Intelligent guide vehicle scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses an intelligent guide vehicle scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a dynamic detection distance based on the crossing time of a rail-mounted gantry crane and the speed of an intelligent guide vehicle when the rail-mounted gantry crane is in an automatic crossing working condition, the dynamic detection distance is the distance from the crossing section of the rail-mounted gantry crane to the direction of the intelligent guide vehicle; the current position of the intelligent guide vehicle is acquired, and the path distance from the crossing section to the current position is calculated; when the path distance is greater than the dynamic detection distance, re-planning the path of the intelligent guide vehicle; and when the path distance is smaller than the dynamic detection distance, the intelligent guide vehicle is controlled to continue to execute the planned path after the rail-mounted gantry crane finishes crossing the street. According to the embodiment of the invention, the problem of poor scheduling flexibility of the intelligent guide vehicle in the related technology is solved.

Description

Technical Field

[0001] The present application relates to an intelligent guided vehicle dispatching method, device, electronic equipment and storage medium, and belongs to the field of automated terminal transshipment. Background Art

[0002] The vehicle management system (VMS) of the automated terminal can generate electronic no-go zones in the yard that prohibit the passage of intelligent guided vehicles (IGVs), and display the images of these no-go zones on the graphical user interface (GUI) for confirmation by the central control personnel. When the automated rail-mounted gantry crane (ARMG), referred to as the "rail crane", needs to execute a crossing instruction, it usually sends an application to the VMS through its rail crane management system (BMS). After receiving the application, the VMS will temporarily generate a no-go preparation area and drive all IGVs in the area away. After confirming that there are no other IGVs in the area, the VMS will generate a formal no-go area. After the BMS system receives the no-go zone generation signal from the VMS, the ARMG will perform automatic crossing operations according to the instructions.

[0003] During the ARMG automatic crossing, the VMS will issue a waiting instruction to the IGVs that have generated a path and passed the crossing section until the ARMG completes the automatic crossing. For the remaining IGVs that have not generated a path, their planned paths will be adjusted to detour. However, this one-size-fits-all waiting and detour decision does not guide the IGV according to the ARMG's crossing status, and it is easy to make a detour decision when the ARMG is about to complete the automatic crossing, which is not conducive to the flexible scheduling of IGVs. Summary of the invention

[0004] In view of this, the present application provides an intelligent guided vehicle scheduling method, device, electronic device and storage medium. The embodiments of the present application at least solve the problem of poor scheduling flexibility of intelligent guided vehicles in related technologies.

[0005] The first aspect of an embodiment of the present application discloses a method for scheduling an intelligent guided vehicle, the method comprising: when a rail crane is in an automatic street crossing condition, obtaining a dynamic detection distance based on the street crossing time of the rail crane and the speed of the intelligent guided vehicle, the dynamic detection distance being the distance from the street crossing section of the rail crane to the direction of the intelligent guided vehicle; obtaining the current position of the intelligent guided vehicle and calculating the path distance from the street crossing section to the current position; when the path distance is greater than the dynamic detection distance, replanning the path of the intelligent guided vehicle; when the path distance is less than the dynamic detection distance, controlling the intelligent guided vehicle to continue executing the planned path after the rail crane completes the street crossing.

[0006] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the street crossing time is determined based on the position of the rail crane.

[0007] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the dynamic detection distance is obtained based on the crossing time of the rail crane and the speed of the intelligent guided vehicle, including: obtaining the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time; obtaining the total street crossing time based on the time when the rail crane arrives at the starting crossing position and the average street crossing time; and obtaining the dynamic detection distance based on the total street crossing time and the average speed of the intelligent guided vehicle.

[0008] In some embodiments, the dynamic detection distance is obtained based on the total street crossing time and the average speed of the intelligent guided vehicle, including: obtaining the dynamic detection distance based on an adjustable patience time, the total street crossing time and the average speed of the intelligent guided vehicle.

[0009] In some embodiments, the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time includes: establishing a motion equation for the rail crane based on the friction resistance between the rail crane and the track and the wind resistance of the rail crane; obtaining a first relationship between the uniform acceleration initial velocity and the uniform acceleration final velocity and the acceleration phase time based on the motion equation for the rail crane; obtaining a second relationship between the uniform acceleration final velocity and the maximum speed that the rail crane can reach and the deceleration phase time based on the motion equation for the rail crane; obtaining a third relationship between the total distance, the acceleration segment distance and the deceleration segment distance and the uniform speed phase time based on the motion equation for the rail crane; and obtaining the time when the rail crane arrives at the starting crossing position based on the annealing algorithm, the first relationship, the second relationship and the third relationship.

[0010] In some embodiments, before acquiring the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a magnetic nail reading antenna of the intelligent guided vehicle and a magnetic nail arrangement of the dock.

[0011] In some embodiments, before obtaining the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a laser radar and / or a Beidou positioning device of the intelligent guided vehicle.

[0012] The second aspect of an embodiment of the present application discloses an intelligent guided vehicle scheduling device, the device comprising: a first distance calculation module, which is used to obtain a dynamic detection distance based on the crossing time of the rail crane and the speed of the intelligent guided vehicle when the rail crane is in an automatic street crossing condition, and the dynamic detection distance is the distance from the street crossing section of the rail crane to the direction of the intelligent guided vehicle; a second distance calculation module, which is used to obtain the current position of the intelligent guided vehicle and calculate the path distance from the street crossing section to the current position; a path planning module, which is used to re-plan the path of the intelligent guided vehicle when the path distance is greater than the dynamic detection distance; and a path execution module, which is used to control the intelligent guided vehicle to continue to execute the planned path after the rail crane completes the street crossing when the path distance is less than the dynamic detection distance.

[0013] A third aspect of an embodiment of the present application discloses a computer-readable storage medium, which includes a stored program, wherein when the program is run, the processor of the device where the program is located controls the execution of the intelligent guided vehicle scheduling method of the above embodiment.

[0014] The fourth aspect of an embodiment of the present application discloses an electronic device, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors execute the intelligent guided vehicle scheduling method of the above embodiment.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0016] The embodiments of the present application provide a method, device, electronic device and storage medium for scheduling an intelligent guided vehicle. The intelligent guided vehicle scheduling method includes: when the rail crane is in an automatic street crossing condition, a dynamic detection distance is obtained based on the street crossing time of the rail crane and the speed of the intelligent guided vehicle, and the dynamic detection distance is the distance from the street crossing section of the rail crane to the direction of the intelligent guided vehicle; the current position of the intelligent guided vehicle is obtained and the path distance from the street crossing section to the current position is calculated; when the path distance is greater than the dynamic detection distance, the path of the intelligent guided vehicle is replanned; when the path distance is less than the dynamic detection distance, the intelligent guided vehicle is controlled to continue to execute the planned path after the rail crane completes the street crossing. This embodiment can change the detection distance according to the position of the rail crane to achieve dynamic adjustment, and through reasonable path planning, the intelligent guided vehicle can continue to maintain efficient transportation tasks without being affected by the street crossing condition of the rail crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0018] Figure 1 A flowchart of an intelligent guided vehicle scheduling method provided in an embodiment of the present application.

[0019] Figure 2 A flowchart of another intelligent guided vehicle scheduling method provided in an embodiment of the present application.

[0020] Figure 3 A schematic diagram of scheduling of an intelligent guided vehicle provided in an embodiment of the present application.

[0021] Figure 4 A schematic diagram of the structure of an intelligent guided vehicle dispatching device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Embodiment 1:

[0025] Figure 1 A flow chart of an intelligent guided vehicle scheduling method provided in an embodiment of the present application is shown in FIG. Figure 1As shown, the method may include the following steps:

[0026] 101 When the rail crane is in automatic street crossing condition, a dynamic detection distance is obtained based on the street crossing time of the rail crane and the speed of the intelligent guided vehicle. The dynamic detection distance is the distance from the street crossing section of the rail crane to the direction of the intelligent guided vehicle.

[0027] 102 obtains the current position of the intelligent guided vehicle and calculates the path distance from the street crossing section to the current position.

[0028] 103 When the path distance is greater than the dynamic detection distance, replan the path of the intelligent guided vehicle.

[0029] 104 When the path distance is less than the dynamic detection distance, the intelligent guided vehicle is controlled to continue executing the planned path after the rail crane completes crossing the street.

[0030] In this embodiment, Figure 2 As shown in the figure, after the BMS system receives the automatic crossing command, the ARMG is in place and applies for a no-go zone. After the VMS system receives the command to generate a no-go zone, it first generates a preliminary no-go zone, then drives the IGVs in the preliminary no-go zone out, and after the driving is completed, it generates a formal no-go zone and guides the IGVs to wait or detour according to the crossing situation. The ARMG starts to cross the street automatically until the crossing is completed, at which time the no-go zone is lifted and the IGVs resume traffic.

[0031] In this embodiment, Figure 3 As shown, when the rail crane is in automatic street crossing condition, a no-crossing zone will be generated, thereby blocking the intelligent guided vehicle that is about to pass. The dynamic detection distance D is the vertical distance from the street crossing section to the direction of the intelligent guided vehicle. It can be understood that the path distance is also the distance in the vertical direction. When the vehicle management system recognizes that the intelligent guided vehicle is within the detection distance, it waits for the rail crane to complete the street crossing and continues to drive the currently planned path; when the vehicle management system recognizes that the intelligent guided vehicle is outside the detection distance, it replans the target path. The planning method is set by the vehicle management system and is a conventional method, which will not be repeated here.

[0032] Exemplarily, the street crossing section includes a side length close to an entrance of the intelligent guided vehicle and a side length away from an exit of the intelligent guided vehicle, and the vertical distance is calculated based on the side length of the entrance.

[0033] In other embodiments, the dynamic detection distance is the straight-line distance from the street crossing section to the direction of the intelligent guided vehicle.

[0034] Exemplarily, the crossing segment includes a rectangular crossing area, and the straight-line distance is calculated based on the center point of the rectangular crossing area.

[0035] In one embodiment, the intelligent guided vehicle is equipped with a camera device, which analyzes the taken photos through visual processing technology to determine the path distance.

[0036] As an optional implementation, the crossing time is a preset time, which is set by the operation and maintenance personnel in the graphical user interface according to the position and movement status of each rail crane.

[0037] It is worth noting that due to different operating positions, the starting position of each rail crane crossing the street is different, so the crossing time is also different. This embodiment can change the detection distance according to the position of the rail crane to achieve dynamic adjustment. At the same time, through reasonable path planning, the intelligent guided vehicle can continue to maintain efficient transportation tasks without being affected by the working conditions of the rail crane crossing the street.

[0038] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the street crossing time is determined based on the position of the rail crane.

[0039] In this embodiment, a corresponding relationship table between the rail crane position and the crossing time is preset, so that the system can automatically calculate the crossing time according to the relationship table, thereby realizing an automated distance calculation operation.

[0040] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the dynamic detection distance is obtained based on the crossing time of the rail crane and the speed of the intelligent guided vehicle, including: obtaining the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time; obtaining the total street crossing time based on the time when the rail crane arrives at the starting crossing position and the average street crossing time; and obtaining the dynamic detection distance based on the total street crossing time and the average speed of the intelligent guided vehicle.

[0041] In this embodiment, the BMS system can calculate the distance that the rail crane needs to reach the starting position for crossing the street based on the position of the rail crane.

[0042] Furthermore, obtaining the dynamic detection distance based on the total street crossing time and the average speed of the intelligent guided vehicle includes: obtaining the dynamic detection distance based on an adjustable patience time, the total street crossing time and the average speed of the intelligent guided vehicle.

[0043] It should be noted that the track is usually accelerated, uniformly accelerated and decelerated when it is hung on the street crossing task. It is usually decelerated to 30% of the maximum speed and crossed the street at this speed.

[0044] Specifically, when the rail crane receives a crossing task, the BMS system extracts the current position information of the rail crane and calculates the time t when it arrives at the starting crossing position. 1 , t 1 It can be expressed as the acceleration phase time t 11 , uniform speed stage time t 12 and deceleration phase time t 13 The sum of:

[0045]

[0046] In the above formula, v 0 is the initial velocity (uniform acceleration stage), v is the final velocity (uniform deceleration stage), a is the acceleration and deceleration, d is the total distance traveled, and d 1 is the acceleration distance, d 2 This is the deceleration section.

[0047] According to the working condition of the rail crane crossing the street, the average crossing time is t 2 , the time for the rail crane to cross the street is generally a fixed value, so the total time for crossing the street is t = t 1 +t 2 .

[0048] Finally, the dynamic detection distance is obtained:

[0049] D=V e *(t+t n )

[0050] Among them, V e is the average speed of IGV, t n For adjustable patience time.

[0051] In some embodiments, the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time includes: establishing a motion equation for the rail crane based on the friction resistance between the rail crane and the track and the wind resistance of the rail crane; obtaining a first relationship between the uniform acceleration initial velocity and the uniform acceleration final velocity and the acceleration phase time based on the motion equation for the rail crane; obtaining a second relationship between the uniform acceleration final velocity and the maximum speed that the rail crane can reach and the deceleration phase time based on the motion equation for the rail crane; obtaining a third relationship between the total distance, the acceleration segment distance and the deceleration segment distance and the uniform speed phase time based on the motion equation for the rail crane; and obtaining the time when the rail crane arrives at the starting crossing position based on the annealing algorithm, the first relationship, the second relationship and the third relationship.

[0052] Specifically, when the rail crane receives a crossing task, the BMS system extracts the current position information of the rail crane and calculates the time t when it arrives at the starting crossing position. 1 , t 1It can be expressed as the acceleration phase time t 11 , uniform speed stage time t 12 and deceleration phase time t 13 The sum of:

[0053] Acceleration (deceleration) stage time:

[0054] Considering the dynamic friction resistance and wind resistance, the motion equation of the rail crane is:

[0055]

[0056] The first relationship is as follows:

[0057]

[0058] The second relationship is as follows:

[0059]

[0060] The third relationship (the time required for the uniform speed stage) is as follows:

[0061]

[0062] in: is the contribution of the net acceleration (deceleration), is a constant related to wind resistance. eng is the thrust of the rail crane motor, m is the mass of the rail crane, μ is the friction coefficient between the rail crane and the track, g is the acceleration of gravity, C d is the drag coefficient, ρ is the air density, A * is the windward area. v is the real-time speed, v 0 is the initial velocity of uniform acceleration, v T is the final velocity of uniform acceleration, v max The maximum speed that the rail crane can reach. 1 is the acceleration distance, d 2 is the deceleration distance. d is the total distance.

[0063] From the above, we can get the time t when the rail crane arrives at the starting crossing position 1 :

[0064] t 1 =t 11 +t 12 +t 13

[0065] From the above, we can get t 1 It can be written as the uniformly accelerated final velocity v T By substituting the above formula into the annealing algorithm, we can calculate the optimal uniform acceleration final speed v when the efficiency is T(speed in the uniform phase) and the minimum time T for the rail crane to reach the crossing position 1 The pseudo code is as follows:

[0066]

[0067]

[0068] According to the working conditions of the rail crane crossing the street, the time for the rail crane to cross the street is generally a fixed value, and the average crossing time can be obtained as T 2 , then the total time to cross the street is T = T 1 +T 2 .

[0069] Finally, the dynamic detection distance is obtained:

[0070] D=V e *(T+T n )

[0071] Among them, V e is the average speed of IGV, T n For adjustable patience time.

[0072] In some embodiments, before acquiring the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a magnetic nail reading antenna of the intelligent guided vehicle and a magnetic nail arrangement of the dock.

[0073] In other embodiments, before obtaining the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a laser radar and / or a Beidou positioning device of the intelligent guided vehicle.

[0074] Exemplarily, positioning is performed by a vehicle-mounted satellite differential positioning integrated device.

[0075] Embodiment 2:

[0076] Figure 4 A schematic diagram of the structure of an intelligent guided vehicle dispatching device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device may include the following modules:

[0077] The first distance calculation module 401 is used to obtain a dynamic detection distance based on the crossing time of the rail crane and the speed of the intelligent guided vehicle when the rail crane is in automatic crossing condition. The dynamic detection distance is the distance from the crossing section of the rail crane to the direction of the intelligent guided vehicle.

[0078] The second distance calculation module 402 is used to obtain the current position of the intelligent guided vehicle and calculate the path distance from the street crossing section to the current position.

[0079] The path planning module 403 is used to replan the path of the intelligent guided vehicle when the path distance is greater than the dynamic detection distance.

[0080] The path execution module 404 is used to control the intelligent guided vehicle to continue to execute the planned path after the rail crane completes crossing the street when the path distance is less than the dynamic detection distance.

[0081] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the street crossing time is determined based on the position of the rail crane.

[0082] In some embodiments, in response to the rail crane receiving a street crossing task, the rail crane is determined to be in an automatic street crossing condition, and the position of the rail crane is determined; the dynamic detection distance is obtained based on the crossing time of the rail crane and the speed of the intelligent guided vehicle, including: obtaining the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time; obtaining the total street crossing time based on the time when the rail crane arrives at the starting crossing position and the average street crossing time; and obtaining the dynamic detection distance based on the total street crossing time and the average speed of the intelligent guided vehicle.

[0083] In some embodiments, the dynamic detection distance is obtained based on the total street crossing time and the average speed of the intelligent guided vehicle, including: obtaining the dynamic detection distance based on an adjustable patience time, the total street crossing time and the average speed of the intelligent guided vehicle.

[0084] In some embodiments, the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time includes: establishing a motion equation for the rail crane based on the friction resistance between the rail crane and the track and the wind resistance of the rail crane; obtaining a first relationship between the uniform acceleration initial velocity and the uniform acceleration final velocity and the acceleration phase time based on the motion equation for the rail crane; obtaining a second relationship between the uniform acceleration final velocity and the maximum speed that the rail crane can reach and the deceleration phase time based on the motion equation for the rail crane; obtaining a third relationship between the total distance, the acceleration segment distance and the deceleration segment distance and the uniform speed phase time based on the motion equation for the rail crane; and obtaining the time when the rail crane arrives at the starting crossing position based on the annealing algorithm, the first relationship, the second relationship and the third relationship.

[0085] In some embodiments, before acquiring the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a magnetic nail reading antenna of the intelligent guided vehicle and a magnetic nail arrangement of the dock.

[0086] In some embodiments, before obtaining the current position of the intelligent guided vehicle, the method includes: obtaining the current position of the intelligent guided vehicle based on a laser radar and / or a Beidou positioning device of the intelligent guided vehicle.

[0087] Embodiment 3:

[0088] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0089] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0090] Embodiment 4:

[0091] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0092] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0093] Embodiment 5:

[0094] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0095] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0096] Embodiment 6:

[0097] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0098] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0099] Embodiment 7:

[0100] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0101] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0102] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0107] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent guided vehicle dispatching method, characterized in that: include: When the rail crane is in an automatic street crossing condition, a dynamic detection distance is obtained based on the street crossing time of the rail crane and the speed of the intelligent guided vehicle. The dynamic detection distance is the distance from the street crossing section of the rail crane to the direction of the intelligent guided vehicle. Acquire the current position of the intelligent guided vehicle and calculate the path distance from the street crossing section to the current position; When the path distance is greater than the dynamic detection distance, replanning the path of the intelligent guided vehicle; When the path distance is less than the dynamic detection distance, the intelligent guided vehicle is controlled to continue executing the planned path after the rail crane completes crossing the street.

2. The intelligent guided vehicle dispatching method according to claim 1, characterized in that: In response to the rail crane receiving a street crossing task, determining that the rail crane is in an automatic street crossing working condition, and determining the position of the rail crane; The crossing time is determined based on the position of the rail crane.

3. The intelligent guided vehicle dispatching method according to claim 1, characterized in that: In response to the rail crane receiving a street crossing task, determining that the rail crane is in an automatic street crossing working condition, and determining the position of the rail crane; The dynamic detection distance is obtained based on the crossing time of the rail crane and the speed of the intelligent guided vehicle, including: The time when the rail crane arrives at the starting crossing position is obtained based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time; The total crossing time is obtained based on the arrival time and average crossing time of the rail crane at the starting crossing position; The dynamic detection distance is obtained based on the total street crossing time and the average speed of the intelligent guided vehicle.

4. The intelligent guided vehicle dispatching method according to claim 3, characterized in that: The dynamic detection distance is obtained based on the total street crossing time and the average speed of the intelligent guided vehicle, including: The dynamic detection distance is obtained based on the adjustable patience time, the total street crossing time and the average vehicle speed of the intelligent guided vehicle.

5. The intelligent guided vehicle dispatching method according to claim 3, characterized in that: The method of obtaining the time when the rail crane arrives at the starting crossing position based on the position of the rail crane, the acceleration phase time, the uniform speed phase time and the deceleration phase time includes: Establishing a motion equation of the track crane based on the friction resistance between the track crane and the track and the wind resistance of the track crane; Based on the motion equation of the rail crane, a first relationship between the uniform acceleration initial velocity, the uniform acceleration final velocity and the acceleration stage time is obtained; Based on the motion equation of the rail crane, a second relationship between the final speed of uniform acceleration and the maximum speed that the rail crane can reach and the deceleration stage time is obtained; Based on the motion equation of the rail crane, a third relationship between the total distance, the acceleration section distance, the deceleration section distance and the uniform speed stage time is obtained; The time when the rail crane arrives at the starting crossing position is obtained based on the annealing algorithm, the first relationship, the second relationship and the third relationship.

6. The intelligent guided vehicle dispatching method according to claim 1, characterized in that: Before obtaining the current position of the intelligent guided vehicle, the method includes: The current position of the intelligent guided vehicle is obtained based on the magnetic nail reading antenna of the intelligent guided vehicle and the magnetic nail arrangement of the dock.

7. The intelligent guided vehicle dispatching method according to claim 1, characterized in that: Before obtaining the current position of the intelligent guided vehicle, the method includes: The current position of the intelligent guided vehicle is obtained based on the laser radar and / or Beidou positioning device of the intelligent guided vehicle.

8. An intelligent guided vehicle dispatching device, characterized in that: include: A first distance calculation module is used to obtain a dynamic detection distance based on the crossing time of the rail crane and the speed of the intelligent guided vehicle when the rail crane is in an automatic crossing condition. The dynamic detection distance is the distance from the crossing section of the rail crane to the direction of the intelligent guided vehicle; A second distance calculation module, used to obtain the current position of the intelligent guided vehicle and calculate the path distance from the street crossing section to the current position; A path planning module, used for replanning the path of the intelligent guided vehicle when the path distance is greater than the dynamic detection distance; The path execution module is used to control the intelligent guided vehicle to continue to execute the planned path after the rail crane completes crossing the street when the path distance is less than the dynamic detection distance.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the intelligent guided vehicle scheduling method according to any one of claims 1 to 7 is executed in a processor of a device where the program is controlled.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors execute the intelligent guided vehicle scheduling method described in any one of claims 1 to 7.

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