CN112392485A - Transparent digital twin self-adaptive mining system and method for fully mechanized coal mining face - Google Patents
Transparent digital twin self-adaptive mining system and method for fully mechanized coal mining face Download PDFInfo
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Abstract
The invention provides a transparent digital twin self-adaptive mining system and method for a fully mechanized coal mining face. The device end is provided with a coal face top plate supporting device, a coal mining device, a transfer device, a power supply device and an auxiliary monitoring device; the server side has the steps of geodetic model correction, equipment data acquisition, equipment model iteration, equipment logic control and geodetic coordinate positioning; the digital twin model database comprises a roadway model, a stratum model, an equipment model and a mining environment. The method comprises the steps of constructing a digital twin three-dimensional virtual mining scene based on a unified geodetic coordinate system, and acquiring equipment terminal information in real time by using an intelligent sensing technology; by driving the twin model in the virtual scene through information, transparent sensing of the mining environment of the underground coal mining operation site, intelligent monitoring of equipment, visual self-adaptive mining, failure prediction and the like are achieved, coal mining operation personnel are reduced, and the intelligent mining level of a coal mine is improved.
Description
Technical Field
The invention relates to the technical field of intelligent mining of coal mines, in particular to a transparent digital twin self-adaptive mining system and method for a fully mechanized coal mining face of a coal mine.
Background
The digital twinning technology in the coal mine industry is still in the initial stage of innovation, and at present, remote visual monitoring on large-scale equipment is mainly researched, information of equipment, mining geological conditions and mining environments is not fused, a transparent digital twinning mining scene based on a unified geodetic coordinate system is formed, and a visual remote control environment and a visual remote control platform are provided for really realizing unmanned or unmanned mining.
In the related technology, the intelligent mining system of the coal mine fully-mechanized mining working face is mainly constructed by means of industrial configuration software, presents the running information of the working face equipment and the mining process flow in a mode of schematic graphics and animation, has the characteristic of simulating the action of the equipment, is lack of spatial position relation expression based on a unified geodetic coordinate system, and cannot realize the coupling and three-dimensional dynamic expression of the equipment and the ground measurement information; meanwhile, the complete mining environment, geological conditions, equipment running state and spatial position, fault diagnosis and equipment prediction maintenance of the fully mechanized mining face are difficult to express through the image splicing mode of the high-definition camera of the working face, and three-dimensional space-time relation live-action contrast support is lacked; in addition, the coal mine virtual reality technology is mainly applied to interactive training examination and equipment information monitoring before the posts of the workers, does not have the function of a geographic information system, and cannot realize transparent expression of the geological conditions of the working face.
In the intelligent construction process of a coal mine, a set of self-adaptive mining system is constructed by utilizing a digital twin technology, so that transparent self-adaptive mining of a fully mechanized coal face is realized, the purposes of reducing personnel, improving efficiency and protecting safety in dangerous operation areas of the coal mine are achieved, and the development trend and the inevitable requirements of the intelligent mining construction of the coal mine are met.
Disclosure of Invention
In view of the above, embodiments of the present invention have been developed to provide a coal mine fully mechanized coal face transparentization digital twin adaptive mining system that overcomes or at least partially solves the above-mentioned problems.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a transparent digital twin adaptive mining system for a fully mechanized coal mining face of a coal mine, including: the device side, the server side and the digital twin model database; the equipment side, the server side and the digital twin model database are connected and communicated through a network;
the equipment end comprises a top plate supporting device, a coal mining device, a transferring device, a power supply device and an auxiliary monitoring device.
Further, the device end provides a communication interface and a protocol to provide key part driving data of a corresponding model in a virtual scene; roof strutting arrangement includes working face hydraulic support, tunnel leading support at least, the data that roof strutting arrangement provided includes at least: upright column pressure, push-pull cylinder stroke, support height, support action, support inclination angle and pitch angle, controller state, alarm and fault information.
Further, the coal mining device provides data at least comprising: the system comprises an oil pump, a traction motor, a cutting motor, a coal mining device, an oil pump, a traction motor, a cutting motor, an encoder, an azimuth angle, a pitch angle, a roll angle, an operation state, an alarm and fault information, wherein the oil pump is used for supplying oil to the coal mining device.
Further, the shipping device includes at least: a face belt conveyor, a front conveyor, a rear conveyor, a transfer conveyor and a crusher, the shipping apparatus providing data including at least: starting state, running state, stopping state, current and voltage, alarm and fault information of each motor.
Further, the power supply device at least comprises a working surface water supply device, an emulsion supply device, a power supply device and an air supply device, and the data provided by the power supply device at least comprises: pump station pressure and power, pipeline pressure, supply current, supply voltage, supply power, ventilation pressure, wind speed, alarm and fault information.
Further, the auxiliary monitoring device at least comprises: safety monitoring device, personnel location and protector, rock burst monitoring devices, communication scheduling device, video monitoring device, supplementary monitoring devices provides exploitation environmental data and personnel monitoring data.
The server side comprises: the device comprises a geodetic model correction service module, a device data acquisition service module, a device model iteration service module, a device logic control service module and a geodetic coordinate positioning service module.
Furthermore, the ground measurement model correction service module is used for dynamically detecting and acquiring latest roadway and coal rock layer boundary data and coal bed data disclosed by a ground measurement project so as to update the ground measurement model data.
Furthermore, the device data acquisition service module is used for acquiring device information provided by the device end in real time, issuing an operation result to other modules for subscription after logical operation, and storing the operation result in the digital twin model database.
Further, the device model iteration service module is used for calculating the spatial position relationship between the devices based on the unified geodetic coordinates and the spatial position relationship between the devices and the ground measurement model in a twin scene in real time, subscribing the device information and the ground measurement model data, dynamically updating the spatial position of the device model, and dynamically optimizing the mining scene by using the boundary and collision point conditions of the device model.
Furthermore, the equipment model is a complete machine model formed by assembling three-dimensional mechanical part drawings of all parts of the mining equipment in equal proportion; the device logic control service module subscribes the sensor data set of the mining device and then distributes the data set to the corresponding device model, the device model receives the data set and splits the data, the split subdata set is distributed to each part of the device model, and the parts perform corresponding action response according to the data values on the basis of following the mechanical constraint relationship.
Further, the device logic control service module is used for realizing real-time mutual feedback linkage of the digital twin body and the device entity in a VR or AR human-computer interaction mode.
Furthermore, the geodetic coordinate positioning service module is used for acquiring equipment information in real time, dynamically calculating the position coordinates of the equipment end based on a unified geodetic coordinate system, and providing the calculated result to the equipment model iteration service module.
The digital twin model database is used for storing a roadway model, a stratum model, an equipment model and a mining environment.
Furthermore, the roadway model and the stratum model are high-precision three-dimensional dynamic measurement model data of the coal face, and new roadway and stratum data can be obtained in the mining process and dynamically updated.
Further, the equipment model is equipment operation information, alarm and fault information generated in the coal mining process, and a coal mining cutting line, a straight line adjusting line, a pitching mining planning path, a mining height and bed bottom adjusting amount and an equipment operation historical track obtained through calculation.
Further, the mining environment refers to ground measurement, ventilation, electromechanics, transportation, processes, personnel and environment elements which are related to a mining scene, and a complete mining area twin data system is formed.
Furthermore, the equipment side adopts a network or bus communication mode, and the communication protocol adopts Modbus TCP/RTU, OPC UA and Socket TCP/UDP.
Furthermore, the geodetic model correction module of the server is used for generating cutting lines (mining height and bedding adjustment amount of the left and right rollers) of the coal mining device based on geodetic coordinates, and aligning lines of a future N-blade mining planning path and a transfer device, wherein N is a positive integer.
Furthermore, the device data acquisition module of the server has the functions of acquiring, publishing and storing the data in a database, the information is published in a message queue mode, and the protocol adopts MQTT, AMQP and Web Socket.
Furthermore, the equipment model iteration module of the server side has the functions of multi-element data fusion, spatial position operation, boundary and collision point detection, finite element analysis, convergence evaluation and mining scene dynamic optimization, and realizes the mutual feedback synchronization of a real mining scene and a twin scene.
Further, the digital twin model database is used for storing all production preparation data and process data, and is a SQL or NoSQL type database.
In a second aspect, an embodiment of the present invention provides a transparent digital twin adaptive mining method for a fully mechanized coal mining face of a coal mine, where the method includes the following steps:
step 1, establishing a ground survey model: an initial three-dimensional geodetic model is constructed through data such as drilling, geophysical prospecting and roadway sketch, and the three-dimensional geodetic model is dynamically corrected by utilizing data such as a coal rock layer identification analysis result or stratum boundary measurement and three-dimensional earthquake dynamic interpretation in the coal mining process, so that the coal bed space form of a mining area is transparent;
step 2, establishing an equipment model: utilizing three-dimensional modeling software, combining an equipment design graph to construct a three-dimensional model of the equipment in a mining area, setting geometric parameters, action parameters, boundary constraint parameters, collision point parameters and conditions aiming at key action parts of the equipment, and associating the set part parameters with the data acquisition information of the equipment to form a data association model;
step 3, establishing a mining environment: collecting data of ground survey, ventilation, electromechanics, transportation, process, personnel and environment of a coal mining area of the fully mechanized coal mining face, and building a mining environment through data modeling and programming;
step 4, establishing a transparent coal face: the method comprises the steps that a ground measurement model, an equipment model and a mining environment are fused to form a digital twin mining scene based on a unified geodetic coordinate system, the scene realizes dynamic updating of the scene and data through a ground measurement model correction service, an equipment model iteration service and a script programming method, and digital twin and virtual-real contrast of the mining environment are formed;
step 5, generating a self-adaptive mining template: dynamically generating a mining template with absolute coordinates by utilizing a geodetic model correction service, wherein the mining template comprises cutting lines of a coal mining top plate and a coal mining bottom plate with the absolute coordinates, a scraper alignment line and a planning path of pitching mining;
step 6, data acquisition and release: acquiring information provided by the equipment, issuing an operation result to a script programming module for subscription after logical operation, simultaneously storing the operation result and important information into the digital twin model database, and respectively acquiring real-time information and historical information by the script programming module in a subscription and query mode to drive a digital twin model of a transparent coal mining working face;
and 7, driving the mining scene by script programming: using a sensor data set subscribed by a script programming module, taking a constraint relation of mechanical and physical motion of the equipment model as a limiting factor, performing data iterative operation, feeding back an operation result to the equipment model parts in each iterative cycle, driving the parts to change the spatial pose, after multiple iterations, enabling the equipment model parts to act according to a set motion route, calculating the spatial pose and position of equipment in the action process to be iteratively contrasted with the data of the self-adaptive mining template in the step 5, calculating an adjustment quantity when the contrast result is inconsistent, and controlling the physical equipment to adjust the pose by the equipment logic control service and to be consistent with the mining template;
step 8, man-machine interaction control: the control information of the human-computer interaction module is coded and input into the equipment logic control service module in a VR or AR human-computer interaction mode, the equipment logic control service module receives the control information and then sends a control instruction to the equipment terminal after logic operation by combining with the subscribed equipment running state, and meanwhile, the execution result is fed back to the human-computer interaction module;
step 9, simulation mining: before the self-adaptive mining mode is actually started, carrying out consistency and reliability verification of complete one-time operation cycle and simulated mining by utilizing equipment space position information, running information, mining environment information and coal cutting lines, alignment lines, pitching mining planning paths, mining height and bedding adjustment data generated by a ground measurement model in a digital twin mining scene, and entering the next step after the verification is passed;
step 10, self-adaptive actual mining: after the simulation mining verification is passed, the digital twin mining scene sends mining information to an equipment logic control service module through a self-adaptive mining mode module, and the information is converted into a control instruction to guide underground mining equipment to automatically operate; and synchronously mirroring feedback of the running information of the equipment and driving a model in a virtual scene to realize data twinning.
Furthermore, the human-computer interaction control has a two-dimensional and three-dimensional visual angle switching function, twin objects (points, lines, surfaces and bodies) in a mining scene can be selected by a mouse or fingers under the two-dimensional and three-dimensional visual angles to perform attribute query, control operation, running state query and evolution process backtracking, and simulation and mutual feedback linkage can be realized through VR or AR under the three-dimensional visual angle.
Furthermore, in the self-adaptive mining process, a convolutional neural network deep learning algorithm is adopted to carry out data mining on the equipment side data, the geodetic model data and the historical data, self learning and self optimization of the digital twin are realized through training and optimizing a simulation model, and the accuracy and the reliability of the self-adaptive mining of the system are improved.
The invention has the beneficial effects that:
(1) the invention can provide a set of coal mine fully mechanized working face transparent self-adaptive mining system, the system carries out equipment and ground measurement model modeling, perception analysis, simulation, iteration optimization and decision control based on a visual three-dimensional mining scene, and the digital twin technology is utilized to realize remote visual intelligent control and self-adaptive analysis decision of the fully mechanized working face.
(2) The invention can provide a construction method of a coal mine fully-mechanized working face transparent mining system, and solves the technical problems that the video image technology is difficult to reflect the real mining environment, geological conditions, coal rock stratum structures, equipment operation states and spatial positions, fault diagnosis, equipment prediction and maintenance of the fully-mechanized working face, and three-dimensional space-time relationship contrast support is lacked by constructing a multi-information fusion transparent fully-mechanized working face.
(3) The invention provides a simulation mining function by applying a digital twin technology, establishes a digital twin model of the details of the mining process of the fully mechanized coal mining face, verifies the mining process in a virtual environment through a simulation mining mode, corrects the mining process in the model in time after finding problems, and improves the intelligent mining accuracy and reliability of the fully mechanized coal mining face of the coal mine through a corrected self-adaptive mining mode.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive labor.
Fig. 1 is a block diagram of a transparent adaptive mining system for a fully mechanized coal mining face according to an embodiment of the present invention;
fig. 2 is a flowchart of a coal mine fully mechanized coal mining face transparentization adaptive mining system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The digital twinning technology in the coal mine industry is still in the initial stage of innovation, and at present, remote visual monitoring on large-scale equipment is mainly researched, information of equipment, mining geological conditions and mining environments is not fused, a transparent digital twinning mining scene based on a unified geodetic coordinate system is formed, and a visual remote control environment and a visual remote control platform are provided for really realizing unmanned or unmanned mining.
In the related technology, the intelligent mining system of the coal mine fully-mechanized mining working face is mainly constructed by means of industrial configuration software, presents the running information of the working face equipment and the mining process flow in a mode of schematic graphics and animation, has the characteristic of simulating the action of the equipment, is lack of spatial position relation expression based on a unified geodetic coordinate system, and cannot realize the coupling and three-dimensional dynamic expression of the equipment and the ground measurement information; meanwhile, the complete mining environment, geological conditions, equipment running state and spatial position, fault diagnosis and equipment prediction maintenance of the fully mechanized mining face are difficult to express through the image splicing mode of the high-definition camera of the working face, and three-dimensional space-time relation live-action contrast support is lacked; in addition, the coal mine virtual reality technology is mainly applied to interactive training examination and equipment information monitoring before the posts of the workers, does not have the function of a geographic information system, and cannot realize transparent expression of the geological conditions of the working face.
In the intelligent construction process of a coal mine, a set of self-adaptive mining system is constructed by utilizing a digital twin technology, so that transparent self-adaptive mining of a fully mechanized coal face is realized, the purposes of reducing personnel, improving efficiency and protecting safety in dangerous operation areas of the coal mine are achieved, and the development trend and the inevitable requirements of the intelligent mining construction of the coal mine are met.
Therefore, the invention explores a new software system, based on a visual three-dimensional mining scene, the modeling of equipment and a ground test model, perception analysis, simulation, iterative optimization and decision control are carried out, and by constructing a transparent working face, the remote visual intelligent control and self-adaptive analysis decision of an on-the-spot comprehensive mining working face are realized by utilizing a digital twin technology, so that the problems in the prior art are solved.
Digital Twin (Digital Twin) is a simulation process integrating multidisciplinary, multi-physical quantity, multi-scale and multi-probability by fully utilizing data such as physical models, sensor updating, operation history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected. The digital twinning technology is applied to the field of intelligent mining of coal mines and has the characteristics of design and construction of a transparent mining scene, simulation of a mining process, fusion and linkage of spatial positions, accurate execution of equipment and feedback.
And (3) establishing a digital twinning model of the details of the mining process of the fully mechanized mining face by using a digital twinning technology, verifying the mining process in a virtual environment, and timely correcting the model after a problem is found. The high-efficiency method superior to the traditional flow verification can predict and improve the performance of the method in the mining design stage, can master accurate information and predict the mining process in the initial stage of the mining flow, ensures that all details are accurate and correct, and improves the accuracy of intelligent mining.
Referring to fig. 1 and 2, fig. 1 is a block diagram of a coal mine fully-mechanized coal mining face transparentization adaptive mining system according to an embodiment of the present invention, and fig. 2 is a flowchart of a coal mine fully-mechanized coal mining face transparentization adaptive mining system according to an embodiment of the present invention, and as shown in fig. 1 and 2, the coal mine fully-mechanized coal mining face transparentization digital twin adaptive mining system includes: the device side, the server side and the digital twin model database; the equipment side, the server side and the digital twin model database are connected and communicated through a network;
the equipment end comprises a top plate supporting device, a coal mining device, a transferring device, a power supply device and an auxiliary monitoring device.
Further, the device end provides a communication interface and a protocol to provide key part driving data of a corresponding model in a virtual scene; roof strutting arrangement includes working face hydraulic support, tunnel leading support at least, the data that roof strutting arrangement provided includes at least: upright column pressure, push-pull cylinder stroke, support height, support action, support inclination angle and pitch angle, controller state, alarm and fault information.
Further, the coal mining device provides data at least comprising: the system comprises an oil pump, a traction motor, a cutting motor, a coal mining device, an oil pump, a traction motor, a cutting motor, an encoder, an azimuth angle, a pitch angle, a roll angle, an operation state, an alarm and fault information, wherein the oil pump is used for supplying oil to the coal mining device.
Further, the shipping device includes at least: a face belt conveyor, a front conveyor, a rear conveyor, a transfer conveyor and a crusher, the shipping apparatus providing data including at least: starting state, running state, stopping state, current and voltage, alarm and fault information of each motor.
Further, the power supply device at least comprises a working surface water supply device, an emulsion supply device, a power supply device and an air supply device, and the data provided by the power supply device at least comprises: pump station pressure and power, pipeline pressure, supply current, supply voltage, supply power, ventilation pressure, wind speed, alarm and fault information.
Further, the auxiliary monitoring device at least comprises: safety monitoring device, personnel location and protector, rock burst monitoring devices, communication scheduling device, video monitoring device, supplementary monitoring devices provides exploitation environmental data and personnel monitoring data.
The server side comprises: the device comprises a geodetic model correction service module, a device data acquisition service module, a device model iteration service module, a device logic control service module and a geodetic coordinate positioning service module.
Furthermore, the geodetic model correction service module is used for dynamically detecting and acquiring latest roadway and coal rock layer boundary data and coal bed data disclosed by a geodetic engineering so as to update the geodetic model data, wherein the geodetic model is a simulation map of a coal mine fully mechanized mining face.
Furthermore, the device data acquisition service module is used for acquiring device information provided by the device end in real time, issuing an operation result to other modules for subscription after logical operation, and storing the operation result in the digital twin model database.
Further, the device model iteration service module is configured to calculate a spatial position relationship between each device and the ground measurement model in a twin scene in real time, subscribe to device information provided by the device side and the ground measurement model data, dynamically update a spatial position of each device model, and dynamically optimize an exploitation scene by using boundary and collision point conditions of the device model, where the device model is a model established based on each device at the device side, and each device corresponds to one device model.
Furthermore, the equipment model is a complete machine model formed by assembling three-dimensional mechanical part drawings of all parts of the mining equipment in equal proportion; the device logic control service module subscribes the sensor data set of the mining device and then distributes the data set to the corresponding device model, the device model receives the data set and splits the data, the split subdata set is distributed to each part of the device model, and the parts perform corresponding action response according to the data values on the basis of following the mechanical constraint relationship.
Further, the device logic control service module is used for realizing real-time mutual feedback linkage of the digital twin body and the device entity in a VR or AR human-computer interaction mode.
Furthermore, the geodetic coordinate positioning service module is used for acquiring equipment information in real time, dynamically calculating the position coordinates of the equipment end based on a unified geodetic coordinate system, and providing the calculated result to the equipment model iteration service module.
The digital twin model database is used for storing a roadway model, a stratum model, an equipment model and a mining environment.
Furthermore, the roadway model and the stratum model are high-precision three-dimensional dynamic measurement model data of the coal face, and new roadway and stratum data can be obtained in the mining process and dynamically updated.
Further, the equipment model is equipment operation information, alarm and fault information generated in the coal mining process, and a coal mining cutting line, a straight line adjusting line, a pitching mining planning path, a mining height and bed bottom adjusting amount and an equipment operation historical track obtained through calculation.
Further, the mining environment refers to ground measurement, ventilation, electromechanics, transportation, processes, personnel and environment elements which are related to a mining scene, and a complete mining area twin data system is formed.
Furthermore, the equipment side adopts a network or bus communication mode, and the communication protocol adopts Modbus TCP/RTU, OPC UA and Socket TCP/UDP.
Furthermore, the geodetic model correction module of the server is used for generating cutting lines (mining height and bedding adjustment amount of the left and right rollers) of the coal mining device based on geodetic coordinates, and aligning lines of a future N-blade mining planning path and a transfer device, wherein N is a positive integer.
Furthermore, the device data acquisition module of the server has the functions of acquiring, publishing and storing the data in a database, the information is published in a message queue mode, and the protocol adopts MQTT, AMQP and Web Socket.
Furthermore, the equipment model iteration module of the server side has the functions of multi-element data fusion, spatial position operation, boundary and collision point detection, finite element analysis, convergence evaluation and mining scene dynamic optimization, and realizes the mutual feedback synchronization of a real mining scene and a twin scene.
Further, the digital twin model database is used for storing all production preparation data and process data, and is a SQL or NoSQL type database.
The method comprises the steps of constructing a digital twin three-dimensional virtual scene of a transparent coal face, and acquiring equipment end operation information in real time by using an intelligent sensing technology; and a twin model in the virtual scene is driven by information, so that transparent perception of the mining environment of an underground coal mining operation site, intelligent monitoring of equipment, simulated mining, fault prediction and the like are realized. The invention solves the problems of non-transparent mining environment of the coal face, large limitation of video images, difficult coupling of equipment and a ground survey model and the like, the system has two application modes of self-adaptive mining and simulated mining, can reduce personnel and operation time of a coal mining operation area, and improves the intelligent mining level of a coal mine.
Based on the coal mine fully mechanized mining face transparent digital twin self-adaptive mining system, the embodiment of the invention provides a coal mine fully mechanized mining face transparent digital twin self-adaptive mining method, which comprises the following steps:
step 1, establishing a ground survey model: an initial three-dimensional geodetic model is constructed through data such as drilling, geophysical prospecting and roadway sketch, and the three-dimensional geodetic model is dynamically corrected by utilizing data such as a coal rock layer identification analysis result or stratum boundary measurement and three-dimensional earthquake dynamic interpretation in the coal mining process, so that the coal bed space form of a mining area is transparent;
step 2, establishing an equipment model: utilizing three-dimensional modeling software, combining an equipment design graph to construct a three-dimensional model of the equipment in a mining area, setting geometric parameters, action parameters, boundary constraint parameters, collision point parameters and conditions aiming at key action parts of the equipment, and associating the set part parameters with the data acquisition information of the equipment to form a data association model;
step 3, establishing a mining environment: collecting data of ground survey, ventilation, electromechanics, transportation, process, personnel and environment of a coal mining area of the fully mechanized coal mining face, and building a mining environment through data modeling and programming;
step 4, establishing a transparent coal face: the method comprises the steps that a ground measurement model, an equipment model and a mining environment are fused to form a digital twin mining scene based on unified geodetic coordinates, the scene realizes dynamic updating of the scene and data through a ground measurement model correction service, an equipment model iteration service and a script programming method, and digital twin and virtual-real contrast of the mining environment are formed;
step 5, generating a self-adaptive mining template: dynamically generating a mining template with absolute coordinates by utilizing a geodetic model correction service, wherein the mining template comprises cutting lines of a coal mining top plate and a coal mining bottom plate with the absolute coordinates, a scraper alignment line and a planning path of pitching mining;
step 6, data acquisition and release: acquiring information provided by the equipment, issuing an operation result to a script programming module for subscription after logical operation, simultaneously storing the operation result and important information into the digital twin model database, and respectively acquiring real-time information and historical information by the script programming module in a subscription and query mode to drive a digital twin model of a transparent coal mining working face;
and 7, driving the mining scene by script programming: using a sensor data set subscribed by a script programming module, taking a constraint relation of mechanical and physical motion of the equipment model as a limiting factor, performing data iterative operation, feeding back an operation result to the equipment model parts in each iterative cycle, driving the parts to change the spatial pose, after multiple iterations, enabling the equipment model parts to act according to a set motion route, calculating the spatial pose and position of equipment in the action process to be iteratively contrasted with the data of the self-adaptive mining template in the step 5, calculating an adjustment quantity when the contrast result is inconsistent, and controlling the physical equipment to adjust the pose by the equipment logic control service and to be consistent with the mining template;
step 8, man-machine interaction control: the control information of the human-computer interaction module is coded and input into the equipment logic control service module in a VR or AR human-computer interaction mode, the equipment logic control service module receives the control information and then sends a control instruction to the equipment terminal after logic operation by combining with the subscribed equipment running state, and meanwhile, the execution result is fed back to the human-computer interaction module;
step 9, simulation mining: before the self-adaptive mining mode is actually started, carrying out consistency and reliability verification of complete one-time operation cycle and simulated mining by utilizing equipment space position information, running information, mining environment information and coal cutting lines, alignment lines, pitching mining planning paths, mining height and bedding adjustment data generated by a ground measurement model in a digital twin mining scene, and entering the next step after the verification is passed;
step 10, self-adaptive actual mining: after the simulation mining verification is passed, the digital twin mining scene sends mining information to an equipment logic control service module through a self-adaptive mining mode module, and the information is converted into a control instruction to guide underground mining equipment to automatically operate; and synchronously mirroring feedback of the running information of the equipment and driving a model in a virtual scene to realize data twinning.
Furthermore, the human-computer interaction control has a two-dimensional and three-dimensional visual angle switching function, twin objects (points, lines, surfaces and bodies) in a mining scene can be selected by a mouse or fingers under the two-dimensional and three-dimensional visual angles to perform attribute query, control operation, running state query and evolution process backtracking, and simulation and mutual feedback linkage can be realized through VR or AR under the three-dimensional visual angle.
Further, in the adaptive mining process, a convolutional neural network deep learning algorithm is adopted to perform data mining on the equipment side data, the geodetic model data and the historical data, self-learning and self-optimization of a digital twin body are realized by training and optimizing a simulation model, and the accuracy and reliability of adaptive mining of the system are improved, wherein the historical data can include: the operation and alarm information of the equipment end of the working face comprises working face historical propelling track information of absolute coordinates, working face equipment space position information in each coal cutting process, calculated and optimized ground measurement model historical cutting data, pitching mining data and straightening data.
The invention can provide a set of coal mine fully-mechanized working face transparent self-adaptive mining system, the system carries out equipment and ground measurement model modeling, perception analysis, simulation, iteration optimization and decision control based on a visual three-dimensional mining scene, and the remote visual intelligent control and analysis decision of the fully-mechanized working face are realized by utilizing a digital twin technology.
The invention can provide a construction method of a coal mine fully-mechanized working face transparent mining system, and solves the technical problems that the video image technology is difficult to reflect the real mining environment, geological conditions, coal rock stratum structures, equipment operation states and spatial positions, fault diagnosis, equipment prediction and maintenance of the fully-mechanized working face, and three-dimensional space-time relationship contrast support is lacked by constructing a multi-information fusion transparent fully-mechanized working face.
The invention provides a simulation mining function by applying a digital twin technology, establishes a digital twin model of the details of the mining process of the fully mechanized coal mining face, verifies the mining process in a virtual environment through a simulation mining mode, corrects the mining process in the model in time after finding problems, and improves the intelligent mining accuracy and reliability of the fully mechanized coal mining face of the coal mine through a corrected self-adaptive mining mode.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The transparent digital twin self-adaptive mining system and method for the fully mechanized coal mining face provided by the invention are introduced in detail, a specific example is applied in the system to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A coal mine fully mechanized coal mining face transparentization digital twin self-adaptive mining system is characterized by comprising: the device side, the server side and the digital twin model database; the equipment side, the server side and the digital twin model database are connected and communicated through a network;
the equipment end comprises a top plate supporting device, a coal mining device, a transfer device, a power supply device and an auxiliary monitoring device; the equipment end provides a communication interface and a protocol so as to provide key part driving data of a corresponding model in a virtual scene; roof strutting arrangement includes working face hydraulic support, tunnel leading support at least, the data that roof strutting arrangement provided includes at least: upright column pressure, push-pull cylinder stroke, support height, support action, support inclination angle and pitch angle, controller state, alarm and fault information; the data provided by the coal mining device at least comprises: the system comprises an oil pump, a traction motor, a cutting motor, a coal mining device, a controller and a controller, wherein the oil temperature and the oil pressure of the oil pump, the temperature, the current and the power of the traction motor and the cutting motor, and the speed, the height of a roller, absolute coordinates, the position of an encoder, an azimuth angle, a pitch angle, a roll angle, an operation state, an alarm and fault information of; the shipping device includes at least: a face belt conveyor, a front conveyor, a rear conveyor, a transfer conveyor and a crusher, the shipping apparatus providing data including at least: starting state, running state, stopping state, current and voltage, alarm and fault information of each motor; the power supply device at least comprises a working surface water supply device, an emulsion supply device, a power supply device and an air supply device, and the data provided by the power supply device at least comprises: pump station pressure and power, pipeline pressure, power supply current, power supply voltage, power supply power, ventilation pressure, wind speed, alarm and fault information; the auxiliary monitoring device at least comprises: the system comprises a safety monitoring device, a personnel positioning and protecting device, a rock burst monitoring device, a communication scheduling device and a video monitoring device, wherein the auxiliary monitoring device provides mining environment data and personnel monitoring data;
the server side comprises: the system comprises a geodetic model correction service module, an equipment data acquisition service module, an equipment model iteration service module, an equipment logic control service module and a geodetic coordinate positioning service module; the ground measurement model correction service module is used for dynamically detecting and acquiring the latest roadway, coal rock layer boundary identification data and coal bed data disclosed by a ground measurement project so as to update the ground measurement model data; the device data acquisition service module is used for acquiring device information provided by the device end in real time, issuing an operation result to other modules for subscription after logical operation, and storing the operation result into the digital twin model database; the equipment model iteration service module is used for calculating the spatial position relationship between each equipment in a twin scene and the spatial position relationship between each equipment and the ground measurement model in real time, subscribing the equipment information and the ground measurement model data, dynamically updating the spatial position of the equipment model, and dynamically optimizing the mining scene by using the boundary and collision point conditions of the equipment model; the equipment logic control service module is used for realizing real-time mutual feedback linkage of a digital twin body and the equipment entity in a VR or AR human-computer interaction mode; the geodetic coordinate positioning service module is used for acquiring equipment information in real time, dynamically calculating the position coordinates of the equipment end based on a unified geodetic coordinate system, and providing the calculated result to the equipment model iteration service module;
the digital twin model database is used for storing a roadway model, a stratum model, an equipment model and a mining environment; the roadway model and the stratum model are high-precision three-dimensional dynamic measurement model data of the coal face, and new roadway and stratum data can be obtained and dynamically updated in the mining process; the equipment model is equipment operation information, alarm and fault information generated in the coal mining process, and a coal mining cutting line, a straight line adjusting line, a pitching mining planning path, a mining height and bedding adjustment amount and an equipment operation historical track obtained through calculation; the mining environment refers to the ground survey, ventilation, electromechanics, transportation, process, personnel and environment elements which are related to a mining scene, and a complete mining area twin data system is formed.
2. The system according to claim 1, wherein the device side adopts a network or bus communication mode, and the communication protocol adopts Modbus TCP/RTU, OPC UA, Socket TCP/UDP.
3. The system according to claim 1, wherein the geodetic model modification module of the server is configured to generate geodetic coordinates-based alignment lines of a cutting line of the coal mining device and future N-point mining planning paths and transfer devices, wherein N is a positive integer.
4. The system of claim 1, wherein the device data acquisition module of the server has functions of acquiring, publishing and storing in a database, and publishes information in a message queue manner, and the protocol employs MQTT, AMQP, and Web Socket.
5. The system of claim 1, wherein the equipment model iteration module of the server side has the functions of multi-element data fusion, space position operation based on unified geodetic coordinates, boundary and collision point detection, convergence evaluation and mining scene dynamic optimization, and realizes the mutual feed synchronization of a real mining scene and a twin scene.
6. The system of claim 1, wherein the equipment model is a complete machine model assembled from three-dimensional mechanical part drawings of the components of the mining equipment in equal proportion; the device logic control service module subscribes a sensor data set of mining equipment and then distributes the data set to a corresponding device model, the device model receives the data set and splits the data, the split subdata set is distributed to each part of the device model, and each part carries out corresponding action response according to a data value on the basis of following a mechanical constraint relation.
7. The system of claim 1, wherein the digital twin model database is configured to store all production preparation data and process data, and is a SQL or NoSQL type database.
8. A coal mine fully mechanized coal mining face transparent digital twin self-adaptive mining method is characterized by comprising the following steps:
step 1, establishing a ground survey model: an initial three-dimensional geodetic model is constructed through data such as drilling, geophysical prospecting and roadway sketch, and the three-dimensional geodetic model is dynamically corrected by utilizing data such as a coal rock layer identification analysis result or stratum boundary measurement and three-dimensional earthquake dynamic interpretation in the coal mining process, so that the coal bed space form of a mining area is transparent;
step 2, establishing an equipment model: utilizing three-dimensional modeling software, combining an equipment design graph to construct a three-dimensional model of the equipment in a mining area, setting geometric parameters, action parameters, boundary constraint parameters, collision point parameters and conditions aiming at key action parts of the equipment, and associating the set part parameters with the data acquisition information of the equipment to form a data association model;
step 3, establishing a mining environment: collecting data of ground survey, ventilation, electromechanics, transportation, process, personnel and environment of a coal mining area of the fully mechanized coal mining face, and building a mining environment through data modeling and programming;
step 4, establishing a transparent coal face: the method comprises the steps that a ground measurement model, an equipment model and a mining environment are fused to form a digital twin mining scene based on a unified geodetic coordinate system, the scene realizes dynamic updating of the scene and data through a ground measurement model correction service, an equipment model iteration service and a script programming method, and digital twin and virtual-real contrast of the mining environment are formed;
step 5, generating a self-adaptive mining template: dynamically generating a mining template with absolute coordinates by utilizing a geodetic model correction service, wherein the mining template comprises cutting lines of a coal mining top plate and a coal mining bottom plate with the absolute coordinates, a scraper alignment line and a planning path of pitching mining;
step 6, data acquisition and release: acquiring information provided by the equipment, issuing an operation result to a script programming module for subscription after logical operation, simultaneously storing the operation result and important information into a digital twin model database, and respectively acquiring real-time information and historical information by the script programming module in a subscription and query mode to drive a transparent coal face digital twin model;
and 7, driving the mining scene by script programming: using a sensor data set subscribed by a script programming module, taking a constraint relation of mechanical and physical motion of the equipment model as a limiting factor, performing data iterative operation, feeding back an operation result to the equipment model parts in each iterative cycle, driving the parts to change the spatial pose, after multiple iterations, enabling the equipment model parts to act according to a set motion route, calculating the spatial pose and position of equipment in the action process to be iteratively contrasted with the data of the self-adaptive mining template in the step 5, calculating an adjustment quantity when the contrast result is inconsistent, and controlling the physical equipment to adjust the pose by using an equipment logic control service and to be consistent with the mining template;
step 8, man-machine interaction control: the control information code of the man-machine interaction module is input into the equipment logic control service module in a VR or AR man-machine interaction mode, the equipment logic control service module receives the control information and then combines the subscribed equipment running state, and sends a control instruction to the equipment terminal after logic operation, and simultaneously feeds back an execution result to the man-machine interaction module;
step 9, simulation mining: before the self-adaptive mining mode is actually started, carrying out consistency and reliability verification of complete one-time operation cycle and simulated mining by utilizing equipment space position information, running information, mining environment information and coal cutting lines, alignment lines, pitching mining planning paths, mining height and bedding adjustment data generated by a ground measurement model in a digital twin mining scene, and entering the next step after the verification is passed;
step 10, self-adaptive actual mining: after the simulation mining verification is passed, the digital twin mining scene sends mining information to the equipment logic control service module through the self-adaptive mining mode module, and the information is converted into a control instruction to guide the underground mining equipment to automatically operate; and synchronously mirroring feedback of the running information of the equipment and driving a model in a virtual scene to realize data twinning.
9. The method according to claim 8, wherein the human-computer interaction control has a two-dimensional and three-dimensional view switching function, a twin object in a mining scene can be selected by a mouse or a finger at the two-dimensional and three-dimensional views to perform attribute query, control operation, running state query and evolution process backtracking, and simulation and mutual feedback linkage can be realized through VR or AR at the three-dimensional view.
10. The method according to claim 8, wherein in the adaptive mining process, a convolutional neural network deep learning algorithm is adopted to perform data mining on the equipment-side data, the geodetic model data and the historical data, and self-learning and self-optimization of the digital twin are realized by training and optimizing a simulation model, so that the accuracy and reliability of adaptive mining of the system are improved.
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