Method and device for automatically marking signal machine for mining area operation

Abstract

The embodiment of the invention provides an automatic marking method and device for an annunciator for mining area operation, and the method comprises the steps: collecting the data of the annunciator through a laser radar and a camera on a mining area locomotive according to a preset collection period; calculating camera pose change data of the mining area locomotive at the interval of a preset period; according to the camera pose change data and the collected signal machine data, calculating a three-dimensional coordinate of the signal machine in a camera coordinate system by using a triangulation algorithm; according to the three-dimensional coordinates of the annunciator in the camera coordinate system, calculating annunciator coordinates of the annunciator in the world map coordinate system.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of computers, and particularly relates to a signal machine automatic labeling method and device for mine operation. BACKGROUND

[0002] The signal machine is installed on the track side and is used for maintaining the order of mine operation and guaranteeing the safety of road traffic. The signal machine guides and dispatches the locomotive that is performing mine operation by lighting different signal lights. When the signal machine lights up the blue light, it indicates the stop signal and prohibits the locomotive from crossing. When the signal machine lights up the white light, it indicates the passing signal and allows the locomotive to cross. In order to enable the camera installed on the mine locomotive to accurately detect the state of the signal machine on the track side, it is necessary to accurately label the world map coordinates (X, Y, Z) of each signal machine on the mine map in advance.

[0003] At present, the coordinate labeling of the signal machine on the world map mainly depends on manual work. First, the labeling personnel need to obtain the world map of the entire mine operation, that is, the three-dimensional point cloud map obtained by laser radar scanning, and then need to know the approximate position of the signal machine on the map and the environmental reference around the signal machine, so as to quickly locate the signal machine. Finally, the labeling personnel observe the three-dimensional point cloud of the signal machine on the map by naked eye and manually label the three-dimensional center point of each light position of the signal machine as the point cloud coordinates of the signal machine. For the existing manual labeling method of the signal machine coordinates, the following problems mainly exist: 1. Time-consuming is too long. The manual labeling method consumes too much time, especially the above-mentioned "determining the approximate position of the signal machine on the map" step. In the mine map, the surrounding environment is relatively open, and most of the environmental references do not have obvious features, so it is also difficult to find the signal machine in the point cloud map. In addition, due to the large number of signal machines in the complex mine road conditions, the manual labeling time also increases exponentially.

[0004] 2. Large manual labeling error. The signal machine is a three-dimensional solid object in the point cloud map, and there is a large subjective error in manually finding the three-dimensional center point of each light position of the signal machine, which can easily introduce a large coordinate error and cause the system to detect the signal machine inaccurately. SUMMARY

[0005] The purpose of the present application is to provide a signal machine automatic labeling method and device for mine operation, which aims to solve the above-mentioned problems in the prior art.

[0006] The present application provides a signal machine automatic labeling method for mine operation, comprising: acquiring the signal machine data by the laser radar and the camera on the mine locomotive at a predetermined acquisition period; calculating the camera pose change data of the mine locomotive at an interval of a predetermined period; According to the camera pose change data and the collected signal machine data, the three-dimensional coordinates of the signal machine in the camera coordinate system are calculated by using a triangulation algorithm. According to the three-dimensional coordinates of the signal machine in the camera coordinate system, the signal machine coordinates of the signal machine in the world map coordinate system are calculated.

[0007] The application provides a signal machine automatic labeling device for mine operation, which comprises: The acquisition module is used for collecting signal machine data at a predetermined acquisition period through a laser radar and a camera on a mine locomotive. The camera pose calculation module is used for calculating camera pose change data of the mine locomotive at an interval of a predetermined period. The three-dimensional coordinate calculation module is used for calculating the three-dimensional coordinates of the signal machine in the camera coordinate system according to the camera pose change data and the collected signal machine data by using a triangulation algorithm. The signal machine coordinate calculation module is used for calculating the signal machine coordinates of the signal machine in the world map coordinate system according to the three-dimensional coordinates of the signal machine in the camera coordinate system.

[0008] The application further provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the computer program is used to realize the steps of the signal machine automatic labeling method for mine operation.

[0009] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores an information transmission implementation program, and the program is used to realize the steps of the signal machine automatic labeling method for mine operation when executed by a processor.

[0010] By using the signal machine automatic labeling method to replace manual labeling, a large amount of human consumption is saved, and the efficiency of signal machine labeling is greatly improved. While improving the labeling efficiency, the subjective error in manual judgment of the three-dimensional center point of the signal machine is eliminated by automatic labeling through the algorithm, and the accuracy of signal machine labeling is improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is the flowchart of the signal machine automatic labeling method for mine operation in the embodiments of the present application. Figure 2 is a motion vehicle pose transformation schematic diagram of an embodiment of the present application; Figure 3 is a schematic diagram of a triangulation algorithm of an embodiment of the present application; Figure 4 is a schematic diagram of a signal machine coordinate in a three-dimensional point cloud map of an embodiment of the present application; Figure 5 is a schematic diagram of a signal machine automatic labeling device for mine operations of an embodiment of the present application; Figure 6 is a schematic diagram of an electronic device of an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order for those skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be described in the following clearly and completely, with reference to the drawings in one or more embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present specification, not all. Based on one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present document.

[0014] Method embodiment According to an embodiment of the present application, a signal machine automatic labeling method for mine operations is provided, Figure 1 is a flowchart of a signal machine automatic labeling method for mine operations of an embodiment of the present application, as Figure 1 shown, the signal machine automatic labeling method for mine operations according to an embodiment of the present application specifically includes: Step S101, collecting signal machine data through a laser radar and a camera on a mine vehicle at a predetermined collection period; specifically including: When the laser radar and the camera on the mine vehicle can both observe the signal machine on the ground, start the mine vehicle and move towards the signal machine, and record the two-dimensional image coordinates of the center points of each light position of the signal machine detected by the camera at each predetermined collection period during running into a log file.

[0015] Step S102, calculating camera pose change data of the mine vehicle at an interval of a predetermined period; specifically including: When the mine vehicle moves between the Nth period and the (N+m)th period, calculate the pose change of the camera during this period through the camera pose R1, t1 of the Nth period and the camera pose R2, t2 of the (N+20)th period R、 , t, where N is a predetermined number of acquisition periods, and m is a positive integer; R1 and R2 represent 3*3 rotation matrices of the camera in the camera coordinate system relative to the world coordinate system in the Nth period and the (N+20)th period, and t1 and t2 represent 3*1 translation vectors of the camera in the camera coordinate system relative to the world coordinate system in the Nth period and the (N+20)th period. Specifically, the camera pose of a certain period can be converted from the radar pose and the camera-radar calibration parameter, and the radar pose [R lidar , t lidar ] of each period can be directly obtained by Beidou positioning without separate calculation. In this case, the camera-radar calibration parameter H cali installed on the Ordos locomotive head is a 4*3 matrix {0.9998, -0.0002, -0.0056, -0.0524, 0, 0.0099, -0.0033, -0.0039, 0.0059, 0.0034, 0.0999, 0}, so the camera pose calculation method is: [R1, t1]= [R lidar , t lidar ]·H cali -1 , and [R2, t2] can be obtained by analogy. R and t represent the change of the camera pose in the Nth period and the (N+20)th period, and the calculation method is: R, t] = [R2, t2]·[R1, t1] -1 .

[0016] In step S103, the three-dimensional coordinates of the signal machine in the camera coordinate system are calculated by using a triangulation algorithm according to the camera pose change data and the acquired signal machine data; specifically, the method comprises the following steps: When the mine locomotive moves from the Nth period to the (N+m)th period, the camera pose change R, t of the camera in this period of time is calculated by using the camera pose R1, t1 of the Nth period and the camera pose R2, t2 of the (N+20)th period, where N is a predetermined number of acquisition periods, and m is a positive integer; R1 and R2 represent 3*3 rotation matrices of the camera in the camera coordinate system relative to the world coordinate system in the Nth period and the (N+20)th period, and t1 and t2 represent 3*1 translation vectors of the camera in the camera coordinate system relative to the world coordinate system in the Nth period and the (N+20)th period. Specifically, the camera pose of a certain period can be converted from the radar pose and the camera-radar calibration parameter, and the radar pose [R lidar , t lidar ] of each period can be directly obtained by Beidou positioning without separate calculation. In this case, the camera-radar calibration parameter Hcali Given a 4x3 matrix {0.9998, -0.0002, -0.0056, -0.0524, 0, 0.0099, -0.0033, -0.0039, 0.0059, 0.0034, 0.0999, 0}, the camera pose is calculated as follows: [R1, t1] = [R lidar , t lidar ] · H cali -1 Similarly, [R2, t2] can be obtained. R and t represents the change in camera pose between the Nth and (N+20th)th periods, calculated as follows: [ R, t] = [R2,t2]·[R1, t1] -1 .

[0017] Step S104: Based on the 3D coordinates of the signal semaphore in the camera coordinate system, calculate the signal semaphore coordinates in the world map coordinate system. Specifically, this includes: The camera coordinates (Camera(x, y, z)) in the camera coordinate system are converted to the radar coordinates (Lidar(x, y, z)) in the radar coordinate system using camera-radar calibration parameters. Based on the radar pose of the lidar in the Nth and (N+m)th cycles, the radar coordinates (Lidar(x, y, z)) in the radar coordinate system are converted to the world map coordinate system to obtain the signal coordinates (World(x, y, z)) in the world map coordinate system.

[0018] As can be seen from the above description, in this embodiment of the invention, taking into account the open environment of the mining area and the lack of distinctive environmental reference objects around the signal, an automatic signal annotation technology was designed. This technology utilizes triangulation algorithms to calculate the depth value of the signal in a three-dimensional scene, thereby obtaining the three-dimensional coordinates of the signal in the camera coordinate system, achieving a conversion from two-dimensional to three-dimensional coordinates. After acquiring the camera and radar data of the signal, the pose changes of the moving vehicle are calculated, the three-dimensional coordinates are calculated using triangulation algorithms, and the signal coordinates are converted from the camera coordinate system to the radar coordinate system and finally to the world map coordinate system. The entire process can be automatically completed by the program, achieving automatic signal annotation.

[0019] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] 1. Signal data acquisition The lidar and camera were mounted on a locomotive in the mining area. Once both sensors could detect the ground signal, the locomotive was started and moved towards the signal. The sensor acquisition cycle was pre-set to 100ms. During operation, the two-dimensional image coordinates (u, v) of the center point of each signal light detected by the camera in each cycle were recorded to a log file.

[0021] 2. Calculate the positional changes of the moving locomotive. like Figure 2 As shown, the locomotive in the mining area moved between the Nth cycle and the (N+20th cycle). Using the camera pose R1, t1 in the Nth cycle and the camera pose R2, t2 in the (N+20th cycle), the pose change of the camera during this period was calculated. R, It should be noted that the interval number of 20 is a preset value in this embodiment of the invention and can be adjusted according to actual conditions.

[0022] 3. Triangulation calculation of the signal machine coordinates (Camera(x, y, z)) in the camera coordinate system. The pose change of the camera during this period has been calculated in the above steps. R, t, through R, Given the image coordinates (u, v) of the signal at the Nth and (N+20)th cycles, the three-dimensional coordinates of the signal in the camera coordinate system, Camera(x, y, z), can be calculated using a triangulation algorithm.

[0023] Figure 3 This is a schematic diagram of the triangulation algorithm. O1 and O2 are the optical centers of the camera at two different times, P1 and P2 are the feature points observed by the camera on the image from the signal receiver at two different times, and point P is the position of the two feature points in the 3D scene. Ideally, O1P1 and O2P2 will intersect at point P, but due to noise, they often cannot intersect at point P. The least squares method can be used to solve this. Assuming that x1 and x2 are the normalized coordinate values ​​corresponding to P1 and P2, and d1 and d2 are the depth values ​​in the corresponding 3D scene, then equation (1) is satisfied, where the pose changes. R, t has been obtained in step 2. Equation (1) is transformed to obtain equation (2). At this time, there is only one unknown on the left side of the equation, d2. Solving the equation can yield the value of d2, which is to say, Camera(x, y, z).

[0024] (1) (2) 4. Calculate the signal machine coordinates World(x, y, z) in the world map coordinate system. Using camera-radar calibration parameters, i.e., camera extrinsic parameters, the signal machine coordinates (Camera(x, y, z)) in the camera coordinate system can be converted to signal machine coordinates (Lidar(x, y, z)) in the radar coordinate system. Finally, using the radar pose of the lidar in the Nth and (N+20th)th cycles, the signal machine coordinates (Lidar(x, y, z)) in the radar coordinate system can be converted to the world map coordinate system, i.e., World(x, y, z). Taking the signal machine in the Ordos Bojianghaizi Station mining area as an example, the world map coordinates of the center point of the lowest light position of the signal machine are calculated to be (-332.889165, -1227.127851, -24.861404). Figure 4 These are the coordinates of the signal horn in the 3D point cloud map, all in meters. For ease of observation, non-high-reflection point clouds and noisy point clouds have been filtered out, leaving only the signal horn point cloud. As you can see, the error between the coordinates calculated above and the actual coordinates is less than 2 centimeters.

[0025] In summary, the technical solution of this invention utilizes triangulation algorithms and coordinate system transformation methods to achieve automatic marking of signal controllers, significantly reducing the time cost of manual marking and improving marking efficiency. While improving marking efficiency, this automatic signal controller marking method also eliminates subjective errors caused by manual marking, improving the coordinate accuracy of signal controller markings.

[0026] Device Example 1 According to an embodiment of the present invention, an automatic marking device for signal machines in mining operations is provided. Figure 5 This is a schematic diagram of an automatic signal marking device for mining operations according to an embodiment of the present invention, such as... Figure 5 As shown, the automatic signal marking device for mining operations according to an embodiment of the present invention specifically includes: The acquisition module 50 is used to acquire signal data at predetermined acquisition cycles using a lidar and camera on a mining locomotive; specifically, it is used for: When the ground signal can be observed by both the lidar and camera on the mining locomotive, the mining locomotive is started and moves toward the signal, and the two-dimensional image coordinates of the center point of each light position of the signal detected by the camera in each predetermined acquisition cycle during the operation are recorded in the log file. Camera pose calculation module 52 is used to calculate the camera pose change data of the mining locomotive at predetermined intervals; specifically used for: When the locomotive moves between the Nth and (N+m)th cycles, the camera pose change during this period is calculated using the camera poses R1 and t1 in the Nth cycle and R2 and t2 in the (N+20)th cycle. R, t, where N is the predetermined number of acquisition cycles, and m is a positive integer; R1 and R2 represent the 3*3 rotation matrices of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles, and t1 and t2 represent the 3*1 translation vectors of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles. Specifically, the camera pose for a certain cycle can be obtained by converting the radar pose and camera-radar calibration parameters, while the radar pose for each cycle [R lidar , t lidar The calibration parameters H of the camera-radar mounted on the front of the Ordos locomotive can be obtained directly from BeiDou positioning without separate calculation. cali Given a 4x3 matrix {0.9998, -0.0002, -0.0056, -0.0524, 0, 0.0099, -0.0033, -0.0039, 0.0059, 0.0034, 0.0999, 0}, the camera pose is calculated as follows: [R1, t1] = [R lidar , t lidar ] · H cali -1 Similarly, [R2, t2] can be obtained. R and t represents the change in camera pose between the Nth and (N+20th)th periods, calculated as follows: [ R, t] = [R2,t2]·[R1, t1] -1 .

[0027] The three-dimensional coordinate calculation module 54 is used to calculate the three-dimensional coordinates of the signal in the camera coordinate system based on the camera pose change data and the acquired signal data, using a triangulation algorithm; specifically, it is used for: Through pose changes R, Given the image coordinates (u, v) of the signal t and the camera at the Nth and (N+m)th periods, the three-dimensional coordinates of the signal t in the camera coordinate system, Camera(x, y, z), are calculated using the triangulation algorithms shown in Formulas 1 and 2: Formula 1; Here, we assume that x1 and x2 are the normalized coordinate values ​​corresponding to P1 and P2, d1 and d2 are the depth values ​​in the corresponding three-dimensional scene, and P1 and P2 are the feature points observed by the camera on the image at two time points. The signal controller coordinate calculation module 56 is used to calculate the signal controller coordinates in the world map coordinate system based on the signal controller's three-dimensional coordinates in the camera coordinate system. Specifically, it is used for: The camera coordinates (Camera(x, y, z)) in the camera coordinate system are converted to the radar coordinates (Lidar(x, y, z)) in the radar coordinate system using camera-radar calibration parameters. Based on the radar pose of the lidar in the Nth and (N+m)th cycles, the radar coordinates (Lidar(x, y, z)) in the radar coordinate system are converted to the world map coordinate system to obtain the signal coordinates (World(x, y, z)) in the world map coordinate system.

[0028] The embodiments of the present invention are device embodiments corresponding to the above method embodiments. The specific operation of each module can be understood with reference to the description of the method embodiments, and will not be repeated here.

[0029] Device Example 2 This invention provides an electronic device, such as... Figure 6 As shown, it includes: a memory 60, a processor 62, and a computer program stored in the memory 60 and executable on the processor 62, wherein the computer program, when executed by the processor 62, performs the steps as described in the method embodiment.

[0030] Device Example 3 This invention provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor 62, performs the steps described in the method embodiment.

[0031] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatically marking signals in mining operations, characterized in that, include: Data from the signal lights is collected at predetermined collection cycles using lidar and cameras on locomotives in the mining area. Calculate the camera pose change data of mining locomotives at predetermined intervals; Based on the camera pose change data and the acquired signal data, the three-dimensional coordinates of the signal in the camera coordinate system are calculated using a triangulation algorithm; Calculate the signal coordinates in the world map coordinate system based on the three-dimensional coordinates of the signal in the camera coordinate system.

2. The method according to claim 1, characterized in that, The collection of signal data using lidar and cameras on mining locomotives at predetermined collection cycles specifically includes: When the ground signal can be observed by both the lidar and camera on the mining locomotive, the mining locomotive is started and moves toward the signal. During the operation, the two-dimensional image coordinates of the center point of each light position of the signal detected by the camera in each predetermined acquisition cycle are recorded in the log file.

3. The method according to claim 1, characterized in that, The specific data on camera pose changes of mining locomotives at predetermined intervals include: When the locomotive in the mining area moves between the Nth cycle and the (N+m)th cycle, the camera pose change during this period is calculated using the camera poses R1 and t1 in the Nth cycle and R2 and t2 in the (N+20)th cycle. R, t, where N is the predetermined number of acquisition cycles, m is a positive integer; R1 and R2 represent the 3*3 rotation matrices of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles, and t1 and t2 represent the 3*1 translation vectors of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles.

4. The method according to claim 3, characterized in that, Based on the camera pose change data and the acquired signal data, the calculation of the three-dimensional coordinates of the signal in the camera coordinate system using a triangulation algorithm specifically includes: Through pose changes R, Given the image coordinates (u, v) of the signal t and the camera at the Nth and (N+m)th periods, the three-dimensional coordinates of the signal t in the camera coordinate system, Camera(x, y, z), are calculated using the triangulation algorithms shown in Formulas 1 and 2: Official 1; Here, we assume that x1 and x2 are the normalized coordinate values ​​corresponding to P1 and P2, d1 and d2 are the depth values ​​in the corresponding 3D scene, and P1 and P2 are the feature points observed by the camera on the image at two time points.

5. The method according to claim 4, characterized in that, Calculating the signal coordinates in the world map coordinate system based on the signal's three-dimensional coordinates in the camera coordinate system specifically includes: The camera coordinates (Camera(x, y, z)) in the camera coordinate system are converted to the radar coordinates (Lidar(x, y, z)) in the radar coordinate system using camera-radar calibration parameters. Based on the radar pose of the lidar in the Nth and (N+m)th cycles, the radar coordinates (Lidar(x, y, z)) in the radar coordinate system are converted to the world map coordinate system to obtain the signal coordinates (World(x, y, z)) in the world map coordinate system.

6. An automatic marking device for signal lights in mining operations, characterized in that, include: The data acquisition module is used to acquire signal data at predetermined acquisition cycles using lidar and cameras on mining locomotives. The camera pose calculation module is used to calculate the camera pose change data of the mining locomotive at predetermined intervals. The three-dimensional coordinate calculation module is used to calculate the three-dimensional coordinates of the signal in the camera coordinate system based on the camera pose change data and the acquired signal data, using a triangulation algorithm. The signal coordinate calculation module is used to calculate the signal coordinates in the world map coordinate system based on the three-dimensional coordinates of the signal in the camera coordinate system.

7. The apparatus according to claim 6, characterized in that, The acquisition module is specifically used for: When the ground signal can be observed by both the lidar and camera on the mining locomotive, the mining locomotive is started and moves toward the signal, and the two-dimensional image coordinates of the center point of each light position of the signal detected by the camera in each predetermined acquisition cycle during the operation are recorded in the log file. The camera pose calculation module is specifically used for: When the locomotive in the mining area moves between the Nth cycle and the (N+m)th cycle, the camera pose change during this period is calculated using the camera poses R1 and t1 in the Nth cycle and R2 and t2 in the (N+20)th cycle. R, t, where N is the predetermined number of acquisition cycles, m is a positive integer; R1 and R2 represent the 3*3 rotation matrices of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles, and t1 and t2 represent the 3*1 translation vectors of the camera in the camera coordinate system relative to the world coordinate system in the Nth and (N+20th)th cycles.

8. The apparatus according to claim 7, characterized in that, The three-dimensional coordinate calculation module is specifically used for: Through pose changes R, Given the image coordinates (u, v) of the signal t and the camera at the Nth and (N+m)th periods, the three-dimensional coordinates of the signal t in the camera coordinate system, Camera(x, y, z), are calculated using the triangulation algorithms shown in Formulas 1 and 2: Official 1; Here, we assume that x1 and x2 are the normalized coordinate values ​​corresponding to P1 and P2, d1 and d2 are the depth values ​​in the corresponding three-dimensional scene, and P1 and P2 are the feature points observed by the camera on the image at two time points. The signal machine coordinate calculation module is specifically used for: The camera coordinates (Camera(x, y, z)) in the camera coordinate system are converted to the radar coordinates (Lidar(x, y, z)) in the radar coordinate system using camera-radar calibration parameters. Based on the radar pose of the lidar in the Nth and (N+m)th cycles, the radar coordinates (Lidar(x, y, z)) in the radar coordinate system are converted to the world map coordinate system to obtain the signal coordinates (World(x, y, z)) in the world map coordinate system.

9. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the automatic marking method for signal machines in mining operations as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information transmission implementation program, which, when executed by a processor, implements the steps of the automatic marking method for signal machines in mining operations as described in any one of claims 1 to 5.

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