An unmanned method, system and terminal for underground rail transportation

Abstract

The application relates to an unmanned driving method, system and terminal for underground rail transportation, and belongs to the technical field of underground mine transportation. The unmanned driving method comprises the following steps: a triple warning area is established in advance, and the triple warning area is represented by different colors; in the process of driving of an electric locomotive, it is judged whether an obstacle exists in the warning area, the obstacle including personnel and / or other objects; if yes, the color of the specific warning area where the obstacle is located is further judged; corresponding warning actions are executed according to the color of the specific warning area, and the warning area and the warning action are in a unique mapping relationship. The application has the beneficial effect that the staff can know the distance between the electric locomotive and the staff in time, and the safety of the roadway is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underground mine transportation, in particular to an underground rail transportation unmanned driving method, system and terminal. BACKGROUND

[0002] Underground mine flat transportation mainly adopts motor car to connect the bottom side of the mine car to complete the one-way cycle transportation in the underground roadway.

[0003] The patent with the publication number CN102700569A and the publication date of October 3, 2012 discloses a mine motor car pedestrian monitoring method and alarm system based on image processing. The system includes a video acquisition module, an image processing module and an audible and visual alarm module. The video acquisition module uses an infrared camera to collect images in front of the motor car. The image processing module includes image preprocessing, rail identification and fitting, and pedestrian identification. The audible and visual alarm module includes an audible and visual alarm and a control circuit. The image preprocessing uses an image adaptive correction based on a genetic algorithm and a normalized incomplete Beta function combined with a pulse-coupled neural network-based image binarization method. The rail identification and fitting use a fuzzy edge detection fast algorithm based on a genetic algorithm threshold improvement to identify the rail and a heuristic connection method to fit the rail. The pedestrian identification uses a pulse-coupled neural network image binarization method based on FPGA to detect moving pedestrians on the track.

[0004] Although the motor car can collect image information in the driving direction to determine whether there is a person in front of the driving direction and alarm when there is a person, that is, as long as a person is detected, the alarm is performed regardless of the distance between the motor car and the person. The person cannot know the distance between the motor car and the person through the alarm, which may cause a situation of not timely avoiding, and the safety needs to be improved. SUMMARY

[0005] In order to enable the staff to know the distance between the motor car and the person in time and further improve the safety of the roadway, the present application provides an underground rail transportation unmanned driving method, system and terminal.

[0006] In the first aspect, the present application provides an underground rail transportation unmanned driving method, which adopts the following technical scheme:

[0007] An underground rail transportation unmanned driving method, comprising:

[0008] A triple warning area is established in advance, and the triple warning area is represented by different colors;

[0009] In the process of driving the motor car, it is determined whether there is an obstacle in the warning area, and the obstacle includes a person and / or other objects;

[0010] If yes, further determine the color of the specific warning area where the obstacle is located;

[0011] According to the color of the specific warning area, a corresponding warning action is performed, and each warning area is uniquely mapped to the warning action.

[0012] By adopting the above technical solution, by establishing three warning areas, when the staff appears in different warning areas, the corresponding warning action can be performed, and the staff can roughly judge the distance between the motor car and itself according to the warning action, so as to avoid in time, thereby further improving the safety of the roadway.

[0013] Optionally, the specific steps of determining whether there is an obstacle in the warning area include:

[0014] Determine whether the tag information of the ranging tag is obtained, the ranging tag is carried by the staff or pasted by other objects, and the ranging base station is pre-installed on the motor car;

[0015] If yes, it is determined that there is an obstacle in the warning area;

[0016] The specific steps of further determining the color of the specific warning area where the obstacle is located include:

[0017] According to the tag information, the distance from the obstacle to the motor car is determined;

[0018] Based on the distance, the color of the warning area where the obstacle falls is determined.

[0019] Optionally, the unmanned driving method further includes:

[0020] Obtain the position information of the motor car;

[0021] Based on the position information and the pre-planned driving route, control the operation and pulling of the nearest electric turnout;

[0022] By adopting the above technical solution, after obtaining the position information, if there is an electric turnout near the motor car, and according to the driving route of the motor car, the electric turnout needs to be operated, then the nearest electric turnout to the motor car is controlled to operate and pull.

[0023] Optionally, the specific steps of obtaining the position information of the motor car include:

[0024] Based on the wireless base station pre-deployed in the roadway, the range position information of the motor car is obtained;

[0025] Based on the optical shaft encoder pre-installed on the driven wheel of the motor car, the driving distance of the motor car is obtained;

[0026] Determine whether the RF card number information has been read. The tunnel is pre-divided into sections, and the RF cards are pre-placed at the intersections of each section of the tunnel. An RF card reader is installed on the locomotive.

[0027] If so, the precise location of the locomotive is determined based on the number information and the travel distance.

[0028] Perform fuzzy matching between the precise location information and the range location information;

[0029] If the two match, then the precise location information is determined to be the actual location information.

[0030] By adopting the above technical solution, the wireless base station can roughly locate the electric locomotive; if the radio frequency card reader reads the serial number information of the radio frequency RF card, the precise location information of the electric locomotive can be determined based on the travel distance and the serial number information. If only the serial number information is relied upon, there may be errors. Then, the precise location information and the range location information are fuzzily matched to further determine whether the precise location information is within the range location, thereby further eliminating errors and achieving precise positioning of the electric locomotive.

[0031] Optionally, the autonomous driving method further includes:

[0032] Based on the location information of the electric locomotive and the pixels of the tunnel map, the image position of the electric locomotive is generated in the tunnel map.

[0033] The location of the screen is shown in the alleyway map.

[0034] By adopting the above technical solution, it is easier for monitoring personnel to locate and track the position of locomotives in the tunnel.

[0035] Optionally, the specific steps for generating the image location of the electric locomotive in the alleyway map based on the locomotive's location information and the alleyway map pixels include:

[0036] Based on the location information of the electric locomotive, determine the area or section to which the electric locomotive belongs;

[0037] Based on the location segment and the lane map pixels, the associated pixel calculation method is invoked;

[0038] Based on the pixel calculation method, obtain the X-axis pixels and Y-axis pixels;

[0039] Based on the X-axis pixels and Y-axis pixels, the location of the electric locomotive is generated in the alleyway map.

[0040] Optionally, the pixel calculation method includes a straight-line pixel calculation method and a curve pixel calculation method. The straight-line pixel calculation method is applicable to straight sections of the roadway, and the curve pixel calculation method is applicable to curved ends of the roadway.

[0041] The method for calculating straight-line pixels includes:

[0042] The pixel value along the X-axis is: Px = Px1 + (L - L1) * Kx1 * J1;

[0043] Wherein, Px1 is the initial pixel value of the electric locomotive at the starting point of the straight line segment; L is the actual distance traveled by the electric locomotive; L1 is the initial actual position value of the electric locomotive at the starting point of the straight line segment; Kx1 is the slope of the straight line traveled by the electric locomotive; J1 is the ratio coefficient between the specific moving position of the electric locomotive and the pixel distance of the image; Px is the real-time X-axis pixel position of the electric locomotive.

[0044] The calculation is the same as for the X-axis. The pixel value for the Y-axis is: Py = Py1 + (L-L1) * Ky1 * J1;

[0045] The curve pixel calculation method includes:

[0046] The X-axis pixels are:

[0047] Px=r*{cos[(L-L1) / L12]* a+b ]-cosb}+Px1;

[0048] Where r is the pixel radius of the image corresponding to the locomotive's running curve; L is the actual distance the locomotive travels; L1 is the initial actual position value of the locomotive at the starting point of the straight segment; L12 is the length of the arc segment; a is the rotation angle of the arc segment; b is the starting angle of the arc segment; Px1 is the initial pixel value of the locomotive at the starting point of the straight segment, and Px is the real-time X-axis pixel position of the locomotive.

[0049] The calculation is the same as for the X-axis; the pixel values ​​for the Y-axis are:

[0050] Py=r*{sin[(L-L1) / L12]* a+b ]-sinb}+Py1.

[0051] By employing the above technical solution, in a straight-moving tunnel, the current pixel coordinates of the locomotive in the image are calculated based on its starting XY coordinates and travel distance, using the slope coefficient. In a curved tunnel, the current pixel coordinates of the locomotive in the image are calculated based on its starting XY coordinates and travel distance, using the arc of the locomotive's travel within the tunnel.

[0052] Secondly, this application provides an unmanned driving system for underground rail transport, which adopts the following technical solution:

[0053] An unmanned underground rail transport system includes:

[0054] The warning zone creation module is used to pre-create triple warning zones, which are represented by different colors.

[0055] The judgment module is used to determine whether there are obstacles within the warning area during the operation of the electric locomotive. The obstacles include people and / or other objects. If so, the module further determines the color of the specific warning area where the obstacle is located.

[0056] The warning action execution module executes the corresponding warning action based on the color of the specific warning area, and each warning area and the warning action have a unique mapping relationship.

[0057] By adopting the above technical solution, a triple warning zone is established through the warning zone establishment module. When staff appear in different warning zones, the warning action execution module can execute the corresponding warning action. Staff can roughly judge the distance of the locomotive based on the warning action and take timely avoidance, thus further improving the safety of the tunnel.

[0058] Thirdly, this application provides a terminal that adopts the following technical solution:

[0059] A terminal, comprising:

[0060] Memory, used to store the unmanned driving program for underground rail transport;

[0061] The processor is used to execute the program stored in the memory to implement the steps of the above-described unmanned underground rail transport method.

[0062] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0063] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by the aforementioned unmanned underground rail transport method.

[0064] In summary, this application has at least the following beneficial effects:

[0065] 1. The purpose of establishing a triple warning zone and implementing corresponding warning actions based on the color of the specific warning zone is to allow staff to roughly judge the distance of the locomotive based on the warning actions, so as to take timely evasive action, thereby further improving the safety of the tunnel.

[0066] 2. Based on the location information of the locomotive and the pixel of the tunnel map, the purpose of generating the locomotive's image position in the tunnel map is to facilitate the location and tracking of the locomotive by monitoring personnel. Attached Figure Description

[0067] Figure 1 This is a flowchart of an embodiment of the method described in this application;

[0068] Figure 2 This is a schematic diagram of the straight line segment of the triple warning area;

[0069] Figure 3 This is a schematic diagram of the curve end of the triple warning area;

[0070] Figure 4 This is a flowchart of the specific judgment steps in S120-S130;

[0071] Figure 5 This is a flowchart of another embodiment of the method of this application;

[0072] Figure 6 This is a flowchart of the specific steps in S210;

[0073] Figure 7 This is a flowchart of another embodiment of the method of this application;

[0074] Figure 8 This is a flowchart of the specific steps in S310;

[0075] Figure 9 This is a structural block diagram of an embodiment of the system described in this application;

[0076] Figure 10 This is a structural block diagram of another embodiment of the system described in this application;

[0077] Figure 11 This is a structural block diagram of another embodiment of the system described in this application.

[0078] Explanation of reference numerals in the attached diagram: 101. Warning area establishment module; 102. Judgment module; 103. Warning action execution module; 104. Location information acquisition module; 105. Control module; 106. Distance acquisition module; 107. Matching module; 108. Screen position generation module; 109. Display module; 110. Calling module; 111. X-axis pixel acquisition module; 112. Y-axis pixel acquisition module. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will be described in conjunction with the appendices in the embodiments of the present invention. Figure 1 - Appendix Figure 11The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] The first embodiment of this application discloses an unmanned driving method for underground rail transport. (Refer to...) Figure 1 As one embodiment of the autonomous driving method, the autonomous driving method may include S110-S140:

[0081] S110, three warning zones are pre-established, and the three warning zones can be represented by different colors;

[0082] S120 determines whether there are obstacles within the warning area while the electric locomotive is in motion;

[0083] S130, if so, then further determine the color of the specific warning area where the obstacle is located;

[0084] S140: Execute the corresponding warning action based on the color of the specific warning area.

[0085] Specifically, the triple warning zones can be yellow, purple, and red, with the purple zone outside the red zone and the yellow zone outside the purple zone. A ranging base station is installed on the locomotive, and the triple warning zones are established based on the range of the ranging base station, with the ranging range from farthest to closest being the yellow, purple, and red zones respectively. For example... Figure 2 and Figure 3 As shown. In other embodiments, other methods can be used to divide the warning area.

[0086] Obstacles include one or both of people and other objects. The warning action corresponding to the yellow zone could be a voice prompt from the locomotive saying, "Locomotive is running, pedestrians please give way." The warning action corresponding to the purple zone could be, "Locomotive decelerates to 0.5 m / s." The warning action corresponding to the red zone could be, "Locomotive immediately stops and brakes." Additionally, different levels of flashing lights can be activated simultaneously, with the alarm becoming more urgent from the yellow zone to the red zone. It should be noted that each warning zone and warning action has a unique mapping relationship.

[0087] Reference Figure 4 The specific steps for the judgment in S120 and S130 may include S121-S124:

[0088] S121, Determine whether the tag information of the ranging tag has been obtained;

[0089] S122, if so, then it is determined that there is an obstacle in the warning area;

[0090] S123, Based on the tag information, determine the distance from the obstacle to the electric locomotive;

[0091] S124, Based on distance, determine the color of the warning zone where an obstacle falls.

[0092] Specifically, the ranging base station installed on the locomotive can be a UWB base station with an effective detection range of 0.5-50 meters. It can achieve 360° monitoring without blind spots and is less affected by line of sight. It adopts ultra-wideband (UWB) positioning technology and does not require the carrier wave in the traditional communication system. Instead, it transmits data by sending and receiving pulses with a duration of nanoseconds or microseconds or less.

[0093] Personnel are pre-wearing distance measuring tags, and other objects are also pre-attached with distance measuring tags. When a worker wearing a distance measuring tag enters the pre-set alarm range of the distance measuring base station, the tag information is received. Based on this tag information, the distance between the distance measuring tag and the locomotive can be determined, thus identifying the color of the warning zone where an obstacle has fallen. After receiving the tag information, the vehicle-mounted voice device begins to issue an alarm, and the distance measuring tag worn by the worker sounds and vibrates. Furthermore, if two or more vehicles equipped with distance measuring base stations enter the pre-set alarm range, the vehicle-mounted device also begins to issue an alarm, providing different levels of audible and visual alarms based on the distance to the other vehicles and the assessed level of danger, effectively preventing and eliminating vehicle collisions.

[0094] Reference Figure 5 As another implementation of this autonomous driving method, the autonomous driving method may further include S210-S220:

[0095] S210, obtain the location information of the electric locomotive;

[0096] The S220 controls the operation and switching of the nearest electric switch machine based on location information and pre-planned travel routes.

[0097] Reference Figure 6 The specific steps for obtaining the location information of the electric locomotive may include S211-S216:

[0098] S211, based on the wireless base stations pre-deployed in the alley, obtain the range and location information of the electric locomotive;

[0099] S212, based on the photoelectric shaft encoder pre-installed on the driven wheel of the locomotive, obtains the travel distance of the locomotive;

[0100] S213, determine whether the number information of the radio frequency (RF) card has been read. The tunnel is pre-divided into sections, and the RF cards are pre-placed at the intersections of each section of the tunnel. The locomotive is equipped with an RF card reader.

[0101] S214, if so, then determine the precise location information of the locomotive based on the number information and the travel distance;

[0102] S215, perform fuzzy matching between precise location information and range location information;

[0103] S216, If the two match, then the precise location information is determined to be the actual location information.

[0104] Specifically, the locomotive's base station positioning utilizes 5G / 4G base stations (wireless base stations) and onboard network bridges deployed in the tunnels to achieve range-based positioning. The positioning accuracy is highly dependent on the density of the base station deployment. The encoder uses continuous counting, with the counted position stored in real-time in the locomotive's controller. The encoder is powered by 24V DC, with interfaces A and B connected to the controller's high-speed input channel for locomotive position pulse code acquisition. The locomotive identifies its location and segment by recognizing the RF card number; simultaneously, it achieves linear positioning by considering the travel distance. In cases of fuzzy matching between precise and range-based positions, a match is considered achieved if the difference between the two positions is within a preset threshold.

[0105] Reference Figure 7 As another implementation of autonomous driving, the autonomous driving method may further include S310-S320:

[0106] S310 generates the image position of the electric locomotive in the alleyway map based on the locomotive's location information and the alleyway map pixels;

[0107] S320, the display screen is located in the alleyway map.

[0108] Specifically, the tunnel map is displayed on a screen in the ground monitoring room. The location of the locomotive is generated in the tunnel map using the locomotive's location information and the tunnel map pixels, and then displayed on the screen.

[0109] Reference Figure 8 The specific steps of S310 may include S311-S314:

[0110] S311, Determine the location section of the locomotive based on its location information;

[0111] S312, based on the location segment and combined with the lane map pixels, calls the associated pixel calculation method;

[0112] S313, obtain the X-axis pixels and Y-axis pixels according to the pixel calculation method;

[0113] S314 generates the location of the electric locomotive in the alleyway map based on X-axis and Y-axis pixels.

[0114] Specifically, the roadway is pre-divided into areas and sections, with each area containing multiple segments. Pixel calculation methods include straight-line pixel calculation and curved-line pixel calculation. The straight-line pixel calculation method is suitable for straight segments of the roadway, while the curved-line pixel calculation method is suitable for curved ends of the roadway. For example, if the locomotive is in segment 1-1, it means the locomotive is in section 1 of zone 1. If this segment is a straight segment, the straight-line pixel calculation method is used; if it is a curved segment, the curved-line pixel calculation method is used. The straight-line pixel calculation method includes:

[0115] The pixel value along the X-axis is: Px = Px1 + (L - L1) * Kx1 * J1;

[0116] Wherein, Px1 is the initial pixel value of the electric locomotive at the starting point of the straight line segment; L is the actual distance traveled by the electric locomotive; L1 is the initial actual position value of the electric locomotive at the starting point of the straight line segment; Kx1 is the slope of the straight line traveled by the electric locomotive; J1 is the ratio coefficient between the specific moving position of the electric locomotive and the pixel distance of the image; Px is the real-time X-axis pixel position of the electric locomotive.

[0117] The calculation is the same as for the X-axis. The pixel value for the Y-axis is: Py = Py1 + (L-L1) * Ky1 * J1;

[0118] The curve pixel calculation method includes:

[0119] The X-axis pixels are:

[0120] Px=r*{cos[(L-L1) / L12]* a+b ]-cosb}+Px1;

[0121] Where r is the pixel radius of the image corresponding to the locomotive's running curve; L is the actual distance the locomotive travels; L1 is the initial actual position value of the locomotive at the starting point of the straight segment; L12 is the length of the arc segment; a is the rotation angle of the arc segment; b is the starting angle of the arc segment; Px1 is the initial pixel value of the locomotive at the starting point of the straight segment, and Px is the real-time X-axis pixel position of the locomotive.

[0122] The calculation is the same as for the X-axis; the pixel values ​​for the Y-axis are:

[0123] Py=r*{sin[(L-L1) / L12]* a+b ]-sinb}+Py1.

[0124] In a straight-moving tunnel, the current pixel coordinates of the locomotive in the image are calculated based on its starting XY coordinates and the distance it has traveled, using the slope coefficient. In a curved tunnel, the current pixel coordinates of the locomotive in the image are calculated based on its starting XY coordinates and the distance it has traveled, using the arc of the locomotive's movement within the tunnel.

[0125] The implementation principle of this embodiment is as follows:

[0126] During the locomotive's operation, it determines whether there are obstacles within the warning area. If so, it further determines the specific warning area where the obstacle is located and executes the corresponding warning action based on the specific warning area. It also acquires the locomotive's location information in real time and controls the operation and switching of nearby electric switch machines based on the location information and the pre-planned travel route.

[0127] Based on the above method embodiments, the second embodiment of this application discloses an unmanned driving system for underground rail transport. (Refer to...) Figure 9 As one implementation of the autonomous driving system, the autonomous driving system may include:

[0128] The warning area creation module 101 is used to pre-create a triple warning area, which can be represented by different colors.

[0129] The judgment module 102 is used to determine whether there are obstacles in the warning area during the operation of the locomotive. The obstacles include staff and / or other objects; if so, it further determines the color of the specific warning area where the obstacle is located.

[0130] The warning action execution module 103 executes the corresponding warning action according to the color of the specific warning area, and each warning area and warning action has a unique mapping relationship.

[0131] In addition, the judgment module 102 will also determine whether the tag information of the ranging tag is obtained. If so, it will determine that there is an obstacle in the warning area. Then, based on the tag information, it will determine the distance from the obstacle to the locomotive, and based on the distance, it will determine the color of the warning area where the obstacle falls.

[0132] Reference Figure 10 As another implementation of the autonomous driving system, the autonomous driving system may further include:

[0133] The location information acquisition module 104 is used to acquire the location information of the locomotive, and also to acquire the range location information of the locomotive based on the wireless base station pre-deployed in the alleyway.

[0134] The distance acquisition module 106 acquires the travel distance of the electric locomotive based on the photoelectric shaft encoder pre-installed on the driven wheel of the electric locomotive; the judgment module 102 determines whether the number information of the RF card is read. If so, the precise location of the electric locomotive is determined based on the number information and the travel distance.

[0135] The matching module 107 is used to perform fuzzy matching between precise location information and range location information. If the two match, the precise location information is determined to be the actual location information.

[0136] The control module 105 controls the operation of nearby electric switch machines based on actual location information and pre-planned travel routes.

[0137] Reference Figure 11 As another implementation of the autonomous driving system, the autonomous driving system may further include:

[0138] The image location generation module 108 generates the image location of the electric locomotive in the alley map based on the locomotive's location information and the alley map pixels.

[0139] Display module 109 is used to display the screen position in the alleyway map;

[0140] The judgment module 102 is used to determine the location segment to which the electric locomotive belongs based on the locomotive's location information;

[0141] Module 110 is invoked to call the associated pixel calculation method based on the location segment and the pixels of the alleyway map.

[0142] X-axis pixel acquisition module 111 is used to obtain X-axis pixels according to the pixel calculation method;

[0143] Y-axis pixel acquisition module 112 is used to obtain Y-axis pixels according to the pixel calculation method.

[0144] The implementation principle of this embodiment is as follows:

[0145] During the locomotive's operation, the judgment module 102 determines whether there is an obstacle within the warning area. If so, the judgment module 102 further determines the specific warning area where the obstacle is located. The warning action execution module 103 executes the corresponding warning action according to the specific warning area. The location information acquisition module 104 acquires the locomotive's location information in real time. The control module 105 controls the operation and switching of the nearby electric switch machine based on the location information and the pre-planned driving route.

[0146] The third embodiment of this application also provides a terminal, which may be a client such as a computer or a smartphone, and the above-mentioned system is built into the terminal. The terminal may include: a memory and a processor.

[0147] The memory is used to store the aforementioned unmanned driving program for underground rail transport;

[0148] The processor is used to execute the program stored in the memory to implement the steps of the above-described unmanned underground rail transport method.

[0149] The memory can communicate with the processor via a communication bus, which can be an address bus, a data bus, a control bus, etc.

[0150] Additionally, the memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0151] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0152] The fourth embodiment of this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed by the above-described unmanned underground rail transport method.

[0153] Computer-readable storage media can be any usable medium that a computer can access, or a data storage device such as a server or data center that integrates one or more usable media. Usable media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0154] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for unmanned driving of underground rail transport, characterized in that, include: A triple warning zone is pre-established, which is based on the range of the ranging base station on the locomotive. The ranging range is divided into yellow, purple and red zones from the farthest to the closest. During the operation of the electric locomotive, it is determined whether there are obstacles within the warning area, including people and / or other objects; If so, then further determine the color of the specific warning area where the obstacle is located; Based on the color of the specific warning area, the corresponding warning action is executed, and each warning area and the warning action have a unique mapping relationship; Based on the location information of the electric locomotive, determine the area or section to which the electric locomotive belongs; Based on the location segment and combined with the lane map pixels, the associated pixel calculation method is invoked; Based on the pixel calculation method, obtain the X-axis pixels and Y-axis pixels; Based on the X-axis pixels and Y-axis pixels, the position of the electric locomotive is generated in the alleyway map; The location of the screen is displayed on the alleyway map; The pixel calculation method includes a straight-line pixel calculation method and a curve pixel calculation method. The straight-line pixel calculation method is applicable to the straight sections of the roadway, and the curve pixel calculation method is applicable to the curved ends of the roadway. The method for calculating straight-line pixels includes: The pixel value along the X-axis is: Px = Px1 + (L - L1) * Kx1 * J1; Wherein, Px1 is the initial pixel value of the electric locomotive at the starting point of the straight line segment; L is the actual distance traveled by the electric locomotive; L1 is the initial actual position value of the electric locomotive at the starting point of the straight line segment; Kx1 is the slope of the straight line traveled by the electric locomotive; J1 is the ratio coefficient between the specific moving position of the electric locomotive and the pixel distance of the image; Px is the real-time X-axis pixel position of the electric locomotive. The calculation is the same as for the X-axis. The pixel value for the Y-axis is: Py = Py1 + (L-L1) * Ky1 * J1; The curve pixel calculation method includes: The X-axis pixels are: Px=r*{cos[(L-L1) / L12]* a+b ]-cosb}+Px1; Where r is the pixel radius of the image corresponding to the locomotive's running curve; L is the actual distance the locomotive travels; L1 is the initial actual position value of the locomotive at the starting point of the straight segment; L12 is the length of the arc segment; a is the rotation angle of the arc segment; b is the starting angle of the arc segment; Px1 is the initial pixel value of the locomotive at the starting point of the straight segment, and Px is the real-time X-axis pixel position of the locomotive. The calculation is the same as for the X-axis; the pixel values ​​for the Y-axis are: Py=r*{sin[(L-L1) / L12]* a+b ]-sinb}+Py1.

2. The method for unmanned driving of underground rail transport according to claim 1, characterized in that, The specific steps for determining whether there are obstacles within the warning area include: Determine whether the tag information of the ranging tag has been obtained. The ranging tag is carried by personnel or affixed by other objects. The electric locomotive is pre-installed with a ranging base station. If so, then it is determined that there is an obstacle within the warning area; The specific steps for further determining the color of the specific warning area where the obstacle is located include: Based on the label information, determine the distance from the obstacle to the electric locomotive; Based on the distance, determine the color of the warning area where the obstacle falls.

3. The method for unmanned driving of underground rail transport according to claim 1, characterized in that, The autonomous driving method also includes: Obtain the location information of the electric locomotive; Based on the location information and the pre-planned travel route, control the operation and switching of the nearest electric switch machine.

4. The unmanned driving method for underground rail transport according to claim 3, characterized in that, The specific steps for obtaining the location information of the electric locomotive include: Based on the wireless base stations pre-deployed in the alley, the range and location information of the electric locomotive is obtained; The travel distance of the electric locomotive is obtained based on the photoelectric shaft encoder pre-installed on the driven wheel of the electric locomotive; Determine whether the RF card number information has been read. The tunnel is pre-divided into sections, and the RF cards are pre-placed at the intersections of each section of the tunnel. An RF card reader is installed on the locomotive. If so, the precise location of the locomotive is determined based on the number information and the travel distance. Perform fuzzy matching between the precise location information and the range location information; If the two match, then the precise location information is determined to be the actual location information.

5. An unmanned driving system for underground rail transport, characterized in that, The method for unmanned underground rail transport as described in any one of claims 1-4 includes: The warning area creation module (101) is used to pre-create a triple warning area, which is represented by different colors. The judgment module (102) is used to determine whether there are obstacles in the warning area during the operation of the electric locomotive, the obstacles including people and / or other objects; if so, it further determines the color of the specific warning area where the obstacle is located. The warning action execution module (103) executes the corresponding warning action according to the color of the specific warning area, and each warning area and the warning action have a unique mapping relationship.

6. A terminal, characterized in that, include: Memory, used to store the unmanned driving program for underground rail transport; A processor for executing a program stored in memory to implement the steps of the unmanned underground rail transport method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program capable of being loaded by a processor and executed as described in any one of claims 1-4 for unmanned underground rail transport.

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