CN115773128B - Cutting control method and system of heading machine and heading machine - Google Patents
Cutting control method and system of heading machine and heading machine Download PDFInfo
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- CN115773128B CN115773128B CN202310095079.XA CN202310095079A CN115773128B CN 115773128 B CN115773128 B CN 115773128B CN 202310095079 A CN202310095079 A CN 202310095079A CN 115773128 B CN115773128 B CN 115773128B
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Abstract
The application discloses a cutting control method and system of a heading machine and the heading machine, relates to the technical field of heading machines, and solves the problems that the existing technology lacks perception of peripheral conditions, once an emergency occurs, tasks cannot be continuously executed, and the cutting efficiency is low. The method comprises the following steps: determining manual operation data and automatic operation data corresponding to each execution behavior in a plurality of execution behaviors; training the obtained automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program; responding to the operation request of the heading machine, acquiring operation test data carried by the operation request of the heading machine, and inputting the operation test data into a target automatic cutting program; and acquiring a cutting control instruction output by the target automatic cutting program to control the heading machine to execute cutting operation according to the cutting control instruction.
Description
Technical Field
The application belongs to the technical field of heading machines, and in particular relates to a cutting control method and system of a heading machine and the heading machine.
Background
The heading machine is a common mechanical device used for tunneling mine tunnels, engineering tunnels and urban underground engineering. Especially in the coal industry, a heading machine is often used for underground operation, and the working environment of the heading machine has a great potential safety hazard.
In the prior art, a manual operation development machine or a positioning automatic cutting technology is often adopted for executing the operation task, and for the manual operation development machine, a person is usually adopted to sit on the development machine for manually driving the development machine for development work, so that the development machine has strong personnel safety hidden trouble; for the positioning automatic cutting technology, the output action of the heading machine is planned in advance, and corresponding actions are executed according to a time flow during operation, but the program of the technology is solidified, the perception of peripheral conditions is lacking, and once an emergency occurs, tasks cannot be continuously executed, so that the cutting efficiency is low.
Disclosure of Invention
In view of the above, the invention provides a cutting control method and a cutting control system for a heading machine, and the heading machine, which mainly aims to solve the problems that the current situation lacks perception of peripheral conditions, and once an emergency occurs, tasks cannot be continuously executed, so that the cutting efficiency is low.
According to a first aspect of the present application, there is provided a cutting control method of a heading machine, including:
Acquiring a plurality of execution behaviors of the heading machine, and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors;
acquiring an automatic cutting program of the heading machine, training the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain a target automatic cutting program;
responding to a development machine operation request, acquiring operation test data carried by the development machine operation request, and inputting the operation test data into the target automatic cutting program;
and acquiring a cutting control instruction output by the target automatic cutting program so as to control the heading machine to execute cutting operation according to the cutting control instruction.
Optionally, before the acquiring the plurality of execution behaviors of the heading machine, the method further includes:
acquiring an initial manual operation data set for operating a manual cutting program, and extracting at least one first data category included in the initial manual operation data set;
classifying a plurality of initial manual operation data included in the initial manual operation data set based on the at least one first data category to obtain a classified initial manual operation data set;
Slicing the classified initial manual operation data set to obtain a sliced initial manual operation data set;
acquiring a first time point of each manual cutting task, and marking initial manual operation data corresponding to the initial manual operation data set after slicing by adopting the first time point to obtain a tidied manual operation data set; the method comprises the steps of,
acquiring an initial automatic operation data set for operating an automatic cutting program, and extracting at least one second data category included in the initial automatic operation data set;
classifying a plurality of initial automatic operation data included in the initial automatic operation data set based on the at least one second data category to obtain a classified initial automatic operation data set;
slicing the classified initial automatic operation data set to obtain a sliced initial automatic operation data set;
and acquiring a second time node for executing the automatic cutting task each time, and marking the initial automatic operation data corresponding to the initial automatic operation data set after slicing by adopting the second time node to obtain a finished automatic operation data set.
Optionally, before the acquiring the plurality of execution behaviors of the heading machine, the method further includes:
acquiring manual execution data which is acquired by controlling the heading machine based on each piece of the sorted manual behavior data in the sorted manual operation data set, and acquiring a manual execution data set;
based on the sorted manual operation data set and the manual execution data set, determining a plurality of execution behaviors and manual operation data corresponding to each execution behavior in the plurality of execution behaviors by adopting a behavior recognition algorithm;
acquiring automatic execution data which is acquired by controlling the heading machine based on each piece of the automatic behavior data after finishing in the automatic operation data set after finishing;
and determining automatic execution data corresponding to each execution behavior in the plurality of execution behaviors.
Optionally, the training the automatic cutting program based on the manual operation data and the automatic operation data corresponding to each execution behavior, and updating the program parameters of the automatic cutting program to obtain the target automatic cutting program, which includes:
inputting the manual operation data corresponding to each execution behavior into the automatic cutting program, controlling the heading machine to acquire first automatic execution data corresponding to each execution behavior, comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating program parameters of the automatic cutting program, and determining at least one target manual operation data meeting preset conditions as training data of the next round;
And determining preset training times, inputting training data obtained from the previous round of training into an updated automatic cutting program in each round of training, and continuously updating program parameters of the automatic cutting program until the training rounds reach the preset training times to obtain the target automatic cutting program.
Optionally, the comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating the program parameters of the automatic cutting program, and determining at least one target manual operation data meeting the preset condition as the training data of the next round includes:
calculating a difference value between the automatic execution data and the first automatic execution data indicated by the same execution behavior;
acquiring a preset threshold value, if the difference value is smaller than the preset threshold value, performing fine adjustment on the program parameters of the automatic cutting program, and determining target manual operation data corresponding to the same execution behavior;
and acquiring the at least one target manual operation data as training data of the next round.
Optionally, the method further comprises:
and if the difference value is larger than the preset threshold value, replacing the program parameters of the automatic cutting program by adopting the program parameters of the manual cutting program.
Optionally, after the obtaining the target automatic cutting program, the method further includes:
and uploading the target program parameters of the target automatic cutting program to a cloud server so that other heading machines update the program parameters of the automatic cutting program by using the target program parameters.
According to a second aspect of the present application, there is provided a cutting control system for a heading machine, comprising:
the determining module is used for acquiring a plurality of execution behaviors of the heading machine and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors;
the training module is used for acquiring an automatic cutting program of the heading machine, training the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain a target automatic cutting program;
the operation module is used for responding to an operation request of the heading machine, acquiring operation test data carried by the operation request of the heading machine and inputting the operation test data into the target automatic cutting program;
and the cutting module is used for acquiring a cutting control instruction output by the target automatic cutting program so as to control the heading machine to execute cutting operation according to the cutting control instruction.
Optionally, the system further comprises: an acquisition module;
the acquisition module comprises: the system comprises a laser ranging unit, a millimeter wave radar unit, an infrared thermal imaging unit and a total station positioning unit, wherein,
the laser ranging unit is used for acquiring absolute distance data of the peripheral wall body of the heading machine and the heading machine body;
the millimeter wave radar unit is used for acquiring surface shape data of the peripheral wall body of the heading machine;
the infrared thermal imaging unit is used for acquiring heat source data around the heading machine;
and the total station positioning unit is used for acquiring absolute position data of the heading machine on a working surface.
According to a third aspect of the present application there is provided a heading machine comprising a cutting control system of a heading machine according to any one of the second aspects described above.
By means of the technical scheme, the cutting control method of the heading machine comprises the steps of firstly obtaining a plurality of execution behaviors of the heading machine, determining manual operation data and automatic operation data corresponding to each execution behavior in the execution behaviors, then obtaining an automatic cutting program of the heading machine, training the automatic cutting program based on the manual operation data and the automatic operation data corresponding to each execution behavior, updating program parameters of the automatic cutting program to obtain a target automatic cutting program, then responding to an operation request of the heading machine, obtaining operation test data carried by the operation request of the heading machine, inputting the operation test data into the target automatic cutting program, finally obtaining a cutting control instruction output by the target automatic cutting program, so that the heading machine is controlled to execute cutting operation according to the cutting control instruction, and when a cutting task is executed, the cutting operation is executed according to the trained cutting program by the automatic cutting program of the heading machine, so that the perception of the heading machine to peripheral conditions is improved, and the accuracy of the cutting operation is improved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 illustrates a flow chart of a method of cutting control of a heading machine in accordance with one embodiment of the present application;
FIG. 2 illustrates a flow chart of a method of cutting control of a heading machine in accordance with another embodiment of the present application;
FIG. 3 shows a schematic structural diagram of a cutting control system of a heading machine according to one embodiment of the present application;
FIG. 4 shows a schematic structural diagram of a cutting control system of a heading machine, according to one embodiment of the present application;
FIG. 5 shows a schematic structural diagram of a cutting control system of a heading machine, according to an embodiment of the present application;
FIG. 6 shows a schematic structural diagram of a cutting control system of a heading machine, according to an embodiment of the present application;
FIG. 7 shows a schematic structural diagram of a cutting control system of a heading machine, according to an embodiment of the present application;
FIG. 8 shows a schematic structural diagram of a mathematical model constructed by binocular vision units in an acquisition module of a cutting control system of a heading machine, according to one embodiment of the present application;
FIG. 9 illustrates a system architecture diagram of a heading machine of a cutting control system of the heading machine in accordance with an embodiment of the present application;
fig. 10 shows a schematic diagram of a three-dimensional point cloud image construction based on distance data collected by a binocular vision unit in an acquisition module of a cutting control system of a heading machine according to one embodiment of the application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the accompanying drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this application will occur to those skilled in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the present application has been described with reference to some specific examples, those skilled in the art can certainly realize many other equivalent forms of the present application.
The foregoing and other aspects, features, and advantages of the present application will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application with unnecessary or excessive detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments as per the application.
Example 1
The cutting control method of the heading machine provided by the embodiment of the application can be applied to a control system of the heading machine shown in fig. 9, specifically, as shown in fig. 9, the heading machine comprises an acquisition module and a control system, when a data acquisition request is received, the control system generates a control instruction and sends the control instruction to the acquisition module, and the acquisition module executes related operations based on the received control instruction and uploads acquired data to the control system.
The embodiment of the application provides a cutting control method of a heading machine, as shown in fig. 1, comprising the following steps:
101. acquiring a plurality of execution behaviors of the heading machine, and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors.
102. Acquiring an automatic cutting program of the heading machine, training the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain the target automatic cutting program.
103. Responding to the operation request of the heading machine, acquiring operation test data carried by the operation request of the heading machine, and inputting the operation test data into the target automatic cutting program.
104. And acquiring a cutting control instruction output by the target automatic cutting program to control the heading machine to execute cutting operation according to the cutting control instruction.
According to the method provided by the embodiment of the application, a plurality of execution behaviors of the development machine are firstly obtained, manual operation data and automatic operation data corresponding to each execution behavior in the execution behaviors are determined, then an automatic cutting program of the development machine is obtained, the automatic cutting program is trained based on the manual operation data and the automatic operation data corresponding to each execution behavior, program parameters of the automatic cutting program are updated to obtain a target automatic cutting program, then an operation test data carried by an operation request of the development machine is obtained in response to the operation request of the development machine, the operation test data are input into the target automatic cutting program, finally a cutting control instruction output by the target automatic cutting program is obtained to control the development machine to execute cutting operation according to the cutting control instruction, the perception of the development machine to peripheral conditions is improved through the automatic cutting program of the training development machine, and when the cutting task is executed, the cutting operation is executed according to the trained cutting program, so that the accuracy of the cutting operation is improved.
The embodiment of the application provides a cutting control method of a heading machine, as shown in fig. 2, the method comprises the following steps:
201. the method comprises the steps of acquiring an initial manual operation data set and an initial automatic operation data set, obtaining the manual operation data set based on the initial manual operation data set, and obtaining the automatic operation data set based on the initial automatic operation data set.
In the embodiment of the application, for a manual cutting program of a heading machine, firstly, a control system of the heading machine acquires an initial manual operation data set for operating the manual cutting program, extracts at least one first data category included in the initial manual operation data set, then classifies a plurality of initial manual operation data included in the initial manual operation data set based on the at least one first data category to obtain a classified initial manual operation data set, then performs slicing processing on the classified initial manual operation data set to obtain a sliced initial manual operation data set, finally acquires a first time point for executing the manual cutting task each time, and marks initial manual operation data corresponding to the sliced initial manual operation data set by adopting the first time point to obtain a finished manual operation data set.
Further, for an automatic cutting program, firstly acquiring an initial automatic operation data set for operating the automatic cutting program, extracting at least one second data category included in the initial automatic operation data set, then classifying a plurality of initial automatic operation data included in the initial automatic operation data set based on the at least one second data category to obtain a classified initial automatic operation data set, then slicing the classified initial automatic operation data set to obtain a sliced initial automatic operation data set, finally acquiring a second time node for executing an automatic cutting task each time, and marking initial automatic operation data corresponding to the sliced initial automatic operation data set by adopting the second time node to obtain a finished automatic operation data set.
In the embodiment of the present application, the first data type and the second data type are data that may be generated when the heading machine is operated, for example, a cutting head operating current, a hydraulic pressure when the heading machine oil pump is operated, and the like.
202. Automatic execution data corresponding to each execution behavior in the plurality of execution behaviors is obtained.
In the embodiment of the application, based on the sorted manual operation data set, the control system controls the development machine to collect manual operation data and obtain the collected manual operation data, wherein each sorted manual operation data in the sorted manual operation data set corresponds to one manual operation data, the manual operation data set is built based on all manual operation data, then based on the sorted manual operation data set and the manual operation data set, a behavior recognition algorithm is adopted to determine a plurality of execution behaviors and manual operation data corresponding to each execution behavior in the plurality of execution behaviors, meanwhile, based on the sorted automatic operation data set, the control system controls the development machine to collect automatic operation data and obtain the collected automatic operation data, each sorted automatic operation data in the sorted automatic operation data set corresponds to one automatic operation data, and each automatic operation data corresponding to each execution behavior in the plurality of execution behaviors is determined.
Specifically, in the actual application process, the sorted manually operated data sets and manually executed data corresponding to each sorted manual data in the sorted manually operated data sets are uploaded to a database, then a data mining algorithm is executed, the same data in the database are automatically classified to determine what action is the current data, and the current manually marked data description, namely the area and the centroid of the data in each dimension, is extracted and defined as a set of behavior recognition P (A).
Further, the data generated in the automatic cutting program is extracted into the database, and P (B) is also obtained when the automatic cutting program is applied, and the closer the joint probability of P (a) and P (B) is to 1, the same content that P (B) is executed with the marked behavior P (a) is represented, so that the known behavior of which mark P (B) is can be determined.
It should be noted that in the practical application process, the operation effect of the manual cutting program is often better than that of the automatic cutting program, and when two events or data occur simultaneously, P (a, B) can be used to represent, that is, the data of the position category is determined by using the data set training model of the known category.
The manual execution data and the automatic execution data are at least one of absolute distance data of a wall body around the heading machine and a heading machine body, which are acquired by a laser ranging unit of the heading machine, millimeter wave radar units, surface shape data of the wall body around the heading machine, heat source data around the heading machine, which are acquired by an infrared thermal imaging unit, and absolute position data of the heading machine on a working face, which are acquired by a total station positioning unit.
203. Acquiring a plurality of execution behaviors of the heading machine, and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors.
204. Training the obtained automatic cutting program based on the manual operation data and the automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain the target automatic cutting program.
In the embodiment of the application, after an automatic cutting program is acquired, manual operation data corresponding to each execution behavior is input into the automatic cutting program, a development machine is controlled to acquire first automatic execution data corresponding to each execution behavior, then the automatic execution data indicated by the same execution behavior is compared with the first automatic execution data, program parameters of the automatic cutting program are updated, and at least one target manual operation data meeting preset conditions is determined to serve as training data of the next round; and then determining the preset training times, inputting training data obtained in the previous round into an updated automatic cutting program in each round of training, and continuously updating program parameters of the automatic cutting program until the round of training reaches the preset training times to obtain the target automatic cutting program.
Further, comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating program parameters of the automatic cutting program, and determining at least one target manual operation data meeting preset conditions as training data of a next round, wherein the method specifically comprises the following steps: firstly, calculating a difference value between automatic execution data and first automatic execution data indicated by the same execution behavior, then acquiring a preset threshold value, if the difference value is smaller than the preset threshold value, performing fine adjustment on program parameters of an automatic cutting program, determining target manual operation data corresponding to the same execution behavior, finally acquiring at least one target manual operation data as training data of the next round, and if the difference value is larger than the preset threshold value, replacing the program parameters of the automatic cutting program by adopting the program parameters of the manual cutting program.
205. And uploading the target program parameters of the target automatic cutting program to the cloud server.
In the embodiment of the application, after the target automatic cutting program is acquired, the control system can upload the target program parameters of the target automatic cutting program to the cloud server, so that other heading machines can update the program parameters of the automatic cutting program by using the uploaded target program parameters, and the automatic cutting operation of the heading machines is optimized.
206. Responding to the operation request of the heading machine, acquiring operation test data carried by the operation request of the heading machine, and inputting the operation test data into the target automatic cutting program.
In the embodiment of the application, after the target automatic cutting program is acquired, the operation test data carried by the operation request of the heading machine is acquired in response to the operation request of the heading machine, and the operation test data is input to the target automatic cutting program.
207. And acquiring a cutting control instruction output by the target automatic cutting program to control the heading machine to execute cutting operation according to the cutting control instruction.
According to the method provided by the embodiment of the application, the initial manual operation data set and the initial automatic operation data set are firstly obtained, the manual operation data set and the automatic operation data set are obtained based on the initial manual operation data set and the initial automatic operation data set, then the automatic execution data corresponding to each execution behavior in the plurality of execution behaviors are obtained, the manual operation data and the automatic operation data corresponding to each execution behavior in the plurality of execution behaviors are determined, then the obtained automatic cutting program is trained based on the manual operation data and the automatic operation data corresponding to each execution behavior, program parameters of the automatic cutting program are updated, the target automatic cutting program is obtained, the target program parameters of the target automatic cutting program are uploaded to a cloud server, finally the operation test data carried by the operation request of the development machine are obtained in response to the operation request of the development machine, the operation test data are input to the target automatic cutting program, the cutting control instruction output by the target automatic cutting program is obtained, the development machine is controlled to execute the cutting operation according to the cutting control instruction, the development machine is trained by the automatic cutting program, the development machine perceiving capability of the peripheral condition is improved, the development machine is executed according to the task execution, the training operation is carried out, and the development operation accuracy is improved.
Example 2
The embodiment of the application provides a cutting control system of a heading machine, as shown in fig. 3, including: a determining module 301, a training module 302, a running module 303 and a cutting module 304.
The determining module 301 is configured to obtain a plurality of execution behaviors of the heading machine, and determine manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors;
the training module 302 is configured to obtain an automatic cutting program of the heading machine, train the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and update program parameters of the automatic cutting program to obtain a target automatic cutting program;
the operation module 303 is configured to obtain operation test data carried by an operation request of the heading machine in response to the operation request of the heading machine, and input the operation test data to a target automatic cutting program;
the cutting module 304 is configured to obtain a cutting control instruction output by the target automatic cutting program, so as to control the heading machine to execute cutting operation according to the cutting control instruction.
As a preferred implementation manner of the embodiment of the present application, in a specific application scenario, as shown in fig. 4, the system further includes: an acquisition module 305;
The acquisition module comprises: the system comprises a laser ranging unit, a millimeter wave radar unit, an infrared thermal imaging unit and a total station positioning unit, wherein,
the laser ranging unit is used for acquiring absolute distance data of the peripheral wall body of the heading machine and the heading machine body;
the millimeter wave radar unit is used for acquiring surface shape data of the peripheral wall body of the heading machine;
the infrared thermal imaging unit is used for acquiring heat source data around the heading machine;
and the total station positioning unit is used for acquiring absolute position data of the heading machine on the working surface.
In this embodiment of the present application, the determining module is connected to the collecting module, after the collecting module collects the target data, the collecting module sends the target data to the confirming module, that is, after the laser ranging unit, the millimeter wave radar unit, the infrared thermal imaging unit and the total station positioning unit acquire the corresponding data, the corresponding data are respectively sent to the confirming module, in a specific application process, the collecting module may further include a binocular vision unit, where the binocular vision unit is used to identify the shape, the distance of the tunneling working face, the current position of the cutting head, whether there is a stacker, a dust state, etc., give the tunneling machine a sense to the front, and send the sensed data to the confirming module, where the mathematical model constructed by the binocular vision unit is shown in fig. 8, and the distance between the tunneling machine and the front working face is as shown in fig. 8 
Can be expressed as:
equation 1:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,z(1, 1) is the distance from the upper left coordinate point of the superimposed image to the binocular vision unit in fig. 8,z(n1) is the distance from the upper right coordinate point of the superimposed image to the binocular vision unit in fig. 8,z(1,m) For the distance of the bottom left coordinate point of the superimposed image to the binocular vision unit in figure 8,z(n,m) For the distance of the bottom right coordinate point of the superimposed image to the binocular vision unit in figure 8,bfor the baseline distance of the two vision cameras,fas the focal length of the camera,X L for the width of the image acquired by the left camera,X R the width of the image acquired for the right camera.
The depth change of the working surface can be estimated according to equation 1.
In the practical application process, the distance Z between the heading machine acquired by the binocular vision unit and the front working surface during working 0 The specific acquisition method is shown in formula 1, in order to avoid ambiguity in the following process, the distance between the heading machine and the front working surface is used in the following processFIs expressed as Z in formula 1 0 Replaced byFBased on distanceFConstructing a three-dimensional point cloud image, extracting point cloud abstract features based on the three-dimensional point cloud image by adopting a machine learning algorithm and a deep learning algorithm, segmenting the point cloud based on the three-dimensional point cloud abstract features through a semantic segmentation model, and finally segmenting the point cloud according to a final segmentation result as shown in fig. 10, for example, in the embodiment of the application, the three-dimensional point cloud image is finally segmented into three parts, each part represents an actual visual area, wherein X, Y and Z coordinates of points in the three-dimensional point cloud image are respectively expressed as:
Equation 2:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,Bfor the baseline distance in the three-dimensional point cloud,dispis the parallax valueu,v) Corresponding pixel coordinates.
Further, the laser ranging unit can construct the relative distance between the heading machine and the working surface when the heading machine moves forwards and backwardsLTime axis during relative movementtThe two are combined to establish a dimension time axis of the heading machine before and after the heading machine during manual cutting, and the dimension time axisL t Can be expressed as:
equation 3:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,n1 is the time stamp of the last manual run.
Further, the millimeter wave radar unit can construct the perception of the heading machine to the left and right positions to position the dimension point cloud of the perception Y of the heading machine to the left and right positions and the time t, and the dimension point cloud is expressed as:
equation 4:
。
further, the infrared thermal imaging unit is used to construct a thermal Cheng Xiangdian Yun Weidu matrix W of the cutting head when moving, and the thermal Cheng Xiangdian Yun Weidu matrix W is expressed as follows in the same coordinate system of the binocular vision unit:
equation 5:
。
wherein, the liquid crystal display device comprises a liquid crystal display device,w(1, 1) the distance from the upper left coordinate point of the superimposed image to the infrared thermal imaging unit,w(1,m) The distance from the lower left coordinate point of the superimposed image to the infrared thermal imaging unit,w(n1) the distance from the upper right coordinate point of the overlapping image to the infrared thermal imaging unit, w(n,m) Is the distance from the lower right coordinate point of the overlapping image to the infrared thermal imaging unit.
Further, the total station positioning unit is used for constructing an absolute position V of the heading machine in the roadway during manual cutting, and the absolute position V is expressed as:
equation 6:
。
wherein x is the coordinate positioning of the total station describing equipment moving left and right, y is the coordinate positioning of the total station describing equipment moving back and forth, and z is the coordinate positioning of the total station describing equipment moving up and down.
All absolute positions are combined into
。
As a preferred implementation manner of the embodiment of the present application, in a specific application scenario, as shown in fig. 5, the system further includes: a first data processing module 306 and a second data processing module 307.
The first data processing module 306 is configured to obtain an initial manually-operated data set for operating the manual cutting program, and extract at least one first data category included in the initial manually-operated data set; classifying a plurality of initial manual operation data included in the initial manual operation data set based on at least one first data category to obtain a classified initial manual operation data set; slicing the classified initial manual operation data set to obtain a sliced initial manual operation data set; acquiring a first time point of each manual cutting task, and marking initial manual operation data corresponding to the initial manual operation data set after slicing by adopting the first time point to obtain a tidied manual operation data set;
The second data processing module 307 is configured to acquire an initial autorun data set for running the autorun program, and extract at least one second data category included in the initial autorun data set; classifying a plurality of initial automatic operation data included in the initial automatic operation data set based on at least one second data category to obtain a classified initial automatic operation data set; slicing the classified initial automatic operation data set to obtain a sliced initial automatic operation data set; and acquiring a second time node for executing the automatic cutting task each time, and marking the initial automatic operation data corresponding to the sliced initial automatic operation data set by adopting the second time node to obtain a tidied automatic operation data set.
As a preferred implementation manner of the embodiment of the present application, in a specific application scenario, as shown in fig. 6, the system further includes: behavior recognition module 308.
The behavior recognition module 308 is configured to obtain manual execution data collected by the heading machine based on each of the sorted manual behavior data in the sorted manual operation data set, and obtain a manual execution data set; based on the sorted manual operation data set and the manual execution data set, determining a plurality of execution behaviors and manual operation data corresponding to each execution behavior in the plurality of execution behaviors by adopting a behavior recognition algorithm; acquiring automatic execution data collected by a heading machine based on each of the sorted automatic behavior data in the sorted automatic operation data set; automatic execution data corresponding to each of a plurality of execution behaviors is determined.
In a specific application scenario, the training module 302 is further configured to: inputting manual operation data corresponding to each execution behavior into an automatic cutting program, controlling a development machine to acquire first automatic execution data corresponding to each execution behavior, comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating program parameters of the automatic cutting program, and determining at least one target manual operation data meeting preset conditions as training data of the next round; and determining preset training times, inputting training data obtained from the previous round of training into an updated automatic cutting program in each round of training, and continuously updating program parameters of the automatic cutting program until the training rounds reach the preset training times to obtain a target automatic cutting program.
In a specific application scenario, the training module 302 is further configured to: calculating a difference value between the automatic execution data and the first automatic execution data indicated by the same execution behavior; acquiring a preset threshold value, if the difference value is smaller than the preset threshold value, performing fine adjustment on the program parameters of the automatic cutting program, and determining target manual operation data corresponding to the same execution behavior; at least one target manual operation data is obtained as training data of the next round.
In a specific application scenario, the training module 302 is further configured to: if the difference is greater than the preset threshold, replacing the program parameters of the automatic cutting program by the program parameters of the manual cutting program.
As a preferred implementation manner of the embodiment of the present application, in a specific application scenario, as shown in fig. 7, the system further includes: a transmission module 309.
The transmission module 309 is configured to upload the target program parameters of the target automatic cutting program to the cloud server, so that other heading machines update the program parameters of the automatic cutting program by using the target program parameters.
According to the cutting control system provided by the embodiment of the application, firstly, a plurality of execution behaviors of the development machine are obtained through the determining module, manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors are determined, then, an automatic cutting program of the development machine is obtained through the training module, the automatic cutting program is trained based on the manual operation data and the automatic operation data corresponding to each execution behavior, program parameters of the automatic cutting program are updated to obtain a target automatic cutting program, then, operation test data carried by the operation request of the development machine are obtained through the operation module in response to the operation request of the development machine, the operation test data are input into the target automatic cutting program, finally, a cutting control instruction output by the target automatic cutting program is obtained through the cutting module, so that the development machine is controlled to execute cutting operation according to the cutting control instruction, the perception capability of the development machine to peripheral conditions is improved through the automatic cutting program of the training development machine, and the accuracy of the cutting operation is improved when the cutting task is executed.
Example 3
The embodiment of the application provides a heading machine, which comprises a cutting control system of the heading machine.
According to the development machine provided by the embodiment of the application, the cutting control system of the development machine is used, the perception capability of the development machine to the peripheral condition is improved through training of the automatic cutting program of the development machine, and the accuracy of cutting operation is improved when a cutting task is executed.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements may be made to the present application by those skilled in the art, which modifications and equivalents are also considered to be within the scope of the present application.
Claims (8)
1. The cutting control method of the heading machine is characterized by comprising the following steps:
acquiring an initial manual operation data set for operating a manual cutting program, and extracting at least one first data category included in the initial manual operation data set;
classifying a plurality of initial manual operation data included in the initial manual operation data set based on the at least one first data category to obtain a classified initial manual operation data set;
Slicing the classified initial manual operation data set to obtain a sliced initial manual operation data set;
acquiring a first time point of each manual cutting task, and marking initial manual operation data corresponding to the initial manual operation data set after slicing by adopting the first time point to obtain a tidied manual operation data set; the method comprises the steps of,
acquiring an initial automatic operation data set for operating an automatic cutting program, and extracting at least one second data category included in the initial automatic operation data set;
classifying a plurality of initial automatic operation data included in the initial automatic operation data set based on the at least one second data category to obtain a classified initial automatic operation data set;
slicing the classified initial automatic operation data set to obtain a sliced initial automatic operation data set;
acquiring a second time node for executing the automatic cutting task each time, and marking the initial automatic operation data corresponding to the initial automatic operation data set after slicing by adopting the second time node to obtain an automatic operation data set after finishing;
Acquiring manual execution data which is acquired by controlling the heading machine based on each piece of the sorted manual behavior data in the sorted manual operation data set, and acquiring a manual execution data set;
based on the sorted manual operation data set and the manual execution data set, determining a plurality of execution behaviors and manual operation data corresponding to each execution behavior in the plurality of execution behaviors by adopting a behavior recognition algorithm;
acquiring automatic execution data which is acquired by controlling the heading machine based on each piece of the automatic behavior data after finishing in the automatic operation data set after finishing;
determining automatic execution data corresponding to each execution behavior in the plurality of execution behaviors;
acquiring a plurality of execution behaviors of the heading machine, and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors;
acquiring an automatic cutting program of the heading machine, training the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain a target automatic cutting program;
responding to a development machine operation request, acquiring operation test data carried by the development machine operation request, and inputting the operation test data into the target automatic cutting program;
And acquiring a cutting control instruction output by the target automatic cutting program so as to control the heading machine to execute cutting operation according to the cutting control instruction.
2. The cutting control method of the heading machine according to claim 1, wherein the training the automatic cutting program based on the manual operation data and the automatic operation data corresponding to each execution behavior, updating the program parameters of the automatic cutting program, and obtaining the target automatic cutting program includes:
inputting the manual operation data corresponding to each execution behavior into the automatic cutting program, controlling the heading machine to acquire first automatic execution data corresponding to each execution behavior, comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating program parameters of the automatic cutting program, and determining at least one target manual operation data meeting preset conditions as training data of the next round;
and determining preset training times, inputting training data obtained from the previous round of training into an updated automatic cutting program in each round of training, and continuously updating program parameters of the automatic cutting program until the training rounds reach the preset training times to obtain the target automatic cutting program.
3. The cutting control method of a heading machine according to claim 2, wherein comparing the automatic execution data indicated by the same execution behavior with the first automatic execution data, updating program parameters of the automatic cutting program, and determining at least one target manual operation data satisfying a preset condition as training data of a next round includes:
calculating a difference value between the automatic execution data and the first automatic execution data indicated by the same execution behavior;
acquiring a preset threshold value, if the difference value is smaller than the preset threshold value, performing fine adjustment on the program parameters of the automatic cutting program, and determining target manual operation data corresponding to the same execution behavior;
and acquiring the at least one target manual operation data as training data of the next round.
4. A cutting control method of a heading machine as claimed in claim 3, further comprising:
and if the difference value is larger than the preset threshold value, replacing the program parameters of the automatic cutting program by adopting the program parameters of the manual cutting program.
5. The cutting control method of a heading machine according to claim 1, characterized in that after the target automatic cutting program is obtained, the method further comprises:
And uploading the target program parameters of the target automatic cutting program to a cloud server so that other heading machines update the program parameters of the automatic cutting program by using the target program parameters.
6. A cutting control system for a heading machine, comprising:
a first data processing module for acquiring an initial manual operation data set for operating a manual cutting program, and extracting at least one first data category included in the initial manual operation data set;
the first data processing module is further used for classifying a plurality of initial manual operation data included in the initial manual operation data set based on the at least one first data category to obtain a classified initial manual operation data set;
the first data processing module is further used for slicing the classified initial manual operation data set to obtain a sliced initial manual operation data set;
the first data processing module is further used for obtaining a first time point of each manual cutting task, marking initial manual operation data corresponding to the initial manual operation data set after slicing by adopting the first time point, and obtaining a tidied manual operation data set;
A second data processing module, configured to obtain an initial autorun data set for running an autorun program, and extract at least one second data category included in the initial autorun data set;
the second data processing module is further used for classifying a plurality of initial automatic operation data included in the initial automatic operation data set based on the at least one second data category to obtain a classified initial automatic operation data set;
the second data processing module is also used for slicing the classified initial automatic operation data set to obtain a sliced initial automatic operation data set;
the second data processing module is further used for acquiring a second time node for executing the automatic cutting task each time, and marking initial automatic operation data corresponding to the sliced initial automatic operation data set by adopting the second time node to obtain a tidied automatic operation data set;
the behavior recognition module is used for acquiring manual execution data which are acquired by controlling the heading machine based on each piece of the sorted manual behavior data in the sorted manual operation data set, and acquiring a manual execution data set;
The behavior recognition module is further used for determining a plurality of execution behaviors and manual operation data corresponding to each execution behavior in the plurality of execution behaviors by adopting a behavior recognition algorithm based on the tidied manual operation data set and the manual execution data set;
the behavior recognition module is further used for acquiring automatic execution data which is acquired by the heading machine and is controlled based on each piece of the automatic behavior data in the automatic operation data set after being tidied;
the behavior recognition module is further used for determining automatic execution data corresponding to each execution behavior in the plurality of execution behaviors;
the determining module is used for acquiring a plurality of execution behaviors of the heading machine and determining manual operation data and automatic operation data corresponding to each execution behavior in the plurality of execution behaviors;
the training module is used for acquiring an automatic cutting program of the heading machine, training the automatic cutting program based on manual operation data and automatic operation data corresponding to each execution behavior, and updating program parameters of the automatic cutting program to obtain a target automatic cutting program;
the operation module is used for responding to an operation request of the heading machine, acquiring operation test data carried by the operation request of the heading machine and inputting the operation test data into the target automatic cutting program;
And the cutting module is used for acquiring a cutting control instruction output by the target automatic cutting program so as to control the heading machine to execute cutting operation according to the cutting control instruction.
7. The cutting control system of a heading machine as recited in claim 6, further comprising: an acquisition module;
the acquisition module comprises: the system comprises a laser ranging unit, a millimeter wave radar unit, an infrared thermal imaging unit and a total station positioning unit, wherein,
the laser ranging unit is used for acquiring absolute distance data of the peripheral wall body of the heading machine and the heading machine body;
the millimeter wave radar unit is used for acquiring surface shape data of the peripheral wall body of the heading machine;
the infrared thermal imaging unit is used for acquiring heat source data around the heading machine;
and the total station positioning unit is used for acquiring absolute position data of the heading machine on a working surface.
8. A heading machine characterised by comprising a cutting control system of a heading machine as claimed in any one of claims 6 to 7.
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