Vehicle dispatching system and method based on multi-vehicle cooperation, electronic device and storage medium

Abstract

A vehicle scheduling system and method based on multi-vehicle cooperation, an electronic device, and a storage medium. The scheduling system includes a parameter module (310) for determining scheduling parameters; a scheduling module (320) for determining scheduling results; a cooperation module (330) for determining a cooperation influence value; a correction module (340) for correcting the scheduling parameters; wherein the correction module determines whether the cooperation influence value is not less than a preset threshold, based on the determination result that the cooperation influence value is not less than the preset threshold, corrects the scheduling parameters, and determines the scheduling results and the cooperation influence value based on the corrected scheduling parameters; and an output module (350) for outputting the scheduling results based on the determination result that the cooperation influence value is less than the preset threshold. The problem of the order and task arrangement of multiple vehicles during scheduling can be effectively solved, and the problems of multi-vehicle conflict, congestion, unreasonable arrangement, etc. caused by multi-vehicle operation are improved and solved, and the completion efficiency of transportation tasks is effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle dispatching technology, and more specifically to a vehicle dispatching system, method, electronic device and storage medium based on multi-vehicle collaboration. Background Technology

[0002] In logistics transportation, multiple vehicles typically complete one or more transport tasks, making proper coordination and allocation of these vehicles crucial. Currently, when multiple vehicles are involved in completing transport tasks, issues such as conflicting routes and traffic congestion arise, resulting in low efficiency in completing the transport tasks.

[0003] It is evident that how to resolve the issue of unreasonable scheduling is a problem that urgently needs to be addressed by those skilled in the art. Summary of the Invention

[0004] This disclosure is made in view of the above-mentioned problems. This disclosure provides a vehicle dispatching system, method, electronic device, and storage medium based on multi-vehicle cooperation.

[0005] This disclosure provides a vehicle dispatching system based on multi-vehicle cooperation. The multi-vehicle cooperation-based vehicle dispatching system includes: a parameter module for determining dispatching parameters; a dispatching module for determining a dispatching result based on the dispatching parameters; a cooperation module for determining a cooperation impact value based on the dispatching result; and a correction module for correcting the dispatching parameters based on the cooperation impact value. The correction module determines whether the cooperation impact value is not less than a preset threshold, corrects the dispatching parameters based on the determination result (if the cooperation impact value is not less than the preset threshold), and redetermines the dispatching result and cooperation impact value based on the corrected dispatching parameters. An output module is used to output the dispatching result when the determination result is less than the preset threshold.

[0006] In another aspect, this disclosure provides an electronic device including a processor, a memory, and an I / O interface, wherein the I / O interface connects the processor and the memory for enabling information interaction between the memory and the processor; the memory stores corresponding modules for implementing the vehicle dispatching system, and the processor runs the modules stored in the memory to execute the vehicle dispatching system.

[0007] In another aspect, this disclosure provides a vehicle scheduling method based on multi-vehicle cooperation, comprising: acquiring scheduling parameters; determining a scheduling result based on the scheduling parameters; determining a cooperative impact value based on the scheduling result; determining whether the cooperative impact value is not less than a preset threshold; correcting the scheduling parameters based on the determination result that the cooperative impact value is not less than the preset threshold, and re-determining the scheduling result and the cooperative impact value based on the corrected scheduling parameters; and outputting the scheduling result when the determination result is less than the preset threshold.

[0008] In another aspect, this disclosure provides a computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of the vehicle scheduling method based on multi-vehicle cooperation.

[0009] This disclosure provides a vehicle scheduling system, method, electronic device, and storage medium based on multi-vehicle collaboration, which can effectively solve the problem of the order and task arrangement of multiple vehicles during scheduling, improve and solve the problems of multi-vehicle conflict, congestion, and unreasonable arrangement caused by multi-vehicle operation, and effectively improve the efficiency of transportation task completion. Attached Figure Description

[0010] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 This diagram illustrates a scenario of a vehicle dispatching system according to an embodiment of the present disclosure.

[0012] Figure 2 A schematic diagram of a vehicle dispatching system according to an embodiment of the present disclosure is shown;

[0013] Figure 3 A schematic block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0014] Figure 4 A schematic flowchart of a scheduling method according to an embodiment of the present disclosure is shown;

[0015] Figure 5 A schematic flowchart illustrating the determination of synergistic effect values ​​according to an embodiment of the present disclosure is shown; and

[0016] Figure 6 A schematic flowchart illustrating the determination of a synergistic effect value according to an embodiment of the present disclosure is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0018] This disclosure proposes a vehicle dispatching system based on multi-vehicle collaboration. In some embodiments, the vehicle dispatching system can be applied to logistics, transportation, taxi, park operation, passenger transport, etc. The vehicle dispatching system can also be used to alleviate congestion in traffic systems. In some embodiments, the vehicle dispatching system can dispatch multiple vehicles within a preset area. These multiple vehicles can be intelligent driving vehicles or manually driven vehicles, wherein the manually driven vehicles can receive dispatches from the vehicle dispatching system. In some embodiments, the preset area can be a defined operating area. In some embodiments, the vehicle dispatching system can have multiple subsystems, where each subsystem manages vehicles in an operating area, and the vehicle dispatching system manages and coordinates vehicles across multiple subsystems and all operating areas. In some embodiments, the vehicle dispatching system can calculate the estimated arrival time of multiple vehicles from their origin to their destination, calculate an impact factor based on the different vehicles' driving conditions, and re-dispatch different vehicles based on the impact factor.

[0019] Figure 1 This is a typical scenario diagram of a vehicle dispatching system provided in an embodiment of this disclosure. The scenario includes a server 100 and multiple vehicles 102-1, 102-2, 102-3...102-N, wherein the server 100 includes a dispatching system 101.

[0020] like Figure 1 As shown, server 100 is used to coordinate and schedule vehicles within a defined area. In some embodiments, server 100 can send scheduling instructions to vehicles to plan their routes. Server 100 can coordinate the processing of multiple vehicle driving conditions, such as vehicle traffic conditions and vehicle scheduling status, to ensure that the routes of all vehicles meet certain requirements, such as the shortest travel time or the shortest total distance.

[0021] Server 100 can send control commands to vehicles to control their coordination with other vehicles.

[0022] Server 100 includes a scheduling system 101. The scheduling system 101 generates scheduling instructions and sends them to vehicles. The scheduling system 101 receives task requests and scheduling conditions, and generates scheduling information based on the received task requests and scheduling conditions. The scheduling information refers to the mapping relationship between vehicles and tasks. This mapping relationship can be one-to-one, one-to-many, or many-to-one.

[0023] Vehicle 102 is used to receive information from server 100 and to plan and control based on that information. The vehicle can be manually driven or autonomously driven. In some embodiments, the vehicle is driverless; the server sends scheduling instructions to the autonomous vehicle, which then controls its movement according to the received instructions. In some embodiments, the vehicle has a driver who operates the vehicle according to the received scheduling instructions. The autonomous vehicle can also receive control instructions from the server.

[0024] Figure 2 A schematic block diagram of a vehicle dispatching system according to an embodiment of the present disclosure is shown, wherein the dispatching system 103 and Figure 1 The scheduling system 101 shown has the same configuration or structure.

[0025] like Figure 2 As shown, the vehicle dispatching system includes a parameter module 310, a dispatching module 320, a coordination module 330, a correction module 340, an output module 350, and other components or modules that can be used for vehicle dispatching.

[0026] Parameter module 310 is used to determine scheduling parameters; parameter module 310 determines scheduling parameters based on the current transportation task request and scheduling conditions. In some embodiments, scheduling parameters include transportation task attributes and scheduling conditions. The transportation task attributes include the start and end points of the transportation task. In some embodiments, the transportation task attributes also include at least one of the following: transportation volume, earliest start time, latest start time, earliest end time, and latest end time. In some embodiments, the transportation task attributes include the duration of completing the transportation task, such as the transportation task must be completed within 30 minutes or within 1 hour. In some embodiments, the transportation task attributes also include transportation temperature requirements, such as ambient temperature transportation, refrigerated transportation, and frozen transportation. The transportation task attributes may also include specific temperature requirements, such as a temperature not higher than 40 degrees Celsius or a temperature not lower than 36 degrees Celsius. The scheduling system schedules vehicles based on one or more transportation tasks.

[0027] In some embodiments, the scheduling parameters further include vehicle attributes. In some embodiments, vehicle attributes include the estimated arrival time of vehicles within the transportation area, wherein the estimated arrival time includes the estimated arrival time of each vehicle between various locations within the operating area; the estimated arrival time is the time required for a vehicle to travel from one location to another, statistically based on historical data. For ease of distinction, the estimated arrival time in the vehicle attributes is referred to as the pre-coordination estimated arrival time. The estimated arrival times of different vehicles at the same origin and the same destination may be the same or different.

[0028] The same vehicle can have multiple estimated arrival times for the same origin and destination. For example, the estimated arrival time will differ depending on the time of day, the route taken, or the traffic conditions on the same route. Furthermore, the estimated arrival time will also vary depending on the vehicle's condition.

[0029] The estimated arrival time is a specific point in time, such as 8:05 or 9:10. For example, if a vehicle starts from location A at time T1 and arrives at location B at time T2, then the estimated arrival time of the vehicle from location A to location B is T2. Different vehicles starting at the same time may have the same or different estimated arrival times from one location A to another location C within the operating area. In some embodiments, a vehicle may have one, two, or more estimated arrival times from one location A to another location C at the same time. For example, if a vehicle's estimated arrival time from location A, through location B, and then to location C at 9:01 is 9:30, and its estimated arrival time from location A, through locations B and D, and then to location C is 9:50, then the estimated arrival time of this vehicle from location A to location C includes 9:30 and 9:50. The estimated arrival time of a vehicle is obtained based on historical data of the vehicle's travel. Understandably, the estimated arrival time of a vehicle can also be calculated based on the distance between locations and the vehicle's speed. In some embodiments, the estimated arrival time of a vehicle also takes into account weather conditions. For example, if a vehicle departs from point A at 8:15 and travels to point E, the estimated arrival time is 8:30 in clear weather; the estimated arrival time is 8:40 in foggy weather; and the estimated arrival time is 8:50 in snowy weather.

[0030] In some embodiments, vehicle attributes also include at least one of vehicle capacity, vehicle load capacity, and vehicle energy consumption. Vehicle capacity includes the total capacity and remaining capacity of the vehicle, such as a total capacity of 7 people, 2 people already loaded, and a remaining capacity of 5 people. When the vehicle is unloaded, the remaining capacity equals the total capacity. Similarly, vehicle load capacity includes the maximum load weight and available load weight, such as a maximum load weight of 1.8 tons, 0.5 tons already loaded, and a available load weight of 1.3 tons. When the vehicle is unloaded, the maximum load weight equals the available load weight. Vehicle energy consumption includes the energy consumed per kilometer and the remaining energy. For example, vehicle capacity includes the total loadable volume and remaining loadable volume of the vehicle. If the vehicle is a gasoline vehicle, its energy consumption includes the amount of gasoline consumed per kilometer and the amount of gasoline remaining; if the vehicle is a diesel vehicle, its energy consumption includes the amount of diesel consumed per kilometer and the amount of diesel remaining; if the vehicle is an electric vehicle, its energy consumption includes the amount of electricity consumed per kilometer and the amount of electricity remaining; if the vehicle is an LNG (Liquefied Natural Gas) vehicle, its energy consumption includes the amount of LNG consumed per kilometer and the amount of LNG remaining.

[0031] In some embodiments, scheduling conditions include at least one of the following: shortest total vehicle travel distance, shortest total vehicle time to complete a transportation task, minimum total vehicle energy consumption, and minimum number of vehicle calls. It is understood that shortest total vehicle travel distance means the shortest total travel distance of vehicles used to complete a transportation task. Shortest total vehicle time to complete a transportation task means the shortest total time from start to stop of vehicles used to complete a transportation task. Minimum number of vehicle calls means the minimum number of vehicles used to complete a transportation task. It is understood that scheduling conditions can vary according to the needs of the transportation task; the above is merely an exemplary description of scheduling conditions, and scheduling conditions can be other suitable conditions that meet the needs of the transportation task.

[0032] The scheduling module 320 is used to determine the scheduling result based on the scheduling parameters; in some embodiments, the scheduling result includes: a many-to-one, one-to-many, or many-to-many mapping between transportation tasks and vehicles.

[0033] A many-to-one mapping between transportation tasks and vehicles means that multiple transportation tasks are completed by one vehicle; a one-to-many mapping means that one transportation task is completed by multiple vehicles; a many-to-many mapping means that multiple transportation tasks are completed by multiple vehicles. In some embodiments, a transportation task may include one or more transportation sub-tasks. For example, one transportation sub-task is to transport W from location A to location B, and another transportation sub-task is to transport Y from location C to location B. For instance, if a transportation task is to transport 2M tons of goods from location A to location B, and the maximum load capacity of a vehicle is M tons, then two vehicles are needed to complete this transportation task. The estimated arrival time of the vehicles in the scheduling result is the estimated arrival time of the vehicles to complete the transportation task.

[0034] In some embodiments, the scheduling module can determine the scheduling result based on static scheduling or dynamic scheduling. Static scheduling refers to determining the scheduling result based on a highly accurate metaheuristic algorithm, while dynamic scheduling refers to determining the scheduling result based on a real-time heuristic algorithm.

[0035] Based on static scheduling, the scheduling module employs a metaheuristic algorithm. This metaheuristic refers to a combination of randomized algorithms and local search algorithms, exploring the search space through continuous iteration. The metaheuristic algorithm typically generates one or more initial solutions using a heuristic algorithm, then randomly transforms these initial solutions to generate new solutions. These new solutions are then evaluated and compared with the original solutions. The new solutions are accepted as the starting point for the next iteration according to certain rules, and this process is repeated to obtain the optimal / approximate optimal solution. In some embodiments, the metaheuristic algorithm includes, but is not limited to, genetic algorithms, simulated annealing algorithms, tabu search, particle swarm optimization, and ant colony optimization.

[0036] Based on dynamic scheduling, the scheduling module employs a heuristic algorithm. A heuristic algorithm is an algorithm constructed based on intuition or experience, providing a feasible solution to the optimization problem under acceptable conditions. In some embodiments, the heuristic algorithm includes, but is not limited to, the saving method, the scanning method, and the insertion method.

[0037] The estimated arrival times of vehicles in the scheduling results can include one or more estimated arrival times of vehicles. For example, if the scheduling task is to transport W from location A to location B, and the road from location A to location B passes through location F, then the estimated arrival times of vehicles in the corresponding scheduling results include the estimated arrival time of the vehicle from location A to location B, and may also include the estimated arrival time of the vehicle from location A to location F and the estimated arrival time of the vehicle from location F to location B.

[0038] The coordination module 330 is used to determine the coordination impact value of the coordination region based on the scheduling result.

[0039] The collaborative impact value refers to the mutual influence on the estimated arrival time when multiple vehicles complete a transportation task. In some embodiments, the collaborative impact value includes the estimated arrival time impact value or the collaborative scheduling impact factor. The estimated arrival time impact value refers to the impact on the estimated arrival time of each vehicle within the area requiring collaboration. The collaboration module 330 determines the area requiring collaboration and the vehicles to be collaborated with based on the scheduling results, determines the release order and waiting time of each collaborative vehicle within the collaboration area, and determines the estimated arrival time of each vehicle after collaboration based on the release order and scheduling time. The impact value is determined based on the estimated arrival time after collaboration and the estimated arrival time before collaboration.

[0040] The estimated arrival time impact value refers to the difference between the estimated arrival time after vehicle collaboration and the estimated arrival time before collaboration. For vehicle 102-8, the estimated arrival time impact value for reaching a subsequent location G after leaving the collaboration area is the difference between the estimated arrival time after collaboration and the estimated arrival time before collaboration. For vehicle 102-8, its estimated arrival time impact value is the sum of the estimated arrival time impact values ​​for each subsequent location after leaving the collaboration area. Because multi-vehicle collaboration may result in some vehicles waiting, the estimated arrival time for these vehicles to reach some task points will increase. The estimated arrival time impact value is the sum of the differences between the estimated arrival time after collaboration and the estimated arrival time before collaboration for each location to complete the transportation task. The estimated arrival time impact value is a positive number. In some embodiments, to complete a transportation task, vehicle 102-1 is assigned the task of traveling from location A to location B, and then from location C to location D. Vehicle 102-2's task is traveling from location E to location F. Before coordination, vehicle 102-1 departs from location A at 8:01:00, completes all tasks in 25 minutes, and is expected to arrive at the destination D at 8:40:00. Before coordination, vehicle 102-2 departs from location E at 8:10:00, completes the transportation task in 30 minutes, and is expected to arrive at the destination F at 8:40:00. After coordination, vehicle 102-1 waits for 5 minutes at the intersection with vehicle 102-2 on its way to location B; vehicle 102-2 does not need to wait. After coordination, the estimated arrival time of vehicle 102-1 to location B increases by 5 minutes, the estimated arrival time to location C increases by 5 minutes, and the estimated arrival time to location D increases by 5 minutes. The impact on the estimated arrival time of vehicle 102-1 in completing its transportation task is 15 minutes. The estimated arrival time of vehicle 102-2 remains unchanged, with an estimated arrival time impact of 0. The estimated arrival time impact value of the coordinated area refers to the sum of the estimated arrival time impact values ​​of all vehicles within that area.

[0041] In some embodiments, the coordination module 330 determines the coordination impact value based on the expected arrival time impact value by first determining a coordination area; determining the release order of each vehicle and the waiting time of each vehicle within the coordination area; and determining the expected arrival time impact value of the coordination area based on the release order of each vehicle and the waiting time of each vehicle.

[0042] In some embodiments, the collaborative scheduling impact factor is the ratio of the estimated arrival time of a vehicle completing a transportation task to the total time taken to complete the transportation task before collaboration. In some embodiments, to complete a transportation task, vehicle 102-1 is assigned the task from location A to location B, and then from location C to location D; vehicle 102-2's task is from location E to location F. Before collaboration, vehicle 102-1 departs from location A at 8:01:00, completes all tasks in 25 minutes, and has an estimated arrival time of 8:40 at the destination D. Before collaboration, vehicle 102-2 departs from location E at 8:10:00, completes the transportation task in 30 minutes, and has an estimated arrival time of 8:40 at the destination F. After collaboration, vehicle 102-1 waits for 5 minutes at the intersection area with vehicle 102-2 on its way to location B; vehicle 102-2 does not need to wait. After coordination, the estimated arrival time of vehicle 102-1 to location B increases by 5 minutes, the estimated arrival time to location C increases by 5 minutes, and the estimated arrival time to location D increases by 5 minutes. The impact value of the estimated arrival time of vehicle 102-1 in completing the transportation task is 15 minutes, and the coordination scheduling impact factor of vehicle 102-1 is 0.6 (15 / 25 = 0.6). The estimated arrival time of vehicle 102-2 remains unchanged, and its estimated arrival time impact value is 0. The coordination scheduling impact factor of vehicle 102-2 is 0 (0 / 30 = 0). Before coordination, the coordination scheduling impact factor of the coordinated area refers to the sum of the coordination scheduling impact factors of all vehicles in that area.

[0043] In some embodiments, the coordination module 330 determines the coordination impact value as a coordination scheduling impact factor by: determining a coordination area; determining the release order and waiting time of each vehicle within the coordination area; determining the expected arrival time impact value of the coordination area based on the release order and waiting time of each vehicle; and determining the coordination scheduling factor of the coordination area based on the expected arrival time impact value and the expected arrival time before coordination.

[0044] The correction module 340 is used to correct scheduling parameters based on the collaborative impact value of the collaborative region. In some embodiments, the correction module can correct scheduling parameters based on the collaborative impact value of the collaborative region. In some embodiments, the correction module 340 can correct scheduling parameters by adjusting the estimated arrival time of vehicles after collaboration; in some embodiments, adjusting the estimated arrival time of vehicles after collaboration can be adjusting the estimated arrival time of vehicles after completing the transportation task, thereby reducing the collaborative impact value.

[0045] In some embodiments, adjusting the estimated arrival time of vehicles after coordination involves translating the impact of coordination into new scheduling parameters. In some embodiments, for all vehicles involved in the coordination area that need to wait, the vehicle order before arriving at the coordination area remains unchanged, and transportation tasks after arriving at the coordination area are rescheduled.

[0046] In some embodiments, the correction module 340 can correct the scheduling parameters by adjusting the vehicle's driving route.

[0047] The correction module 340 determines whether the collaborative impact value of the collaborative area is not less than a preset threshold. Based on the determination result that the collaborative impact value is not less than the preset threshold, it corrects the scheduling parameters and redetermines the scheduling result and collaborative impact value based on the corrected scheduling parameters. Adjusting the estimated arrival time in the area with the largest collaborative impact value means adjusting the estimated arrival time of the vehicle after it leaves the collaborative area to the start and end points of the subsequent tasks.

[0048] This will result in one or more of the following: changing the release order of vehicles outside the area with the greatest synergistic impact, changing the driving routes of vehicles, changing the waiting time of vehicles, etc.

[0049] Adjusting the vehicle's route will result in one or more of the following: the vehicle changes its route to avoid entering the area with the greatest cooperative impact value, thereby reducing the impact of multi-vehicle cooperation; the number of vehicles in the area with the greatest cooperative impact value is reduced, thereby reducing the impact of multi-vehicle cooperation.

[0050] The output module 350 is used to output a scheduling result when the judgment result is less than a preset threshold.

[0051] The output module 350 receives the scheduling result from the correction module and sends the scheduling result to the vehicle.

[0052] Those skilled in the art will understand that the content disclosed in this application can be varied and modified in many ways. For example, the different functional components described above are all implemented through hardware devices, but they may also be implemented solely through software solutions. For example, installing a system on an existing server. The content disclosed herein may be implemented through firmware, a combination of firmware and software, a combination of firmware and hardware, or a combination of hardware / firmware / software. For example, the coordination module and the correction module may be integrated to uniformly complete the coordinated scheduling of vehicles. As another example, the correction module and the output module may be integrated to uniformly complete the coordinated scheduling of vehicles.

[0053] In another aspect, embodiments of this disclosure provide an electronic device, such as... Figure 3 As shown, it includes: processor, memory and I / O interface.

[0054] The memory stores corresponding modules for implementing a vehicle dispatching system according to embodiments of the present disclosure.

[0055] The processor is used to run modules stored in the memory to execute a vehicle dispatching system according to an embodiment of the present disclosure.

[0056] The processor is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH). In some embodiments, the processor can be a server or a server cluster. The server cluster can be centralized or distributed. In some embodiments, the server can be local or remote.

[0057] I / O interfaces (read / write interfaces) are connected between the processor and memory to enable information exchange between the memory and the processor. They include, but are not limited to, data buses.

[0058] On the other hand, this disclosure provides a vehicle scheduling method based on multi-vehicle cooperation. The steps of the vehicle scheduling method provided in this embodiment are executed by the respective modules of the vehicle scheduling system described above. The following only describes the main steps of the vehicle scheduling method, omitting the details already described above. See also... Figure 4 The vehicle dispatching method of this disclosure includes:

[0059] In step 410, the server obtains scheduling parameters.

[0060] In some embodiments, scheduling parameters include transportation task attributes and scheduling conditions.

[0061] The transportation task attributes include the origin and destination of the transportation task. In some embodiments, the task attributes also include at least one of the following: the transportation volume of the transportation task, the earliest start time of the transportation task, the latest start time of the transportation task, the earliest end time of the transportation task, and the latest end time of the transportation task.

[0062] In some embodiments, the scheduling parameters further include vehicle attributes. In some embodiments, vehicle attributes include at least one of vehicle capacity, vehicle load capacity, and vehicle energy consumption.

[0063] In some embodiments, vehicle attributes include the estimated arrival time of vehicles within the transport area, wherein the estimated arrival time includes the estimated arrival time of each vehicle between various locations within the operating area; the estimated arrival time of a vehicle is the time required for a vehicle to travel from one location to another, based on historical data. For ease of distinction, the estimated arrival time in the vehicle attributes is referred to as the pre-cooperation estimated arrival time.

[0064] In some embodiments, vehicle attributes include at least one of vehicle capacity, vehicle load capacity, and vehicle energy consumption.

[0065] In some embodiments, the scheduling conditions include at least one of the following: shortest total vehicle travel distance, shortest total time for vehicles to complete transportation tasks, minimum total vehicle energy consumption, and minimum number of vehicle calls.

[0066] In step 420, the server determines the scheduling result based on the scheduling parameters.

[0067] In some embodiments, determining the scheduling result based on scheduling parameters includes: determining the scheduling result using a metaheuristic algorithm based on static scheduling; and determining the scheduling result using a heuristic algorithm based on dynamic scheduling.

[0068] In some embodiments, the scheduling result includes: a many-to-one, one-to-many, or many-to-many mapping between transportation tasks and vehicles; and the estimated arrival time of the vehicles. Based on the scheduling result, one or more of the following are determined: the departure order, departure time, and completion time of the vehicles.

[0069] A many-to-one mapping of transportation tasks to vehicles means that multiple transportation tasks are completed by one vehicle; a one-to-many mapping of transportation tasks to vehicles means that one transportation task is completed by multiple vehicles; a many-to-many mapping of transportation tasks to vehicles means that multiple transportation tasks are completed by multiple vehicles.

[0070] In step 430, the server determines the collaborative impact value based on the scheduling result.

[0071] The collaborative impact value refers to the mutual influence on the estimated arrival time when multiple vehicles complete a transportation task. In some embodiments, the collaborative impact value includes the estimated arrival time impact value or the collaborative scheduling impact factor. The estimated arrival time impact value refers to the impact on the estimated arrival time of each vehicle within the area requiring collaboration. The collaboration module 330 determines the area requiring collaboration and the vehicles to be collaborated with based on the scheduling results, determines the release order and waiting time of each collaborative vehicle within the collaboration area, and determines the estimated arrival time of each vehicle after collaboration based on the release order and scheduling time. The impact value is determined based on the estimated arrival time after collaboration and the estimated arrival time before collaboration.

[0072] The estimated arrival time impact value refers to the difference between the estimated arrival time after vehicle collaboration and the estimated arrival time before the corresponding vehicle collaboration. Because multi-vehicle collaboration will result in some vehicles waiting, the estimated arrival time of these vehicles to some task points will increase. The estimated arrival time impact value is the estimated arrival time after collaboration minus the estimated arrival time before collaboration. The estimated arrival time impact value is a positive number.

[0073] The estimated arrival time impact value of a collaborative area refers to the sum of the estimated arrival time impact values ​​of all vehicles in that area.

[0074] In some embodiments, the coordinated scheduling impact factor is the ratio of the estimated arrival time of a vehicle to the estimated arrival time before coordination. The estimated arrival time before coordination refers to the total time required for the vehicle to complete the transportation task before coordination.

[0075] The collaborative scheduling impact factor of a collaborative region refers to the sum of the collaborative scheduling impact factors of all vehicles in that region.

[0076] In some embodiments, the collaborative impact value is the expected arrival time impact value, and determining the collaborative impact value includes: determining the collaborative area; determining the release order of each vehicle and the waiting time of each vehicle within the collaborative area; and determining the expected arrival time impact value of the collaborative area.

[0077] In some embodiments, the collaborative impact value is a collaborative scheduling impact factor. Determining the collaborative impact value includes: determining a collaborative area; determining the release order of each vehicle and the waiting time of each vehicle within the collaborative area; determining the expected arrival time impact value of each vehicle within the collaborative area; and determining the collaborative scheduling factor of the collaborative area based on the expected arrival time impact value and the expected arrival time before collaboration.

[0078] In step 440, the server determines whether the collaborative impact value is not less than a preset threshold. Based on the determination result that the collaborative impact value is not less than the preset threshold, the server corrects the scheduling parameters and redetermines the scheduling result and collaborative impact value based on the corrected scheduling parameters.

[0079] In some embodiments, modifying the scheduling parameters includes at least one of the following: adjusting the estimated arrival time within the region with the largest cooperative influence value; adjusting the estimated arrival time of the vehicle to complete the transportation task; and adjusting the vehicle's driving route.

[0080] The estimated arrival time within the area with the greatest adjustment in the collaborative impact value is the estimated arrival time after the vehicle leaves the collaborative area and reaches the starting and ending points of the subsequent tasks.

[0081] In step 450, the server outputs the scheduling result when the judgment result is less than a preset threshold.

[0082] In summary, the vehicle scheduling method of this disclosure can be used to schedule multiple vehicles. It can effectively solve the problems of the order and task arrangement of multiple vehicles during scheduling, improve and solve the problems of multiple vehicle conflicts, congestion, and unreasonable arrangement caused by multi-vehicle operation, and effectively improve the efficiency of transportation task completion.

[0083] In another aspect, embodiments of this disclosure provide a computer-readable medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the vehicle scheduling method of any one of the embodiments of this disclosure.

[0084] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different systems to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0085] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, features of this disclosure are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this disclosure. However, this disclosure should not be construed as reflecting an intention that the claimed disclosure requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all the features of a single embodiment of a particular disclosure. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this disclosure.

[0086] It should be noted that the above embodiments are illustrative of this disclosure and not restrictive, and that alternative embodiments can be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This disclosure can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0087] The above description is merely a specific embodiment or illustration of this disclosure, and the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. The scope of protection of this disclosure shall be determined by the scope of the claims.

Claims

1. A vehicle dispatching system based on multi-vehicle coordination, characterized in that, The method comprises the following steps: a parameter module is configured to determine a scheduling parameter, wherein the scheduling parameter comprises a scheduling condition, and the scheduling condition comprises at least one of the following: shortest total travel distance of the vehicle, shortest total time for the vehicle to complete the transportation task, minimum total energy consumption of the vehicle, and least number of vehicle calls; a scheduling module is configured to determine a scheduling result based on the scheduling parameter; a coordination module is configured to determine a coordination influence value based on the scheduling result; a correction module is configured to correct the scheduling parameter based on the coordination influence value; wherein the correction module determines whether the coordination influence value is not less than a preset threshold value, and based on the result of the determination that the coordination influence value is not less than the preset threshold value, the scheduling parameter is corrected, and the scheduling result and the coordination influence value are determined again based on the corrected scheduling parameter; an output module is configured to output the scheduling result when the result of the determination is less than the preset threshold value. The coordination influence value comprises a predicted arrival time influence value or a coordination scheduling influence factor, wherein The predicted arrival time influence value refers to the sum of the difference between the post-coordination predicted arrival time and the pre-coordination predicted arrival time of the vehicle for completing the transportation task at each location after coordination; The coordination scheduling influence factor is the ratio of the predicted arrival time influence value to the total time for completing the transportation task before coordination.

2. The system of claim 1, wherein, The scheduling parameter comprises a transportation task attribute, and the transportation task attribute comprises a starting point and an ending point of the transportation task.

3. The system of claim 2, wherein, The scheduling parameter further comprises a vehicle attribute.

4. The system of claim 2, wherein, The transportation task attribute further comprises at least one of the following: transportation volume of the transportation task, earliest start time of the transportation task, latest start time of the transportation task, earliest end time of the transportation task, and latest end time of the transportation task.

5. The system of claim 3, wherein, The vehicle attribute comprises a vehicle pre-coordination predicted arrival time, wherein The predicted arrival time refers to the vehicle predicted arrival time between each location in the operating area of the plurality of vehicles; The vehicle pre-coordination predicted arrival time is the time required for the vehicle to travel from one location to another location according to historical data.

6. The system of claim 5, wherein, The vehicle attribute further comprises at least one of the following: capacity of the vehicle, load of the vehicle, and energy consumption of the vehicle.

7. The system of claim 1, wherein, The determination of the scheduling result based on the scheduling parameter comprises: based on static scheduling, determining the scheduling result by using a meta-heuristic algorithm; based on dynamic scheduling, determining the scheduling result by using a heuristic algorithm.

8. The system of claim 1, wherein, The scheduling result comprises: one-to-multiple or multiple-to-multiple mapping between the transportation task and the vehicle.

9. The system of claim 8, wherein, The vehicle post-coordination predicted arrival time is determined based on the scheduling result; and one or more of the following: departure order, departure time, and completion time of the vehicle.

10. The system of claim 1, the synergy impact value is a predicted time of arrival impact value, characterized by, The determination of the coordination influence value comprises: determining a coordination area; determining the release order of each vehicle in the coordination area and the waiting time of each vehicle; determining the coordination influence value of the coordination area based on the release order, the waiting time, and the scheduling result.

11. The system of claim 1, the synergistic impact value is a synergistic dispatch impact factor, characterized in that, The determination of the coordination influence value comprises: determining a coordination area; determining the release order of each vehicle in the coordination area and the waiting time of each vehicle; determining the coordination influence value of the coordination area based on the release order, the waiting time, and the scheduling result. Determine a coordination scheduling factor of the coordination area based on the predicted arrival time influence value and the pre-coordination predicted arrival time.

12. The system of claim 1, wherein, The modification of the scheduling parameter at least includes one of: Adjusting the predicted arrival time in the coordination influence value maximum area; Adjusting the post-coordination predicted arrival time of the vehicle; Adjusting the driving route of the vehicle.

13. The system of claim 12, wherein, The adjustment of the predicted arrival time in the coordination influence value maximum area is the adjustment of the post-coordination predicted arrival time of the vehicle from the exit of the coordination area to the start and end points of the subsequent task.

14. A method for vehicle dispatching based on multi-vehicle coordination, characterized in that, It includes: Obtain scheduling parameters, wherein the scheduling parameters include scheduling conditions, and the scheduling conditions include at least one of the shortest total driving distance of the vehicle, the shortest total time for the vehicle to complete the transportation task, the minimum total energy consumption of the vehicle, and the least number of vehicle calls; Determine a scheduling result based on the scheduling parameters; Determine a coordination influence value based on the scheduling result; Determine whether the coordination influence value is not less than a preset threshold based on the judgment result, and modify the scheduling parameters based on the judgment result that the coordination influence value is not less than the preset threshold, and determine the scheduling result and the coordination influence value again based on the modified scheduling parameters; When the judgment result is less than the preset threshold, output the scheduling result; The coordination influence value includes a predicted arrival time influence value or a coordination scheduling influence factor, wherein, The predicted arrival time influence value is the sum of the difference between the post-coordination predicted arrival time of the vehicle for completing the transportation task at each location and the pre-coordination predicted arrival time before coordination; The coordination scheduling influence factor is the ratio of the predicted arrival time influence value to the total time for completing the transportation task before coordination.

15. The method of claim 14, wherein, The scheduling parameters include transportation task attributes and vehicle attributes, wherein, The transportation task attributes include the start and end points of the transportation task, and at least one of the transportation task attributes includes the transportation volume of the transportation task, the earliest start time of the transportation task, the latest start of the transportation task, the earliest end time of the transportation task, and the latest end time of the transportation task; The vehicle attributes include at least one of the capacity of the vehicle, the load of the vehicle, and the energy consumption of the vehicle.

16. The method of claim 15, wherein, The vehicle attributes also include the pre-coordination predicted arrival time of the vehicle, wherein The predicted arrival time refers to the predicted arrival time of the vehicle between each location in the operating area; The pre-coordination predicted arrival time of the vehicle is the time required for the vehicle to travel from one location to another location according to historical data.

17. The method of claim 14, wherein, The scheduling result includes: One-to-many, one-to-many, or many-to-many mapping of transportation tasks and vehicles.

18. The method of claim 14, wherein, The coordination influence value is the predicted arrival time influence value, and the determination of the coordination influence value includes: Determine a coordination area; Determine the release sequence of each vehicle in the coordination area and the waiting time of each vehicle; Determine the coordination influence value of the coordination area based on the release sequence, the waiting time, and the scheduling result.

19. The method of claim 14, wherein, The coordination influence value is the coordination scheduling influence factor, and the determination of the coordination influence value includes: Determine a coordination area; Determine the release sequence of each vehicle in the coordination area and the waiting time of each vehicle; Determine the coordination influence value based on the release sequence, the waiting time, and the scheduling result. determining a cooperative scheduling factor of the cooperative region based on the predicted arrival time influence value and the pre-cooperation predicted arrival time.

20. The method of claim 14, wherein, the adjusting the scheduling parameter comprises adjusting the predicted arrival time in the cooperative influence value maximum region, the adjusting the predicted arrival time in the cooperative influence value maximum region is adjusting the post-cooperation predicted arrival time from the cooperative region exit point to the start point and the end point of the subsequent task.

21. An electronic device, comprising: comprise: a processor, a memory and an I / O interface; the I / O interface connects the processor and the memory, and is used for realizing information interaction between the memory and the processor; the memory stores a program for realizing the method in any one of claims 14 to 20, and the processor is used for running the program stored in the memory to realize the method in any one of claims 14 to 20.

22. A computer-readable storage medium, characterized in that, The computer readable storage medium stores programs or instructions, and the programs or instructions enable the computer to execute the steps of the method in any one of claims 14 to 20.

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