application of the existing automated mining equipment system is limited, and it is difficult to adapt to this drastically changing application environment.
- invention patent 201910064818.2 discloses a virtual reality physical engine-based collaborative advancement simulation method for fully mechanized mining equipment. The method uses the virtual reality physical engine to model and repair the coal seam and various equipment at the fully mechanized mining face, and update the virtual coal seam data in real time. The information truly reproduces the advancement process of underground equipment.
- the invention patent 201711138800.X discloses a method for solving and predicting the attitude of the mining and transportation equipment in a fully mechanized mining face.
- Attitude data and predict the shape of the scraper conveyor and the working state of the shearer in the next cycle according to the cutting roof and floor curve of the shearer in this cycle.
- the invention patent 201811422886.3 discloses an unmanned intelligent fully mechanized mining face.
- the intelligent fully mechanized mining face is composed of the face body, signal transceiver, control mechanism, and monitoring mechanism. These mechanisms are used to replace artificial fully mechanized mining face.
- the invention patent 201510527484.X provides a method for implementing a centralized control platform for large-scale equipment in a fully mechanized coal mining face. This method is based on a video monitoring system to achieve centralized control of large-scale equipment in a fully mechanized coal mining face. These methods are based on sensors, downhole network technology, video technology, etc. to centrally control the fully mechanized mining face equipment, but the control methods are relatively simple. They are feedback control based on sensor signals, and lack the comprehensive utilization of data and the entire fully mechanized mining face. It is difficult to deal with the control of face equipment under complex conditions.
- the full pose measurement system of the fully mechanized mining equipment is used to measure 15 necessary parameters that comprehensively describe the real-time operating status of the equipment;
- the rock pressure monitoring system is used to collect and analyze the rock pressure data of the fully mechanized mining face, and provide a data basis for the analysis and decision-making system.
- the virtual simulation system is used to receive the necessary parameters obtained by the full attitude measurement system of the fully mechanized mining equipment, and load the rock pressure data along the face direction monitored by the mine pressure monitoring system and update it over time.
- the data obtained from the pose measurement system drives the 3D model of the equipment to simulate the real mining process;
- the analysis and decision-making system is used to calculate the abnormal deviation of the equipment position caused by the complicated geological conditions, the random tilt of the direction and the prediction of the deformation, fall and slab of surrounding rock, and automatically give the automatic learning results based on the known process methods and historical data. Eliminate the control strategy of pose errors and surrounding rock changes, and determine the control parameters of the next coal cutting cycle;
- the distributed control system is used for full-process collaborative management and control, sending equipment status information and operating parameters to the shearer, hydraulic support and scraper conveyor, and implementing comprehensive decision-making control of the fully mechanized mining equipment system.
- the minimum parameter set of the relationship including the three rotation angles of the shearer and the height of the rocker arm; the angle of the base of the hydraulic support, the inclination of the top beam, the height of the support, the moving distance, and the state of the guard; the horizontal bending of the scraper conveyor, the undulation of the bottom plate, and the torsion angle ; 3 relative positions between the equipment, including the distance between the shearer drum and the bracket guard, the distance between the shearer and the head of the scraper conveyor, and the angle between the middle groove of the scraper conveyor and the bracket push rod.
- the mine pressure monitoring system not only has the mining pressure data collection function of the working face, but also has the mining pressure data analysis and prediction function of the complex geological conditions based on deep learning or expert feature database, and equipped with pose data. Calculate the subsequent mining control parameters together.
- the virtual simulation system is developed with a modular concept, and has a motion simulation module, a scene generation module, a simulation scene, and a feedback control module driven by real data.
- the said full pose measurement system enters the real-time database for storage and analysis through the underlying data interface, and the interface parameters correspond to the driving parameters of the simulation model one-to-one.
- the motion simulation module filters out data that has large deviations and does not meet the actual working conditions, stores reliable data, completes coordinated simulation and deduction of equipment operating status, drives virtual model motion, and can generate historical data change trends and time-lapse curves of key equipment parameters
- the scene generation module collects geological conditions and mine pressure monitoring system data through the data interface to reconstruct the underground 3D geological environment, and transfers the reconstructed scene data to the simulation scene; equipment operation data and The geological environment data is supported by the analysis and decision system to generate simulation scenarios, and analyze and predict; the decision results are transmitted back to the feedback control module through the virtual link to complete the graphical interface display, and the actual equipment of the working face is feedback controlled through the data interface.
- the analysis and decision-making system is the core of the background operation service of the entire system. Based on real data and virtual simulation, it models specific working scenarios, equipment objects, and technological processes in the underground, and runs the intelligent decision-making control method of comprehensive mining equipment to complete the underground Optimization and decision-making of control parameters of fully mechanized mining equipment.
- the distributed control system includes a central main controller and an application program expansion interface module, a coal mining machine, a hydraulic support and a scraper conveyor controller and a coding and decoding module, and the module establishes data based on the underground industrial Ethernet
- the overall architecture of the link layer, protocol layer, and application layer connects the above-mentioned devices to complete control signal transmission, interface communication, and distributed coordinated control.
- the distributed control system has a three-layer architecture of data link layer, protocol layer and application layer, corresponding to system signal transmission, interface communication and control functions respectively; wherein, the protocol layer system interface is compatible with multiple communication protocols , Can exchange information with equipment of different manufacturers through its codec modules; the application layer has API (application program extension interface), and realizes unified control of hardware of different manufacturers by calling the underlying control commands in the function library.
- the protocol layer system interface is compatible with multiple communication protocols , Can exchange information with equipment of different manufacturers through its codec modules
- the application layer has API (application program extension interface), and realizes unified control of hardware of different manufacturers by calling the underlying control commands in the function library.
- the present invention also provides an intelligent decision-making control method for fully mechanized mining equipment, which includes: fusing the spatial posture of the fully mechanized mining equipment operation process with surrounding rock geological parameters and mine pressure data to form a spatial field of equipment operation along the time dimension. Model and stress field model.
- the two fields of data are superimposed to determine the state of equipment such as the shape of the surrounding rock at a certain moment, the mining height of the working face, the roof fall and the straightness, the pitching and the up and down sliding, and judge whether it is normal; based on the normal state, Improve equipment operation efficiency and adaptability goals, and calculate ahead of time the support resistance of hydraulic supports, the best moving time, the cutting speed and mining height of the coal shearer, the time of guard board retracting, roof sinking amount, scraper conveying
- the operating control parameters such as the distance of the machine travel and the amount of sliding up and down; and after each cutting cycle is completed, the model is automatically re-corrected according to the actual data and the predicted calculation data is updated to ensure the consistency of the preset control and the actual geological conditions, and improve the work Surface running quality.
- Figure 1 is a block diagram of the intelligent decision-making control system of fully mechanized mining equipment used in complex conditions of the present invention
- Figure 2 is a diagram of the full posture composition of the fully mechanized mining equipment used to describe the working face of the present invention
- Figure 3 is the full pose measurement scheme of the fully mechanized mining equipment used in the working face of the present invention
- the embodiment of the present invention provides a block diagram of an intelligent decision-making control system for fully mechanized mining equipment for working faces with complex conditions, which is used to complete the optimization and decision-making of the control parameters of underground fully mechanized mining equipment and improve the operation quality of fully mechanized mining equipment. .
- the intelligent decision-making control system for fully mechanized mining equipment includes a fully-mechanized mining equipment full-position measurement system 100, a mine pressure monitoring system 200, a virtual simulation system 300, an analysis and decision-making system 400, and a distributed control system 500; Attitude measurement system 100, used to measure 15 necessary parameters that fully describe the real-time operating status of the equipment;
- the mine pressure monitoring system 200 is used to collect and analyze the mine pressure data of the fully mechanized mining face, and provide a data basis for the analysis and decision-making system;
- the virtual simulation system 300 is used to receive the necessary parameters obtained by the full attitude measurement system of the fully mechanized mining equipment, and load the rock pressure data along the working face monitored by the mine pressure monitoring system and update it over time, according to the full position of the fully mechanized mining equipment
- the data obtained from the attitude measurement system drives a three-dimensional model of the equipment to simulate a real mining process; in one example, the virtual simulation system 300 updates the loaded rock pressure data over time.
- the analysis and decision-making system 400 is used to calculate the abnormal deviation of the equipment position caused by the complicated geological conditions, the random inclination of the direction and the prediction of the deformation, caving and slabs of the surrounding rock (roof, coal wall and floor). It is based on known technological methods and Historical data learning results provide control strategies to automatically eliminate pose errors and surrounding rock changes, and determine the control parameters of the next coal cutting cycle;
- the distributed control system 500 is used for the entire process of collaborative management and control, sending equipment status information and operating parameters to the shearer, hydraulic support and scraper conveyor, and implementing comprehensive decision-making control of the fully mechanized mining equipment system.
- the fully-mechanized mining equipment full-position measurement system is installed in the global coordinate system of the working surface, and the description equipment itself and the image analysis method are simultaneously obtained through inertial navigation devices, inclination and displacement sensors, and image analysis methods.
- the mathematical expression of the minimum parameter set of mutual spatial constraints and pose relations is:
- S i is the shearer's 4 pose parameters, including three rotation inclination angles and rocker arm height
- H j is the 5 hydraulic support pose parameters, including the base inclination angle around the Y axis, the top beam inclination angle, the support height, and the shift Distance, protection status
- C k is the 3 posture parameters of the scraper conveyor, including horizontal bending, bottom plate undulation (rotating around the Y axis), and torsion angle
- R m is the 3 relative postures between the equipment, including the shearer The distance between the roller and the bracket guard, the distance between the shearer and the head of the scraper conveyor, and the angle between the middle groove of the scraper conveyor and the bracket push rod.
- the minimum parameter set describing the equipment itself, the mutual spatial constraints, and the pose relationship can be measured by the fusion vision scheme.
- the inertial navigation system installed on the shearer measures its rotation angle along the three axes
- the high-precision shaft encoder measures the rocker arm rotation angle S 4
- the walking wheel shaft encoder measures the shearer travel displacement R 14
- the top beam of the hydraulic support Install the inclination sensor on the connecting rod and the base, measure the overall posture of the bracket and calculate the height of the bracket H 7 ; install the displacement sensor on the bracket pushing jack to measure the distance H 8 , install the fiber grating bending measurement device on the scraper conveyor to monitor horizontal bending Degree C 10 ;
- C 11 and C 12 are calculated by fusion of S 1 , S 3 and H 5 ; the distance between the shearer and the support R 18 , the angle between the scraper conveyor and the support R 15 and the guard
- the board state H 9 can be analyzed and calculated from the image collected by the vision sensor.
- the mining pressure monitoring system 200 having the mining pressure data collection function, it also has the mining pressure data analysis and prediction function of the complex geological conditions. This function is based on deep learning or expert feature database, and equipment The pose data is used to calculate the subsequent mining control parameters.
- the virtual simulation system 300 is developed using a modular concept, and includes a motion simulation module 310, a scene generation module 320, a simulation scene 330, and a feedback control module 340 driven by real data.
- the full pose measurement system 100 enters the real-time database through the bottom data interface for data storage and analysis, and the interface parameters correspond to the driving parameters of the simulation model one-to-one.
- the motion simulation module 310 filters out data with large deviations and does not meet the actual working conditions, stores reliable data, completes collaborative simulation and deduction calculation of equipment operating status, drives virtual model motion, and can generate historical data change trends and equipment key parameters Time-lapse curve, etc.; after receiving the motion simulation instruction, the scene generation module 320 collects geological conditions and mine pressure monitoring system data through the data interface to reconstruct the underground 3D geological environment, and transfers the reconstructed scene data to the simulation scene;
- the equipment operating data and geological environment data are supported by the analysis and decision system 400 to generate simulation scenarios, and analyze and predict; the decision results are transmitted back to the feedback control module 340 through the virtual link to complete the graphical interface display, and the actual working surface is displayed through the data interface.
- the equipment realizes feedback control.
- the decision result refers to the operation control command of the equipment at the fully mechan
- the scene generation module 320 reconstructs the downhole three-dimensional geological environment through the image acquisition device in the pose measurement system, and transmits the reconstructed scene data into the simulation scene.
- the motion simulation module 310 includes the motion simulation of the hydraulic support, the motion simulation of the shearer, the motion simulation of the scraper conveyor, and the coordinated motion simulation of the fully mechanized mining equipment group.
- the motion simulation state of the hydraulic support mainly includes: retracting the fender, lowering the column, moving the frame, raising the column, extending the fender, and pushing and sliding.
- the hydraulic support performs coordinated movement as a whole.
- the single machine movement process of the shearer mainly includes: the drum cuts the coal seam, the shearer moves in a straight line, the rocker arm rotates, the shearer turns at the end of the roadway, and the shearer cutting depth advances.
- the body is the parent node
- the rocker arm is the first-level child node
- the drum is the second-level child node.
- the node tree is created according to the logic controlled by the shearer, and each child node is driven when the parent node is operated, and the operation of each child node is relative to the parent node.
- the scraper conveyor model is loaded into the simulation system by means of segmented establishment. In the process of pushing and sliding of the hydraulic support, the scraper conveyor moves forward with the hydraulic support as a fulcrum, and the scraper conveyor is approximately bent under the action of the time difference of the cylinders of different hydraulic supports.
- Cooperative motion simulation of fully mechanized mining equipment group is carried out on the basis of single machine equipment motion simulation. The models and parts in the scene are independent of each other, and the motion between them can be realized by establishing the parent-child relationship and the motion driving equation.
- the feedback control module 340 feeds back the control parameters obtained by analysis calculation and simulation optimization through the virtual control link back to the scene generation module 320 and the motion simulation module 310.
- the scene generation module 320 and the motion simulation module 310 are based on the optimized data.
- the generated control signal controls the operation of the fully mechanized mining equipment in the virtual simulation system. If the operation result of the comprehensive mining equipment does not match the optimization expectation, the data is transmitted back to the decision analysis system 400 to re-enter the parameter optimization calculation process until the expected control effect is achieved.
- the motion simulation module 310 confirms that the operating parameters of the fully mechanized mining equipment are correctly optimized and then transmitted to the central control center of the coal mining face corresponding to the virtual simulation system through the network, and the central control center transmits the obtained control parameters to the underground network through the underground ring network.
- Fully mechanized mining equipment controller The comprehensive mining equipment controller judges the received control signal. If the control parameters do not meet the current working conditions of the comprehensive mining equipment controller, it sends a feedback signal to the virtual simulation system to request the recalculation of the control parameters until the correct control is received. parameter. After being confirmed by the equipment's own control system, the control parameters are transmitted to the fully mechanized mining equipment actuators through Profibus, CAN, Modbus, RS232/485 and other fieldbus communication protocols, and the control commands generated by the virtual simulation system are executed.
- the motion simulation module 310 confirms that the operating parameters of the fully mechanized mining equipment are correctly optimized and then transmitted to the central control center of the coal mining face corresponding to the virtual simulation system via the network, and the central control center sends the obtained control parameters to the central control center of the mining face corresponding to the virtual simulation system.
- the distributed control system 500 sends equipment status information and operating parameters to the shearer, hydraulic support, scraper conveyor, etc., to implement comprehensive decision-making control of the fully mechanized mining equipment system.
- the analysis and decision-making system 400 is the core of the background operation service of the entire system. Based on real data and virtual simulation, it models specific underground work scenes, equipment objects, and technological processes, and runs the intelligent decision-making control method of fully mechanized mining equipment. Optimization and decision-making of control parameters of underground fully mechanized mining equipment.
- the analysis and decision-making system 400 uses an intelligent decision-making method to obtain the next operation mode of the comprehensive mining equipment based on the received data, and transmits it to the virtual simulation system for simulation.
- the virtual simulation system is responsible for displaying the operation status of the fully mechanized mining face and fully mechanized mining equipment, and transmits control commands to the distributed control system, which receives the commands to control the operation of the equipment.
- the analysis and decision-making system 400 can establish a coupling model of equipment space attitude and force state under general conditions, calculate its pressure controllable area and attitude instability area for different control parameters, and determine the specific adjustment target.
- a set of optimal control parameters including the support resistance of the hydraulic support, the optimal moving time, the cutting speed and mining height of the shearer, the time of retracting the guard plate, the sinking amount of the roof, and the moving distance of the scraper conveyor And the amount of upward movement and downward movement.
- This parameter group can be transferred back to the simulation model to simulate the mining effect before actual operation, so as to avoid conflicting and potentially risky mining processes.
- the distributed control system 500 includes a central main controller and an application program expansion interface module, a coal mining machine, a hydraulic support, and a scraper conveyor controller and a codec module, which is based on the underground industrial Ethernet.
- the overall architecture of the data link layer, protocol layer and application layer connects the above-mentioned devices to complete control signal transmission, interface communication and distributed coordinated control.
- the distributed control system 500 has a three-layer architecture of a data link layer, a protocol layer, and an application layer, corresponding to system signal transmission, interface communication, and control functions; among them, the protocol layer system interface is compatible with multiple communications
- the protocol can exchange information with equipment of different manufacturers through its codec modules;
- the application layer has API (application program extension interface), which realizes unified control of hardware of different manufacturers by calling the underlying control commands in the function library.
- the embodiment of the present invention also provides an intelligent decision-making control method for fully mechanized mining equipment, which includes: fusing the spatial pose of the fully mechanized mining equipment operation process with surrounding rock geological parameters, and mine pressure data together to form an equipment operating profile along the time dimension.
- Spatial field model and stress field model the two fields of data are superimposed to determine the state of equipment such as the shape of the surrounding rock, the mining height of the working face, the roof fall and the straightness, the pitch and the up and down at a certain time, and determine whether it is normal; based on the return to normal State, improve equipment operation efficiency and adaptability goals, and calculate in advance the support resistance of hydraulic supports, the best moving time, the cutting speed and mining height of the coal shearer, the retracting time of the guard plate, the amount of roof sinking, and the scraping Operating control parameters such as the distance of the slab conveyor and the amount of sliding and sliding; and after each cutting cycle is completed, the model is automatically re-corrected according to the actual data and the predicted calculation data is updated to ensure the consistency of the preset control and the actual geological conditions. Improve the operating quality of the working face.
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Mechanical Engineering (AREA)
- Excavating Of Shafts Or Tunnels (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种用于复杂条件工作面的综采装备智能决策控制方法及系统被公开。该系统包括综采装备全位姿测量系统（100）、矿压监测系统（200）、虚拟仿真系统（300）、分析决策系统（400）和分布式控制系统（500）。该系统测量装备实时运行状态的15个空间参数和矿压数据，通过数据叠加模拟真实开采过程，计算复杂地质条件引起的装备异常并预测围岩状态，提出基于已知工艺方法和历史数据学习结果的误差消除和围岩控制方法，预测后续开采控制参数，基于真实数据和虚拟仿真结果完成对井下综采装备控制参数的优化和决策，从而保证复杂地质下的装备控制能够和实际环境条件相吻合，提升综采装备运行质量。
Description
本申请要求于2020年3月14日提交中国专利局的申请号为202010178157.9，发明名称为“一种用于复杂条件工作面的综采装备智能控制方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
本发明涉及煤矿综采工作面装备控制领域，尤其涉及一种综采工作面装备智能决策控制方法及系统。
煤炭经过几十年的持续大规模开发，浅部资源越来越少，开采深度不断增加。深部开采面临高地压、高地温及复杂地质条件等多因素制约，作为井工开采核心作业系统的工作面综采成套装备(液压支架、采煤机、刮板输送机、转载机及超前支护装备等)，处于围岩变形、矿压冲击的动态变化环境中，原本队列整齐、协调一致的设备群随顶底板及煤层条件随机倾斜、错动，无法保持正常空间位姿和力学状态。现有自动化开采装备系统的适用范围有限，难以适应这种大幅动态变化的应用环境。
现有的井下综采工作面集控系统虽然与各个设备单机控制系统相连，但只是将各个设备的信息汇集到一起，没有进一步的数据挖掘和应用，也无法进行智能决策和综采装备协同控制。发明专利201910064818.2公开了一种基于虚拟现实物理引擎的综采装备协同推进仿真方法，该方法在虚拟现实物理引擎中对综采工作面的煤层以及各装备进行建模并修补，实时更新虚拟煤层数据信息，真实再现井下装备的推进过程。发明专利201711138800.X公开了一种综采工作面采运装备的姿态求解与预测方法，该方法能够在综采工作面底板不平整的符合工况下，求解采煤机和刮板输送机的姿态数据，并根据采煤机本循环的截割顶底板曲线对下一循环的刮板输送机形态和采煤机的工作状态进行预测。这些方法仅提供了一种将现实装备映射至虚拟现实中并对其进行仿真，虽然能够指导综采装备运行但无法对其进行直接控制。
发明专利201811422886.3公开了一种无人化智能综采工作面，该智能综采工作面由工作面本体、信号收发机构、控制机构、监测机构等部分组成，通过这些机构代替人工实现综采工作面无人化。发明专利201510527484.X提供了一种煤矿综采工作面大型装备集中控制平台的实现方法，该方法基于视频监控系统实现了煤矿井下综采工作面大型装备的集中控制。这些方法基于传感器、井下网络技术、视频技术等对综采工作面装备进行集中控制，但是控制方法较为简单，均为根据传感器信号进行反馈控制，缺乏对数据的综合利用以及对整个综采工作面的建模，难以应对复杂条件下的工作面装备控制。
发明内容
有鉴于此，本发明提供了一种复杂条件下工作面的综采装备智能决策控制方法及系统，可以根据真实数据和虚拟仿真结果完成对井下综采装备控制参数的优化和决策，从而保证复杂地质下的装备控制能够和实际环境条件相吻合，大幅提升综采装备运行质量。
为达到上述目的，本发明采用如下技术方案：
本发明提供一种用于复杂条件工作面的综采装备智能决策控制系统，包括综采装备全位姿测量系统、矿压监测系统、虚拟仿真系统、分析决策系统和分布式控制系统，其中，
所述综采装备全位姿测量系统，用于测量全面描述装备实时运行状态的15个必要参数；
所述矿压监测系统，用于采集并分析综采工作面矿压数据，为分析决策系统提供数据基础。
所述虚拟仿真系统，用于接收由综采装备全位姿测量系统所得的必要参数，并加载矿压监测系统监测得到的沿工作面方向的矿压数据并随时间更新，根据综采装备全位姿测量系统所得数据驱动装备三维模型模拟真实开采过程；
所述分析决策系统，用于计算复杂地质条件引起的装备位置异常偏移、方向随机倾斜和预测围岩变形、冒落及片帮情况，并基于已知工艺方法和历史数据学习结果给出自动消除位姿误差和围岩变化的控制策略，确定下一割煤循环 控制参数；
所述分布式控制系统，用于进行全流程协同管控，向采煤机、液压支架和刮板输送机发送设备状态信息及操作参数，实施综采装备系统的综合决策控制。
优选地，所述的综采装备全位姿测量系统安设在在工作面全局坐标系下，通过惯性导航装置、倾角和位移传感器、图像分析方法同时获取描述装备自身及相互空间约束、位姿关系的最少参数集：包括采煤机三个转动倾角和摇臂高度；液压支架底座倾角，顶梁倾角，支架高度，推移距离，护帮状态；刮板输送机水平弯曲，底板起伏，扭转角度；装备间3个相对位姿，包括采煤机滚筒和支架护帮板距离，采煤机距刮板输送机机头距离，刮板输送机中部槽与支架推杆的夹角。
优选地，所述的矿压监测系统除具有工作面矿压数据采集功能之外，还具有基于深度学习或专家特征库的复杂地质条件工作面矿压数据分析与预测功能，与装备位姿数据共同计算后续开采控制参数。
优选地，所述的虚拟仿真系统采用模块化思想开发，具有基于真实数据驱动的运动仿真模块、场景生成模块、仿真场景以及反馈控制模块。所述的全位姿测量系统通过底层数据接口进入实时数据库进行存储及分析，接口参数与仿真模型驱动参数一一对应。运动仿真模块过滤掉偏差较大且不满足实际工况的数据，存储可靠数据，完成装备运行状态协同仿真和推演计算、驱动虚拟模型运动，并可生成历史数据变动趋势、设备关键参数时移曲线等；场景生成模块在接收运动仿真指令后，通过数据接口采集地质条件及矿压监测系统数据对井下三维地质环境进行重构，并将重构的场景数据传入仿真场景中；装备运行数据和地质环境数据在分析决策系统的支持下生成仿真场景，并进行分析预测；将决策结果通过虚拟链路传回反馈控制模块完成图形界面显示，并通过数据接口对工作面实际装备实现反馈控制。
优选地，所述的分析决策系统是整个系统后台运行服务的核心，基于真实数据和虚拟仿真对井下特定工作场景、设备对象、工艺流程进行建模，运行综采装备智能决策控制方法，完成井下综采装备控制参数的优化和决策。
优选地，所述的分布式控制系统包括中央主控制器和应用程序扩展接口模 块，采煤机、液压支架和刮板输送机的控制器及编解码模块，该模块基于井下工业以太网建立数据链路层、协议层和应用层的整体架构，连接上述设备，完成控制信号传输、接口通信和分布式协调控制。
优选地，所述的分布式控制系统具有数据链路层、协议层和应用层的三层架构，分别对应系统信号传输、接口通信和控制功能；其中，协议层系统接口可兼容多种通信协议，能够与不同厂家的设备通过其编解码模块进行信息交换；应用层具有API(应用程序扩展接口)，通过调用函数库中的底层控制命令实现对不同厂商硬件的统一控制。
本发明还提供一种综采装备智能决策控制方法，包括：将工作面综采装备运行过程的空间位姿和围岩地质参数、矿压数据融合在一起，沿时间维度形成装备运行的空间场模型及应力场模型，两场数据叠加确定某一时刻围岩形态、工作面采高、冒顶片帮及直线度、仰俯及上窜下滑等装备状态，并判断是否正常；基于恢复正常状态、提升装备运行效率和适应性目标，超前计算得出液压支架支护阻力、最佳移架时间、采煤机割煤速度和采高、护帮板收放时间、顶板下沉量、刮板输送机推移距离和上窜下滑量等运行控制参数；且每一截割循环完成后，均根据实际数据自动重新修正模型和更新预测计算数据，保证预设控制和实际地质条件的吻合度，提升工作面运行质量。
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为本发明用于复杂条件工作面的综采装备智能决策控制系统组成框图；
图2为本发明用于描述工作面综采装备全位姿组成图
图3为本发明用于工作面综采装备全位姿测量方案
下面结合附图对本发明实施例进行详细描述。
应当明确，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例，为了更加清楚说明本发明，在以下的具体实施例中描述了众多技术细节，本领域技术人员应当理解，没有其中的某些细节，本发明同样可以实施。另外，为了凸显本发明的发明主旨，涉及的一些本领域技术人员所熟知的方法、手段、零部件及其应用等未作详细描述，但是，这并不影响本发明的实施。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
参看图1所示，本发明实施例提供的用于复杂条件工作面的综采装备智能决策控制系统组成框图，用于完成对井下综采装备控制参数的优化和决策，提升综采装备运行质量。
所述的综采装备智能决策控制系统包括综采装备全位姿测量系统100、矿压监测系统200、虚拟仿真系统300、分析决策系统400及分布式控制系统500；其中，综采装备全位姿测量系统100，用于测量全面描述装备实时运行状态的15个必要参数；
矿压监测系统200，用于采集并分析综采工作面矿压数据，为分析决策系统提供数据基础；
虚拟仿真系统300，用于接收由综采装备全位姿测量系统所得的必要参数，并加载矿压监测系统监测得到的沿工作面方向的矿压数据并随时间更新，根据综采装备全位姿测量系统所得数据驱动装备三维模型模拟真实开采过程；在一个例子中，虚拟仿真系统300将加载的所述矿压数据随时间更新。
分析决策系统400，用于计算复杂地质条件引起的装备位置异常偏移、方向随机倾斜和预测围岩(顶板、煤壁和底板)变形、冒落及片帮等情况，基于已知工艺方法和历史数据学习结果给出自动消除位姿误差和围岩变化的控制策略，确定下一割煤循环控制参数；
分布式控制系统500，用于进行全流程协同管控，向采煤机、液压支架和刮板输送机发送设备状态信息及操作参数，实施综采装备系统的综合决策控制。
具体地，如图2所示，所述的综采装备全位姿测量系统安设在在工作面全局坐标系下，通过惯性导航装置、倾角和位移传感器、图像分析方法同时获取 描述装备自身及相互空间约束、位姿关系的最少参数集，其数学表达式为：
P
mq＝{(S
i|i＝1～4)，(H
j|j＝5～9)，(C
k|k＝10～12)，(R
m|m＝13～15)}
其中，S
i为采煤机4个位姿参数，包括三个转动倾角和摇臂高度；H
j为5个液压支架位姿参数，包括底座绕Y轴倾角，顶梁倾角，支架高度，推移距离，护帮状态；C
k为3个刮板输送机位姿参数，包括水平弯曲，底板起伏(绕Y轴旋转)，扭转角度；R
m为设备间3个相对位姿，包括采煤机滚筒和支架护帮板距离，采煤机距刮板输送机机头距离，刮板输送机中部槽与支架推杆的夹角。
具体地，如图3所示，所述的描述装备自身及相互空间约束、位姿关系的最少参数集可通过融合视觉方案测量得到。安装在采煤机上的惯导系统测量其沿三个轴的转动角度，高精度轴编码器测量摇臂转动角度S
4；行走轮轴编码器测量采煤机行走位移R
14；在液压支架顶梁、连杆和底座上安装倾角传感器，测量支架整体姿态并计算支架高度H
7；在支架推移千斤顶上安装位移传感器测量推移距离H
8，在刮板输送机上安装光纤光栅测弯装置监测器水平弯曲度C
10；C
11和C
12由S
1，S
3和H
5融合计算得出；采煤机和支架之间的距离R
18、刮板输送机和支架之间的角度R
15以及护帮板状态H
9可由视觉传感器采集的图像进行分析计算。这些传感器先经过各自解算模块得出精确可靠数值，再发送至统一的全位姿融合解算系统中，并连接至虚拟仿真系统300的虚拟仿真模块310。
具体地，以端头液压支架为基准布设视觉测量装置(如视觉传感器)，且每隔5～10架布置一个，用于测量液压支架护帮板状态、采煤机与液压支架的相对位姿、液压支架与刮板输送机的相对位姿，并且通过多视觉测量装置的融合还可以测量工作面的直线度。视觉测量装置由其安装所在的支架位姿监测装置校正。在采煤机上安装惯导系统，结合采煤机自带的轴编码器也可以实现采煤机的位姿测量和刮板输送机的直线度测量，在有视觉测量融合纠正的情况下降低惯导系统的精度要求和矫正时间。
具体地，所述的矿压监测系统200除具有工作面矿压数据采集功能之外，还具有复杂地质条件工作面矿压数据分析与预测功能，该功能基于深度学习或专家特征库，与装备位姿数据共同计算后续开采控制参数。
具体地，所述的虚拟仿真系统300采用模块化思想开发，包括基于真实数 据驱动的运动仿真模块310、场景生成模块320、仿真场景330以及反馈控制模块340。
所述的全位姿测量系统100通过底层数据接口进入实时数据库进行数据存储及分析，接口参数与仿真模型驱动参数一一对应。所述运动仿真模块310过滤掉偏差较大且不满足实际工况的数据，存储可靠数据，完成装备运行状态协同仿真和推演计算、驱动虚拟模型运动，并可生成历史数据变动趋势、设备关键参数时移曲线等；场景生成模块320在接收运动仿真指令后，通过数据接口采集地质条件及矿压监测系统数据对井下三维地质环境进行重构，并将重构的场景数据传入仿真场景中；装备运行数据和地质环境数据在分析决策系统400的支持下生成仿真场景，并进行分析预测；将决策结果通过虚拟链路传回反馈控制模块340完成图形界面显示，并通过数据接口对工作面实际装备实现反馈控制。在一个例子中，决策结果是指综采工作面装备运行控制指令。
在一个例子中，场景生成模块320在接收运动仿真指令后，通过位姿测量系统中的图像采集装置对井下三维地质环境进行重构，并将重构的场景数据传入仿真场景中。
具体地，所述的运动仿真模块310包括液压支架运动仿真、采煤机运动仿真、刮板输送机运动仿真、综采装备群协同运动仿真。其中液压支架的运动仿真状态主要包括：收护帮板、降柱、移架、升柱、伸护帮板、推溜。升、降柱过程中液压支架整体做协同运动。采煤机单机运动过程主要包括：滚筒切割煤层、采煤机直线运动、摇臂旋转、采煤机在巷道端点的转向、采煤机切割深度推进。按照采煤机的机身、摇臂和滚筒的不同运动方式将其划分为3个节点层次，其中机身为父节点，摇臂为一级子节点，滚筒作为二级子节点。根据采煤机控制的逻辑创建节点树，操作父节点时带动其各个子节点，各子节点操作都相对于父节点。采用分段建立的方式将刮板输送机模型加载到仿真系统中。在液压支架推溜过程中，刮板输送机以液压支架为支点进行前移，在不同液压支架推移油缸时间差作用下刮板输送机成近似弯曲状态。在单机装备运动仿真的基础上进行综采装备群协同运动仿真，场景中各模型和各零部件相互独立，它们之间的运动可通过建立父子关系和运动驱动方程来实现。
具体地，所述的场景生成模块320利用Creo、Solidworks、UG等三维软件完成综采装备三维建模并导入Unity3D。在Unity3D中对不同模型、不同部件进行父子关系约束，建立坐标系及碰撞运动规则，完成工作面综采装备虚拟仿真模型的建立。通过Unity3D软件场景生成模块，利用Line Renderer线渲染器及Mesh网格组件完成工作面围岩环境的构建。
具体地，所述的运动仿真模块330在实际工况感知数据的基础上，控制虚拟仿真系统中各装备及其部件运动，实时反映采煤过程中各装备的运动状态。
具体地，所述的反馈控制模块340将分析计算及模拟优化得到的控制参数通过虚拟控制链路反馈回场景生成模块320与运动仿真模块310，场景生成模块320与运动仿真模块310根据优化后数据所产生的控制信号控制虚拟仿真系统中的综采装备运作。若综采装备运作结果与优化预期不符则将数据传回决策分析系统400重新进入参数优化计算过程，直至达到预期的控制效果。运动仿真模块310确认综采装备运行参数正确优化后通过网络传输至与虚拟仿真系统相对应的采煤工作面的顺槽集控中心，顺槽集控中心将所得控制参数通过井下环网传输至综采装备控制器。综采装备控制器对接收到的控制信号进行判别，若控制参数不符合当前综采装备控制器的工作条件，则向虚拟仿真系统发出反馈信号，请求重新计算控制参数，直至接收到正确的控制参数。控制参数经装备自身控制系统确认后通过Profibus、CAN、Modbus、RS232/485等现场总线通讯协传输至综采装备执行机构，执行虚拟仿真系统所产生的控制命令。
在一个例子中，运动仿真模块310确认综采装备运行参数正确优化后通过网络传输至与虚拟仿真系统相对应的采煤工作面的顺槽集控中心，顺槽集控中心将所得控制参数发送给分布式控制系统500，分布式控制系统500向采煤机、液压支架和刮板输送机等发送设备状态信息及操作参数，实施综采装备系统的综合决策控制。
在一个例子中，反馈控制模块340，将决策系统的控制指令通过反馈控制模块传输至分布式控制系统。
具体地，所述的分析决策系统400是整个系统后台运行服务的核心，基于真实数据和虚拟仿真对井下特定工作场景、设备对象、工艺流程进行建模，运 行综采装备智能决策控制方法，完成井下综采装备控制参数的优化和决策。
在一个例子中，分析决策系统400根据接收到的数据利用智能决策方法得出综采装备下一步的运行方式，将其传输至虚拟仿真系统中进行仿真。虚拟仿真系统负责显示综采工作面及综采装备运行状态，并将控制指令传输至分布式控制系统中，分布式控制系统接收指令控制装备运行。
具体地，所述的分析决策系统400可建立一般条件下的装备空间位姿和受力状态耦合模型，针对不同的控制参数计算其压力可控区和姿态失稳区，结合具体的调整目标确定出一组最佳的控制参数，包括液压支架支护阻力、最佳移架时间、采煤机割煤速度和采高、护帮板收放时间、顶板下沉量、刮板输送机推移距离和上窜下滑量等。该参数组可以在实际运行之前传回仿真模型中模拟开采效果，从而避免有冲突、有潜在风险的开采工艺。
具体地，所述的分布式控制系统500包括中央主控制器和应用程序扩展接口模块，采煤机、液压支架和刮板输送机的控制器及编解码模块，该模块基于井下工业以太网建立数据链路层、协议层和应用层的整体架构，连接上述设备，完成控制信号传输、接口通信和分布式协调控制。
具体地，所述的分布式控制系统500具有数据链路层、协议层和应用层的三层架构，分别对应系统信号传输、接口通信和控制功能；其中，协议层系统接口可兼容多种通信协议，能够与不同厂家的设备通过其编解码模块进行信息交换；应用层具有API(应用程序扩展接口)，通过调用函数库中的底层控制命令实现对不同厂商硬件的统一控制。
本发明实施例还提供一种综采装备智能决策控制方法，包括：将工作面综采装备运行过程的空间位姿和围岩地质参数、矿压数据融合在一起，沿时间维度形成装备运行的空间场模型及应力场模型，两场数据叠加确定某一时刻围岩形态、工作面采高、冒顶片帮及直线度、仰俯及上窜下滑等装备状态，并判断是否正常；基于恢复正常状态、提升装备运行效率和适应性目标，超前计算得出液压支架支护阻力、最佳移架时间、采煤机割煤速度和采高、护帮板收放时间、顶板下沉量、刮板输送机推移距离和上窜下滑量等运行控制参数；且每一截割循环完成后，均根据实际数据自动重新修正模型和更新预测计算数据，保 证预设控制和实际地质条件的吻合度，提升工作面运行质量。
本发明实施例提出的用于复杂条件工作面的智能决策控制系统及方法，基于真实数据和虚拟仿真结果完成对井下综采装备控制参数的优化和决策，从而保证复杂地质下的装备控制能够和实际环境条件相吻合，大幅提升综采装备运行质量满足了煤矿工作人员对于综采工作面自动化控制的需求，填补了市场上该综采装备智能决策控制系统的空白，对采煤工作面智能化系统研发具有重要意义。
需要说明的是，本说明书中的各个实施例均采用相关的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。另外，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。
Claims (8)
- 一种用于复杂条件工作面的综采装备智能决策控制系统，其特征在于：包括综采装备全位姿测量系统、矿压监测系统、虚拟仿真系统、分析决策系统和分布式控制系统；其中，所述综采装备全位姿测量系统，用于测量全面描述装备实时运行状态的15个必要参数；所述矿压监测系统，用于采集并分析采煤工作面矿压数据；所述虚拟仿真系统，用于接收由所述综采装备全位姿测量系统所得的必要参数，加载矿压监测系统监测得到的沿工作面方向的矿压数据并随时间更新，根据综采装备全位姿测量系统所得数据驱动装备三维模型模拟真实开采过程；所述分析决策系统，用于计算复杂地质条件引起的装备位置异常偏移、方向随机倾斜和预测围岩变形、冒落及片帮情况，并基于已知工艺方法和历史数据学习结果给出自动消除位姿误差和围岩变化的控制策略，确定下一割煤循环控制参数；所述分布式控制系统，用于进行全流程协同管控，向采煤机、液压支架和刮板输送机发送设备状态信息及操作参数，实施综采装备系统的综合决策控制。
- 根据权利要求1所述的综采装备智能决策控制系统，其特征在于：所述的综采装备全位姿测量系统安设在工作面全局坐标系下，通过惯性导航装置、倾角和位移传感器、图像分析方法同时获取描述装备自身及相互空间约束、位姿关系的最少参数集：包括采煤机三个转动倾角和摇臂高度；液压支架底座倾角、顶梁倾角、支架高度、推移距离、护帮状态；刮板输送机水平弯曲、底板起伏、扭转角度；装备间3个相对位姿，包括采煤机滚筒和支架护帮板距离，采煤机距刮板输送机机头距离，刮板输送机中部槽与支架推杆的夹角。
- 根据权利要求1所述的综采装备智能决策控制系统，其特征在于：所述的矿压监测系统，除具有工作面矿压数据采集功能之外，还具有复杂地质条件工作面矿压数据分析与预测功能，该功能基于深度学习或专家特征库，与装备位姿数据共同计算后续开采控制参数。
- 根据权利要求1所述的综采装备智能决策控制系统，其特征在于：所述的虚拟仿真系统，包括基于真实数据驱动的运动仿真模块、场景生成模块、仿真场景以及反馈控制模块；所述全位姿测量系统通过底层数据接口进入实时数据库进行数据存储及分析，接口参数与仿真模型驱动参数一一对应；所述运动仿真模块，过滤掉偏差较大且不满足实际工况的数据，存储可靠数据，完成装备运行状态协同仿真和推演计算、驱动虚拟模型运动；并可生成历史数据变动趋势、设备关键参数时移曲线；所述场景生成模块在接收运动仿真指令后，通过数据接口采集地质条件及对井下三维地质环境进行重构，并将重构的场景数据传入仿真场景中；装备运行数据和地质环境数据在分析决策系统的支持下生成仿真场景，并进行分析预测；将决策结果通过虚拟链路传回反馈控制模块完成图形界面显示，并通过数据接口对工作面实际装备实现反馈控制。
- 根据权利要求1所述的综采装备智能决策控制系统，其特征在于：作为整个系统后台运行服务的核心；基于真实数据和虚拟仿真对井下特定工作场景、设备对象、工艺流程进行建模，运行综采装备智能决策控制方法，完成井下综采装备控制参数的优化和决策。
- 根据权利要求1所述的综采装备智能决策控制系统，其他特征在于：包括中央主控制器和应用程序扩展接口模块，采煤机、液压支架和刮板输送机的控制器及编解码模块；基于井下工业以太网建立数据链路层、协议层和应用层的整体架构，连接上述设备，完成控制信号传输、接口通信和分布式协调控制。
- 根据权利要求6所述的综采装备智能决策控制系统，其他特征在于：具有数据链路层、协议层和应用层的三层架构，分别对应系统信号传输、接口通信和控制功能；其中，协议层系统接口可兼容多种通信协议，能够与不同厂家的设备通过其编解码模块进行信息交换；应用层具有API(应用程序扩展接口)，通过调用函数库中的底层控制命令实现对不同厂商硬件的统一控制。
- 一种综采装备智能决策控制方法，其特征在于：包括：将工作面综采装备运行过程的空间位姿和围岩地质参数、矿压数据融合在 一起，沿时间维度形成装备运行的空间场模型及应力场模型，两场数据叠加确定某一时刻围岩形态、工作面采高、冒顶片帮及直线度、仰俯及上窜下滑这些装备状态，并判断是否正常；基于恢复正常状态、提升装备运行效率和适应性目标，超前计算得出液压支架支护阻力、最佳移架时间、采煤机割煤速度和采高、护帮板收放时间、顶板下沉量、刮板输送机推移距离和上窜下滑量等运行控制参数；且每一截割循环完成后，均根据实际数据自动重新修正模型和更新预测计算数据，保证预设控制和实际地质条件的吻合度，提升工作面运行质量。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010178157.9A CN111173510A (zh) | 2020-03-14 | 2020-03-14 | 一种用于复杂条件工作面的综采装备智能控制方法及系统 |
| CN202010178157.9 | 2020-03-14 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2021184614A1 true WO2021184614A1 (zh) | 2021-09-23 |
Family
ID=70626150
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2020/102159 WO2021184614A1 (zh) | 2020-03-14 | 2020-07-15 | 一种用于复杂条件工作面的综采装备智能决策控制方法及系统 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111173510A (zh) |
| WO (1) | WO2021184614A1 (zh) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113820953A (zh) * | 2021-09-26 | 2021-12-21 | 北京航空航天大学 | 导叶伺服系统的建模方法、装置及电子设备 |
| CN113887111A (zh) * | 2021-11-08 | 2022-01-04 | 太原理工大学 | 面向综采工作面地质、煤层与装备的虚拟综合测试方法 |
| CN114091233A (zh) * | 2021-10-18 | 2022-02-25 | 中煤科工开采研究院有限公司 | 综采工作面采煤机自适应跟随煤层截割路径优化方法 |
| CN114329936A (zh) * | 2021-12-22 | 2022-04-12 | 太原理工大学 | 基于多智能体深度强化学习的虚拟综采生产系统推演方法 |
| CN114352336A (zh) * | 2021-12-17 | 2022-04-15 | 北京天玛智控科技股份有限公司 | 综采工作面智能控制系统和方法 |
| CN114355944A (zh) * | 2022-01-05 | 2022-04-15 | 天津华宁电子有限公司 | 一种矿用工作面双车控制系统 |
| CN114357637A (zh) * | 2021-12-02 | 2022-04-15 | 中煤科工开采研究院有限公司 | 复杂起伏变化煤层工作面采煤机自适应截割路径优化方法 |
| CN114439528A (zh) * | 2021-12-16 | 2022-05-06 | 中国矿业大学 | 一种智能充填液压支架结构干涉自主控制方法 |
| CN114439527A (zh) * | 2021-12-16 | 2022-05-06 | 中国矿业大学 | 一种智能固体充填液压支架工况位态表征方法 |
| CN114560256A (zh) * | 2022-02-28 | 2022-05-31 | 国能神东煤炭集团有限责任公司 | 一种刮板输送机故障检测方法、系统及存储介质 |
| CN114743160A (zh) * | 2022-04-01 | 2022-07-12 | 中煤科工开采研究院有限公司 | 基于视觉三维重建的采煤工作面可视化监控系统及方法 |
| CN115348181A (zh) * | 2022-10-18 | 2022-11-15 | 苏州浪潮智能科技有限公司 | 一种数据传输建模方法、系统、设备及存储介质 |
| CN115788438A (zh) * | 2023-02-09 | 2023-03-14 | 西安华创马科智能控制系统有限公司 | 一种综采工作面的调整方法及装置 |
| CN115826465A (zh) * | 2022-12-02 | 2023-03-21 | 中国煤炭科工集团太原研究院有限公司 | 一种用于连续采煤机多级行走的可视化远程控制系统和方法 |
| CN115877898A (zh) * | 2023-01-31 | 2023-03-31 | 山东华宜同创自动化科技有限公司 | 一种提升机控制系统 |
| CN115906336A (zh) * | 2023-01-06 | 2023-04-04 | 常熟天地煤机装备有限公司 | 基于硬件在环仿真的采煤机数字孪生模型建模方法及系统 |
| CN116300517A (zh) * | 2022-12-26 | 2023-06-23 | 北京卫星环境工程研究所 | 面向航天器在轨运行任务的多人协同推演仿真平台及方法 |
| CN114033369B (zh) * | 2021-11-10 | 2023-11-28 | 中煤科工开采研究院有限公司 | 一种基于采煤机位置架号的双向割煤循环分析方法 |
| US11899410B1 (en) | 2022-12-15 | 2024-02-13 | Halliburton Energy Services, Inc. | Monitoring a wellbore operation using distributed artificial intelligence |
| US11899438B1 (en) | 2022-12-15 | 2024-02-13 | Halliburton Energy Services, Inc. | Distributed control system with failover capabilities for physical well equipment |
| CN117685982A (zh) * | 2024-01-29 | 2024-03-12 | 宁波长壁流体动力科技有限公司 | 一种液压支架群的数字孪生体的管理方法和系统 |
| CN115826465B (zh) * | 2022-12-02 | 2024-06-04 | 中国煤炭科工集团太原研究院有限公司 | 一种用于连续采煤机多级行走的可视化远程控制系统和方法 |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111173510A (zh) * | 2020-03-14 | 2020-05-19 | 天地科技股份有限公司 | 一种用于复杂条件工作面的综采装备智能控制方法及系统 |
| CN111833462B (zh) * | 2020-07-14 | 2024-05-17 | 深圳市瑞立视多媒体科技有限公司 | 基于虚幻引擎的切割方法、装置、设备及存储介质 |
| CN113296429B (zh) * | 2020-07-24 | 2024-05-24 | 盒马(中国)有限公司 | 输送机系统、物联网控制器、控制方法及配置方法 |
| CN112160750B (zh) * | 2020-09-21 | 2022-08-16 | 三一重型装备有限公司 | 采煤机状态控制、预测的系统及方法 |
| CN112395661B (zh) * | 2020-11-23 | 2023-03-24 | 太原理工大学 | 一种针对刮板输送机上窜下滑问题的预警方法 |
| CN112668109B (zh) * | 2020-11-30 | 2024-01-30 | 天地(榆林)开采工程技术有限公司 | 一种综采工作面截割路线模型建立方法 |
| CN112832867B (zh) * | 2020-12-31 | 2024-01-19 | 西安合智宇信息科技有限公司 | 一种融合开采数据及地质信息的开采视频建模方法 |
| CN112879061A (zh) * | 2021-01-20 | 2021-06-01 | 河南理工大学 | 姿态角度自调式沿空留巷智慧型控顶装置 |
| EP4036370A1 (en) * | 2021-01-29 | 2022-08-03 | Sandvik Mining and Construction G.m.b.H. | Mining machine and method for controlling movement of a movable element of a mining machine |
| CN112983417B (zh) * | 2021-03-15 | 2023-12-12 | 中国煤炭科工集团太原研究院有限公司 | 一种煤矿采掘装备数据分析预警方法 |
| CN113124797B (zh) * | 2021-03-24 | 2022-10-21 | 太原理工大学 | 一种基于可调节底板的液压支架群位姿模拟系统 |
| CN113128109B (zh) * | 2021-04-08 | 2022-11-29 | 太原理工大学 | 一种面向智能化综采机器人生产系统的测试与评估方法 |
| CN114415624B (zh) * | 2021-12-21 | 2024-04-19 | 煤科(北京)检测技术有限公司 | 电液控制器的仿真测试系统 |
| CN114622912B (zh) * | 2022-03-17 | 2022-12-27 | 中国矿业大学 | 一种采煤机智能控制装置及其控制方法 |
| CN115237082A (zh) * | 2022-09-22 | 2022-10-25 | 太原向明智控科技有限公司 | 基于可编程的方式实现综采工作面规划开采的系统及方法 |
| CN115853594B (zh) * | 2023-01-20 | 2023-04-28 | 太原理工大学 | 基于fbg传感器的综采三机状态监测系统 |
| CN116291659B (zh) * | 2023-05-24 | 2023-08-08 | 太原理工大学 | 液压支架人机协同控制策略推荐方法 |
| CN116816342B (zh) * | 2023-06-07 | 2024-03-08 | 河南平煤神马电气股份有限公司 | 一种煤矿智能掘进工作面多机协同集中控制系统 |
| CN117348500B (zh) * | 2023-12-04 | 2024-02-02 | 济南华科电气设备有限公司 | 一种煤矿综采工作面自动化控制方法及系统 |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102221832A (zh) * | 2011-05-10 | 2011-10-19 | 江苏和光天地科技有限公司 | 一种煤矿无人工作面开发系统 |
| CN102759909A (zh) * | 2012-06-14 | 2012-10-31 | 中国矿业大学 | 基于不同地质条件的电牵引采煤机工作状态虚拟仿真系统 |
| CN106089278A (zh) * | 2016-06-28 | 2016-11-09 | 太原理工大学 | 煤矿无人值守综采工作面液压支架电液控制系统试验台 |
| CN106127324A (zh) * | 2016-04-26 | 2016-11-16 | 山东科技大学 | 一种用于无人化采掘工作面的远程可视化监控方法 |
| US20170335688A1 (en) * | 2014-08-28 | 2017-11-23 | Joy Mm Delaware, Inc. | Roof support monitoring for longwall system |
| DE102018111938A1 (de) * | 2018-05-17 | 2019-11-21 | EEP Elektro-Elektronik Pranjic GmbH | Anordnung und Verfahren zur Fernbedienung eines elektrohydraulischen Steuerungssystems eines Strebausbaus |
| CN111173510A (zh) * | 2020-03-14 | 2020-05-19 | 天地科技股份有限公司 | 一种用于复杂条件工作面的综采装备智能控制方法及系统 |
-
2020
- 2020-03-14 CN CN202010178157.9A patent/CN111173510A/zh active Pending
- 2020-07-15 WO PCT/CN2020/102159 patent/WO2021184614A1/zh active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102221832A (zh) * | 2011-05-10 | 2011-10-19 | 江苏和光天地科技有限公司 | 一种煤矿无人工作面开发系统 |
| CN102759909A (zh) * | 2012-06-14 | 2012-10-31 | 中国矿业大学 | 基于不同地质条件的电牵引采煤机工作状态虚拟仿真系统 |
| US20170335688A1 (en) * | 2014-08-28 | 2017-11-23 | Joy Mm Delaware, Inc. | Roof support monitoring for longwall system |
| CN106127324A (zh) * | 2016-04-26 | 2016-11-16 | 山东科技大学 | 一种用于无人化采掘工作面的远程可视化监控方法 |
| CN106089278A (zh) * | 2016-06-28 | 2016-11-09 | 太原理工大学 | 煤矿无人值守综采工作面液压支架电液控制系统试验台 |
| DE102018111938A1 (de) * | 2018-05-17 | 2019-11-21 | EEP Elektro-Elektronik Pranjic GmbH | Anordnung und Verfahren zur Fernbedienung eines elektrohydraulischen Steuerungssystems eines Strebausbaus |
| CN111173510A (zh) * | 2020-03-14 | 2020-05-19 | 天地科技股份有限公司 | 一种用于复杂条件工作面的综采装备智能控制方法及系统 |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113820953A (zh) * | 2021-09-26 | 2021-12-21 | 北京航空航天大学 | 导叶伺服系统的建模方法、装置及电子设备 |
| CN113820953B (zh) * | 2021-09-26 | 2024-04-02 | 北京航空航天大学 | 导叶伺服系统的建模方法、装置及电子设备 |
| CN114091233A (zh) * | 2021-10-18 | 2022-02-25 | 中煤科工开采研究院有限公司 | 综采工作面采煤机自适应跟随煤层截割路径优化方法 |
| CN114091233B (zh) * | 2021-10-18 | 2024-03-08 | 中煤科工开采研究院有限公司 | 综采工作面采煤机自适应跟随煤层截割路径优化方法 |
| CN113887111A (zh) * | 2021-11-08 | 2022-01-04 | 太原理工大学 | 面向综采工作面地质、煤层与装备的虚拟综合测试方法 |
| CN113887111B (zh) * | 2021-11-08 | 2024-03-22 | 太原理工大学 | 面向综采工作面地质、煤层与装备的虚拟综合测试方法 |
| CN114033369B (zh) * | 2021-11-10 | 2023-11-28 | 中煤科工开采研究院有限公司 | 一种基于采煤机位置架号的双向割煤循环分析方法 |
| CN114357637A (zh) * | 2021-12-02 | 2022-04-15 | 中煤科工开采研究院有限公司 | 复杂起伏变化煤层工作面采煤机自适应截割路径优化方法 |
| CN114357637B (zh) * | 2021-12-02 | 2024-02-27 | 中煤科工开采研究院有限公司 | 复杂起伏变化煤层工作面采煤机自适应截割路径优化方法 |
| CN114439527B (zh) * | 2021-12-16 | 2023-04-28 | 中国矿业大学 | 一种智能固体充填液压支架工况位态表征方法 |
| CN114439527A (zh) * | 2021-12-16 | 2022-05-06 | 中国矿业大学 | 一种智能固体充填液压支架工况位态表征方法 |
| CN114439528A (zh) * | 2021-12-16 | 2022-05-06 | 中国矿业大学 | 一种智能充填液压支架结构干涉自主控制方法 |
| CN114352336A (zh) * | 2021-12-17 | 2022-04-15 | 北京天玛智控科技股份有限公司 | 综采工作面智能控制系统和方法 |
| CN114329936A (zh) * | 2021-12-22 | 2022-04-12 | 太原理工大学 | 基于多智能体深度强化学习的虚拟综采生产系统推演方法 |
| CN114329936B (zh) * | 2021-12-22 | 2024-03-29 | 太原理工大学 | 基于多智能体深度强化学习的虚拟综采生产系统推演方法 |
| CN114355944B (zh) * | 2022-01-05 | 2023-11-28 | 天津华宁电子有限公司 | 一种矿用工作面双车控制系统 |
| CN114355944A (zh) * | 2022-01-05 | 2022-04-15 | 天津华宁电子有限公司 | 一种矿用工作面双车控制系统 |
| CN114560256A (zh) * | 2022-02-28 | 2022-05-31 | 国能神东煤炭集团有限责任公司 | 一种刮板输送机故障检测方法、系统及存储介质 |
| CN114743160B (zh) * | 2022-04-01 | 2024-02-27 | 中煤科工开采研究院有限公司 | 基于视觉三维重建的采煤工作面可视化监控系统及方法 |
| CN114743160A (zh) * | 2022-04-01 | 2022-07-12 | 中煤科工开采研究院有限公司 | 基于视觉三维重建的采煤工作面可视化监控系统及方法 |
| CN115348181A (zh) * | 2022-10-18 | 2022-11-15 | 苏州浪潮智能科技有限公司 | 一种数据传输建模方法、系统、设备及存储介质 |
| CN115826465A (zh) * | 2022-12-02 | 2023-03-21 | 中国煤炭科工集团太原研究院有限公司 | 一种用于连续采煤机多级行走的可视化远程控制系统和方法 |
| CN115826465B (zh) * | 2022-12-02 | 2024-06-04 | 中国煤炭科工集团太原研究院有限公司 | 一种用于连续采煤机多级行走的可视化远程控制系统和方法 |
| US11899410B1 (en) | 2022-12-15 | 2024-02-13 | Halliburton Energy Services, Inc. | Monitoring a wellbore operation using distributed artificial intelligence |
| US11899438B1 (en) | 2022-12-15 | 2024-02-13 | Halliburton Energy Services, Inc. | Distributed control system with failover capabilities for physical well equipment |
| CN116300517B (zh) * | 2022-12-26 | 2023-11-24 | 北京卫星环境工程研究所 | 面向航天器在轨运行任务的多人协同推演仿真平台及方法 |
| CN116300517A (zh) * | 2022-12-26 | 2023-06-23 | 北京卫星环境工程研究所 | 面向航天器在轨运行任务的多人协同推演仿真平台及方法 |
| CN115906336A (zh) * | 2023-01-06 | 2023-04-04 | 常熟天地煤机装备有限公司 | 基于硬件在环仿真的采煤机数字孪生模型建模方法及系统 |
| CN115877898B (zh) * | 2023-01-31 | 2023-05-05 | 山东华宜同创自动化科技有限公司 | 一种提升机控制系统 |
| CN115877898A (zh) * | 2023-01-31 | 2023-03-31 | 山东华宜同创自动化科技有限公司 | 一种提升机控制系统 |
| CN115788438B (zh) * | 2023-02-09 | 2023-05-05 | 西安华创马科智能控制系统有限公司 | 一种综采工作面的调整方法及装置 |
| CN115788438A (zh) * | 2023-02-09 | 2023-03-14 | 西安华创马科智能控制系统有限公司 | 一种综采工作面的调整方法及装置 |
| CN117685982A (zh) * | 2024-01-29 | 2024-03-12 | 宁波长壁流体动力科技有限公司 | 一种液压支架群的数字孪生体的管理方法和系统 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111173510A (zh) | 2020-05-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021184614A1 (zh) | 一种用于复杂条件工作面的综采装备智能决策控制方法及系统 | |
| CN109356608B (zh) | 一种掘进机、系统及方法 | |
| CN112392485B (zh) | 煤矿综采工作面透明化数字孪生自适应开采系统和方法 | |
| CN114120785B (zh) | 一种煤矿掘进设备与地质模型、巷道设计模型的耦合系统 | |
| CN112883559B (zh) | 基于大数据体系的规划截割方法和装置、存储介质及电子装置 | |
| CN114329936B (zh) | 基于多智能体深度强化学习的虚拟综采生产系统推演方法 | |
| CN109630110A (zh) | 一种综采工作面煤层厚度自适应截割控制方法及电子设备 | |
| CN107066313B (zh) | 一种基于局域网协同的综采工作面虚拟监测方法 | |
| CN109214076B (zh) | 一种支撑综采工作面地理环境及装备的虚拟规划方法 | |
| LU501938B1 (en) | Method and system for intelligent analysis of big data on unmanned mining in mine | |
| CN115454057B (zh) | 一种煤矿机器人群数字孪生智能管控建模系统与方法 | |
| CN113128109B (zh) | 一种面向智能化综采机器人生产系统的测试与评估方法 | |
| CN103745648B (zh) | 盾构机双模型仿真设备与方法 | |
| Ge et al. | A virtual adjustment method and experimental study of the support attitude of hydraulic support groups in propulsion state | |
| CN103867205A (zh) | 一种掘进机远程控制系统及方法 | |
| CN102759909A (zh) | 基于不同地质条件的电牵引采煤机工作状态虚拟仿真系统 | |
| CN112832867B (zh) | 一种融合开采数据及地质信息的开采视频建模方法 | |
| CN111140231B (zh) | 面向综采装备时空运动学的煤层顶底板路径虚拟规划方法 | |
| CN109635367A (zh) | 一种掘进机三维模拟方法、装置及系统 | |
| CN106089201A (zh) | 一种用于无人化采煤工作面的截割路径规划方法 | |
| WO2022001760A1 (zh) | 一种基于5g技术的远程可监控多轴协同智能控制器 | |
| CN113340305A (zh) | 一种基于mems的液压支架姿态监测方法 | |
| Jiao et al. | Intelligent decision method for the position and attitude self-adjustment of hydraulic support groups driven by a digital twin system | |
| CN117291959A (zh) | 基于激光slam的工作面整体工作空间虚拟重构方法 | |
| CN109783962A (zh) | 基于虚拟现实物理引擎的综采装备协同推进仿真方法 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20925679 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 20925679 Country of ref document: EP Kind code of ref document: A1 |