CN114004103B - Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face - Google Patents
Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face Download PDFInfo
- Publication number
- CN114004103B CN114004103B CN202111314262.1A CN202111314262A CN114004103B CN 114004103 B CN114004103 B CN 114004103B CN 202111314262 A CN202111314262 A CN 202111314262A CN 114004103 B CN114004103 B CN 114004103B
- Authority
- CN
- China
- Prior art keywords
- coal
- fully mechanized
- data
- model
- virtual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005065 mining Methods 0.000 title claims abstract description 192
- 238000012360 testing method Methods 0.000 title claims abstract description 97
- 238000011160 research Methods 0.000 title claims abstract description 22
- 239000003245 coal Substances 0.000 claims abstract description 211
- 238000000034 method Methods 0.000 claims abstract description 53
- 230000008569 process Effects 0.000 claims abstract description 45
- 238000004088 simulation Methods 0.000 claims abstract description 37
- 230000003993 interaction Effects 0.000 claims abstract description 20
- 230000000007 visual effect Effects 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims description 32
- 230000006870 function Effects 0.000 claims description 28
- 238000005520 cutting process Methods 0.000 claims description 26
- 238000004458 analytical method Methods 0.000 claims description 22
- 238000004422 calculation algorithm Methods 0.000 claims description 22
- 238000007689 inspection Methods 0.000 claims description 15
- 230000033001 locomotion Effects 0.000 claims description 13
- 238000012800 visualization Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 9
- 239000000463 material Substances 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 8
- 238000013135 deep learning Methods 0.000 claims description 7
- 238000003745 diagnosis Methods 0.000 claims description 7
- 230000036541 health Effects 0.000 claims description 7
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 238000007726 management method Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 4
- 238000011835 investigation Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 238000005498 polishing Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000002360 explosive Substances 0.000 claims description 3
- 239000006261 foam material Substances 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 11
- 238000010586 diagram Methods 0.000 description 13
- 230000007246 mechanism Effects 0.000 description 7
- 230000008878 coupling Effects 0.000 description 6
- 238000010168 coupling process Methods 0.000 description 6
- 238000005859 coupling reaction Methods 0.000 description 6
- 230000005856 abnormality Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 239000000428 dust Substances 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000005452 bending Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 239000006260 foam Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- 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]
Abstract
The invention relates to a collaborative operation test platform capable of supporting basic research of a digital twin fully mechanized coal mining face, which comprises a multifunctional digital test platform of the fully mechanized coal mining face, a visual virtual test simulation system of the collaborative operation of the fully mechanized coal mining face, digital twin data and an intelligent service system of the test platform of the fully mechanized coal mining face. The method has the advantages that the operation reproduction, exploitation previewing and fault early warning are carried out on the exploitation process of the fully mechanized mining face, the real-time interaction of the virtual prototype test platform and the physical prototype test platform is realized, the underground common engineering problems can be researched, and research environments and platform supports are created for researching the application of some new technologies.
Description
Technical Field
The invention relates to a fully mechanized mining face operation test platform, in particular to a collaborative operation test platform capable of supporting basic research of a digital twin fully mechanized mining face.
Background
The digital twin technology is one of key technologies of intelligent manufacturing, can be applied to multiple levels of unit-level, system-level and complex system-level objects based on models, data double driving and virtual-real closed loop interaction, and can be used for monitoring and visualizing physical entity behaviors, evaluating the states of physical entities, predicting future trends of the physical entities and the like.
Coal mine is one of the important energy sources in China, and coal is still the main energy source in China for a quite long time. The digital twin technology is applied to the coal mining process, and a digital twin fully-mechanized mining face is established, so that the digital twin fully-mechanized mining face has important significance in building intelligent coal mines, intelligent mining and green mining.
In the prior art, the invention patent with publication number CN111208759A discloses a digital twin intelligent monitoring system and method for an unmanned fully mechanized mining face of a mine, wherein a digital twin model is constructed, and based on a three-dimensional visual virtual scene, a convolutional network deep learning algorithm is utilized to perform perception analysis, simulation, iterative optimization and decision control; the data driving based on the data twinning technology realizes real-time monitoring, intelligent sensing, accurate positioning and health prediction of the unmanned fully-mechanized mining working face of the remote physical space mine through the virtual space digital twinning unmanned fully-mechanized mining working face. The invention patent with publication number of CN111210359A discloses a digital twin evolution mechanism and method for an intelligent mine scene, wherein digital twin is formed by mutually coupling and evolving and integrating a physical model, a logic model, a simulation model and a data model, and by constructing the digital twin model, the data mirror image and information interaction between a digital twin body and the physical entity are realized, and the object twin, the process twin and the performance twin of the physical space physical entity and the virtual space digital twin body are realized. The invention patent with publication number of CN110021224A discloses a pushing and sliding process simulation experiment device for a scraper conveyor under a complex underground condition, wherein a pushing and sliding mechanism in the experiment device is a key part connecting the scraper conveyor with a hydraulic support base, and pushing and sliding operation can be simulated through the pushing and sliding mechanism to push and slide the scraper conveyor. However, in order to obtain the pushing angle of each structure of the pushing mechanism, the pushing distance of the scraper conveyor and the displacement of the joint of the pushing angle and the scraper conveyor, an electronic element and an electronic instrument such as an axial encoder, an inclination angle sensor, an infrared distance measuring sensor, a camera and the like are required to be installed.
The digital twin working surface is obviously difficult to support from the technical levels of equipment, sensors, information communication, geological security and the like, and the current prototype platform can only realize a single function and cannot be continuously propelled, so that the digital twin working surface has no practicability and practicality. Therefore, a related digital twin prototype platform is built under laboratory conditions, some key technologies of the working face are researched, and after the key technologies are thoroughly researched, support and guarantee are further provided for the true digital twin fully mechanized mining face with the practicability.
Disclosure of Invention
The invention aims to provide a collaborative operation test platform capable of supporting basic research of a digital twin fully-mechanized mining face, so that the engineering problems in the advancing process of the underground fully-mechanized mining face are subjected to equipment pose monitoring, pose monitoring method and control program verification, virtual-real fusion key technology and other expanded virtual-real research such as fault diagnosis.
In order to achieve the above purpose, the invention adopts the following technical scheme: the collaborative operation test platform capable of supporting basic research of the digital twin fully mechanized mining face comprises a multifunctional digital test platform of the fully mechanized mining face, a visual virtual test simulation system of the collaborative operation of the fully mechanized mining face, digital twin data and an intelligent service system of the test platform of the fully mechanized mining face;
the multifunctional digital test platform for the fully mechanized coal mining face is a test platform for the fully mechanized coal mining face, which is built on the basis of the existing coal seam detection information by scaling in an equal proportion according to the selected matching relation of the three machines of the actual fully mechanized coal mining face, and comprises a coal mining machine model, a scraper conveyor model, a hydraulic support group model, a coal seam model, a routing inspection robot model and an information sensing system, wherein the three machines of the fully mechanized coal mining face are built in a physical entity space and accord with the real working condition;
the 'three-machine' collaborative operation visual virtual test simulation system of the fully mechanized coal mining face comprises a virtual simulation description model and a simulation system intelligent model;
the virtual simulation description model is a three-dimensional virtual model imported on a physical test platform based on a Unity3D virtual simulation engine, and after the virtual model is rendered, the virtual model is set from the shape, the material, the quality, the friction factors, the interaction forces between equipment and the virtual coal layers, so that the virtualization of the fully mechanized coal mining face three-machine test platform is completed;
the simulation system intelligent model is used for carrying out algorithm analysis on the acquired data through real-time data and original historical data acquired by the information sensing system, predicting the pose of a hydraulic support group, the cutting track of a coal mining machine, the pose of a scraper conveyor, the service life and the health state of equipment, and realizing intelligent fault diagnosis of the equipment;
the twin data comprises all simulation bottom data generated when a virtual digital twin fully mechanized mining face three-machine collaborative simulation system operates, full-period operation data generated when a fully mechanized mining face three-machine multifunctional digital test platform operates, and intermediate data generated in the process of realizing interaction and scene operation;
the intelligent service system of the fully mechanized coal mining face three-machine test platform comprises a management service subsystem based on knowledge driving and an intelligent service subsystem based on data driving, and is used for completing data output and input, algorithm integration, virtual modeling, track prediction, interface operation and fault identification alarm work.
Further, in the multifunctional digital test platform of the fully-mechanized coal face, the coal bed model is a fully-mechanized coal face coal bed three-dimensional model constructed by utilizing limited survey data, and based on a deep learning algorithm, the coal bed three-dimensional model is optimized and predicted by utilizing monitoring data in the cutting process of the coal mining machine to obtain construction information of the coal bed, and a coal bed model constructed by polishing is constructed by utilizing foam materials.
Further, in the multifunctional digital test platform of the fully mechanized mining face, the information sensing system is a system for detecting the whole pose of equipment and monitoring the surrounding environment on a prototype platform by utilizing related sensors and comprises a hydraulic support information sensing system, a coal mining machine information sensing system and a coal mining space information sensing system.
Further, in the multifunctional digital test platform of the fully mechanized working face, the inspection robot model can monitor the running state of equipment, the environmental state of a working space and whether foreign matters exist on line when the fully mechanized working face runs or stops; abnormal sounds during the running of the equipment can be identified, fault identification and analysis can be carried out, and running data can be obtained; the multi-parameter sensor is arranged, so that the parameters of flammable and explosive gas and temperature in the inspection track can be detected, and the data can be automatically acquired and analyzed; when fault information is obtained, warning can be timely carried out when faults are found, and emergency braking instructions can be sent to the system when serious faults are encountered.
Further, in the virtual simulation description model, the virtual coal bed is a fully-mechanized coal face coal bed three-dimensional model constructed by utilizing limited survey data, the coal bed three-dimensional model is optimized and predicted by utilizing monitoring data in the cutting process of a coal mining machine based on a deep learning algorithm to obtain construction information of the coal bed, a dynamic coal bed model capable of self-optimizing is built in Unity3D based on real-time inversion geological data information, the dynamic coal bed model is integrated in the virtual fully-mechanized coal face, and the virtual coal bed model updated in real time along with mining is built.
Further, the simulation bottom layer data comprise motion parameters, pushing and sliding forces, frame moving forces, supporting heights of the hydraulic support, cutting heights of the coal mining machine, cutting tracks of the roller, rotating speeds of the roller and running resistance of the scraper conveyor on the virtual coal seam bottom plate, wherein the motion parameters, pushing and sliding forces, frame moving forces and the running resistance of the structure of the hydraulic support are generated during running.
Further, the full-period operation data are three-machine whole-course operation data acquired by the test bed measurement and control system, and the three-machine whole-course operation data comprise hydraulic support height, scraper conveyor track, coal cutter posture and three-machine real-time posture related information in the mining process, scanned equipment point cloud information, and equipment operation parameters and environment parameters monitored by the inspection robot.
Further, the intermediate data includes data generated when the data is fused by an intermediate equation or a related algorithm in the analysis process and when the information measured by the sensor is analyzed and fused.
Further, the knowledge-driven management-based service subsystem is a service system established for direct demand of operators, and comprises the steps of demand proposing, tutorial guiding, scene selecting and function selecting; the requirement is that an operator inputs own requirement to inquire related functions of the system, and the requirement is a precondition of the operation of the other three parts; the guiding course is a process that an operator is familiar with the operation flow of the simulation system, and is the basis of the operation of the whole subsystem; the scene selection refers to that an operator selects an operation scene according to the requirement, and is a key of service-oriented operation of the whole subsystem; the function selection is to directly convert the operator demand into a function for simulation system use, and is the final function of the whole service subsystem.
Further, the intelligent service subsystem based on data driving realizes on-line planning, intelligent prediction, fault investigation, exploitation previewing and scene visualization of fully mechanized mining face exploitation on the basis of establishing virtual-real interaction channels; the comprehensive mining working face mining on-line planning is to carry out on-line planning on the operation and mining process of the coal machine equipment according to the requirements of operators under the drive of historical data; the intelligent prediction refers to predicting the state of future coal machine equipment and the next mining process based on historical mining data in the mining process; the fault checking is to classify faults based on equipment history operation data when the exploitation is not started or is in progress, and identify the faults through an intelligent algorithm by the data output by a previewing result in a simulation system; the exploitation and prediction are to build a coal bed model and a coal machine equipment model according to the detected coal bed information and the coal machine equipment matching information, and deduce the whole exploitation process in advance; scene visualization is the visualization that enables the production process and production data.
Compared with the prior art, the digital twin fully mechanized mining face three-machine collaborative operation test platform has the following advantages and outstanding innovation points:
1. the method aims at the problems that the global view angle is lacking in coal mining, underground light is dim, the three-machine equipment group of the fully mechanized mining face is large in size and relevant sensors are difficult to install, and experimental verification cannot be carried out, so that a model machine model is built under laboratory conditions according to a modeling mode of equal-proportion scaling, an equivalent mining environment of the fully mechanized mining face under the laboratory conditions is built, and a solution idea is provided for solving the underground relevant problems.
2. The self-adaptive operation action of the coal machine equipment under the virtual scene and the cooperative propulsion of the fully mechanized coal mining face equipment are realized based on the multidisciplinary theory. The method comprises the steps of establishing a complete virtual fully-mechanized mining face three-machine collaborative operation test platform, realizing high-precision reproduction during the actual fully-mechanized mining face three-machine collaborative propulsion, and realizing pre-selection planning, virtual monitoring and virtual control of a virtual mining process.
3. Aiming at the problems of unknown property, lack of planning property and danger elimination in the coal mining process, a prototype platform with functions of virtual-real interaction, on-line planning, operation and environment monitoring, fault diagnosis and the like is established, and a supporting research thought is provided for researching the basic problem of fully-mechanized mining face mining.
4. The inspection robot is integrated into a test prototype platform to serve as an 'inspection person' when the whole working face operates, so that on-line monitoring of faults and reading of real-time operation data are realized, parameter detection is carried out on the environment, early warning is carried out on detection results, and when abnormality is found, warning signals can be timely sent or emergency braking instructions can be sent to the system.
5. The invention can realize real-time measurement of the pose of the coal machine equipment and directly measure the coal seam morphology. The coupling test model between the coal cutter and the coal seam is established, the roller of the coal cutter is made of steel materials, the coal seam is made of foam plates and other materials, and the coal cutting operation of the coal cutter can be realized. Based on the real-time inversion geological data information, a self-optimized dynamic coal bed model and a working space updated in real time along with exploitation can be obtained.
6. In the physical layer, the platform support can be provided for the actual running pose research of the complex equipment group; on a virtual level, providing support for related algorithm researches of virtual planning of equipment running states (three plane roller heightening, straightness control and equipment pushing) (big data analysis algorithm based on data); and at the virtual-real interaction level, verifying that a virtual-real interaction bidirectional channel, virtual monitoring and virtual control provide support.
Drawings
FIG. 1 shows the components of a test platform according to the present invention.
Fig. 2 is a virtual model framework diagram.
Fig. 3 is a data content contained in the twin data.
FIG. 4 is a construction process of a model of 'coal seam+coal machine equipment' in a physical fully mechanized coal mining face 'three-machine' collaborative operation test platform.
Fig. 5 is a schematic diagram of a prototype hydraulic bracket model.
FIG. 6 is a schematic diagram of a middle trough prototype of the scraper conveyor.
FIG. 7 is a schematic diagram of a prototype model of a shearer.
Fig. 8 shows the components and related functions of the electronically controlled monitoring system.
FIG. 9 is a schematic diagram of an information sensing system.
FIG. 10 is a schematic diagram of a test platform service system.
Fig. 11 is a workflow diagram of virtual-real interaction.
FIG. 12 is an exemplary diagram of a downhole engineering problem that may be studied with the subject test stand.
Fig. 13 is a coal cutter attitude acquisition result.
Fig. 14 is a schematic drawing of a flight conveyor trajectory.
Detailed Description
The collaborative operation test platform capable of supporting basic research of the digital twin fully mechanized mining face comprises a physical model, a virtual model, digital twin data and a test platform service system. The method can perform operation reproduction, exploitation previewing and fault early warning on the exploitation process of the fully mechanized mining face, achieves real-time interaction of the virtual prototype test platform and the physical prototype test platform, can study common engineering problems in the pit by establishing the test platform, and creates a study environment and a platform support for studying application of some new technologies.
As shown in FIG. 1, the digital collaborative operation test platform capable of supporting the digital twin fully mechanized mining face multifunctional basic research comprises a fully mechanized mining face 'three-machine' multifunctional digital test platform, a fully mechanized mining face 'three-machine' collaborative operation visual virtual test simulation system, digital twin data and a fully mechanized mining face 'three-machine' test platform intelligent service system.
Multifunctional digital test platform for three machines of fully mechanized mining face
The multifunctional digital test platform for the fully mechanized coal mining face is characterized in that a model machine system is built after scaling in equal proportion according to the matching relation of the three machines of the selected actual fully mechanized coal mining face, a coal seam model is built on the basis of the existing coal seam detection information, and accordingly the three machines of the fully mechanized coal mining face under the real working condition are built in a physical entity space, and the multifunctional digital test platform mainly comprises a coal mining machine model, a scraper conveyor model, a hydraulic support model, a coal seam model, a routing inspection robot model and an information sensing system. The platform can realize collaborative propulsion of a fully mechanized mining work 'three-machine' model machine model on a coal bed model, the model machine is monitored through the inspection robot and the information sensing system, the whole platform can realize self-adaptive cutting of coal walls of the coal mining machine and automatic height adjustment, and the hydraulic support is self-adaptive to support, and meanwhile, the engineering problems of common straightness, upward movement, downward sliding and memory cutting in the fully mechanized mining working face propulsion process can be studied.
The model of the model machine of the coal mining machine is obtained by modeling according to the CAD drawing of the coal mining machine in scaling according to equal proportion, and model building is carried out according to connection and movement relation among all structures of the equipment.
The model of the scraper conveyor is obtained by modeling according to CAD drawing of a head, a tail and a middle groove of the scraper conveyor in an equal proportion scaling mode and performing model building according to connection and movement relation among all structures of equipment, and can be used as a walking track and a crushed coal carrier of a model of a coal mining machine.
The hydraulic support model machine model is formed by scaling according to the CAD drawing of a hydraulic support single machine in an equal proportion, single machine model building is performed according to connection and movement relation among all structures of equipment, a plurality of hydraulic supports are built according to the number requirement of equipment on a working face, and the finally obtained model can realize functions of the underground hydraulic support, including self-adaptive coal seam support, pushing, sliding, moving and adjusting functions.
The coal bed model is a fully-mechanized coal face three-dimensional model constructed by utilizing limited survey data. Based on a deep learning algorithm, optimizing and predicting a three-dimensional model of the coal seam by using monitoring data in the cutting process of the coal mining machine to obtain structural information of the coal seam, and polishing the coal seam model by using materials such as foam and the like; and the coal cutter prototype performs autonomous cutting of the coal wall under the control of the instruction, so that the updating of the coal bed model is changeable.
The inspection robot model can monitor the running state of equipment, the environmental state of a working space and whether foreign matters exist on line when the fully mechanized mining face runs or stops; abnormal sounds during the running of the equipment can be identified, fault identification and analysis can be carried out, and running data can be obtained; the multi-parameter sensor is arranged, so that the parameters of flammable and explosive gas and temperature in the inspection track can be detected, and the data can be automatically acquired and analyzed; when fault information is obtained, warning can be timely carried out when faults are found, and emergency braking instructions can be sent to the system when serious faults are encountered.
The information sensing system is a system for detecting the whole pose of equipment and monitoring the surrounding environment on a prototype platform by utilizing related sensors, and is mainly divided into three types, namely a hydraulic support information sensing system, a coal cutter information sensing system and a coal mining space information sensing system.
The hydraulic support information sensing system mainly comprises a sensor system, an optical positioning navigation system and a three-dimensional laser radar system. The hydraulic support comprises a hydraulic support top beam, a shield beam and an upright post, wherein the hydraulic support top beam, the shield beam and the upright post are respectively provided with an inclination sensor for measuring the inclination angle of each hydraulic support, an ultrasonic sensor and a nine-axis attitude sensor are respectively arranged on a base for measuring the supporting height of the hydraulic support and the attitude of the base, an infrared correlation sensor is arranged on a pushing mechanism for measuring pushing quantity, a resistance type film pressure sensor is arranged on the top beam for measuring the supporting pressure of the hydraulic support, and an optical positioning navigation system and a three-dimensional laser radar system are arranged on a coal mining machine for jointly positioning the hydraulic support;
the coal mining machine information sensing system is characterized in that two rocker arms of the coal mining machine are respectively provided with a rocker arm inclination angle sensor for measuring the rocker arm inclination angle, and a shaft encoder and a strapdown inertial navigation system are arranged on a machine body for combined positioning;
the coal mining space information sensing system comprises a sensor set which comprises a patrol robot and can monitor the mining environment, and particularly comprises the patrol robot which can patrol the mining process of the fully-mechanized mining face, and an Arduion main board of the integrated dust, temperature and gas monitoring sensor is arranged at a top plate of an air return roadway close to the working face to monitor the environment.
Visual virtual test simulation system for 'three-machine' collaborative operation of fully mechanized mining face
The virtual model framework diagram is shown in fig. 2, the three-machine collaborative operation visual virtual test simulation system of the fully mechanized coal mining face refers to a virtual fully mechanized coal mining face three-machine collaborative operation test platform established by using multidisciplinary cross knowledge under a virtual environment, and comprises two parts of a virtual simulation description model and a simulation system intelligent model, so that real mapping of the three-machine multifunctional digital test platform of the fully mechanized coal mining face can be realized, underground related tests can be studied, including positioning and attitude determination of equipment of a virtual coal machine, edge detection of the equipment, and local pose detection by utilizing three-dimensional laser scanning, point cloud analysis of the equipment, coal and rock identification and linearity research of the fully mechanized coal mining face are realized, inspection and fault identification diagnosis of the virtual fully mechanized coal mining face are performed, and the related tests of a test bed can be pre-tested, so that problems can be found in time and solutions are provided.
The multidisciplinary cross knowledge refers to the knowledge related to mechanical design, mechanical dynamics, related control theory, mechanical principle, robot kinematics, fault diagnosis and analysis which are integrated by using UG, solidWorks, labVIEW, unity3D, MATLAB simulation calculation software.
Fig. 2 is a virtual model frame diagram, and the virtual simulation description model is a model for realizing virtual 'coal seam + equipment' based on a highly real mapping of a Unity3D virtual simulation engine for virtualizing the interaction forces among the appearance, materials (materials), quality (Mass), friction factors and equipment of a physical test platform, so as to complete the simulation and real-time monitoring process of the working process of the test platform. The modeling process of the virtual 'coal seam+equipment' refers to the modeling process of the virtual 'coal seam+equipment' by performing model format conversion after fitting and modeling of coal seam point cloud and model selection and modeling of coal machine equipment are completed and importing the model format conversion into Unity 3D.
The parameterized expression process of the mechanical system needs three steps of coal machine equipment kinematics equation, determination of dynamics equation and dynamic parameter setting of 'coal seam + equipment'.
The parameterized expression process of the control system refers to the establishment process of realizing the functions of the control system and equations of the control system.
The virtual simulation description model is a three-dimensional virtual model imported on a physical test platform based on a Unity3D virtual simulation engine, and after the model is rendered, the three-dimensional virtual model is set from the shape, the Material (Material), the quality (Mass), the friction factors, the interaction force between equipment and the virtual coal layer, so that the virtualization of the fully mechanized coal face three-machine test platform is completed, and the establishment of a simulation model with high real mapping characteristics is ensured.
The fully mechanized coal face three-machine means a coal mining machine, a hydraulic support and a scraper conveyor.
The virtual coal bed model is a three-dimensional model of the fully-mechanized coal face, which is constructed by using limited survey data. Based on a deep learning algorithm, optimizing and predicting a coal seam three-dimensional model by using monitoring data in the cutting process of a coal mining machine to obtain structural information of a coal seam, establishing a dynamic coal seam model capable of self-optimizing in Unity3D based on real-time inversion geological data information, integrating the dynamic coal seam model into a virtual fully-mechanized mining working face, and establishing a virtual coal seam model updated in real time along with mining.
The finite survey data refers to the prospecting information of geology of the whole working face and the monitoring information in the cutting process of the coal mining machine in the earlier stage of coal mining, such as the inclination angle of the coal mining machine, the cutting height of a drum of the coal mining machine and the positioning information of the coal mining machine.
As shown in fig. 2, the intelligent model of the simulation system refers to real-time data and original historical data acquired through the information sensing system, on the basis of realizing parameterization expression of the mechanical system and parameterization expression of the control system, algorithm analysis is performed on the acquired data by using MATLAB data analysis software to realize prediction, so that the pose of a hydraulic support group, the cutting track of the coal mining machine, the pose of the scraper conveyor, the service life and the health state of equipment can be predicted, and intelligent fault diagnosis of the equipment is also realized.
Digital twin data
FIG. 3 is a data content contained in the twinning data, including all simulation data generated when the virtual digital twinning fully mechanized mining face is operated in a three-machine cooperation mode, all real-time operation data generated when the fully mechanized mining face is operated by the three-machine prototype test platform, and intermediate data generated in the process of realizing interaction and scene operation.
The simulation bottom layer data comprises motion parameters, pushing and sliding forces, frame moving forces, supporting heights of the hydraulic supports, cutting heights of the coal mining machine, cutting tracks of the roller, rotating speeds of the roller, running resistance of the scraper conveyor on the virtual coal seam bottom plate and the like of each structure of the hydraulic support pushing mechanism;
the full-period operation data refer to three-machine whole-course operation data collected by the test bed measurement and control system, and the three-machine whole-course operation data comprise all data of hydraulic support height, scraper conveyor track, coal cutter posture, three-machine real-time posture related information and scanned equipment point cloud information, and equipment operation parameters and environment parameters monitored by the inspection robot, including wind speed, temperature, humidity, dust, air pressure, gas concentration and equipment current.
The intermediate data include necessary, but not decisive, data which are generated when the data are fused by intermediate equations or by using a correlation algorithm in the analysis process and when the information measured by the sensor are analyzed and fused.
Intelligent service system of fully mechanized mining face three-machine test platform
The intelligent service system of the fully mechanized mining face three-machine test platform comprises two major parts of a management service subsystem based on knowledge driving and an intelligent service subsystem based on data driving, integrates three functions of fully mechanized mining three-machine operation data, monitoring various environment parameters simulated by a laboratory table and whole operation process visualization, and is used for completing data output and input, algorithm integration, virtual modeling, track prediction, interface operation and fault identification alarm work.
The knowledge-driven management-based service subsystem is a service system established for direct demand of operators, and mainly comprises four parts of demand, instruction course, scene selection and function selection. The four systems function as follows: the requirement is that an operator inputs own requirement to query related functions of the system, and the requirement is the premise of the operation of the other three parts; the guiding course is a process that an operator is familiar with the operation flow of the simulation system, and is the basis of the operation of the whole subsystem; the scene selection refers to that an operator selects an operation scene according to the requirement, and is a key of service-oriented operation of the whole subsystem; the function selection is to directly convert the operator demand into a function for simulation system use, and is the final function of the whole service subsystem.
The intelligent service subsystem based on data driving can realize on-line planning, intelligent prediction, fault investigation, exploitation previewing and scene visualization of the fully mechanized mining face on the basis of establishing the virtual-real interaction channel. The comprehensive mining working face mining on-line planning is to carry out on-line planning on the operation and mining process of the coal machine equipment according to the requirements of operators under the drive of historical data; the intelligent prediction refers to predicting the state of future coal machine equipment and the next mining process based on historical mining data in the mining process; the fault investigation means that when the exploitation is not started or is in progress, fault classification is carried out based on equipment history operation data, and the faults are identified through an intelligent algorithm by the data output by a previewing result in a simulation system; the exploitation and pre-modeling are to build a coal bed model and a coal machine equipment model according to the detected coal bed information and the coal machine equipment matching information, and to deduce the whole exploitation process in advance; scene visualization refers to the visualization of the production process and production data that is achieved.
The operation reproduction means that after an equal-proportion scaling test platform and a virtual simulation test platform which are mapped with the actual fully-mechanized mining face three-machine equipment are established, the obtained underground real-time related data are subjected to data processing, the self-adaptive collaborative propulsion of the fully-mechanized mining face three-machine is respectively realized through the control of a singlechip and a C# script, and the operation reproduction is realized.
The exploitation and prediction means that a coal bed model is respectively built in a laboratory environment and a virtual environment created by Unity3D according to detected complete coal bed information, a fully mechanized working face three-machine virtual and actual model machine model is built according to a drawing, and when the coal machine equipment model machine is self-adaptively and cooperatively propelled on the coal bed model, the whole exploitation process is deduced in advance, so that problems can be found timely.
The fault early warning means that when the platform monitors that a certain parameter exceeds the limit, the system sends an alarm signal, if the parameter exceeds the limit seriously, the system alarms and is powered off, and the virtual simulation test platform pops up a jumping red exclamation mark on a virtual monitoring interface and sends an early warning signal.
The real-time interaction refers to outputting data on a three-machine collaborative operation test platform of a virtual fully mechanized mining face in real time, and transmitting the data to a control module on the three-machine collaborative operation test platform of the fully mechanized mining face under laboratory conditions, so that virtual control and real control are realized; the physical test bed collects data in real time through the sensor, converts the data into signals which can be identified by a computer, analyzes the information through a computer technology, feeds back the analysis result to the virtual test bed, and adjusts the motion parameters and motions of the virtual entity according to the obtained feedback information so as to realize 'real-time virtual adjustment'.
The underground common engineering problems often occur in the propulsion process, mainly comprise two major aspects of common engineering problems in the cooperative propulsion of the fully mechanized mining face and common engineering problems in the coupling propulsion of the fully mechanized mining face and the coal seam, and the included engineering problems have important roles in the health, safety and efficient mining of the fully mechanized mining face.
Common engineering problems in the three-machine collaborative propulsion of the fully mechanized mining face comprise mutual perception among hydraulic supports, attitude prediction of a hydraulic support group, attitude abnormality detection of the hydraulic support group, independent following of the hydraulic support, and analysis of correlation between the hydraulic support supporting attitude and a cutting track; accurate pushing of the hydraulic support, S-shaped bending of the scraper conveyor and straightening of the fully mechanized mining face; the method comprises the steps of coal cutter positioning and attitude determination research, middle trough attitude analysis, inversion of a scraper conveyor track by a coal cutter track and prediction of the scraper conveyor track;
the coupling aspect of fully-mechanized mining 'three machines' and the coal seam comprises the whole pose analysis of coal machine equipment, automatic height adjustment of the coal machine, cutting track prediction of the coal machine, influence of coal seam fluctuation on straightness, hydraulic support supporting pose monitoring, health monitoring of coal machine equipment groups and mining environment safety.
FIG. 4 is a construction process of a model of coal seam+coal machine equipment in a three-machine cooperative operation test platform of a physical fully mechanized coal mining face, wherein the model of the coal machine equipment is selected firstly, the scaling of the model is determined according to a selected number drawing, the design of a model machine of the coal machine equipment is carried out, the plates of the model machine are spliced and built, the construction of the model machine of the coal machine equipment is completed, and the construction results are shown in FIG. 5, FIG. 6 and FIG. 7; and setting a position reference of the point cloud according to the obtained coal seam point cloud set, further obtaining the height of each point relative to the reference, and cutting and polishing the coal seam plate to obtain the coal seam model of the test platform.
FIG. 8 shows the components and related functions of an electronic control monitoring system, wherein an Arduion main board is used as a lower computer, a built information sensing system is matched for completing the monitoring function, a PC is used as an upper computer for data display analysis and laboratory control, and the system monitoring function is realized; labVIEW graphical development software is used as an upper computer, UI design is carried out, a good man-machine interface is provided, and control and detection analysis are convenient.
FIG. 9 is a schematic diagram of an information sensing system. The information sensing system is a system for detecting the whole pose of equipment and monitoring the surrounding environment on a prototype platform by utilizing related sensors, and is mainly divided into three types, namely a hydraulic support information sensing system, a coal cutter information sensing system and a coal mining space information sensing system. The hydraulic support information sensing system specifically refers to that inclination sensors are respectively arranged on a top beam, a shield beam and an upright post of the hydraulic support to measure the respective inclination angles, an ultrasonic sensor and a nine-axis attitude sensor are arranged on a base to measure the supporting height of the hydraulic support and the attitude of the base respectively, an infrared correlation sensor is arranged on a pushing mechanism to measure pushing quantity, and a resistance type film pressure sensor is arranged on the top beam to measure the supporting pressure of the hydraulic support; the coal cutter information sensing system is characterized in that two rocker arms of the coal cutter are respectively provided with a rocker arm inclination angle sensor for measuring the rocker arm inclination angle, and a shaft encoder and a strapdown inertial navigation system are arranged on a machine body for combined positioning; the coal mining space information sensing system is characterized in that an Arduion main board integrating dust, temperature and gas monitoring sensors is arranged at a top plate of an air return roadway close to a working surface.
Fig. 10 is a schematic diagram of a test platform service system, which is a service system integrating three functions of collecting operation data of fully-mechanized mining, monitoring various environment parameters simulated by a test platform and visualizing the whole operation process, and is used for completing data output and input, algorithm integration, virtual modeling, track prediction and interface operation.
FIG. 11 is a virtual-real interaction workflow diagram, which outputs data on a virtual fully mechanized mining face three-machine cooperative operation test platform in real time, and transmits the data to a control module on the fully mechanized mining face three-machine cooperative operation test platform under laboratory conditions, so as to realize virtual control and real state; the physical test bed collects data in real time through the sensor, converts the data into signals which can be identified by a computer, analyzes the information through a computer technology, feeds back the analysis result to the virtual test bed, and adjusts the motion parameters and motions of the virtual entity according to the obtained feedback information so as to realize 'real-time virtual adjustment'.
FIG. 12 illustrates the common engineering difficulties under the well that can be studied by the test bed, including five aspects of cooperation among hydraulic supports, cooperation of the hydraulic supports and a coal mining machine, cooperation of the hydraulic supports and a scraper conveyor, cooperation of the coal mining machine and the scraper conveyor, and coupling propulsion of a fully mechanized mining face 'three machines' and a coal seam, wherein the cooperation among the hydraulic supports comprises mutual sensing among the hydraulic supports, attitude prediction of a hydraulic support group and attitude abnormality detection of the hydraulic support group; the hydraulic support and the coal mining machine cooperatively comprise the independent following of the hydraulic support, and the analysis of the correlation between the support posture of the hydraulic support and the cutting track; the hydraulic support and the scraper conveyor cooperatively comprise accurate pushing of the hydraulic support, S-shaped bending of the scraper conveyor and straightening of a fully mechanized mining working face; the cooperation of the coal mining machine and the scraper conveyor comprises joint positioning and attitude determination research, middle trough attitude analysis, inversion of the scraper conveyor track by the coal mining machine track and prediction of the scraper conveyor track; the coupling aspect of the fully-mechanized mining three-machine and the coal seam comprises the problems of full pose analysis of coal machine equipment, automatic height adjustment of the coal machine, cutting track prediction of the coal machine, influence of coal seam fluctuation on straightness, hydraulic support supporting pose monitoring, health monitoring of coal machine equipment groups and mining environment safety.
Performing single-machine test on the coal mining machine, respectively performing single-machine test on a virtual fully-mechanized mining face three-machine cooperative operation test platform and a physical fully-mechanized mining face three-machine cooperative operation test platform, and performing height adjustment, travelling speed regulation and reversing control on the coal mining machine, wherein the single-machine test can perfectly realize the controlled action through the test, the sensor data acquisition condition is good, and the acquired coal mining machine track is shown in figure 13; and starting the hydraulic support, controlling the hydraulic support to lift and lower the support, verifying the pushing and moving functions, detecting the track of the scraper conveyor and the positioning data of the position of the hydraulic support, wherein the track of the scraper conveyor is shown in fig. 14.
Claims (10)
1. The collaborative operation test platform capable of supporting basic research of the digital twin fully mechanized coal mining face is characterized by comprising a multifunctional digital test platform of the fully mechanized coal mining face, a visual virtual test simulation system of the collaborative operation of the fully mechanized coal mining face, digital twin data and an intelligent service system of the test platform of the fully mechanized coal mining face;
the multifunctional digital test platform for the fully mechanized coal mining face is a test platform for the fully mechanized coal mining face, which is built on the basis of the existing coal seam detection information by scaling in an equal proportion according to the selected matching relation of the three machines of the actual fully mechanized coal mining face, and comprises a coal mining machine model, a scraper conveyor model, a hydraulic support group model, a coal seam model, a routing inspection robot model and an information sensing system, wherein the three machines of the fully mechanized coal mining face are built in a physical entity space and accord with the real working condition;
the 'three-machine' collaborative operation visual virtual test simulation system of the fully mechanized coal mining face comprises a virtual simulation description model and a simulation system intelligent model;
the virtual simulation description model is a three-dimensional virtual model imported on a physical test platform based on a Unity3D virtual simulation engine, and after the virtual model is rendered, the virtual model is set from the shape, the material, the quality, the friction factors, the interaction forces between equipment and the virtual coal layers, so that the virtualization of the fully mechanized coal mining face three-machine test platform is completed;
the simulation system intelligent model is used for carrying out algorithm analysis on the acquired data through real-time data and original historical data acquired by the information sensing system, predicting the pose of a hydraulic support group, the cutting track of a coal mining machine, the pose of a scraper conveyor, the service life and the health state of equipment, and realizing intelligent fault diagnosis of the equipment;
the twin data comprises all simulation bottom data generated when a virtual digital twin fully mechanized mining face three-machine collaborative simulation system operates, full-period operation data generated when a fully mechanized mining face three-machine multifunctional digital test platform operates, and intermediate data generated in the process of realizing interaction and scene operation;
the intelligent service system of the fully mechanized coal mining face three-machine test platform comprises a management service subsystem based on knowledge driving and an intelligent service subsystem based on data driving, and is used for completing data output and input, algorithm integration, virtual modeling, track prediction, interface operation and fault identification alarm work.
2. The collaborative operation testing platform for supporting basic research on a digital twin fully mechanized mining face according to claim 1, wherein: in the multifunctional digital test platform of the fully mechanized coal face, the coal bed model is a fully mechanized coal face coal bed three-dimensional model constructed by utilizing limited survey data, and based on a deep learning algorithm, the coal bed three-dimensional model is optimized and predicted by utilizing monitoring data in the cutting process of a coal mining machine to obtain construction information of the coal bed, and a coal bed model constructed by polishing by using a foam material is obtained.
3. The collaborative operation testing platform for supporting a digital twin fully mechanized mining face foundation study of claim 1 or 2, wherein: in a multifunctional digital test platform of a fully mechanized mining face, the information sensing system is a system for detecting the full pose of equipment and monitoring the surrounding environment on a prototype platform by utilizing related sensors and comprises a hydraulic support information sensing system, a coal mining machine information sensing system and a coal mining space information sensing system.
4. The collaborative operation testing platform for supporting foundation research of a digital twin fully mechanized mining face according to claim 3, wherein: in a multifunctional digital test platform of a fully mechanized mining face, the inspection robot model is used for monitoring the running state of equipment, the environmental state of a working space and whether foreign matters exist on line when the fully mechanized mining face runs or stops; abnormal sounds during the running of the equipment can be identified, fault identification and analysis can be carried out, and running data can be obtained; the multi-parameter sensor is arranged, so that the parameters of flammable and explosive gas and temperature in the inspection track can be detected, and the data can be automatically acquired and analyzed; when fault information is obtained, warning can be timely carried out when faults are found, and emergency braking instructions can be sent to the system when serious faults are encountered.
5. The collaborative operation testing platform for supporting a digital twin fully mechanized mining face foundation study of claim 1 or 4, wherein: in the virtual simulation description model, the virtual coal bed is a fully-mechanized coal face coal bed three-dimensional model constructed by utilizing limited survey data, the coal bed three-dimensional model is optimized and predicted by utilizing monitoring data in the cutting process of a coal mining machine based on a deep learning algorithm to obtain construction information of the coal bed, a dynamic coal bed model capable of self-optimizing is built in Unity3D based on real-time inversion geological data information, the dynamic coal bed model is integrated in the virtual fully-mechanized coal face, and a virtual coal bed model updated in real time along with mining is built.
6. The collaborative operation testing platform for supporting basic research on a digital twin fully mechanized mining face according to claim 5, wherein: the simulation bottom layer data comprise motion parameters, pushing and sliding forces, frame moving forces, supporting heights of the hydraulic support, cutting heights of the coal mining machine, cutting tracks of the roller, rotating speed of the roller and running resistance of the scraper conveyor on the virtual coal seam bottom plate, wherein the motion parameters, pushing and sliding forces, frame moving forces, supporting heights of the hydraulic support are generated in running.
7. The collaborative operation testing platform for supporting a digital twin fully mechanized mining face foundation study of claim 1 or 6, wherein: the full-period operation data are three-machine whole-course operation data acquired by the test bed measurement and control system, and comprise the relevant information of the height of a hydraulic support, the track of a scraper conveyor, the posture of a coal mining machine and the real-time posture of the three machines in the mining process, scanned equipment point cloud information, and equipment operation parameters and environment parameters monitored by a patrol robot.
8. The collaborative operation testing platform for supporting basic research on a digital twin fully mechanized mining face according to claim 7, wherein: the intermediate data comprise data generated when the data are fused by an intermediate equation or a related algorithm in the analysis process and when the information measured by the sensor are analyzed and fused.
9. The collaborative operation testing platform for supporting a digital twin fully mechanized mining face foundation study of claim 1 or 8, wherein: the knowledge-driven-based management type service subsystem is a service system which is established for direct demand of operators and comprises the steps of demand proposing, tutorial guiding, scene selecting and function selecting; the requirement is that an operator inputs own requirement to inquire related functions of the system, and the requirement is a precondition of the operation of the other three parts; the guiding course is a process that an operator is familiar with the operation flow of the simulation system, and is the basis of the operation of the whole subsystem; the scene selection refers to that an operator selects an operation scene according to the requirement, and is a key of service-oriented operation of the whole subsystem; the function selection is to directly convert the operator demand into a function for simulation system use, and is the final function of the whole service subsystem.
10. The collaborative operation testing platform for supporting basic research on a digital twin fully mechanized mining face according to claim 9, wherein: the intelligent service subsystem based on data driving realizes on-line planning, intelligent prediction, fault investigation, exploitation previewing and scene visualization of fully mechanized mining face exploitation on the basis of establishing virtual-real interaction channels; the comprehensive mining working face mining on-line planning is to carry out on-line planning on the operation and mining process of the coal machine equipment according to the requirements of operators under the drive of historical data; the intelligent prediction refers to predicting the state of future coal machine equipment and the next mining process based on historical mining data in the mining process; the fault checking is to classify faults based on equipment history operation data when the exploitation is not started or is in progress, and identify the faults through an intelligent algorithm by the data output by a previewing result in a simulation system; the exploitation and prediction are to build a coal bed model and a coal machine equipment model according to the detected coal bed information and the coal machine equipment matching information, and deduce the whole exploitation process in advance; scene visualization is the visualization that enables the production process and production data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111314262.1A CN114004103B (en) | 2021-11-08 | 2021-11-08 | Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111314262.1A CN114004103B (en) | 2021-11-08 | 2021-11-08 | Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114004103A CN114004103A (en) | 2022-02-01 |
| CN114004103B true CN114004103B (en) | 2024-03-29 |
Family
ID=79928236
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111314262.1A Active CN114004103B (en) | 2021-11-08 | 2021-11-08 | Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114004103B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114708393B (en) * | 2022-03-28 | 2023-04-25 | 中国矿业大学 | Mine stress field twinning modeling assimilation system and method in full-time air mining process |
| CN114775724A (en) * | 2022-06-17 | 2022-07-22 | 山东捷瑞数字科技股份有限公司 | Well control emergency rescue remote cooperative operation method based on digital twins |
| CN115345034B (en) * | 2022-10-18 | 2023-02-03 | 中煤科工开采研究院有限公司 | Management method and system of digital twin body of hydraulic support group |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020229841A1 (en) * | 2019-05-15 | 2020-11-19 | Roborace Limited | A metaverse data fusion system |
| CN113128109A (en) * | 2021-04-08 | 2021-07-16 | 太原理工大学 | Test and evaluation method for intelligent fully-mechanized mining robot production system |
-
2021
- 2021-11-08 CN CN202111314262.1A patent/CN114004103B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020229841A1 (en) * | 2019-05-15 | 2020-11-19 | Roborace Limited | A metaverse data fusion system |
| CN113128109A (en) * | 2021-04-08 | 2021-07-16 | 太原理工大学 | Test and evaluation method for intelligent fully-mechanized mining robot production system |
Non-Patent Citations (1)
| Title |
|---|
| 工业互联网驱动的透明综采工作面运行系统及关键技术;谢嘉成;王学文;郝尚清;李娟莉;葛星;史恒波;;计算机集成制造系统;20191215(12);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114004103A (en) | 2022-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114004103B (en) | Collaborative operation test platform capable of supporting foundation research of digital twin fully mechanized mining face | |
| WO2021184614A1 (en) | Intelligent decision control method and system for fully-mechanized mining equipment used for working surface under complex condition | |
| CN112883559B (en) | Planning cutting method and device based on big data system, storage medium and electronic device | |
| Li et al. | Application and outlook of information and intelligence technology for safe and efficient TBM construction | |
| CN115454057B (en) | Digital twin intelligent management and control modeling system and method for coal mine machine crowd | |
| CN113128109B (en) | Test and evaluation method for intelligent fully-mechanized mining robot production system | |
| CN111177942A (en) | Digital twin intelligent monitoring system for unmanned fully-mechanized excavation working face of mine | |
| CN109214076B (en) | Virtual planning method for supporting geographical environment and equipment of fully mechanized coal mining face | |
| CN111622735B (en) | Environment-friendly drilling machine automatic control and monitoring system and method based on Internet of things | |
| CN111159793A (en) | Digital twin five-dimensional model based 3D printer modeling method and model system | |
| Qiao et al. | Advancing measurement science to assess monitoring, diagnostics, and prognostics for manufacturing robotics | |
| CN112666845A (en) | Simulation test system and simulation test method for fully mechanized coal mining face | |
| CN106407624A (en) | Memory cutting method of virtual coal cutter | |
| CN101259615A (en) | Detection of condition changes in an industrial robot system | |
| CN111140231B (en) | Coal seam roof and floor path virtual planning method oriented to space-time kinematics of fully-mechanized mining equipment | |
| CN111445170A (en) | Autonomous operation system and method for unmanned rolling machine group | |
| Xie et al. | A virtual test and evaluation method for fully mechanized mining production system with different smart levels | |
| CN115728776A (en) | Underground coal mine mobile measurement robot, safety evaluation method and system | |
| CN113124797B (en) | Hydraulic support group pose simulation system based on adjustable bottom plate | |
| CN117314264A (en) | Web-combined function modularized building structure operation and maintenance supervision system and method | |
| CN117094056A (en) | Urban underground loop digital twin intelligent construction method and system | |
| CN113051756B (en) | Virtual-real fusion coal mining machine memory cutting test system and method | |
| Khan et al. | Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines | |
| CN115329922A (en) | Coal mine underground multifunctional four-legged robot digital twin system and operation method thereof | |
| Li et al. | Digital twin for intelligent tunnel construction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |