CN117328869A - Automatic control system and method for coal mining machine - Google Patents
Automatic control system and method for coal mining machine Download PDFInfo
- Publication number
- CN117328869A CN117328869A CN202311613822.2A CN202311613822A CN117328869A CN 117328869 A CN117328869 A CN 117328869A CN 202311613822 A CN202311613822 A CN 202311613822A CN 117328869 A CN117328869 A CN 117328869A
- Authority
- CN
- China
- Prior art keywords
- coal
- coal mining
- mining machine
- module
- real
- 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.)
- Pending
Links
- 239000003245 coal Substances 0.000 title claims abstract description 191
- 238000005065 mining Methods 0.000 title claims abstract description 103
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 31
- 230000000007 visual effect Effects 0.000 claims abstract description 22
- 239000000126 substance Substances 0.000 claims abstract description 15
- 238000013136 deep learning model Methods 0.000 claims description 15
- 239000011435 rock Substances 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000013135 deep learning Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000013527 convolutional neural network Methods 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 abstract 1
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 241000233866 Fungi Species 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21C—MINING OR QUARRYING
- E21C35/00—Details of, or accessories for, machines for slitting or completely freeing the mineral from the seam, not provided for in groups E21C25/00 - E21C33/00, E21C37/00 or E21C39/00
- E21C35/24—Remote control specially adapted for machines for slitting or completely freeing the mineral
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21C—MINING OR QUARRYING
- E21C41/00—Methods of underground or surface mining; Layouts therefor
- E21C41/16—Methods of underground mining; Layouts therefor
- E21C41/18—Methods of underground mining; Layouts therefor for brown or hard coal
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Mechanical Engineering (AREA)
- Remote Sensing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention relates to the technical field of coal mining machines, and particularly discloses an automatic control system and method for a coal mining machine, wherein the system comprises the following components: the system comprises a sensor module, an automatic control module, a visual identification module, a safety module and a remote monitoring module; according to the invention, various parameters of the coal face are monitored and acquired through various sensors arranged on the coal mining machine, real-time data are transmitted to the control system, the real-time data are processed through a computer, and the visual recognition unit is combined, so that the coal mining machine has a visual recognition function, and can automatically recognize coal and non-coal substances, thereby reducing the mixing of coal gangue, improving the quality of the mined coal, regulating parameters such as the coal mining speed of the coal mining machine by using a feedback control algorithm, ensuring the accurate positioning and movement of the coal mining machine in a coal mine by matching with an automatic navigation and positioning unit, improving the coal mining safety and efficiency, and monitoring various operation indexes of the coal mining machine by using a remote monitoring module, so as to ensure the stable operation of the coal mining.
Description
Technical Field
The invention belongs to the technical field of coal mining machines, and particularly relates to an automatic control system and method of a coal mining machine.
Background
Coal mine intellectualization is a core technical support for high-quality development of coal industry. The coal mining machine is used as core equipment of the intelligent fully-mechanized coal mining face, and the intelligent degree of the coal mining machine determines the intelligent level of the fully-mechanized coal mining face. Meanwhile, as various policy documents about intelligent mines and the like are issued by countries, the requirements on the intelligent degree of the coal mining machine are higher and higher.
When the coal mining machine is operated, the on-site operation of the working personnel is used as the main operation, and the on-site equipment of the coal mining face is more, the environment is complex, the manual operation is not only high in strength, but also difficult to accurately cut the coal seam, more gangue is easy to generate, the quality of coal is reduced, the follow-up fussy screening work is required to be performed, the cost is increased, and in addition, the intelligent degree of the manual operation is lower, and safety accidents are also easy to generate.
Disclosure of Invention
The invention aims to provide an automatic control system and method for a coal mining machine, which are used for solving the problems that the manual operation is high in strength, coal seam cutting is difficult to accurately perform, more coal gangue is easy to generate, the quality of coal is reduced, the follow-up complicated screening work is required, the cost is increased and the intelligence is low.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an automated shearer control system comprising:
a sensor module for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensor units mounted on the coal cutter;
the automatic control module is used for receiving the real-time data in the sensor module, transmitting the real-time data to the control system and processing the sensor data through a computer or a PLC; the speed, the position and the working parameters of the coal mining machine are adjusted through a feedback control algorithm unit, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with an automatic navigation and positioning unit, so that the coal mining safety and the coal mining efficiency are improved;
the visual recognition module enables the real-time data to pass through the deep learning unit, so that the coal mining machine has a visual recognition function and can automatically recognize coal and non-coal substances, thereby reducing the mixing of coal gangue and improving the quality of the acquired coal;
a safety module for monitoring potential dangerous conditions of the coal face, such as gas leakage or rock stratum collapse, by integrating a safety system in the automatic control module;
and the remote monitoring module is used for monitoring each operation index of the coal mining machine through a remote platform, so that the stable coal mining operation and the problem of communication with staff at any time are ensured.
Preferably, the deep learning unit specifically includes:
a data acquisition unit: collecting image data of the coal mining machine under different coal seam, rock type and gas concentration operation conditions, and using the data for training a deep learning model;
a data preprocessing unit: performing image enhancement, size adjustment and denoising operation on the acquired image data to improve the performance of the model;
building and training a deep learning model unit: training a deep learning model by adopting a Convolutional Neural Network (CNN) model and using marked image data, and marking images in a training dataset with correct categories or attributes so that the model can learn how to identify different coal seams and non-coal substances;
deployment and integration unit: the trained and optimized deep learning model is deployed into a visual system of the coal mining machine, and when the coal mining machine operates, image data acquired in real time are transmitted to the deep learning model for real-time prediction so as to judge whether the current position is a coal bed or a non-coal substance.
Preferably, the automatic navigation and positioning unit comprises GPS, laser ranging and inertial navigation technologies.
The automatic control method of the coal mining machine comprises the automatic control system of the coal mining machine, and the control method comprises the following steps:
installing sensors for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensors installed on the coal mining machine;
the automatic control is applied, real-time data in the sensor are received and transmitted to a control system, the sensor data are processed through a computer or a PLC, the speed, the position and the working parameters of the coal mining machine are adjusted through a feedback control algorithm, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with automatic navigation and positioning, so that the coal mining safety and the coal mining efficiency are improved;
visual recognition, through a deep learning algorithm, the coal mining machine has a visual recognition function, and can automatically recognize coal and non-coal substances, so that the mixing of coal gangue is reduced, and the quality of the obtained coal is improved;
safety monitoring, by integrating a safety system in an automated control module, to monitor potential dangerous conditions of the coal face, such as gas leakage or formation collapse;
and the remote monitoring is performed by monitoring each operation index of the coal mining machine on a remote platform, so that the stable coal mining work and the problem of communication with staff at any time are ensured.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, various sensors arranged on the coal mining machine are used for monitoring and acquiring real-time data of various parameters in the coal mining working face, the real-time data are transmitted to the control system, and the real-time data are processed by a computer and combined with the visual recognition unit, so that the coal mining machine has a visual recognition function, and can automatically recognize coal and non-coal substances, thereby reducing the mixing of coal gangue and improving the quality of the acquired coal.
(2) According to the invention, parameters such as the coal mining speed of the coal mining machine are adjusted by utilizing a feedback control algorithm, and the automatic navigation and positioning unit is matched, so that the accurate positioning and movement of the coal mining machine in a coal mine are ensured, the coal mining safety and efficiency are improved, and the operation indexes of the coal mining machine are monitored by a remote monitoring module, so that the stable coal mining operation and the problem of communication with staff at any time are ensured.
Drawings
FIG. 1 is a block diagram of an automated control system for a shearer of the present invention;
FIG. 2 is a flow chart of an automated control method of a shearer of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1 to 2, an automatic control system for a coal mining machine includes:
the sensor module is used for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensor units installed on the coal mining machine, including a SICK laser range finder, a rock hardness sensor, a Mine Safety Appliances gas concentration sensor, an insulation resistance temperature sensor, an MEMS acceleration sensor, a differential pressure sensor and a microphone type sound sensor;
the automatic control module is used for receiving real-time data in the sensor module, transmitting the real-time data to the control system and processing the sensor data through a computer or a PLC (programmable logic controller), for example, the model Siemens SIMATIC S7-1500 automatic control module; the speed, the position and the working parameters of the coal mining machine (Eickhoff SL 300) are adjusted through a feedback control algorithm unit, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with an automatic navigation and positioning unit such as GPS, laser ranging and inertial navigation technology, so that the coal mining safety and the coal mining efficiency are improved;
the visual recognition module is used for enabling the real-time data to pass through the deep learning unit, so that the coal mining machine has a visual recognition function and can automatically recognize coal and non-coal substances, mixing of coal gangue is reduced, and quality of the obtained coal is improved;
a safety module, such as Pilz PNOZmulti Safety PLC safety module, for monitoring the coal face for potentially dangerous conditions, such as gas leaks or formation collapse, by integrating a safety system in the automated control module;
the remote monitoring module, such as the model Sierra Wireless AirLink GX450 remote monitoring module, monitors each operation index of the coal mining machine on a remote platform, and ensures that the coal mining work is stably carried out and the problems occurring in communication with staff at any time.
Wherein the cutting speed is adjusted by the feedback control algorithm unit as follows:
setting a target: setting a desired coal seam cutting speed (v 1 ）；
And (3) error calculation: calculate the actual cutting speed (v 2 ) Error from the expected value;
speed error E v =v 1 -v 2 Depth error E d =d 1 -d 2
Control input calculation: based on the error signal calculated from the error, a PID (proportional-integral-derivative) controller is used to calculate the control input:
control input:
wherein Kp, ki, kd are gain parameters of the PID controller;
control output: according to the control input, cutting parameters (such as cutterhead speed and conveyor belt speed) of the coal cutter are adjusted to realize more efficient coal seam cutting, and the cutting parameters of the coal cutter are continuously monitored and adjusted to ensure efficient cutting of the coal seam and improve the recovery rate of coal.
Specifically, the deep learning unit specifically includes:
a data acquisition unit: collecting image data of the coal mining machine under different coal seam, rock type and gas concentration operation conditions, and using the data for training a deep learning model;
a data preprocessing unit: performing image enhancement, size adjustment and denoising operation on the acquired image data to improve the performance of the model;
building and training a deep learning model unit: training a deep learning model by adopting a Convolutional Neural Network (CNN) model and using marked image data, and marking images in a training dataset with correct categories or attributes so that the model can learn how to identify different coal seams and non-coal substances;
deployment and integration unit: the trained and optimized deep learning model is deployed into a visual system of the coal mining machine, and when the coal mining machine operates, image data acquired in real time are transmitted to the deep learning model for real-time prediction so as to judge whether the current position is a coal bed or a non-coal substance.
According to the method, various sensors arranged on the coal mining machine are used for monitoring and acquiring real-time data of various parameters in a coal mining working face, the real-time data are transmitted to a control system, and the real-time data are processed by a computer and combined with a visual recognition unit, so that the coal mining machine has a visual recognition function, and can automatically recognize coal and non-coal substances, thereby reducing the mixing of coal gangue and improving the quality of the acquired coal;
the coal mining machine is controlled by a feedback control algorithm, parameters such as the coal mining speed of the coal mining machine are adjusted, and an automatic navigation and positioning unit is matched, so that accurate positioning and movement of the coal mining machine in a coal mine are ensured, the coal mining safety and efficiency are improved, and through a remote monitoring module, the operation indexes of the coal mining machine are monitored, and the stable coal mining operation and the communication with staff at any time are ensured.
The automatic control method of the coal mining machine comprises the automatic control system of the coal mining machine, and the control method comprises the following steps:
installing sensors for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensors installed on the coal mining machine;
the application automation control is used for receiving real-time data in the sensor, transmitting the real-time data to the control system and processing the sensor data through a computer or a PLC; the speed, the position and the working parameters of the coal mining machine are adjusted through a feedback control algorithm, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with automatic navigation and positioning, so that the coal mining safety and the coal mining efficiency are improved;
visual identification, namely enabling the coal mining machine to have a visual identification function through a deep learning algorithm, and automatically identifying coal and non-coal substances, so that mixing of coal gangue is reduced, and quality of the obtained coal is improved;
safety monitoring, by integrating a safety system in an automated control module, to monitor potential dangerous conditions of the coal face, such as gas leakage or formation collapse;
and the remote monitoring is performed by monitoring each operation index of the coal mining machine on a remote platform, so that the stable coal mining work and the problem of communication with staff at any time are ensured.
The beneficial effects are the same as the technical effects of the embodiment of the automatic control system of the coal mining machine, and the description is omitted here.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. An automated control system for a shearer, comprising:
a sensor module for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensor units mounted on the coal cutter;
the automatic control module is used for receiving the real-time data in the sensor module, transmitting the real-time data to the control system and processing the sensor data through a computer or a PLC; the speed, the position and the working parameters of the coal mining machine are adjusted through a feedback control algorithm unit, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with an automatic navigation and positioning unit, so that the coal mining safety and the coal mining efficiency are improved;
the visual recognition module enables the real-time data to pass through the deep learning unit, so that the coal mining machine has a visual recognition function and can automatically recognize coal and non-coal substances, thereby reducing the mixing of coal gangue and improving the quality of the acquired coal;
a safety module for monitoring potential dangerous conditions of the coal face, such as gas leakage or rock stratum collapse, by integrating a safety system in the automatic control module;
and the remote monitoring module is used for monitoring each operation index of the coal mining machine through a remote platform, so that the stable coal mining operation and the problem of communication with staff at any time are ensured.
2. The automated shearer control system of claim 1, wherein: the deep learning unit is specifically as follows:
a data acquisition unit: collecting image data of the coal mining machine under different coal seam, rock type and gas concentration operation conditions, and using the data for training a deep learning model;
a data preprocessing unit: performing image enhancement, size adjustment and denoising operation on the acquired image data to improve the performance of the model;
building and training a deep learning model unit: training a deep learning model by adopting a Convolutional Neural Network (CNN) model and using marked image data, and marking images in a training dataset with correct categories or attributes so that the model can learn how to identify different coal seams and non-coal substances;
deployment and integration unit: the trained and optimized deep learning model is deployed into a visual system of the coal mining machine, and when the coal mining machine operates, image data acquired in real time are transmitted to the deep learning model for real-time prediction so as to judge whether the current position is a coal bed or a non-coal substance.
3. The automated shearer control system of claim 1, wherein: the automatic navigation and positioning unit comprises GPS, laser ranging and inertial navigation technologies.
4. An automatic control method of a coal mining machine is characterized by comprising the following steps of: a shearer loader automation control system comprising any one of claims 1-3, said control method comprising the steps of:
installing sensors for monitoring various parameters such as coal seam thickness, rock hardness, gas concentration and temperature through various sensors installed on the coal mining machine;
the automatic control is applied, real-time data in the sensor are received and transmitted to a control system, the sensor data are processed through a computer or a PLC, the speed, the position and the working parameters of the coal mining machine are adjusted through a feedback control algorithm, and the accurate positioning and the movement of the coal mining machine in a coal mine are ensured by matching with automatic navigation and positioning, so that the coal mining safety and the coal mining efficiency are improved;
visual recognition, through a deep learning algorithm, the coal mining machine has a visual recognition function, and can automatically recognize coal and non-coal substances, so that the mixing of coal gangue is reduced, and the quality of the obtained coal is improved;
safety monitoring, by integrating a safety system in an automated control module, to monitor potential dangerous conditions of the coal face, such as gas leakage or formation collapse;
and the remote monitoring is performed by monitoring each operation index of the coal mining machine on a remote platform, so that the stable coal mining work and the problem of communication with staff at any time are ensured.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311613822.2A CN117328869A (en) | 2023-11-29 | 2023-11-29 | Automatic control system and method for coal mining machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311613822.2A CN117328869A (en) | 2023-11-29 | 2023-11-29 | Automatic control system and method for coal mining machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117328869A true CN117328869A (en) | 2024-01-02 |
Family
ID=89292039
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311613822.2A Pending CN117328869A (en) | 2023-11-29 | 2023-11-29 | Automatic control system and method for coal mining machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117328869A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117967307A (en) * | 2024-04-01 | 2024-05-03 | 枣庄矿业集团新安煤业有限公司 | Data processing method for remotely controlling rotation adjustment mining of coal mining machine |
-
2023
- 2023-11-29 CN CN202311613822.2A patent/CN117328869A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117967307A (en) * | 2024-04-01 | 2024-05-03 | 枣庄矿业集团新安煤业有限公司 | Data processing method for remotely controlling rotation adjustment mining of coal mining machine |
| CN117967307B (en) * | 2024-04-01 | 2024-06-07 | 枣庄矿业集团新安煤业有限公司 | Data processing method for remotely controlling rotation adjustment mining of coal mining machine |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2021254666B2 (en) | Method and system for online monitoring and optimization of mining and mineral processing operations | |
| CN117328869A (en) | Automatic control system and method for coal mining machine | |
| Ralston et al. | Sensing for advancing mining automation capability: A review of underground automation technology development | |
| CN106121621A (en) | A kind of intelligent drilling specialist system | |
| Hao et al. | A geometry surveying model and instrument of a scraper conveyor in unmanned longwall mining faces | |
| CN111622735B (en) | Environment-friendly drilling machine automatic control and monitoring system and method based on Internet of things | |
| CN102797462A (en) | Automatic cutting control system and automatic cutting control method for coal cutter | |
| CN109469484A (en) | Automation coal-mining method based on host computer planning | |
| MX2014007972A (en) | Determining optimal parameters for a downhole operation. | |
| Onifade et al. | Challenges and applications of digital technology in the mineral industry | |
| CN105064997A (en) | Coal mine unmanned mining system and method | |
| CN106401585A (en) | Memory coal cutting control method based on cooperative control over coal cutter and supports | |
| AU2014223301B2 (en) | A method of generating a drill hole sequence plan and drill hole sequence planning equipment | |
| CN105201507A (en) | Mining system and mining method for high-dipping super-high seam shortwall working face | |
| Liu et al. | Research progress and prospects of intelligent technology in underground mining of hard rock mines | |
| US20220349257A1 (en) | Apparatus, rock drilling rig and method for mining navigation | |
| CN115795369B (en) | Coal-rock interface identification method and device | |
| CN117291086A (en) | Digital twin intelligent construction method and system based on extra-long tunnel TBM engineering | |
| CN110806859A (en) | Modular drilling data monitoring and design system based on machine learning | |
| US11767714B2 (en) | Boundary line generation for controlling drilling operations | |
| CN213902110U (en) | Multifunctional mixed emulsion explosive loading vehicle | |
| Hague | Strategic Implementation of Digital Technologies to Optimize Operations in an Underground Mine | |
| CN113236343A (en) | Intelligent gas extraction system used for underground coal mine | |
| Holm et al. | Concept of shield-data-based horizon control for longwall coal mining automation | |
| Li et al. | Adaptive height adjusting strategy research of shearer cutting drum |
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 |