CN111255497B - Intelligent rock stratum control method for fully mechanized coal mining face - Google Patents
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Abstract
The invention discloses an intelligent rock stratum control method for a fully mechanized coal mining face, which comprises the following steps: s1, establishing a coal wall stability judging system; s2, collecting a support operation process system library, a fully mechanized coal mining face working resistance database and a fully mechanized coal mining face support posture model library; s3, acquiring a three-dimensional dynamic image of a real-time roof and a coal wall of the fully mechanized coal mining face; s4, analyzing the collected data of the steps S2 and S3 to obtain a collected working face pressure parameter and a coal wall state parameter; s5, inputting the data obtained in the step S4 into a coal wall stability judging system, and outputting a judging result by the coal wall stability judging system; and when the judgment result is that the coal wall is stable, storing the data into a database as a case, and when the judgment result is that the coal wall is unstable, feeding the judgment result back to the electric hydraulic control system, implementing protection measures on the coal wall by the electric hydraulic control system until the coal wall is stable, and storing the data into the database as a case.
Description
Technical Field
The invention relates to the field of coal mining, in particular to an intelligent rock stratum control method for a fully mechanized coal mining face.
Background
At present, coal mines are in the key nodes of revolution, and the requirements of intelligent coal mine exploitation are urgent on the premise of high development and fusion of coal mine exploitation automation and informatization. One of the key contents in intelligent coal mining is intelligent rock stratum control, and because the coal mining object is a complex geologic body, the coal mining process is not only influenced by the structure, bedding and the like of the geologic body, but also greatly influenced by the secondary stress effect brought by mining and the stability of surrounding rocks of a working face. The traditional control of surrounding rocks of a working face mainly depends on manual identification and judgment, and the hydraulic support is controlled manually by operating an electric hydraulic control system of the hydraulic support, so that the rock stratum of the working face is controlled. Because artificial recognition is greatly influenced by a main observation and has obvious aftereffect, the control of the rock stratum of the working face cannot be timely and accurately controlled, the mining progress and the production efficiency of the working face are influenced, meanwhile, along with the great promotion of intelligent mining, the aim of realizing the purposes of few people and no humanization of the working face is fulfilled early. The intelligent rock stratum control of the fully mechanized mining face is achieved to an unbearable ground step.
Disclosure of Invention
The invention aims to solve the problems and provides an intelligent rock stratum control method for a fully mechanized mining face, which can acquire data information in real time so as to accurately control a hydraulic support in time.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an intelligent rock stratum control method for a fully mechanized mining face comprises the following steps:
s1, establishing a coal wall stability judging system;
s2, collecting control source signal data, support resistance data and support posture data of an electric hydraulic control system of the fully mechanized coal mining face hydraulic support; then, generating a support operation flow system library, a fully mechanized mining face working resistance database and a fully mechanized mining face support attitude model library from the control source signal data, the support resistance data and the support attitude data of the electro-hydraulic control system of the hydraulic support through a data analysis processor;
s3, acquiring three-dimensional images of the top plate and the coal wall of the fully mechanized coal mining face through the laser radar detectors, splicing the data acquired by the plurality of laser radar detectors through the image processor, and generating three-dimensional dynamic images of the top plate and the coal wall of the fully mechanized coal mining face in real time;
s4, analyzing a support operation process system library, a fully mechanized mining face working resistance database, a fully mechanized mining face support posture model library and a fully mechanized mining face real-time top plate and coal wall three-dimensional dynamic image to obtain a collected working face pressure parameter and a coal wall state parameter;
s5, inputting the acquired pressure parameters of the collecting working face, the coal wall state parameters and the coal bed parameters (coal bed parameter information is obtained through a mine production geological report) into a coal wall stability judging system, and judging the stability of the coal wall and outputting a judgment result by the coal wall stability judging system; and when the judgment result is that the coal wall is stable, storing the data into a database as a case, and when the judgment result is that the coal wall is unstable, feeding the judgment result back to the electric hydraulic control system, implementing protection measures on the coal wall by the electric hydraulic control system until the coal wall is stable, and storing the data into the database as a case.
Further, the coal wall stability determination system in step S1 is built based on a BP neural network model, and the building process of the BP neural network model includes the following steps:
s11, taking the coal wall stability influence factors as an input layer of the BP neural network model, and carrying out quantitative processing;
s12, taking the coal wall stability grade as an output layer of the BP neural network model;
s13, presetting model parameters of the BP neural network model to obtain an initial BP neural network model;
s14, collecting coal wall stability influence factors and coal wall stability grades in historical data and respectively using the coal wall stability influence factors and the coal wall stability grades as an input layer and an output layer of the initial BP neural network model, dividing the historical data into a training set and a test set, training the initial BP neural network model by using the training set, testing the trained initial BP neural network model by using the test set, and correcting model parameters to obtain the BP neural network model.
Further, the factors influencing the stability of the coal wall in step S11 include a pressure parameter of the collecting working surface, a state parameter of the coal wall, and a parameter of the coal bed.
Further, the coal wall stability in the step S12 is classified into four grades of stable, more stable, unstable and extremely unstable.
Further, the rack posture data in step S2 includes data of an included angle between the rack and the horizontal plane along the working surface running direction, and a rack supporting height. The method is characterized in that a double-shaft tilt angle sensor (capable of acquiring tilt angles between the surface where the sensor is located and a horizontal plane in two directions) arranged at each part (a base, a front connecting rod and a top beam) of all hydraulic supports on a working surface is used for acquiring data of included angles between each part (the base, the front connecting rod and the top beam) of all supports on the working surface and the horizontal plane along the trend direction of the working surface and data of included angles between each part (the base, the front connecting rod and the top beam) and the horizontal plane along the inclination direction of the working surface. And calculating the real-time heights of all the supports on the working surface respectively by utilizing sine and cosine theorems according to the inclination angle data of each part of each support and a support design drawing (measuring the size information of each part).
Further, in the step S3, the lidar detector is disposed below the top beam, and the detection range is the height of the entire coal wall.
Further, the coal wall state parameters in the step S4 include a caving depth, a caving position, a caving area, and a caving speed, and are obtained by comparing elevation differences of the three-dimensional dynamic images of the front and rear fully mechanized coal mining faces.
Further, the coal seam parameters in the step S4 are mine geological exploration data, which are obtained from mine production geological reports.
Further, in step S5, when the judgment result of the coal wall stability judgment system is that the coal wall is unstable, the coal wall stability judgment system matches the technical measures under the similar conditions in the database, and then feeds back the matched technical measures to the electric hydraulic control system for controlling the hydraulic support.
Further, in the coal mining process of the fully mechanized coal mining face, the coal wall stability judging system feeds back the stable state of the coal wall in real time, and finally records the feedback information of the control process of the whole fully mechanized coal mining face to form a control learning case and store the control learning case in the database.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention provides an intelligent rock stratum control method for a fully mechanized coal mining face, which detects data such as a coal wall structure by a laser radar, detects stress and attitude information of a hydraulic support by a hydraulic support sensor, fuses the detection data of the hydraulic support and the detection data of the hydraulic support, judges the stability of the coal wall by a coal wall stability judging system, and controls by combining an electric hydraulic control system for controlling the hydraulic support, so that the electric hydraulic control system can automatically control the behavior of the hydraulic support, and realizes self-sensing, self-judging and self-control of the hydraulic support; on the other hand, the invention makes beneficial supplement for the intelligent mining of coal mines, can fully liberate human resources and greatly reduces the human cost of coal mining; according to the scheme, a large number of learning cases can be formed, the bp neural network model database is perfected, an important model foundation is established for realizing intelligent popularization of the coal mine, and great contribution is made to realizing intelligent mining of the coal mine.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block diagram of the framework of the present invention;
fig. 2 is a connection structure diagram 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived from the embodiments of the present invention by a person skilled in the art without any creative effort, should be included in the protection scope of the present invention.
As shown in fig. 1 and fig. 2, the invention provides an intelligent rock stratum control method for a fully mechanized coal mining face, which comprises the following steps: in the normal stoping process of a working face, acquiring and analyzing monitoring data of a hydraulic support in historical coal mining construction and working face top plate and coal wall image data monitored by a laser radar detector on the hydraulic support to obtain acquired working face pressure parameters and coal wall state parameters, acquiring coal layer parameter information through a mine production geological report, establishing a coal wall stability judging system by combining the parameter data with a BP (back propagation) neural network, analyzing the stability of the working face top plate and the coal wall of the fully mechanized coal mining at present through the system, and recording and storing to form a case if the stability is stable; and if the working face top plate and the coal wall are judged to be unstable, automatically controlling and adjusting the working face support through feedback information and an electric hydraulic control system until the working face top plate and the coal wall are stable, recording the whole process and forming a learning case.
The monitoring data of the hydraulic support of the working face are firstly obtained through a pressure sensor and an attitude sensor in real time through control source signal data of an electric hydraulic control system of the hydraulic support of the fully mechanized mining face, support resistance data and support attitude data, then a support operation flow system library, a working resistance database of the fully mechanized mining face and a support attitude model library of the fully mechanized mining face are generated through the control source signal data of the electric hydraulic control system of the support, the support resistance data and the support attitude data through a hydraulic support data analysis processor, and the data are transmitted to a coal wall stability judging system.
The laser radar detector is arranged on a top beam of the hydraulic support, namely the top end of the hydraulic support, the detection range of the laser radar detector can be controlled to the height of the whole coal wall, the laser radar sends out radar waves in real time and receives echoes to form three-dimensional dynamic images of a working face top plate and the coal wall in a certain range, data detected by a plurality of radars are spliced by the image processor to generate the three-dimensional dynamic images of the working face top plate and the coal wall in real time, the three-dimensional dynamic images reflect the change condition of the coal wall distance, and the data of the rib caving depth, the rib caving position, the rib caving area and the rib caving speed of the coal wall can be obtained by dynamically comparing the height difference of time images before and after dynamic comparison, and are transmitted to the working face top plate and coal wall stability judging system.
The judgment algorithm of the coal wall stability judgment system is based on a BP neural network, training is carried out before the system is applied on site, and the training process is as follows:
firstly, determining an input layer of a neural network, namely a coal wall stability influence factor, and carrying out quantitative processing by using a working face pressure parameter, a coal wall state parameter and a coal bed parameter as influence factors by the system; the quantification processing is to give specific values of each data, such as the number of working resistance values, the inclination angle of the bracket, the number of parameter values of the coal wall state and the like; the coal bed parameters are mine geological exploration data and are obtained by a mine production geological report;
then, determining coal wall stability grades, dividing the coal wall stability into four grades of stable, more stable, unstable and extremely unstable, and respectively representing the corresponding output layer information by (1000), (0100), (0010) and (0001);
and then determining model parameters of the BP neural network, wherein the model parameters comprise an activation function type, the number of neurons in a hidden layer, a connection weight value between an input layer and the hidden layer, a connection weight value between the hidden layer and an output layer, a threshold value of each neuron in the hidden layer, a threshold value of each neuron in the output layer, an error function, the maximum training step number and the like.
And finally, dividing the ready-collected data of the input layer and the output layer into a training set and a testing set, training by using the established BP neural network, testing the trained BP neural network by using the testing set, and correcting parameters of the neural network until the accuracy of the test result reaches more than 95%.
The trained BP neural network is applied to on-site coal wall stability judgment, in the judgment process, working face pressure parameters, coal wall state parameters and coal bed parameters are led into a coal wall stability judgment system, the system can automatically give out a coal wall stability evaluation result and judge the stability condition of the working face coal wall in real time, a feedback relation exists between the coal wall stability judgment system and a hydraulic support electric hydraulic control system, and the electric hydraulic control system can be manually controlled and simultaneously responds to the judgment result of the judgment system; if the judgment result is stable or more stable, the system can directly record and store the working state and the stable state at the moment as a success case; if the judgment result is unstable or extremely unstable, the coal wall stability judgment system feeds the judgment information back to the electro-hydraulic control system for controlling the hydraulic support, the coal wall stability judgment system matches technical measures and means under similar conditions in the database through deep learning, and then feeds the matching result back to the electro-hydraulic control system for controlling the hydraulic support, and the electro-hydraulic control system can adopt measures of increasing the working resistance of the support or increasing the pressure of a side protection plate and the like, so that the working resistance of the support is increased, the acting force of a top plate on the coal wall can be reduced, and the possibility of continuous damage of the coal wall is reduced; the pressure of the side protection plate is increased, so that the loosened coal wall cannot slide to the working face, the working face hydraulic support is automatically controlled by the electric hydraulic control system in real time until the working face top plate and the coal wall are stable, a success case is formed at the same time, and the success case is recorded and stored in a database. The success case can be used as a reference in the stability judgment of the working face top plate and the coal wall, provides reference for the later exploitation, and can perform water injection, grouting, anchoring measures and the like under the condition of poor stability of the coal wall.
The invention provides an intelligent rock stratum control method for a fully mechanized coal mining face, which detects data such as a coal wall structure by a laser radar, detects stress and attitude information of a hydraulic support by a hydraulic support sensor, fuses the detection data of the hydraulic support and the detection data of the hydraulic support, judges the stability of the coal wall by a coal wall stability judging system, and controls by combining an electric hydraulic control system for controlling the hydraulic support, so that the electric hydraulic control system can automatically control the behavior of the hydraulic support, and realizes self-sensing, self-judging and self-control of the hydraulic support; on the other hand, the invention makes beneficial supplement for the intelligent mining of coal mines, can fully liberate human resources and greatly reduces the human cost of coal mining; according to the scheme, a large number of learning cases can be formed, the bp neural network model database is perfected, an important model foundation is established for realizing intelligent popularization of the coal mine, and great contribution is made to realizing intelligent mining of the coal mine.
Claims (10)
1. An intelligent rock stratum control method for a fully mechanized mining face is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing a coal wall stability judging system;
s2, collecting control source signal data, support resistance data and support posture data of an electric hydraulic control system of the fully mechanized coal mining face hydraulic support; then, generating a support operation flow system library, a fully mechanized mining face working resistance database and a fully mechanized mining face support attitude model library from the control source signal data, the support resistance data and the support attitude data of the electro-hydraulic control system of the hydraulic support through a data analysis processor;
s3, acquiring three-dimensional images of the top plate and the coal wall of the fully mechanized coal mining face through the laser radar detectors, splicing the data acquired by the plurality of laser radar detectors through the image processor, and generating three-dimensional dynamic images of the top plate and the coal wall of the fully mechanized coal mining face in real time;
s4, analyzing a support operation process system library, a fully mechanized mining face working resistance database, a fully mechanized mining face support posture model library and a fully mechanized mining face real-time top plate and coal wall three-dimensional dynamic image to obtain a collected working face pressure parameter and a coal wall state parameter;
s5, inputting the acquired pressure parameters of the collecting working face, the coal wall state parameters and the coal bed parameters into a coal wall stability judging system, and judging the stability of the coal wall and outputting a judging result by the coal wall stability judging system; and when the judgment result is that the coal wall is stable, storing the data into a database as a case, and when the judgment result is that the coal wall is unstable, feeding the judgment result back to the electric hydraulic control system, implementing protection measures on the coal wall by the electric hydraulic control system until the coal wall is stable, and storing the data into the database as a case.
2. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: the coal wall stability determination system in the step S1 is established based on a BP neural network model, and the establishment process of the BP neural network model includes the following steps:
s11, taking the coal wall stability influence factors as an input layer of the BP neural network model, and carrying out quantitative processing;
s12, taking the coal wall stability grade as an output layer of the BP neural network model;
s13, presetting model parameters of the BP neural network model to obtain an initial BP neural network model;
s14, collecting coal wall stability influence factors and coal wall stability grades in historical data and respectively using the coal wall stability influence factors and the coal wall stability grades as an input layer and an output layer of the initial BP neural network model, dividing the historical data into a training set and a test set, training the initial BP neural network model by using the training set, testing the trained initial BP neural network model by using the test set, and correcting model parameters to obtain the BP neural network model.
3. The intelligent rock stratum control method for the fully mechanized mining face of claim 2, wherein: the factors influencing the stability of the coal wall in the step S11 include the pressure parameter of the collected working surface, the state parameter of the coal wall, and the parameters of the coal bed.
4. The intelligent rock stratum control method for the fully mechanized mining face of claim 3, wherein: the coal wall stability in the step S12 is classified into four grades of stable, more stable, unstable and extremely unstable.
5. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: the rack posture data in the step S2 includes data of an included angle between the rack and the horizontal plane along the direction of the working surface, and a rack supporting height.
6. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: in the step S3, the lidar detector is disposed below the top beam, and the detection range is the height of the entire coal wall.
7. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: the coal wall state parameters in the step S4 include the caving depth, the caving position, the caving area, and the caving speed, and are obtained by comparing the elevation difference between the real-time top plate of the front and rear fully mechanized coal mining faces and the three-dimensional dynamic images of the coal wall by the image processor.
8. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: the coal seam parameters in the step S4 are mine geological exploration data, which are obtained from mine production geological reports.
9. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: in the step S5, when the judgment result of the coal wall stability judgment system indicates that the coal wall is unstable, the coal wall stability judgment system matches the technical measures under the similar conditions in the database, and then feeds back the matched technical measures to the electric hydraulic control system for controlling the hydraulic support.
10. The intelligent rock stratum control method for the fully mechanized mining face of claim 1, wherein: and in the coal mining process of the fully mechanized coal face, the coal wall stability judging system feeds back the stable state of the coal wall in real time, and finally records the feedback information of the control process of the whole fully mechanized coal face to form a control learning case and store the control learning case in a database.
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| CN112906182A (en) * | 2021-01-11 | 2021-06-04 | 中国矿业大学(北京) | Simulation platform and method for large-scale simulation of coal mine stope system and realization of intelligent control of stope surrounding rock in laboratory |
| CN114360176A (en) * | 2021-12-15 | 2022-04-15 | 中煤科工开采研究院有限公司 | Fully mechanized coal mining face safety monitoring method |
| CN114352336A (en) * | 2021-12-17 | 2022-04-15 | 北京天玛智控科技股份有限公司 | Fully-mechanized coal mining face intelligent control system and method |
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