Automatic driving vehicle charging management method, electronic equipment and storage medium

Abstract

The invention relates to an automatic driving vehicle charging management method, electronic equipment and a storage medium, and the automatic driving vehicle charging management method applied to a master control platform comprises the steps: responding to monitoring that the remaining electric quantity of a target automatic driving vehicle is smaller than a low-power threshold value, and adding the target automatic driving vehicle to a low-power queue, the low power threshold value is determined according to current task information; determining the comprehensive priority of each to-be-charged automatic driving vehicle in the low-power queue; based on the comprehensive priority of each to-be-charged automatic driving vehicle, determining a charging sequence of each to-be-charged automatic driving vehicle in the low-power queue; and controlling each to-be-charged automatic driving vehicle to be charged according to the charging sequence of each to-be-charged automatic driving vehicle. According to the invention, the unified management of the charging resources and the flexible adjustment of the low-power threshold are realized, thereby achieving the purpose of taking both charging and task execution into consideration, and obtaining the task execution efficiency as high as possible and the utilization rate of the charging resources.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a charging management method, electronic device, and storage medium for an autonomous driving vehicle. Background Art

[0002] In recent years, with the rapid development of unmanned driving technology, unmanned vehicles have been widely used in various scenarios, such as airport logistics transportation, passenger ferrying and site patrol.

[0003] Usually, vehicles are responsible for tasks with high density and diverse scenarios. Therefore, how to ensure the endurance of these vehicles and efficiently manage charging resources without affecting the vehicle's mission execution has become a problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide an autonomous driving vehicle charging management method, an electronic device and a storage medium. The charging of each autonomous driving vehicle is monitored through a master control platform, and charging resources are uniformly managed, and the charging sequence of each autonomous driving vehicle to be charged is uniformly coordinated, thereby realizing unified management of charging resources and each autonomous driving vehicle to be charged; by dynamically determining the low power threshold according to the vehicle type of the target autonomous driving vehicle, the last charging date and the current task information, the low power threshold is flexibly adjusted, thereby achieving the purpose of taking into account both charging and task execution, and obtaining the highest possible task execution efficiency.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for managing charging of an autonomous driving vehicle, which is applied to a master control platform. The method includes:

[0006] In response to monitoring that the remaining power of the target autonomous driving vehicle is less than a low power threshold, adding the target autonomous driving vehicle to a low power queue, wherein the low power threshold is determined according to the vehicle type, the last charging date, and the current mission information of the target autonomous driving vehicle;

[0007] Determining the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue;

[0008] Determining a charging order for each autonomous driving vehicle to be charged in the low-power queue based on the comprehensive priority of each autonomous driving vehicle to be charged;

[0009] The charging of each autonomous driving vehicle to be charged is controlled according to the charging sequence of each autonomous driving vehicle to be charged.

[0010] In a second aspect, the embodiments of the present disclosure further provide a method for managing charging of an autonomous driving vehicle, which is applied to a sub-control platform, including:

[0011] Receiving a low-power threshold for a target autonomous driving vehicle issued by a master control platform, wherein the low-power threshold is determined according to a vehicle type, a last charging date, and current mission information of the target autonomous driving vehicle;

[0012] The target autonomous driving vehicle is assigned a task based on the low electrical threshold.

[0013] In a third aspect, the embodiments of the present disclosure further provide an autonomous driving vehicle management system, including a master control platform and a plurality of sub-control platforms;

[0014] Among them, the master control platform is used to determine the low-power threshold of the target autonomous driving vehicle according to the vehicle type, the last charging date and the current task information of the target autonomous driving vehicle, and send the low-power threshold to the sub-control platform corresponding to the target autonomous driving vehicle; and when it is monitored that the remaining power of the target autonomous driving vehicle is less than the low-power threshold, the target autonomous driving vehicle is added to the low-power queue, and the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue is determined; based on the comprehensive priority of each autonomous driving vehicle to be charged, the charging order of each autonomous driving vehicle to be charged in the low-power queue is determined; the charging of each autonomous driving vehicle to be charged is controlled according to the charging order of each autonomous driving vehicle to be charged; each sub-control platform is used to assign tasks to each autonomous driving vehicle according to the low-power threshold for each autonomous driving vehicle issued by the master control platform.

[0015] In a fourth aspect, an embodiment of the present disclosure further provides an electronic device, comprising: 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 implement the autonomous driving vehicle charging management method as described above.

[0016] In a fifth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the autonomous driving vehicle charging management method as described above.

[0017] A charging management method for an autonomous driving vehicle provided by an embodiment of the present disclosure monitors the charging of each autonomous driving vehicle through a master control platform, manages charging resources in a unified manner, coordinates the charging sequence of each autonomous driving vehicle to be charged, and realizes unified management of charging resources and each autonomous driving vehicle to be charged; by dynamically determining the low-battery threshold according to the vehicle type of the target autonomous driving vehicle, the last charging date, and the current task information, the low-battery threshold is flexibly adjusted, thereby achieving the purpose of balancing charging and task execution and obtaining the highest possible task execution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0019] Figure 1 The process of a charging management method for an autonomous driving vehicle in an embodiment of the present disclosure is as follows Figure 1 ;

[0020] Figure 2 The process of a charging management method for an autonomous driving vehicle in an embodiment of the present disclosure is as follows Figure 2 ;

[0021] Figure 3 A schematic diagram of an automatic driving vehicle management system in an embodiment of the present disclosure Figure 1 ;

[0022] Figure 4 A schematic diagram of an automatic driving vehicle management system in an embodiment of the present disclosure Figure 2 ;

[0023] Figure 5 It is a structural schematic diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0027] Figure 1 Flow chart of a method for managing charging of an autonomous driving vehicle in an embodiment of the present disclosure. The method is applied to a master control platform and can be executed by an autonomous driving vehicle management device, which can be implemented in software and / or hardware and can be configured in an electronic device. Figure 1As shown, the method specifically comprises the following steps:

[0028] S110. In response to monitoring that the remaining power of the target autonomous driving vehicle is less than a low-power threshold, the target autonomous driving vehicle is added to a low-power queue, wherein the low-power threshold is determined based on the vehicle type of the target autonomous driving vehicle, the last charging date, and the current mission information.

[0029] Among them, a communication link is established in advance between each autonomous driving vehicle and the master control platform. Through this communication link, the autonomous driving vehicle actively reports its own remaining power to the master control platform at a certain period, or the master control platform requests the remaining power of each autonomous driving vehicle from each autonomous driving vehicle at a certain period. When each autonomous driving vehicle receives the request sent by the master control platform, it reports its own remaining power to the master control platform.

[0030] When the remaining power of the target autonomous driving vehicle is less than the low-power threshold, it means that the target autonomous driving vehicle needs to be charged. At this time, the master control platform adds the target autonomous driving vehicle to the low-power queue to prepare for charging.

[0031] The low power threshold is determined by the master control platform based on the vehicle type, the last charging date and the current mission information of the target autonomous driving vehicle. Since the autonomous driving vehicle needs to be charged once in a while, the last charging date is continuously updated over time. In addition, the mission information of the autonomous driving vehicle is different at different times, so the current mission information is also dynamically changing. By determining the low power threshold with reference to dynamic information of different dimensions, the environmental adaptability of the low power threshold can be improved, making the determined low power threshold more reasonable, thereby taking into account the mission execution and endurance guarantee of the autonomous driving vehicle, thereby improving the efficiency of mission execution.

[0032] The master control platform can perform a low power threshold determination operation at regular intervals according to a certain cycle, and send the determined low power threshold to the matching sub-control platform, so that the sub-control platform controls the target autonomous driving vehicle according to the low power threshold. Specifically, when the sub-control platform detects that the remaining power of the target autonomous driving vehicle is less than the low power threshold, it stops assigning tasks to the target autonomous driving vehicle to avoid the parking of the autonomous driving vehicle due to insufficient power during the execution of the task, thereby affecting the execution of the task and the charging of the autonomous driving vehicle. In other words, the sub-control platform is responsible for assigning tasks to the target autonomous driving vehicle. In particular, different sub-control platforms are responsible for assigning tasks to autonomous driving vehicles of different vehicle types (such as autonomous driving trailers, autonomous driving buses, autonomous driving patrol cars and other specific vehicle types). The task allocation process, logic, etc. for autonomous driving vehicles of different vehicle types are different. Therefore, task allocation management for autonomous driving vehicles with different task allocation processes and logics through different sub-control platforms can improve the efficiency of task allocation; at the same time, when assigning tasks to autonomous driving vehicles, referring to the low power threshold issued by the master control platform is conducive to achieving a balance between task execution and charging resource utilization.

[0033] The master control platform is responsible for coordinating the charging sequence of each autonomous driving vehicle and managing charging resources in a unified manner, thereby realizing charging resource sharing and improving the utilization rate of charging resources. That is, charging resources and vehicle resources are managed independently, with the aim of improving the efficiency of task allocation while realizing charging resource sharing and improving the utilization rate of charging resources.

[0034] In some embodiments, determining a low power threshold of a target autonomous driving vehicle based on the vehicle type, the last charging date, and current mission information of the target autonomous driving vehicle includes:

[0035] The low power threshold of the target autonomous driving vehicle is determined by the following second calculation formula:

[0036] H=H0+m1×W+m2×C

[0037] Among them, H represents the low power threshold, H0 represents the basic threshold, and the basic threshold H0 is determined according to the vehicle type of the target autonomous driving vehicle. For example, when the vehicle type is "unmanned trailer", the basic threshold H0 is 30%, and when the vehicle type is "unmanned bus", the basic threshold H0 is 40%. The corresponding relationship between the vehicle type and the basic threshold can be pre-established, and the corresponding basic threshold can be found from the corresponding relationship when needed.

[0038] W represents the task impact factor, which is determined according to the current task information of the target autonomous driving vehicle. The weight m1 corresponding to the task impact factor W is a negative value, m1∈[-1,-0.1]. The current task information shows that the more tasks to be executed, or the more power consumption is required to execute the task, the higher the value of the task impact factor W is, so as to achieve the purpose of lowering the low-power threshold, making it difficult for the autonomous driving vehicle to reach the condition of stopping the execution of the task (that is, the remaining power is less than the low-power threshold), so as to facilitate the autonomous driving vehicle to prioritize the task.

[0039] C represents the charging impact factor, which is determined according to the time interval from the last charging of the target autonomous driving vehicle. The longer the time interval from the last charging of the target autonomous driving vehicle, the higher the value of the charging impact factor C, so as to achieve the purpose of raising the low power threshold, making it easier for the autonomous driving vehicle to reach the condition of stopping the task (that is, the remaining power is less than the low power threshold), so as to facilitate the autonomous driving vehicle to charge in time; m2∈[0.1, 1].

[0040] By determining a basic threshold according to the vehicle type of the autonomous driving vehicle, and further adding a dynamic adjustment part based on the basic threshold according to the current tasks to be performed by the autonomous driving vehicle and the time interval from the last charge, the low-battery threshold can be flexibly adjusted and the determined low-battery threshold is more reasonable, which is conducive to balancing task execution and vehicle endurance.

[0041] S120: Determine the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue.

[0042] By determining the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue, further screening of the autonomous driving vehicles to be charged in the low-power queue is achieved, and autonomous driving vehicles that are more in need of emergency charging are determined, and priority is given to charging the autonomous driving vehicles that are more in need of emergency charging, which further takes into account the differences between different autonomous driving vehicles, thereby facilitating improving the overall task execution efficiency and efficient use of limited charging resources.

[0043] In some embodiments, determining the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue includes:

[0044] For an autonomous driving vehicle to be charged, the comprehensive priority of the autonomous driving vehicle to be charged is determined based on the current remaining power of the autonomous driving vehicle to be charged, the urgency factor of the task it is responsible for performing, the distance from the charging pile, and the last charging date.

[0045] Among them, if the current remaining power of the autonomous driving vehicle to be charged is lower, it should be charged as soon as possible. Therefore, if the current remaining power of the autonomous driving vehicle to be charged is lower, the value of the corresponding impact factor should be higher, so as to achieve the purpose of raising the comprehensive priority. If the urgency of the task that the autonomous driving vehicle to be charged is higher, the urgency factor should be higher, so as to achieve the purpose of raising the comprehensive priority. If the distance between the autonomous driving vehicle to be charged and the charging pile is closer, it should be charged as soon as possible to avoid the charging pile being idle for a long time, causing a waste of charging resources, so as to achieve the purpose of improving the utilization rate of charging resources. If the current time is longer than the last charging date of the autonomous driving vehicle to be charged, it means that the possibility that the autonomous driving vehicle needs to be charged is greater, and the corresponding impact factor is larger at this time, so as to achieve the purpose of raising the comprehensive priority.

[0046] By comprehensively considering the current remaining power of the autonomous driving vehicle to be charged, the urgency factor of the task it is responsible for, the distance from the charging pile, and the last charging date, it is helpful to improve the rationality of determining the comprehensive priority and achieve the goal of ensuring task execution efficiency and charging resource utilization.

[0047] Exemplarily, the charging comprehensive reference value of the autonomous driving vehicle to be charged is determined by the following first calculation formula;

[0048] Determine the comprehensive priority of the autonomous driving vehicle to be charged according to the comprehensive charging reference value, wherein the greater the comprehensive charging reference value, the higher the comprehensive priority;

[0049] P=w1×B+w2×T+w3×D+w4×L

[0050] Among them, P represents the comprehensive charging reference value of the autonomous driving vehicle to be charged, B represents the influencing factor of the current remaining power of the autonomous driving vehicle to be charged, and the lower the current remaining power, the larger the value of B; T represents the urgency factor of the task that the autonomous driving vehicle to be charged is responsible for performing, and the higher the urgency of the task to be performed, the larger the value of the urgency factor; D represents the distance factor between the autonomous driving vehicle to be charged and the charging pile, and the closer the distance between the autonomous driving vehicle to be charged and the charging pile, the larger the value of the distance factor; L represents the influencing factor of the last charging date of the autonomous driving vehicle to be charged, and the longer the time from the last charging date, the larger the value of L; w1, w2, w3 and w4 are the weights of each of the factors respectively.

[0051] S130. Determine a charging order for each autonomous driving vehicle to be charged in the low-power queue based on the comprehensive priority of each autonomous driving vehicle to be charged.

[0052] Specifically, the higher the comprehensive priority of the autonomous driving vehicle to be charged, the earlier it is in the charging sequence and the faster it can be charged.

[0053] S140. Control the charging of each autonomous driving vehicle to be charged according to the charging sequence of each autonomous driving vehicle to be charged.

[0054] For example, each autonomous driving vehicle to be charged is controlled to charge at the nearest idle charging pile according to the charging order of each autonomous driving vehicle to be charged, so as to achieve efficient use of charging resources.

[0055] Specifically, the management of charging resources and vehicle charging by the master control platform includes the following:

[0056] The master control platform and the charging pile realize data docking: determine whether the charging pile is idle, whether the charging gun is occupied, whether the charging pile is faulty, etc.; real-time monitoring of charging progress information, current charging power, charging time, remaining estimated charging time, etc. Perform resource allocation management: maintain the relationship between charging piles, charging guns, and charging positions. If the charging pile fails, the charging gun will be unavailable; if the charging position is occupied by a vehicle, the charging gun will be unavailable; if the vehicle is charging, the charging gun and the charging position are set to a locked state. Charging resource allocation process: according to the vehicle type and sub-control information, find a list of all available charging positions. According to the status of the charging gun and the charging position, find a list of all available charging positions. Calculate the path length from the vehicle to the charging position, find the nearest charging position, and lock the charging gun and charging position resources.

[0057] Automatic charging process: low-power vehicle identification and queue management, obtain vehicle power and status information. The master control platform obtains the power of all vehicles from the sub-control platform or vehicle status monitoring module, searches for low-power thresholds based on the vehicle, finds the corresponding threshold from the low-power threshold parameters maintained by the master control platform according to different vehicle types and business scenarios, and determines whether the vehicle power is lower than the threshold. Determine whether to enter the low-power queue. If the vehicle power is lower than the corresponding threshold, it enters the low-power queue; otherwise, it returns to the monitoring state and continues to wait.

[0058] Add to low-power queue and adjust priority: Add eligible vehicles to the low-power queue and adjust the low-power queue according to the dynamic comprehensive priority strategy to ensure priority allocation of charging resources.

[0059] Charging resource allocation and execution: Get information about vehicles to be charged from the low-power queue. The master control platform selects the vehicle with the highest priority from the low-power queue and prepares to allocate charging resources. Determine whether there are charging resources available and check whether there are currently available charging pile resources. If there are no available resources, add the vehicle back to the low-power queue and wait for the next round of allocation.

[0060] Charging resource allocation and locking: If charging resources are available, a charging pile is allocated to the vehicle and the charging resource is locked to avoid resource conflicts.

[0061] Issue dispatch instructions: The master control platform issues instructions to the sub-control platform or vehicle dispatch module, notifying the vehicle to go to the designated charging pile for charging. The vehicle arrives and starts charging. After the vehicle arrives at the charging position according to the instructions, the master control platform interacts with the charging pile to start the charging process, recording the charging start time and related parameters. Wait for charging to complete and trigger the return instruction. After charging is completed, the master control platform issues a return instruction to the vehicle, guiding the vehicle to complete the charging task and return to the task area or parking space, ready to receive new task assignments.

[0062] A charging management method for an autonomous driving vehicle provided by an embodiment of the present disclosure monitors the charging of each autonomous driving vehicle through a master control platform, manages charging resources in a unified manner, coordinates the charging sequence of each autonomous driving vehicle to be charged, and realizes unified management of charging resources and each autonomous driving vehicle to be charged; by dynamically determining the low-battery threshold according to the vehicle type of the target autonomous driving vehicle, the last charging date, and the current task information, the low-battery threshold is flexibly adjusted, thereby achieving the purpose of balancing charging and task execution and obtaining the highest possible task execution efficiency.

[0063] Figure 2 Flow chart of a method for managing charging of an autonomous driving vehicle in an embodiment of the present disclosure. The method is applied to a sub-control platform and can be executed by an autonomous driving vehicle management device, which can be implemented in software and / or hardware, and can be configured in an electronic device. Figure 2 As shown, the method specifically comprises the following steps:

[0064] S210. Receive a low-battery threshold for a target autonomous driving vehicle issued by a master control platform, wherein the low-battery threshold is determined based on a vehicle type, a last charging date, and current mission information of the target autonomous driving vehicle.

[0065] S220. Assign tasks to the target autonomous driving vehicle according to the low electrical threshold.

[0066] Exemplarily, the assigning of tasks to the target autonomous driving vehicle according to the low power threshold includes:

[0067] In response to monitoring that the remaining power of the target autonomous driving vehicle is less than the low power threshold, stop allocating tasks to the target autonomous driving vehicle; in response to monitoring that the remaining power of the target autonomous driving vehicle is greater than the low power threshold, calculate the difference between the remaining power of the target autonomous driving vehicle and the low power threshold; in response to the difference being greater than the power consumption required to perform the task, allocate tasks to the target autonomous driving vehicle; in response to the difference being less than the power consumption required to perform the task, stop allocating tasks to the target autonomous driving vehicle. The purpose is to avoid the parking of the autonomous driving vehicle due to low power during the execution of the task, thereby affecting the execution of the task and the charging of the vehicle.

[0068] Different sub-control platforms are responsible for task allocation for autonomous vehicles of different vehicle types (such as autonomous trailers, autonomous buses, autonomous patrol cars and other specific vehicle types). The task allocation process and logic for autonomous vehicles of different vehicle types are different. Therefore, task allocation efficiency can be improved by managing the task allocation of autonomous vehicles with different task allocation processes and logics through different sub-control platforms. At the same time, when allocating tasks to autonomous vehicles, referring to the low power threshold issued by the master control platform is conducive to achieving a balance between task execution and charging resource utilization.

[0069] Based on the above embodiments, Figure 3 As shown, this embodiment provides an autonomous driving vehicle management system, including a master control platform 310 and multiple sub-control platforms 320; wherein the master control platform 310 is used to determine the low power threshold of the target autonomous driving vehicle according to the vehicle type, the last charging date and the current task information of the target autonomous driving vehicle, and send the low power threshold to the sub-control platform 320 corresponding to the target autonomous driving vehicle; and when it is detected that the remaining power of the target autonomous driving vehicle is less than the low power threshold, the target autonomous driving vehicle is added to the low power queue, and the comprehensive priority of each autonomous driving vehicle to be charged in the low power queue is determined; based on the comprehensive priority of each autonomous driving vehicle to be charged, the charging order of each autonomous driving vehicle to be charged in the low power queue is determined; the charging of each autonomous driving vehicle to be charged is controlled according to the charging order of each autonomous driving vehicle to be charged; each sub-control platform 320 is used to assign tasks to each autonomous driving vehicle according to the low power threshold for each autonomous driving vehicle issued by the master control platform. That is, charging resources and vehicle resources are managed independently, the purpose is to improve the efficiency of task allocation while realizing the sharing of charging resources and improving the utilization rate of charging resources.

[0070] like Figure 4As shown, the master control platform 410 is connected to the charging pile 400 and the sub-control platform 420 respectively. The master control platform 410 can perform a low-power threshold determination operation at regular intervals according to a certain period, and send the determined low-power threshold to the matching sub-control platform. In addition, the power of all vehicles is obtained, the low-power threshold is found according to the vehicle, and the corresponding threshold is found from the low-power threshold parameters maintained by the master control platform according to different vehicle types and business scenarios, and it is determined whether the vehicle power is lower than the threshold. Determine whether to enter the low-power queue. If the vehicle power is lower than the corresponding threshold, it enters the low-power queue; otherwise, it returns to the monitoring state and continues to wait. Add qualified vehicles to the low-power queue, and adjust the low-power queue according to the dynamic comprehensive priority strategy to ensure priority charging resource allocation. The master control platform selects the vehicle with the highest priority from the low-power queue, prepares for charging resource allocation, determines whether charging resources are available, checks whether there are currently available charging pile resources, and if there are no available resources, adds the vehicle to the low-power queue again, waiting for the next round of allocation. If charging resources are available, a charging pile is allocated to the vehicle, and the charging resource is locked to avoid resource conflicts. Issue dispatch instructions: The master control platform issues instructions to the sub-control platform, notifying the vehicle to go to the designated charging pile for charging. The vehicle arrives and starts charging. After the vehicle arrives at the charging position according to the instructions, the master control platform interacts with the charging pile to start the charging process, recording the charging start time and related parameters. Wait for charging to complete and trigger the return instruction. After charging is completed, the master control platform issues a return instruction to the vehicle, guiding the vehicle to complete the charging task and return to the task area or parking space, ready to receive new task assignments.

[0071] By adopting an architecture that separates the master control platform and the sub-control platform, charging resources can be shared, and the needs of collaborative work of vehicles in multiple scenarios and vehicle types can be adapted, which is conducive to improving task execution efficiency.

[0072] Figure 5 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 5 , which shows a structural schematic diagram of an electronic device 500 suitable for implementing the embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0073] like Figure 5As shown, the electronic device 500 may include a processing device 501, a ROM 502, a RAM 503, a bus 504, an input / output (I / O) interface 505, an input device 506, an output device 507, a storage device 508, and a communication device 509. The processing device (e.g., a central processing unit, a graphics processor, etc.) 501 can perform various appropriate actions and processes to implement the method of the embodiment described in the present disclosure according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage device 508 to the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0074] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the automatic driving vehicle charging management method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0075] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0076] The computer-readable medium may be included in the electronic device; or it may exist separately without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to monitoring that the remaining power of the target autonomous driving vehicle is less than the low power threshold, the target autonomous driving vehicle is added to the low power queue, wherein the low power threshold is determined according to the vehicle type, the last charging date and the current task information of the target autonomous driving vehicle; the comprehensive priority of each autonomous driving vehicle to be charged in the low power queue is determined; based on the comprehensive priority of each autonomous driving vehicle to be charged, the charging order of each autonomous driving vehicle to be charged in the low power queue is determined; and the charging of each autonomous driving vehicle to be charged is controlled according to the charging order of each autonomous driving vehicle to be charged. Alternatively, the low power threshold for the target autonomous driving vehicle issued by the master control platform is received, wherein the low power threshold is determined according to the vehicle type, the last charging date and the current task information of the target autonomous driving vehicle; and the target autonomous driving vehicle is assigned tasks according to the low power threshold.

[0077] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0078] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0079] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A charging management method for an autonomous driving vehicle, applied to a master control platform, characterized in that: The method comprises: In response to monitoring that the remaining power of the target autonomous driving vehicle is less than a low power threshold, adding the target autonomous driving vehicle to a low power queue, wherein the low power threshold is determined according to the vehicle type, the last charging date, and the current mission information of the target autonomous driving vehicle; Determining the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue; Determining a charging order for each autonomous driving vehicle to be charged in the low-power queue based on the comprehensive priority of each autonomous driving vehicle to be charged; The charging of each autonomous driving vehicle to be charged is controlled according to the charging sequence of each autonomous driving vehicle to be charged.

2. The method according to claim 1, characterized in that: The determining of the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue includes: For an autonomous driving vehicle to be charged, the comprehensive priority of the autonomous driving vehicle to be charged is determined based on the current remaining power of the autonomous driving vehicle to be charged, the urgency factor of the task it is responsible for performing, the distance from the charging pile, and the last charging date.

3. The method according to claim 2, characterized in that The method of determining the comprehensive priority of an autonomous driving vehicle to be charged according to the current remaining power of the autonomous driving vehicle to be charged, the urgency factor of the task to be performed, the distance from the charging pile, and the last charging date includes: Determine the charging comprehensive reference value of the autonomous driving vehicle to be charged by the following first calculation formula; Determine the comprehensive priority of the autonomous driving vehicle to be charged according to the comprehensive charging reference value, wherein the greater the comprehensive charging reference value, the higher the comprehensive priority; P=w1×B+w2×T+w3×D+w4×L Among them, P represents the comprehensive charging reference value of the autonomous driving vehicle to be charged, N represents the influencing factor of the current remaining power of the autonomous driving vehicle to be charged, and the lower the current remaining power, the larger the value of B; T represents the urgency factor of the task that the autonomous driving vehicle to be charged is responsible for performing, and the higher the urgency of the task to be performed, the larger the value of the urgency factor; D represents the distance factor between the autonomous driving vehicle to be charged and the charging pile, and the closer the distance between the autonomous driving vehicle to be charged and the charging pile, the larger the value of the distance factor; L represents the influencing factor of the last charging date of the autonomous driving vehicle to be charged, and the longer the time from the last charging date, the larger the value of L; w1, w2, w3 and w4 are the weights of each of the factors respectively.

4. The method according to claim 1, characterized in that Also includes: Determine a low power threshold of the target autonomous vehicle based on the vehicle type, the last charging date, and current mission information of the target autonomous vehicle; The low power threshold is sent to a matching sub-control platform so that the sub-control platform controls the target autonomous driving vehicle according to the low power threshold. When the sub-control platform detects that the remaining power of the target autonomous driving vehicle is less than the low power threshold, the sub-control platform stops allocating tasks to the target autonomous driving vehicle.

5. The method according to claim 4, characterized in that The step of determining the low power threshold of the target autonomous driving vehicle according to the vehicle type, the last charging date, and the current mission information of the target autonomous driving vehicle includes: The low power threshold of the target autonomous driving vehicle is determined by the following second calculation formula: H=H0+m1×W+m2×C Among them, H represents the low-power threshold, H0 represents the basic threshold, which is determined according to the vehicle type of the target autonomous driving vehicle; W represents the task influence factor, which is determined according to the current task information of the target autonomous driving vehicle; C represents the charging influence factor, which is determined according to the time interval from the last charge of the target autonomous driving vehicle; m1 and m2 are the corresponding weights, m1∈[-1,-0.1], m2∈[0.1,1].

6. A charging management method for an autonomous driving vehicle, applied to a sub-control platform, characterized in that: include: Receiving a low-power threshold for a target autonomous driving vehicle issued by a master control platform, wherein the low-power threshold is determined according to a vehicle type, a last charging date, and current mission information of the target autonomous driving vehicle; The target autonomous driving vehicle is assigned a task based on the low electrical threshold.

7. The method according to claim 6, characterized in that The assigning of tasks to the target autonomous driving vehicle according to the low power threshold comprises: In response to monitoring that the remaining power of the target autonomous driving vehicle is less than the low power threshold, stopping allocating tasks to the target autonomous driving vehicle; In response to monitoring that the remaining power of the target autonomous driving vehicle is greater than the low power threshold, calculating a difference between the remaining power of the target autonomous driving vehicle and the low power threshold; In response to the difference being greater than the power consumption required to perform the task, the task is assigned to the target autonomous driving vehicle; in response to the difference being less than the power consumption required to perform the task, the task is stopped being assigned to the target autonomous driving vehicle.

8. An automatic driving vehicle management system, characterized in that: Including master control platform and multiple sub-control platforms; The master control platform is used to determine the low-power threshold of the target autonomous driving vehicle according to the vehicle type, the last charging date and the current mission information of the target autonomous driving vehicle, and send the low-power threshold to the sub-control platform corresponding to the target autonomous driving vehicle; and when it is monitored that the remaining power of the target autonomous driving vehicle is less than the low-power threshold, the target autonomous driving vehicle is added to the low-power queue, and the comprehensive priority of each autonomous driving vehicle to be charged in the low-power queue is determined; based on the comprehensive priority of each autonomous driving vehicle to be charged, the charging order of each autonomous driving vehicle to be charged in the low-power queue is determined; and the charging of each autonomous driving vehicle to be charged is controlled according to the charging order of each autonomous driving vehicle to be charged; Each of the sub-control platforms is used to assign tasks to each autonomous driving vehicle according to the low power threshold for each autonomous driving vehicle issued by the master control platform.

9. An electronic device, characterized in that: The electronic device comprises: 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 implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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