CN116151043A - Pose inversion method and device for scraper conveyor - Google Patents
Pose inversion method and device for scraper conveyor Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for inverting the pose of a scraper conveyor, wherein the method comprises the following steps: the collaborative operation data of the coal mining machine and the scraper conveyor are re-carved through a preset digital twin technology; performing model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor; the heights of the front roller and the rear roller of the coal mining machine are controlled and adjusted according to the pose of the scraper conveyor; the position and the posture of the scraper conveyor can be accurately determined.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to a pose inversion method and device of a scraper conveyor.
Background
As key equipment for leading the intelligent working face to cooperatively operate, the scraper conveyor is a pivot link between a base plate with complex working conditions and the coal mining machine, and the key problem of unmanned intelligent production of the fully-mechanized working face is that how to establish the pose coupling relation between the coal mining machine and the scraper conveyor by means of inertial navigation positioning and pose information of the existing coal mining machine. In the process, although the digitalization degree of the current coal mining machine is higher, more accurate pose information can be obtained, the scraper conveyor is difficult to arrange stable and reliable sensors due to the special working environment and the working characteristics of the scraper conveyor, and the form of the scraper conveyor cannot be directly obtained. With the development of virtual reality and communication technology, a digital twin concept is introduced into the coal industry, and virtual planning is carried out on the fully mechanized coal mining equipment operation process by simulating the full life cycle process of coal production, so that the safety and the high efficiency in the coal production process are improved.
At present, the automatic detection of the pose of the scraper conveyor at home and abroad is mainly studied from the following three angles: firstly, a position and orientation detection method of a scraper conveyor based on position information of a hydraulic support, but the position and orientation measurement precision of the scraper conveyor cannot be ensured due to pin-ear gaps at the mechanical connection positions of the support and the scraper conveyor; secondly, the method for detecting the pose of the scraper conveyor based on the visual technology has higher environmental requirements and can only be suitable for monitoring the pose of the scraper conveyor with the working face and higher visibility. Thirdly, according to the position and posture detection method of the scraper conveyor based on the running track of the coal mining machine, as the supporting skid shoes and the travelling wheels of the coal mining machine are in contact with the scraper conveyor for running, the form of the scraper conveyor can be directly reflected by the running track of the coal mining machine. Therefore, on the basis of the travelling track of the coal mining machine, the pose of the scraper conveyor is calculated by combining the matching relation of the coal mining machine and the scraper conveyor.
The inventor knows that in the prior art, in order to know the pose of the scraper conveyor in real time and solve the problem of alignment of the scraper conveyor, an intelligent perception method for the pose of the scraper conveyor is provided. According to the technology, the three-dimensional pose sensors are arranged in the middle grooves of each section of the scraper conveyor, the bending degree of the scraper in each direction in the three-dimensional space is detected, so that the angle of the current scraper is obtained, the actual state of the scraper conveyor is known in real time, and the coal mining efficiency is improved.
The inventor finds that the scraper conveyor arranged on the whole fully-mechanized mining working face is long, a large number of sensors are required to be installed in the method, the requirements on the performance of the sensors are high, a large number of sensors special for coal mines are used, the cost is high, the data transmission is difficult, the long-term stable acquisition of the pose information of the scraper conveyor is difficult, and therefore, the method still has a great problem in the practical application of the coal mine working face.
The inventor knows that in the prior art, in order to solve the problems that the coal mining machine is difficult to pass due to bending of the scraper conveyor, the load of the scraper conveyor is increased, and the production efficiency of a working face is low. The technology provides a dynamic straightening method of a scraper conveyor based on an absolute motion track of a coal mining machine, which utilizes an accurate positioning track of the coal mining machine to reversely push the straightness of the scraper conveyor, analyzes the track of the coal mining machine running along the scraper conveyor under a set space coordinate system, and pushes the scraper conveyor to a target reference track position of a next cutter by calculating a pushing distance of a hydraulic support.
The inventor finds that although the method analyzes the running track of the scraper conveyor through the accurate positioning of the coal mining machine, only the deviation degree of the scraper conveyor on the horizontal working surface is calculated, and the pose state of the scraper conveyor in the vertical direction under the complex working condition that the bottom plate of the fully mechanized mining working surface is uneven is not considered.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a method and a device for inverting the pose of a scraper conveyor, which can accurately determine the pose of the scraper conveyor.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a method for inverting a pose of a scraper conveyor, including:
the collaborative operation data of the coal mining machine and the scraper conveyor are re-carved through a preset digital twin technology;
performing model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor;
and controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
Further, the collaborative operation data of the re-engraving coal mining machine and the scraper conveyor equipment by the preset digital twin technology comprises the following steps:
virtual mapping is carried out on the collaborative operation of the coal mining machine and the scraper conveyor equipment in a virtual space through a preset digital twin technology, and the angle of the coal mining machine, the angle of the scraper conveyor and the height of the scraper conveyor are obtained;
downsampling the coal cutter angle, and reserving coal cutter angle data when the coal cutter passes 25%, 50% and 75% of the scraper blade.
Further, before the model inversion of the scraper conveyor according to the cooperative operation data and a preset deep learning model, the method includes:
acquiring the angles of the coal cutter when the right sliding shoe and the left sliding shoe of the coal cutter pass through the scraping plate from the cooperative operation data, wherein the angles of the scraping plate are obtained after the two supporting sliding shoes of the coal cutter pass through the scraping plate;
and carrying out digital twin-engraving on the angle of the coal cutter and the angle of the scraping plate by a digital twin technology, and constructing a training data set and a testing data set of a preset deep learning model.
Further, before the model inversion is performed on the scraper conveyor according to the collaborative operation data and a preset deep learning model, the method further comprises:
mapping the training data set and the test data set into a set value interval by adopting a maximum and minimum normalization method for normalization processing;
and inputting the training data set and the test data set which are subjected to normalization processing into a preset convolutional neural network for model training, and obtaining a trained deep learning model.
Further, the model inversion of the scraper conveyor according to the collaborative operation data and a preset deep learning model comprises the following steps:
Inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain a current scraper angle which is inverted and output by the preset deep learning model;
and calculating the relative height of the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained through inversion, and determining the pose of the scraper conveyor according to the relative height.
Further, before the determining the pose of the scraper conveyor according to the relative height, the method further comprises:
and carrying out relative height correction through the serial number of the current scraping plate, a preset error correction factor and an error correction factor of the height of the current scraping plate to obtain corrected relative height.
In a second aspect, the present application provides a scraper conveyor pose inversion apparatus comprising:
the digital twin re-engraving module is used for re-engraving the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology;
the deep learning inversion module is used for carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor;
and the scraper pose determining module is used for controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
Further, the digital twin copy module includes:
the virtual mapping unit is used for virtually mapping the cooperative operation of the coal mining machine and the scraper conveyor equipment in a virtual space through a preset digital twin technology to obtain the angle of the coal mining machine, the angle of the scraper conveyor and the height of the scraper conveyor;
the downsampling unit is used for downsampling the angles of the coal mining machine and reserving the data of the angles of the coal mining machine when the coal mining machine passes through 25%, 50% and 75% of the scraper.
Further, the deep learning inversion module includes:
the data acquisition unit is used for acquiring the coal cutter angle of the right sliding shoe and the left sliding shoe of the coal cutter passing through the scraping plate and the scraping plate angle of the two supporting sliding shoes of the coal cutter after all the two supporting sliding shoes of the coal cutter pass through from the cooperative operation data;
the data set determining unit is used for carrying out digital twin re-engraving on the coal cutter angle and the scraper angle through a digital twin technology, and constructing a training data set and a testing data set of a preset deep learning model.
Further, the deep learning inversion module further includes:
the normalization processing unit is used for mapping the training data set and the test data set into a set value interval by adopting a maximum and minimum normalization method to perform normalization processing;
The model training unit is used for inputting the training data set and the test data set which are subjected to normalization processing into a preset convolutional neural network to perform model training, and obtaining a trained deep learning model.
Further, the deep learning inversion module further includes:
the scraper angle inversion unit is used for inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain the current scraper angle which is inversed and output by the preset deep learning model;
and the relative height calculating unit is used for calculating the relative height between the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained through inversion, and determining the pose of the scraper conveyor according to the relative height.
Further, the deep learning inversion module further includes:
the error correction unit is used for carrying out relative height correction through the serial number of the current scraping plate, a preset error correction factor and an error correction coefficient of the current scraping plate height to obtain corrected relative height.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for inverting the pose of a scraper conveyor when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the scraper conveyor pose inversion method.
In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method for inverting the pose of a scraper conveyor.
According to the technical scheme, the position and posture inversion method and the position and posture inversion device for the scraper conveyor are provided, the collaborative operation process of the coal mining machine and the scraper conveyor is virtually simulated through a digital twin technology, then a position and posture relation model between the coal mining machine and the scraper conveyor is established by using deep learning on the basis, and position and posture inversion of the scraper conveyor based on operation data of the coal mining machine is achieved. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is one of the flow diagrams of a method for inverting the pose of a scraper conveyor in an embodiment of the present application;
FIG. 2 is a second flow chart of a method for inverting the pose of a scraper conveyor according to an embodiment of the present disclosure;
FIG. 3 is a third flow chart of a method for inverting the pose of a scraper conveyor according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of a method for inverting the pose of a scraper conveyor in an embodiment of the present application;
FIG. 5 is a fifth flow chart of a method for inverting the pose of a scraper conveyor in an embodiment of the present application;
FIG. 6 is one of the block diagrams of the blade conveyor pose inversion apparatus in the embodiment of the present application;
FIG. 7 is a second block diagram of a blade conveyor pose inversion apparatus in an embodiment of the present application;
FIG. 8 is a third block diagram of a blade conveyor pose inversion apparatus in an embodiment of the present application;
FIG. 9 is a fourth block diagram of a blade conveyor pose inversion apparatus in an embodiment of the present application;
FIG. 10 is a fifth block diagram of a blade conveyor pose inversion apparatus in an embodiment of the present application;
FIG. 11 is a sixth block diagram of a blade conveyor pose inversion apparatus in an embodiment of the present application;
FIG. 12 is a schematic diagram of data collection of a dataset in accordance with an embodiment of the present application;
FIG. 13 is a schematic diagram of height calculation in an embodiment of the present application;
FIG. 14 is a schematic overall flow diagram of a method for inverting the pose of a scraper conveyor in an embodiment of the present application;
fig. 15 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
In consideration of the problems in the prior art, the application provides a method and a device for inverting the pose of a scraper conveyor, which are used for virtually simulating the cooperative operation working process of the coal mining machine and the scraper conveyor through a digital twin technology, and then establishing a pose relation model between the coal mining machine and the scraper conveyor by using deep learning on the basis, so as to realize the pose inversion of the scraper conveyor based on the operation data of the coal mining machine. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
In order to accurately determine the pose of the scraper conveyor, the application provides an embodiment of a method for inverting the pose of the scraper conveyor, referring to fig. 1, wherein the method for inverting the pose of the scraper conveyor specifically comprises the following contents:
step S101: and (3) carrying out repeated engraving on the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology.
Alternatively, the present application may first re-score the shearer and blade conveyor equipment co-operating data using digital twinning techniques.
Firstly, in view of high risk of underground coal mine production and the particularity of working face environments, collaborative operation data of fully mechanized coal mining equipment in a real scene are difficult to obtain. In order to obtain a large amount of accurate data of the coal mining machine and the scraper conveyor at a low cost, the invention selects and utilizes a digital twin technology to virtually map the collaborative operation of the coal mining machine and the scraper conveyor equipment in a virtual space so as to obtain the data of the angle of the coal mining machine, the angle of the scraper conveyor, the height of the scraper conveyor and the like.
Then, after the shearer angle data is obtained, different data are generated when the shearer passes different positions of a certain scraper due to the problem of the shearer speed. Considering that the angle change of the coal mining machine at different positions of the same scraper blade is not obvious, and in order to reduce the calculated amount, the method selects downsampling the generated coal mining machine angle, and only retains the coal mining machine angle when the coal mining machine passes through 25%, 50% and 75% of the scraper blade.
Step S102: and carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor.
Optionally, in the application, virtual simulation and re-engraving of the collaborative operation process of the coal cutter and the scraper conveyor equipment can be performed based on a digital twin technology, a relation model between the angle of the coal cutter and the angle of the scraper conveyor is established by using a deep learning technology, inversion calculation of the angle of the scraper conveyor is realized, the relative height of the scraper conveyor is calculated by using a trigonometric function on the basis, and accumulated errors generated in the calibration process are calculated, so that the pose of the scraper conveyor is finally obtained, and data guarantee is provided for height adjustment of front and rear rollers of the coal cutter.
Step S103: and controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
From the above description, the pose inversion method of the scraper conveyor provided by the embodiment of the application can virtually simulate the cooperative operation working process of the coal mining machine and the scraper conveyor through a digital twin technology, and then a pose relation model between the coal mining machine and the scraper conveyor is established by using deep learning on the basis, so that the pose inversion of the scraper conveyor based on the operation data of the coal mining machine is realized. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
In an embodiment of the method for inverting the pose of the scraper conveyor of the present application, referring to fig. 2, the step S101 may further specifically include the following:
step S201: and virtually mapping the collaborative operation of the coal cutter and the scraper conveyor equipment in a virtual space by a preset digital twin technology to obtain the angle of the coal cutter, the angle of the scraper conveyor and the height of the scraper conveyor.
Step S202: downsampling the coal cutter angle, and reserving coal cutter angle data when the coal cutter passes 25%, 50% and 75% of the scraper blade.
Firstly, in view of high risk of underground coal mine production and the particularity of working face environments, collaborative operation data of fully mechanized coal mining equipment in a real scene are difficult to obtain. In order to obtain a large amount of accurate data of the coal mining machine and the scraper conveyor at a low cost, the invention selects and utilizes a digital twin technology to virtually map the collaborative operation of the coal mining machine and the scraper conveyor equipment in a virtual space so as to obtain the data of the angle of the coal mining machine, the angle of the scraper conveyor, the height of the scraper conveyor and the like.
Then, after the shearer angle data is obtained, different data are generated when the shearer passes different positions of a certain scraper due to the problem of the shearer speed. Considering that the angle change of the coal mining machine at different positions of the same scraper blade is not obvious, and in order to reduce the calculated amount, the method selects downsampling the generated coal mining machine angle, and only retains the coal mining machine angle when the coal mining machine passes through 25%, 50% and 75% of the scraper blade.
In an embodiment of the method for inverting the pose of the scraper conveyor of the present application, referring to fig. 3, the step S102 may further specifically include the following:
step S301: and acquiring the angles of the coal cutter when the right sliding shoe and the left sliding shoe of the coal cutter pass through the scraping plate and the scraping plate angles of the two supporting sliding shoes of the coal cutter after all the supporting sliding shoes of the coal cutter pass through the scraping plate from the cooperative operation data.
Step S302: and carrying out digital twin-engraving on the angle of the coal cutter and the angle of the scraping plate by a digital twin technology, and constructing a training data set and a testing data set of a preset deep learning model.
Optionally, firstly, selecting the right sliding shoe of the coal mining machine to pass through the firstiThe angles of the shearer at 25%, 50% and 75% of the scraper blade are respectively recorded as
And the angles of the coal mining machine when the left skid shoe of the coal mining machine passes through 25 percent, 50 percent and 75 percent of the scraper blade after a period of time are recorded as +.>
The 6 data together form a group of model input data, and then the angle of the scraping plate is recorded as +.>
And outputting the data as a corresponding model. Finally, the 210-cutter data reproduced using the digital twin technique is processed as a final sample data set (with 200-cutter data used as training data and the remaining 10-cutter data used as test data) in accordance with the operations described above.
In an embodiment of the method for inverting the pose of the scraper conveyor of the present application, referring to fig. 4, the step S102 may further specifically include the following:
step S401: and mapping the training data set and the test data set into a set numerical value interval by adopting a maximum and minimum normalization method for normalization processing.
Step S402: and inputting the training data set and the test data set which are subjected to normalization processing into a preset convolutional neural network for model training, and obtaining a trained deep learning model.
Optionally, the present application may map the result of the preprocessing to the [0,1] interval by using a maximum and minimum normalization method to perform normalization processing, so as to eliminate the dimension difference between the training sample and the test sample. The normalization formula is as follows:
in the method, in the process of the invention,
for maximum value in training dataset, +.>
Is the minimum in the training dataset.
(3) And then training the convolutional neural network by using the normalized training data set. The specific formula for training the convolutional neural network model is as follows:
in the method, in the process of the invention,xis composed of
Angle of combined coal cutter->
And->
Is the firstlWeights and biases of layer network layer +.>
Is the firstlOutputting a result of the layer network layer; mFor training the number of samples, +.>
Representing the calculated blade angle of the convolutional neural network, < >>
Representing the angle of the squeegee reproduced using digital twinning techniques.
After multiple rounds of iterative training, the convolutional neural network can reverse the current flight angle from the shearer angle. And if the inversion accuracy of the model on the test data set meets the requirement, the model can be stored. In the actual application stage, only the coal machine angle measured by the inertial navigation system is directly input into the trained convolutional neural network, and the model can automatically give the angle of each scraper so as to finish the inversion of the scraper angle of the scraper conveyor.
In an embodiment of the method for inverting the pose of the scraper conveyor of the present application, referring to fig. 5, the step S102 may specifically include the following:
step S501: and inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain the current scraper angle which is inverted and output by the preset deep learning model.
Step S502: and calculating the relative height of the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained through inversion, and determining the pose of the scraper conveyor according to the relative height.
Optionally, the present application may calculate the relative height of the current blade compared to the first blade (where no gap exists between two adjacent blades by default) according to the current blade angle output by the convolutional neural network, and the specific formula is as follows:
in the method, in the process of the invention,
is the firstiThe height of the block screed compared to the first block screed,Wfor the width of the scraper->
Is the firstiBlade angle of block blade.
In an embodiment of the method for inverting the pose of the scraper conveyor of the present application, the following may be specifically included:
and carrying out relative height correction through the serial number of the current scraping plate, a preset error correction factor and an error correction factor of the height of the current scraping plate to obtain corrected relative height.
Alternatively, as can be seen from the formula (4), the height of the current blade is calculated based on the previous blade, and then there is an error accumulation problem in this process. In order to solve the problem, the invention designs an error correction algorithm, and the specific operation is as shown in a formula (5):
in the method, in the process of the invention,
for the scratch board serial number->
Is error correction factor, +>
The error correction coefficient for the current flight height is the value:
after error correction of equation (5), the pose of the final scraper conveyor is obtained. On the basis, the upper computer can directly control and adjust the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
In order to accurately determine the pose of the scraper conveyor, the application provides an embodiment of a pose inversion device of the scraper conveyor for realizing all or part of the pose inversion method of the scraper conveyor, referring to fig. 6, the pose inversion device of the scraper conveyor specifically comprises the following contents:
the digital twin resculpting module 10 is used for resculpting the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology.
And the deep learning inversion module 20 is used for carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor.
And the scraper pose determining module 30 is used for controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
From the above description, the pose inversion device of the scraper conveyor provided by the embodiment of the application can virtually simulate the cooperative operation working process of the coal mining machine and the scraper conveyor through a digital twin technology, and then a pose relation model between the coal mining machine and the scraper conveyor is established by using deep learning on the basis, so that the pose inversion of the scraper conveyor based on the operation data of the coal mining machine is realized. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
In one embodiment of the scraper conveyor pose inversion apparatus of the present application, referring to fig. 7, the digital twin re-engraving module 10 includes:
the virtual mapping unit 11 is configured to virtually map the collaborative operation of the coal cutter and the scraper conveyor equipment in the virtual space by using a preset digital twin technology, so as to obtain a coal cutter angle, a scraper conveyor angle and a scraper conveyor height.
The downsampling unit 12 is used for downsampling the angle of the coal mining machine, and retains the data of the angle of the coal mining machine when the coal mining machine passes 25%, 50% and 75% of the scraper blade.
In one embodiment of the blade conveyor pose inversion apparatus of the present application, referring to fig. 8, the deep learning inversion module 20 includes:
and the data acquisition unit 21 is used for acquiring the angle of the coal cutter when the right sliding shoe and the left sliding shoe of the coal cutter pass through the scraping plate and the scraping plate angle after the two supporting sliding shoes of the coal cutter pass through the scraping plate from the cooperative operation data.
The data set determining unit 22 is configured to perform digital twin re-engraving on the coal cutter angle and the scraper angle through a digital twin technology, and construct a training data set and a testing data set of a preset deep learning model.
In an embodiment of the pose inversion apparatus of the scraper conveyor of the present application, referring to fig. 9, the deep learning inversion module 20 further includes:
And the normalization processing unit 23 is used for mapping the training data set and the test data set into a set value interval by adopting a maximum and minimum normalization method for normalization processing.
The model training unit 24 is configured to input the normalized training data set and the test data set into a preset convolutional neural network for model training, and obtain a trained deep learning model.
In an embodiment of the pose inversion apparatus of the scraper conveyor of the present application, referring to fig. 10, the deep learning inversion module 20 further includes:
and the scraper angle inversion unit 25 is used for inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain the current scraper angle outputted by inversion of the preset deep learning model.
And the relative height calculating unit 26 is used for calculating the relative height of the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained by inversion, and determining the pose of the scraper conveyor according to the relative height.
In an embodiment of the pose inversion apparatus of the scraper conveyor of the present application, referring to fig. 11, the deep learning inversion module 20 further includes:
the error correction unit 27 is configured to perform relative height correction by using the serial number of the current squeegee, the preset error correction factor, and the error correction coefficient of the current squeegee height, to obtain a corrected relative height.
In order to further explain the scheme, the application also provides a specific application example of the position and orientation inversion device of the scraper conveyor for realizing the position and orientation inversion method of the scraper conveyor, referring to fig. 14, which specifically comprises the following contents:
first, the data of the collaborative operation of the coal mining machine and the scraper conveyor equipment are re-carved by using the digital twin technology, and the data are shown in fig. 12.
(1) In view of the high risk of underground coal mine production and the particularity of the working face environment, it is difficult to obtain the collaborative operation data of fully-mechanized coal mining equipment in a real scene. In order to obtain a large amount of accurate data of the coal mining machine and the scraper conveyor at a low cost, the invention selects and utilizes a digital twin technology to virtually map the collaborative operation of the coal mining machine and the scraper conveyor equipment in a virtual space so as to obtain the data of the angle of the coal mining machine, the angle of the scraper conveyor, the height of the scraper conveyor and the like.
(2) After the shearer angle data is acquired, different data can be generated when the shearer passes through different positions of a certain scraper due to the problem of the shearer speed. Considering that the angle change of the coal mining machine at different positions of the same scraper blade is not obvious, and in order to reduce the calculated amount, the invention selects to downsample the generated coal mining machine angle, and only retains the coal mining machine angle when the coal mining machine passes 25%, 50% and 75% of the scraper blade.
The pose of the scraper conveyor is then inverted using a deep learning technique.
(1) Firstly, selecting a right sliding shoe of the coal mining machine to pass throughiThe angles of the shearer at 25%, 50% and 75% of the scraper blade are respectively recorded as
And the angles of the coal mining machine when the left skid shoe of the coal mining machine passes through 25 percent, 50 percent and 75 percent of the scraper blade after a period of time are recorded as +.>
The 6 data together form a group of model input data, and then the angle of the scraping plate is recorded as +.>
As a result ofData is output for the corresponding model. Finally, the 210-cutter data reproduced using the digital twin technique is processed as a final sample data set (with 200-cutter data used as training data and the remaining 10-cutter data used as test data) in accordance with the operations described above.
(2) And then mapping the preprocessed result into a [0,1] interval by adopting a maximum and minimum normalization method to perform normalization processing so as to eliminate dimension difference between the training sample and the test sample. The normalization formula is as follows:
in the method, in the process of the invention,
for maximum value in training dataset, +.>
Is the minimum in the training dataset.
(3) And then training the convolutional neural network by using the normalized training data set. The specific formula for training the convolutional neural network model is as follows:
In the method, in the process of the invention,xis composed of
Angle of combined coal cutter->
And->
Is the firstlWeights and biases of layer network layer +.>
Is the firstlOutputting a result of the layer network layer;mfor training the number of samples, +.>
Representing the calculated blade angle of the convolutional neural network, < >>
Representing the angle of the squeegee reproduced using digital twinning techniques.
After multiple rounds of iterative training, the convolutional neural network can reverse the current flight angle from the shearer angle. And if the inversion accuracy of the model on the test data set meets the requirement, the model can be stored. In the actual application stage, only the coal machine angle measured by the inertial navigation system is directly input into the trained convolutional neural network, and the model can automatically give the angle of each scraper so as to finish the inversion of the scraper angle of the scraper conveyor.
(4) Referring to fig. 13, the relative height of the current blade compared with the first blade (no gap exists between two adjacent blades by default here) needs to be calculated according to the current blade angle output by the convolutional neural network, and the specific formula is as follows:
in the method, in the process of the invention,
is the firstiThe height of the block screed compared to the first block screed,Wfor the width of the scraper->
Is the firstiBlade angle of block blade.
As can be seen from the formula (4), the height of the current blade is calculated based on the previous blade, and there is an error accumulation problem in this process. In order to solve the problem, the invention designs an error correction algorithm, and the specific operation is as shown in a formula (5):
in the method, in the process of the invention,
for the scratch board serial number->
Is error correction factor, +>
The error correction coefficient for the current flight height is the value:
after error correction of equation (5), the pose of the final scraper conveyor is obtained. On the basis, the upper computer can directly control and adjust the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
According to the method, the digital twin technology is utilized to re-etch the collaborative operation data of the coal mining machine and the scraper conveyor equipment, and the method fundamentally solves the problem that reliable coal mining machine operation parameters and scraper conveyor pose information in a real scene cannot be stably obtained for a long time. On the basis, the invention provides a deep learning-based scraper conveyor pose inversion algorithm, which not only can accurately obtain the scraper angle converted from the angle of the coal mining machine and calculate the height of the scraper according to a trigonometric function, but also can eliminate the accumulated error in the process as much as possible, and finally provides data guarantee for the height adjustment of front and rear rollers of the coal mining machine so as to reduce the manual intervention in the coal production process and further improve the coal production efficiency and safety.
In order to accurately determine the pose of the scraper conveyor from the aspect of hardware, the application provides an embodiment of an electronic device for implementing all or part of the content in the pose inversion method of the scraper conveyor, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the pose inversion device of the scraper conveyor and related equipment such as a core service system, a user terminal, a related database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the method for inverting the pose of the scraper conveyor in the embodiment and the embodiment of the device for inverting the pose of the scraper conveyor, and the contents thereof are incorporated herein, and the repetition is omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the method for inverting the pose of the scraper conveyor can be performed on the electronic equipment side as described above, or all operations can be completed in the client equipment. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 15 is a schematic block diagram of a system configuration of the electronic device 9600 of the embodiment of the present application. As shown in fig. 15, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 15 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the flight conveyor pose inversion method functionality may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
step S101: and (3) carrying out repeated engraving on the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology.
Step S102: and carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor.
Step S103: and controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
From the above description, the electronic device provided by the embodiment of the application virtually simulates the cooperative operation process of the coal mining machine and the scraper conveyor through the digital twin technology, and then uses deep learning to build a pose relation model between the coal mining machine and the scraper conveyor on the basis of the cooperative operation process, so as to realize pose inversion of the scraper conveyor based on the operation data of the coal mining machine. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
In another embodiment, the blade conveyor pose inversion device may be configured separately from the central processor 9100, for example, the blade conveyor pose inversion device may be configured as a chip connected to the central processor 9100, and the blade conveyor pose inversion method function is implemented by the control of the central processor.
As shown in fig. 15, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 15; in addition, the electronic device 9600 may further include components not shown in fig. 15, and reference may be made to the related art.
As shown in fig. 15, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiments of the present application further provide a computer readable storage medium capable of implementing all the steps in the method for inverting the pose of a scraper conveyor in which the execution subject in the above embodiment is a server or a client, where the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the method for inverting the pose of a scraper conveyor in which the execution subject in the above embodiment is a server or a client, for example, the processor implements the following steps when executing the computer program:
Step S101: and (3) carrying out repeated engraving on the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology.
Step S102: and carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor.
Step S103: and controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
As can be seen from the above description, the computer readable storage medium provided in the embodiments of the present application virtually simulates the cooperative operation process of the coal mining machine and the scraper conveyor by using the digital twin technology, and then uses deep learning to build a pose relationship model between the coal mining machine and the scraper conveyor on the basis of the cooperative operation process, so as to implement pose inversion of the scraper conveyor based on the operation data of the coal mining machine. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
The embodiments of the present application further provide a computer program product capable of implementing all the steps in the method for inverting the pose of a scraper conveyor in which the execution subject in the above embodiments is a server or a client, where the steps of the method for inverting the pose of a scraper conveyor are implemented by a processor, for example, the computer program/instructions implement the following steps:
Step S101: and (3) carrying out repeated engraving on the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology.
Step S102: and carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor.
Step S103: and controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
As can be seen from the above description, the computer program product provided in the embodiments of the present application virtually simulates the cooperative operation process of the coal mining machine and the scraper conveyor by using the digital twin technology, and then uses deep learning to build a pose relationship model between the coal mining machine and the scraper conveyor on the basis of the cooperative operation process, so as to implement pose inversion of the scraper conveyor based on the operation data of the coal mining machine. Finally, guiding the self-adaptive height adjustment of the roller of the coal mining machine according to the inversion result of the scraper conveyor, and further improving the safety and the high efficiency in the coal production process.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (15)
1. A method for inverting the pose of a scraper conveyor, the method comprising:
the collaborative operation data of the coal mining machine and the scraper conveyor are re-carved through a preset digital twin technology;
performing model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor;
And controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
2. The method of claim 1, wherein the collaborative operation data of the resculpting shearer and the scraper conveyor equipment by the preset digital twin technique comprises:
virtual mapping is carried out on the collaborative operation of the coal mining machine and the scraper conveyor equipment in a virtual space through a preset digital twin technology, and the angle of the coal mining machine, the angle of the scraper conveyor and the height of the scraper conveyor are obtained;
downsampling the coal cutter angle, and reserving coal cutter angle data when the coal cutter passes 25%, 50% and 75% of the scraper blade.
3. The method of inverting the pose of a scraper conveyor according to claim 1, comprising, before said model inverting the scraper conveyor based on the cooperative operation data and a preset deep learning model:
acquiring the angles of the coal cutter when the right sliding shoe and the left sliding shoe of the coal cutter pass through the scraping plate from the cooperative operation data, wherein the angles of the scraping plate are obtained after the two supporting sliding shoes of the coal cutter pass through the scraping plate;
and carrying out digital twin-engraving on the angle of the coal cutter and the angle of the scraping plate by a digital twin technology, and constructing a training data set and a testing data set of a preset deep learning model.
4. The method of claim 3, further comprising, prior to said model inversion of said scraper conveyor based on said co-operating data and a predetermined deep learning model:
mapping the training data set and the test data set into a set value interval by adopting a maximum and minimum normalization method for normalization processing;
and inputting the training data set and the test data set which are subjected to normalization processing into a preset convolutional neural network for model training, and obtaining a trained deep learning model.
5. The method of claim 1, wherein the model inverting the scraper conveyor according to the cooperative operation data and a preset deep learning model comprises:
inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain a current scraper angle which is inverted and output by the preset deep learning model;
and calculating the relative height of the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained through inversion, and determining the pose of the scraper conveyor according to the relative height.
6. The method of claim 5, further comprising, prior to said determining the pose of the scraper conveyor from said relative heights:
and carrying out relative height correction through the serial number of the current scraping plate, a preset error correction factor and an error correction factor of the height of the current scraping plate to obtain corrected relative height.
7. The utility model provides a scraper conveyor position appearance inversion device which characterized in that includes:
the digital twin re-engraving module is used for re-engraving the cooperative operation data of the coal mining machine and the scraper conveyor through a preset digital twin technology;
the deep learning inversion module is used for carrying out model inversion on the scraper conveyor according to the cooperative operation data and a preset deep learning model, and determining the pose of the scraper conveyor;
and the scraper pose determining module is used for controlling and adjusting the heights of the front roller and the rear roller of the coal mining machine according to the pose of the scraper conveyor.
8. The blade conveyor pose inversion apparatus of claim 7 wherein said digital twin replication module comprises:
the virtual mapping unit is used for virtually mapping the cooperative operation of the coal mining machine and the scraper conveyor equipment in a virtual space through a preset digital twin technology to obtain the angle of the coal mining machine, the angle of the scraper conveyor and the height of the scraper conveyor;
The downsampling unit is used for downsampling the angles of the coal mining machine and reserving the data of the angles of the coal mining machine when the coal mining machine passes through 25%, 50% and 75% of the scraper.
9. The blade conveyor pose inversion device of claim 7 wherein said deep learning inversion module comprises:
the data acquisition unit is used for acquiring the coal cutter angle of the right sliding shoe and the left sliding shoe of the coal cutter passing through the scraping plate and the scraping plate angle of the two supporting sliding shoes of the coal cutter after all the two supporting sliding shoes of the coal cutter pass through from the cooperative operation data;
the data set determining unit is used for carrying out digital twin re-engraving on the coal cutter angle and the scraper angle through a digital twin technology, and constructing a training data set and a testing data set of a preset deep learning model.
10. The blade conveyor pose inversion apparatus of claim 9, wherein said deep learning inversion module further comprises:
the normalization processing unit is used for mapping the training data set and the test data set into a set value interval by adopting a maximum and minimum normalization method to perform normalization processing;
the model training unit is used for inputting the training data set and the test data set which are subjected to normalization processing into a preset convolutional neural network to perform model training, and obtaining a trained deep learning model.
11. The blade conveyor pose inversion apparatus of claim 7, wherein said deep learning inversion module further comprises:
the scraper angle inversion unit is used for inputting the coal cutter angle in the collaborative operation data into a preset deep learning model to obtain the current scraper angle which is inversed and output by the preset deep learning model;
and the relative height calculating unit is used for calculating the relative height between the current scraper angle and the initial scraper according to the trigonometric function relation of the current scraper angle obtained through inversion, and determining the pose of the scraper conveyor according to the relative height.
12. The blade conveyor pose inversion apparatus of claim 7, wherein said deep learning inversion module further comprises:
the error correction unit is used for carrying out relative height correction through the serial number of the current scraping plate, a preset error correction factor and an error correction coefficient of the current scraping plate height to obtain corrected relative height.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for inverting the pose of a scraper conveyor according to any of claims 1 to 6 when the program is executed by the processor.
14. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for inverting the pose of a scraper conveyor according to any one of claims 1 to 6.
15. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method for inverting the pose of a scraper conveyor according to any of claims 1 to 6.
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