WO2021184265A1 - 基于多车协同的车辆调度系统、方法、电子设备及存储介质 - Google Patents

基于多车协同的车辆调度系统、方法、电子设备及存储介质 Download PDF

Info

Publication number
WO2021184265A1
WO2021184265A1 PCT/CN2020/080052 CN2020080052W WO2021184265A1 WO 2021184265 A1 WO2021184265 A1 WO 2021184265A1 CN 2020080052 W CN2020080052 W CN 2020080052W WO 2021184265 A1 WO2021184265 A1 WO 2021184265A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
scheduling
coordination
arrival
time
Prior art date
Application number
PCT/CN2020/080052
Other languages
English (en)
French (fr)
Inventor
张晓濛
鲜余强
Original Assignee
驭势（上海）汽车科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 驭势（上海）汽车科技有限公司 filed Critical 驭势（上海）汽车科技有限公司
Priority to EP20925452.3A priority Critical patent/EP4120156A4/en
Priority to KR1020227032898A priority patent/KR102537002B1/ko
Priority to US17/912,533 priority patent/US12106242B2/en
Priority to PCT/CN2020/080052 priority patent/WO2021184265A1/zh
Priority to JP2022556642A priority patent/JP7474525B2/ja
Priority to CN202080047407.3A priority patent/CN114008647A/zh
Publication of WO2021184265A1 publication Critical patent/WO2021184265A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G06Q10/08355—Routing methods

Definitions

  • the present disclosure relates to the technical field of vehicle scheduling, and more specifically to a vehicle scheduling system, method, electronic device, and storage medium based on multi-vehicle coordination.
  • the present disclosure is made in consideration of the above-mentioned problems.
  • the present disclosure provides a vehicle scheduling system, method, electronic device, and storage medium based on multi-vehicle coordination.
  • a vehicle scheduling system based on multi-vehicle coordination includes: a parameter module for determining scheduling parameters; a scheduling module for determining a scheduling result based on the scheduling parameters; a coordination module for determining a coordination impact value based on the scheduling result; a correction module , Used to modify the scheduling parameter based on the coordinated influence value; wherein the correction module determines whether the coordinated influence value is not less than a preset threshold, and based on the judgment result that the coordinated influence value is not less than the preset threshold, correct the Scheduling parameters, and re-determine the scheduling result and the coordination influence value based on the revised scheduling parameters; the output module is configured to output the scheduling result based on the judgment result being less than a preset threshold.
  • 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 realizing the memory and the processing
  • the storage of the memory is used to implement the corresponding modules in the vehicle scheduling system, and the processor is used to run the modules stored in the memory to execute the vehicle scheduling system.
  • a vehicle scheduling method based on multi-vehicle coordination including: obtaining scheduling parameters; determining a scheduling result based on the scheduling parameters; determining a coordinated influence value based on the scheduling result; judging the coordinated influence Whether the value is not less than a preset threshold, based on the judgment result that the collaborative influence value is not less than the preset threshold, revise the scheduling parameter, and re-determine the scheduling result and the collaborative influence value based on the revised scheduling parameter; based on the judgment result When it is less than the preset threshold, output the scheduling result.
  • Another aspect of the present disclosure provides a computer-readable storage medium that stores a program or instruction that causes a computer to execute the steps of the vehicle scheduling method based on multi-vehicle coordination.
  • the embodiments of the present disclosure provide a vehicle scheduling system, method, electronic device, and storage medium based on multi-vehicle coordination, which can effectively solve the sequence and task scheduling problems of multiple vehicles during scheduling, and improve and solve the problem of multi-vehicle operation. It causes problems such as multi-vehicle conflicts, congestion, and unreasonable arrangements, which effectively improves the efficiency of transportation tasks.
  • Fig. 1 shows a scene diagram of a vehicle scheduling system according to an embodiment of the present disclosure
  • Fig. 2 shows a schematic diagram of a vehicle dispatching system according to an embodiment of the present disclosure
  • Fig. 3 shows a schematic block diagram of an electronic device according to an embodiment of the present disclosure
  • Fig. 4 shows a schematic flowchart of a scheduling method according to an embodiment of the present disclosure
  • Fig. 5 shows a schematic flowchart of determining a synergy influence value according to an embodiment of the present disclosure
  • Fig. 6 shows a schematic flowchart of determining a synergy influence value according to an embodiment of the present disclosure.
  • the present disclosure proposes a vehicle scheduling system based on multi-vehicle coordination.
  • the vehicle scheduling system can be applied to logistics, transportation, leasing, park operations, manned operations, and so on.
  • the vehicle scheduling system can also be used to relieve congestion in the traffic system.
  • the vehicle dispatching system may dispatch multiple vehicles in a preset area.
  • the multiple vehicles may be intelligently driven vehicles or manually driven vehicles, where the manually driven vehicles may be Receive the dispatch from the vehicle dispatch system.
  • the preset area may be a limited operation area.
  • the vehicle scheduling system may have multiple subsystems, where each subsystem is used to manage vehicles in one operating area, and the vehicle scheduling system is used to manage and coordinate multiple subsystems and vehicles in all operating areas.
  • the vehicle scheduling system may calculate the estimated time of arrival of multiple vehicles from the start to the destination, and calculate the impact factor according to the driving situation of different vehicles, and perform different vehicles based on the impact factor. Reschedule.
  • FIG. 1 is a typical scene diagram of a vehicle scheduling system provided by an embodiment of the disclosure. This scenario includes a server 100 and multiple vehicles 102-1, 102-2, 102-3...102-N, where the server 100 includes a dispatch system 101.
  • the server 100 is used to coordinate and dispatch vehicles in a limited area.
  • the server 100 may send a scheduling instruction to the vehicle for the vehicle to plan its itinerary.
  • the server 100 may perform collaborative processing based on the driving conditions of multiple vehicles, such as vehicle traffic conditions and vehicle scheduling conditions during the scheduling process, so that the trips of all vehicles meet certain requirements, such as the shortest driving time or the shortest total distance.
  • the server 100 may send control instructions to the vehicle to control the vehicle to cooperate with other vehicles.
  • the server 100 includes a dispatch system 101.
  • the dispatch system 101 is used to generate dispatch instructions to send 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.
  • scheduling information refers to the mapping relationship between vehicles and tasks.
  • the mapping relationship can be one-to-one, one-to-many, or many-to-one.
  • the vehicle 102 is used to receive information from the server 100 and perform planning and control based on the information.
  • the vehicle may be a manually driven vehicle or an autonomously driven vehicle.
  • the vehicle is an unmanned vehicle
  • the server sends a dispatch instruction to the autonomous vehicle
  • the autonomous vehicle controls the vehicle to travel according to the received dispatch instruction.
  • the vehicle has a driver, and the driver manipulates the vehicle according to a dispatch command received by the vehicle.
  • the self-driving vehicle may also receive control instructions from the server.
  • FIG. 2 shows a schematic block diagram of a vehicle dispatching system according to an embodiment of the present disclosure.
  • the dispatching system 103 has the same configuration or structure as the dispatching system 101 shown in FIG. 1.
  • the vehicle scheduling system includes a parameter module 310, a scheduling 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 scheduling.
  • the parameter module 310 is used to determine the scheduling parameters; the parameter module 310 determines the scheduling parameters based on the current transportation task request and scheduling conditions.
  • the scheduling parameters include transportation task attributes and scheduling conditions.
  • the transport task attributes include the starting point and the end point of the transport task.
  • the transportation task attribute further includes at least one of 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. kind.
  • the attributes of the transportation task include the length of time to complete the transportation task, for example, the transportation task must be completed within 30 minutes, the transportation task must be completed within 1 hour, and so on.
  • the transportation task attributes also include transportation temperature requirements, such as room temperature transportation, refrigerated transportation, and refrigerated transportation.
  • Transport task attributes can also include specific temperature requirements, such as temperature not higher than 40 degrees Celsius or temperature not lower than 36 degrees Celsius.
  • the dispatch system dispatches vehicles based on one or more transportation tasks.
  • the scheduling parameters also include vehicle attributes.
  • the vehicle attributes include the estimated time of arrival of vehicles in the transportation area, where the estimated time of arrival includes the estimated time of arrival of each vehicle between various points in the operating area; the estimated time of arrival of the vehicle is based on historical data statistics. The time it takes for a vehicle to travel from one place to another.
  • the estimated time of arrival in the vehicle attributes is called the estimated time of arrival before coordination. The estimated time of arrival of different vehicles at the same starting point and the same destination may be the same or different.
  • the estimated time of arrival is a point in time, such as 8:5, 9:10, etc.
  • the estimated time of arrival of the vehicle from location A to location B is T2.
  • Different vehicles start at the same time, and the estimated time of arrival from one location A to another location C in the operating area may be the same or different.
  • the estimated time of arrival of a vehicle from one place A to another place C at the same time can be one or two or more.
  • the estimated time of arrival of a vehicle from point A to point B and then to point C at 9:1 is 9:30
  • the estimated time of arrival from point A to point B and point D to point C is 9:50.
  • the estimated time of arrival of this vehicle from location A to location C includes 9:30 and 9:30.
  • the estimated time of arrival of the vehicle is calculated based on the historical data of the vehicle. Understandably, the estimated time of arrival of the vehicle may also be calculated based on the distance between the locations and the speed of the vehicle.
  • the estimated time of arrival of the vehicle also considers weather conditions. For example, a vehicle departs from location A to location E at 8:01, and its expected arrival time in fine weather is 8:30; in foggy weather, its expected arrival time from location A to location E is 8:40. The estimated time of arrival in snow from location A to location E is 8:50.
  • the vehicle attributes further include at least one of the capacity of the vehicle, the load of the vehicle, and the energy consumption of the vehicle.
  • the capacity of the vehicle includes the total capacity and the remaining capacity of the vehicle. For example, the total capacity of the vehicle is 7 people, 2 people have been carried, and the remaining capacity is 5 people. When the vehicle is empty, the remaining capacity of the vehicle is equal to the total capacity of the vehicle.
  • the load of a vehicle includes the maximum load weight of the vehicle and the available load weight. For example, the maximum load weight of the vehicle is 1.8 tons, the loaded 0.5 tons, and the available load weight is 1.3 tons. When the vehicle is empty, the maximum load weight of the vehicle is equal to the available load weight of the vehicle.
  • Vehicle energy consumption includes the energy consumed by the vehicle per kilometer and the remaining energy.
  • the capacity of the vehicle includes the total loadable volume and the remaining loadable volume of the vehicle.
  • the vehicle is a gasoline vehicle, the energy consumption of the vehicle includes the gasoline consumption per kilometer and the remaining gasoline; if the vehicle is a diesel vehicle, the vehicle energy consumption of the vehicle includes the diesel consumption per kilometer and the remaining gasoline.
  • the vehicle is an electric vehicle, the energy consumption of the vehicle includes the electricity consumed per kilometer and the remaining electricity; if the vehicle is an LNG (Liquefied Natural Gas) vehicle, the energy consumption of the vehicle includes The amount of LNG consumed per kilometer of the vehicle and the remaining amount of LNG.
  • LNG Liquefied Natural Gas
  • the scheduling condition includes at least one of the shortest total distance traveled by the vehicle, the shortest total time for the vehicle to complete the transportation task, the smallest total energy consumption of the vehicle, and the smallest number of vehicle calls.
  • the total travel distance of the vehicle is the shortest, that is, the total travel distance of the vehicle used to complete a transportation task is the shortest.
  • the vehicle has the shortest total time to complete a transportation task, that is, the shortest total time from start to stop of the vehicle used to complete a transportation task.
  • the minimum number of vehicles used refers to the minimum number of vehicles used to complete a transportation task.
  • the scheduling conditions can be changed according to the needs of the transportation task. The above is only an exemplary description of the scheduling conditions, and the scheduling conditions may be other suitable conditions that meet the needs of the transportation task.
  • the scheduling module 320 is configured to determine a 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 a transportation task and a vehicle.
  • the transportation task may include one or more transportation subtasks, for example, one transportation subtask is to transport W from location A to location B, and another transportation subtask is to transport Y from location C to location B; For example, if a transportation task is to transport 2M tons of goods from location A to location B, and the maximum load of a vehicle is M tons, two vehicles are required to complete this transportation task together.
  • the estimated time of arrival of the vehicle in the scheduling result is the estimated time of arrival of the vehicle to complete the transportation task.
  • the scheduling module may determine the scheduling result based on static scheduling or dynamically.
  • the static scheduling refers to determining the scheduling result based on a meta-heuristic algorithm with higher precision
  • the dynamic scheduling refers to determining the scheduling result based on a heuristic algorithm with strong real-time performance.
  • the scheduling module uses a meta-heuristic algorithm.
  • the meta-heuristic refers to the combination of random algorithm and local search algorithm to explore in the search space through continuous iteration.
  • Meta-heuristic algorithms usually use heuristic algorithms to generate one or a set of initial solutions, and then randomly transform the initial solution to generate a new solution. Then, the new solution is evaluated and compared with the original solution, and the new solution is received through certain rules as the next solution. The beginning of an iteration, so as to obtain the optimal/approximately optimal solution through continuous iteration.
  • the meta-heuristic algorithm includes, but is not limited to, genetic algorithm, simulated annealing algorithm, tabu search, particle swarm algorithm, ant colony algorithm, and the like. .
  • the scheduling module uses a heuristic algorithm.
  • a heuristic algorithm is an algorithm based on intuition or empirical construction, which gives a feasible solution to the optimization problem under acceptable conditions.
  • the heuristic algorithm includes, but is not limited to, the economy method, the scanning method, the insertion method, and the like.
  • the estimated time of arrival of the vehicle in the scheduling result may include one or more estimated time of arrival of the vehicle. 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, the expected arrival time of the vehicle in the corresponding scheduling result includes the expected arrival time of the vehicle from location A to location B. It may also include the estimated time of arrival of the vehicle from location A to location F and the estimated time of arrival of the vehicle from location F to location B.
  • the coordination module 330 is configured to determine the coordination influence value of the coordination area based on the scheduling result.
  • the coordinated impact value refers to the impact on the estimated time of arrival caused by each other when there are multiple vehicles completing the transportation task.
  • the cooperative influence value includes an estimated time of arrival influence value or a cooperative scheduling influence factor.
  • the estimated time of arrival impact value refers to the impact on the estimated time of arrival of each vehicle in the area that requires coordination.
  • the coordination module 330 determines the need for coordination area and coordination vehicle according to the scheduling result, determines the release sequence and waiting time of each coordinated vehicle in the coordination area, and determines the coordinated post-coordination of each vehicle based on the release sequence and scheduling time Estimated time of arrival, based on the estimated time of arrival after coordination and the estimated time of arrival before coordination to determine the impact value.
  • the estimated time of arrival influence value refers to the difference between the estimated time of arrival after the vehicle is coordinated and the estimated time of arrival before the corresponding vehicle is coordinated.
  • the impact value of the estimated time of arrival at a subsequent location G refers to the difference between the estimated time of arrival of the vehicle 102-8 after the coordination to the location G and the estimated time of arrival before the coordination.
  • its estimated time of arrival influence value refers to the sum of the estimated time of arrival influence value of the vehicle 102-8 to each subsequent location after driving out of the coordination area. Due to multi-vehicle coordination, some vehicles will wait. The estimated arrival time of these vehicles at some mission points will increase.
  • the estimated arrival time impact value is the reduction in the estimated arrival time after the vehicles arrive at various locations to complete the transportation task after the coordination.
  • the sum of the difference of the estimated time of arrival before the coordination before de-coordination, and the influence value of the estimated time of arrival is a positive number.
  • the task assigned to the vehicle 102-1 is from location A to location B, and then from location C to location D, and the task of vehicle 102-2 is from location E to location F.
  • vehicle 102-1 departed from location A at 8:1, and it took 25 minutes to complete all tasks.
  • the estimated time to reach transportation task terminal D was 8:40; before coordination, vehicle 102-2 was at 8:40.
  • the estimated time-of-arrival influence value of the collaboration area refers to the sum of the estimated time-of-arrival influence values of all vehicles in the area.
  • the determination of the coordinated impact value by the coordination module 330 includes: first determining the coordination area; determining the release sequence of each vehicle in the coordination area and the time of each vehicle Waiting time; based on the release sequence of each vehicle and the waiting time of each vehicle to determine the impact value of the expected arrival time of the coordination area.
  • the coordinated scheduling impact factor is the ratio of the estimated time of arrival impact value for a vehicle to complete the transportation task and the total time to complete the transportation task before coordination.
  • the task assigned to the vehicle 102-1 is from location A to location B, and then from location C to location D
  • the task of vehicle 102-2 is from location E to location F.
  • vehicle 102-1 departed from location A at 8:1, and it took 25 minutes to complete all tasks.
  • the estimated time to reach transportation task terminal D was 8:40; before coordination, vehicle 102-2 was at 8:40. Departing from location E at 10 o'clock, it takes 30 minutes to complete the transportation task, and the estimated arrival time to reach the end of the transportation task F is 8:40.
  • the vehicle 102-1 waits for 5 minutes in the intersection area with the vehicle 102-2 on its way to the location B, and the vehicle 102-2 does not need to wait.
  • the estimated time of arrival of the vehicle 102-1 at location B is increased by 5 minutes
  • the estimated time of arrival at location C is increased by 5 minutes
  • the estimated time of arrival at location D is increased by 5 minutes
  • the vehicle 102-1 is expected to complete the transportation task.
  • the impact value of the arrival time is 15 minutes
  • the estimated time of arrival of the vehicle 102-2 remains unchanged
  • the impact value of the estimated time of arrival is 0, and the vehicle 102-2
  • the coordinated scheduling impact factor of a coordinated area refers to the sum of the coordinated scheduling impact factors of all vehicles in the area.
  • the coordinated module 330 determining the coordinated influence value includes: determining a coordinated area; determining the release sequence of each vehicle in the coordinated area and the waiting time of each vehicle Time; determine the estimated arrival time influence value of the coordination area based on the release sequence of each vehicle and the waiting time of each vehicle; determine the coordination scheduling factor of the coordination area based on the estimated arrival time influence value and the expected arrival time before coordination.
  • the correction module 340 is configured to correct the scheduling parameters based on the coordination influence value of the coordination area.
  • the modification module may modify the scheduling parameter based on the coordination influence value of the coordination area.
  • the modification module 340 may modify the scheduling parameters by adjusting the estimated time of arrival of vehicles after coordination; in some embodiments, the adjustment of the estimated time of arrival of vehicles after coordination may be adjusting the coordination of vehicles to complete transportation tasks. After the estimated time of arrival, thereby reducing the value of synergy.
  • the adjustment of the estimated time of arrival after coordination of vehicles is to transform the influence of the coordination into a new scheduling parameter.
  • the estimated time of arrival after coordination is the estimated time of arrival after coordination.
  • the order of the vehicles before reaching the coordination area remains unchanged, and the transportation tasks after reaching the coordination area are rescheduled.
  • the modification module 340 may modify the scheduling parameters by adjusting the driving route of the vehicle.
  • the modification module 340 judges whether the cooperative influence value of the cooperative area is not less than a preset threshold, and based on the judgment result that the cooperative influence value is not less than the preset threshold, revises the scheduling parameters, and renews the scheduling parameters based on the revised scheduling parameters. Determine the results of the scheduling and the value of the synergy impact. Adjusting the estimated time of arrival in the area with the largest coordination influence value is to adjust the estimated time of arrival of the vehicle after the vehicle has left the coordination area to the starting point and the end of the subsequent task.
  • Adjust the driving route of the vehicle which will bring about one or more of the following results, including but not limited to: the vehicle changes the driving route and does not drive into the area with the largest synergistic influence value, thereby reducing the impact of multi-vehicle synergy; The number of vehicles in the region with the maximum synergy impact value is reduced, thereby reducing the impact of multi-vehicle synergy.
  • the output module 350 is configured to output the scheduling result based on the judgment result being less than the preset threshold.
  • the output module 350 receives the scheduling result of the correction module, and sends the scheduling result to the vehicle.
  • the content disclosed in this application may have various modifications and improvements.
  • the different functional components described above are all realized by hardware devices, but they may also be realized only by software solutions.
  • installing the system on an existing server The content disclosed here may be realized through a firmware, a combination of firmware/software, a combination of firmware/hardware, or a combination of hardware/firmware/software.
  • the coordination module and the correction module may be integrated to complete coordinated scheduling of vehicles in a unified manner.
  • the correction module and the output module may be integrated to complete coordinated scheduling of vehicles in a unified manner.
  • an embodiment of the present disclosure provides an electronic device, as shown in FIG. 3, which includes a processor, a memory, and an I/O interface.
  • the memory stores the corresponding modules used to implement the vehicle dispatching system according to the embodiments of the present disclosure.
  • the processor is used to run the module stored in the memory to execute the vehicle dispatching system according to the embodiment of the present disclosure.
  • the processor is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically such as SDRAM). , DDR, etc.), read-only memory (ROM), charged erasable programmable read-only memory (EEPROM), flash memory (FLASH).
  • the processor may be a server or a server group. Server groups can be centralized or distributed. In some embodiments, the server may be local or remote.
  • the I/O interface (read and write interface) is connected between the processor and the memory, and is used to realize the information interaction between the memory and the processor, which includes, but is not limited to, a data bus (Bus), etc.
  • the embodiments of the present disclosure provide a vehicle scheduling method based on multi-vehicle coordination.
  • the steps of the vehicle scheduling method provided in this implementation are respectively executed by the various modules of the vehicle scheduling system described above. In the following, only the main steps of the vehicle scheduling method are described, and the details that have been described above are omitted. 4, the vehicle scheduling method of the embodiment of the present disclosure includes:
  • step 410 the server obtains scheduling parameters.
  • the scheduling parameters include transportation task attributes and scheduling conditions.
  • the attributes of the transportation task include the starting point and the end point of the transportation task.
  • the task attribute further includes at least one of 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.
  • the scheduling parameters further include vehicle attributes.
  • 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.
  • the vehicle attributes include the estimated time of arrival of vehicles in the transportation area, where the estimated time of arrival includes the estimated time of arrival of each vehicle between various points in the operating area; the estimated time of arrival of the vehicle is based on historical data statistics. The time it takes for a vehicle to travel from one place to another. In order to facilitate the distinction, the estimated time of arrival in the vehicle attributes is called the estimated time of arrival before coordination.
  • 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.
  • the scheduling condition includes at least one of the shortest total distance traveled by the vehicle, the shortest total time for the vehicle to complete the transportation task, the smallest total energy consumption of the vehicle, and the smallest number of vehicle calls.
  • step 420 the server determines a scheduling result based on the scheduling parameter.
  • determining the scheduling result based on the scheduling parameters includes: using a meta-heuristic algorithm to determine the scheduling result based on static scheduling; and using a heuristic algorithm to determine the scheduling result based on dynamic scheduling.
  • the scheduling result includes: a many-to-one, one-to-many, or many-to-many mapping between the transportation task and the vehicle; and the estimated time of arrival of the vehicle.
  • One or more of the departure sequence, departure time, and completion time of the vehicle is determined based on the scheduling result.
  • Many-to-one mapping between transportation tasks and vehicles means that multiple transportation tasks are completed by one vehicle; one-to-many mapping between transportation tasks and vehicles means that one transportation task is completed by multiple vehicles; many-to-many transportation tasks and vehicles Mapping means that multiple transportation tasks are completed by multiple vehicles.
  • step 430 the server determines a collaborative influence value based on the scheduling result.
  • the coordinated impact value refers to the impact on the estimated time of arrival caused by each other when there are multiple vehicles completing the transportation task.
  • the cooperative influence value includes an estimated time of arrival influence value or a cooperative scheduling influence factor.
  • the estimated time of arrival impact value refers to the impact on the estimated time of arrival of each vehicle in the area that requires coordination.
  • the coordination module 330 determines the need for coordination area and coordination vehicle according to the scheduling result, determines the release sequence and waiting time of each coordinated vehicle in the coordination area, and determines the coordinated post-coordination of each vehicle based on the release sequence and scheduling time Estimated time of arrival, based on the estimated time of arrival after coordination and the estimated time of arrival before coordination to determine the impact value.
  • the estimated time of arrival impact value refers to the difference between the estimated time of arrival after the vehicle is coordinated and the estimated time of arrival before the corresponding vehicle is coordinated; because multi-vehicle coordination will cause some vehicles to wait, the estimated time of arrival of these vehicles at some mission points will be affected. If it becomes larger, the estimated time of arrival impact value is the calculated estimated time of arrival after coordination after coordination minus the estimated time of arrival before coordination before coordination, and the impact value of estimated time of arrival is a positive number.
  • the estimated time-of-arrival influence value of the collaboration area refers to the sum of the estimated time-of-arrival influence values of all vehicles in the area.
  • the collaborative scheduling impact factor is the ratio of the impact value of the estimated time of arrival of a vehicle to complete the transportation task and the estimated time of arrival before coordination.
  • the estimated time of arrival before coordination refers to the total time required for the vehicle to complete the transportation task before coordination.
  • the coordinated scheduling impact factor of a coordinated area refers to the sum of the coordinated scheduling impact factors of all vehicles in the area.
  • the coordinated influence value is the estimated time of arrival influence value
  • determining the coordinated influence value includes: determining the coordinated area; determining the release sequence of each vehicle in the coordinated area and the waiting time of each vehicle; determining the expected arrival time of the coordinated area Impact value.
  • the coordinated influence value is a coordinated scheduling influence factor
  • the determination of the coordinated influence value includes: determining the coordinated area; determining the release sequence of each vehicle in the coordinated area and the waiting time of each vehicle; determining the prediction of each vehicle in the coordinated area Arrival time influence value; based on the estimated time of arrival influence value and the estimated time of arrival before coordination, the coordination scheduling factor of the coordination area is determined.
  • step 440 the server determines whether the coordinated influence value is not less than a preset threshold, based on the judgment result that the coordinated influence value is not less than the preset threshold, revises the scheduling parameters, and re-determines the scheduling based on the revised scheduling parameters Results and synergy impact values.
  • modifying the scheduling parameter includes at least one of the following: adjusting the estimated time of arrival in the area with the largest coordinated influence value; adjusting the estimated time of arrival of the vehicle to complete the transportation task; adjusting the driving route of the vehicle.
  • Adjusting the estimated time of arrival in the area with the largest coordination influence value is to adjust the estimated time of arrival after the coordination of the vehicle to the start and end of the subsequent task after the vehicle leaves the coordination area.
  • step 450 the server outputs the scheduling result based on the judgment result being less than a preset threshold.
  • the vehicle scheduling method of the embodiments of the present disclosure can be used to schedule multiple vehicles. It can effectively solve the problems of the sequence and task arrangement of multiple vehicles during dispatch, improve and solve the problems of multi-vehicle conflicts, congestion, and unreasonable arrangements caused by multi-vehicle operations, and effectively improve the efficiency of transportation tasks.
  • an embodiment of the present disclosure provides a computer-readable medium on which a computer program or instruction is stored, and when the program is executed by a processor, the vehicle scheduling method of any one of the embodiments of the present disclosure is implemented.

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

一种基于多车协同的车辆调度系统、方法、电子设备及存储介质。调度系统包括参数模块(310)，用于确定调度参数；调度模块(320)，用于确定调度结果；协同模块(330)，用于确定协同影响值；修正模块(340)，用于修正调度参数；其中，修正模块判断协同影响值是否不小于预设阈值，基于判断结果为协同影响值不小于预设阈值，修正调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值；输出模块(350)，用于基于判断结果为小于预设阈值时，输出调度结果。能够有效解决多个车辆在调度时的先后顺序及任务安排问题，完善并解决了多车运营所造成多车冲突、拥堵、安排不合理等问题，有效地提高运输任务的完成效率。

Description

基于多车协同的车辆调度系统、方法、电子设备及存储介质 技术领域
本公开涉及车辆调度技术领域，更具体地涉及一种基于多车协同的车辆调度系统、方法、电子设备及存储介质。
背景技术
在物流运输中，通常由多个车辆完成一项或者多项运输任务，对车辆进行合理的统筹安排十分重要。目前多个车辆在完成运输任务时会出现车辆行驶路线冲突、多车拥堵等问题，造成运输任务完成的效率低下。
可见，如何解决出现的不合理调度问题，是本领域技术人员亟待解决的问题。
发明内容
考虑到上述问题而提出了本公开。本公开提供了一种基于多车协同的车辆调度系统、方法、电子设备及存储介质。
本公开一方面，提供了一种基于多车协同的车辆调度系统。基于多车协同的车辆调度系统包括：参数模块，用于确定调度参数；调度模块，用于基于所述调度参数确定调度结果；协同模块，用于基于所述调度结果确定协同影响值；修正模块，用于基于所述协同影响值修正调度参数；其中，所述修正模块判断所述协同影响值是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值；输出模块，用于基于所述判断结果为小于预设阈值时，输出所述调度结果。
本公开另一方面，提供了一种电子设备，包括处理器、存储器和I/O接口，其中，I/O接口连接所述处理器和所述存储器，用于实现所述存储器与所述处理器的信息交互；所述存储器存储用于实现所述车辆调度系统中的相应模块，所述处理器用于运行所述存储器中存储的模块，以执行所述车辆调度系统。
本公开的另一方面，提供了一种基于多车协同的车辆调度方法，包括：获取调度参数；基于所述调度参数确定调度结果；基于所述调度结果确定协同影响值；判断所述协同影响值 是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值；基于所述判断结果为小于预设阈值时，输出所述调度结果。
本公开另一方面，提供了一种计算机可读存储介质，所述计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行所述基于多车协同的车辆调度方法的步骤。
本公开实施例通过提供一种基于多车协同的车辆调度系统、方法、电子设备及存储介质，能够有效解决多个车辆在调度时的先后顺序及任务安排问题，完善并解决了多车运营所造成多车冲突、拥堵、安排不合理等问题，有效地提高运输任务的完成效率。
附图说明
通过结合附图对本公开实施例进行更详细的描述，本公开的上述以及其它目的、特征和优势将变得更加明显。附图用来提供对本公开实施例的进一步理解，并且构成说明书的一部分，与本公开实施例一起用于解释本公开，并不构成对本公开的限制。在附图中，相同的参考标号通常代表相同部件或步骤。
图1示出根据本公开一个实施例的车辆调度系统的场景图；
图2示出根据本公开一个实施例的车辆调度系统的示意图；
图3示出根据本公开一个实施例的电子设备的示意性框图；
图4示出根据本公开一个实施例的调度方法的示意性流程图；
图5示出根据本公开一个实施例的确定协同影响值的示意性流程图；以及
图6示出根据本公开一个实施例的确定协同影响值的示意性流程图。
具体实施方式
为了使得本公开的目的、技术方案和优点更为明显，下面将参照附图详细描述根据本公开的示例实施例。显然，所描述的实施例仅仅是本公开的一部分实施例，而不是本公开的全部实施例，应理解，本公开不受这里描述的示例实施例的限制。
本公开提出了一种基于多车协同的车辆调度系统。在一些实施例中，所述车辆调度系统可以应用于物流、运输、出租、园区运营、载人等方面。所述车辆调度系统还可以用于缓解交通系统中出现的拥堵现象。在一些实施例中，所述车辆调度系统可以对预设区域内的多个车辆进行调度，所述多个车辆可以是智能驾驶车辆，也可以是人工驾驶车辆，其中，所述人工驾驶车辆可以接收来自所述车辆调度系统的调度。在一些实施例中，所述预设区域可以是限定的运营区域。在一些实施例中，所述车辆调度系统可以存在多个子系统，其中每个子系 统用于管理一个运营区域的车辆，而所述车辆调度系统用于管理协调多个子系统和所有运营区域的车辆。在一些实施例中，所述车辆调度系统可以通过计算多个车辆从起始到目的地的预计到达时间，并根据不同车辆在行驶中的情况来计算影响因子，并基于影响因子对不同车辆进行重新调度。
图1为本公开实施例所提供的一种车辆调度系统的典型场景图。该场景包括服务器100，和多个车辆102-1、102-2、102-3……102-N，其中所述服务器100包括调度系统101。
如图1所示，服务器100用于对限定区域内的车辆进行协同与调度。在一些实施例中，所述服务器100可以发送调度指令给车辆，用于车辆规划其行程。所述服务器100可以基于在调度过程中的车辆通行状况、车辆的调度情况等多个车辆的行车情况进行协同处理，以使得所有车辆的行程满足一定要求，例如行驶时间最短或者行驶总路程最短。
服务器100可以发送控制指令给车辆以控制车辆与其他车辆协同。
服务器100包括调度系统101。调度系统101用于产生调度指令发给车辆。调度系统101接收任务请求以及调度条件，并基于接收到的任务请求以及调度条件生成调度信息。其中，调度信息是指车辆与任务之间的映射关系。所述的映射关系可以是一对一、一对多或者多对一。
车辆102用于接收来自服务器100的信息并基于信息进行规划和控制。所述车辆可以是人工驾驶车辆，也可以是自动驾驶车辆。在一些实施例中，所述车辆是无人驾驶车辆，服务器发送调度指令给自动驾驶车辆，自动驾驶车辆根据接收到的调度指令控制车辆行驶。在一些实施例中，所述车辆有驾驶员，驾驶员根据车辆接收的调度指令操控车辆。所述自动驾驶车辆还可以接收来自于服务器的控制指令。
图2示出了根据本公开一个实施例的车辆调度系统的示意性框图，所述调度系统103与图1所示的调度系统101有相同的配置或结构。
如图2所示，车辆调度系统包括参数模块310、调度模块320、协同模块330、修正模块340、输出模块350及其他可以用于进行车辆调度的组件或模块。
参数模块310，用于确定调度参数；参数模块310基于当前的运输任务请求和调度条件确定调度参数。在一些实施例中，调度参数包括运输任务属性和调度条件。其中，运输任务属性包括运输任务的起点及终点。在一些实施例中，运输任务属性还包括运输任务的运输量、运输任务最早开始时间、运输任务的最晚的开始时间、运输任务的最早结束时间和运输任务的最晚结束时间中的至少一种。在一些实施例中，运输任务属性包括完成运输任务的时长，如运输任务必须在30分钟内完成，运输任务必须在1个小时内完成等。在一些实施例中，运输任务属性还包括运输温度要求，如常温运输、冷藏运 输和冷冻运输等。运输任务属性也可以包括具体的温度要求，如温度不高于40摄氏度或温度不低于36摄氏度等。调度系统基于一个或者多个运输任务，对车辆进行调度。
在一些实施例中，调度参数还包括车辆属性。在一些实施例中，车辆属性包括运输区域内的车辆的预计到达时间，其中预计到达时间包括每个车辆在运行区域内各地点之间的车辆预计到达时间；车辆预计到达时间是根据历史数据统计的车辆从一个地点行驶到另一个地点所需的时间。为了便于区分，将车辆属性中的预计到达时间称为协同前预计到达时间。其中不同车辆在相同起点和相同目的地的预计达到时间，可以相同，也可不同。
同一车辆在相同起点和目的地的预计到达时间，可以具备多个。例如，在不同时间段行驶、以不同的路线行驶会有不同的预计达到时间，同一路线在不同的道路拥堵情况下预计到达时间不同。例如，车辆的车况不同，预计到达时间也不同。
所述预计到达时间是一个时间点，如8点5分，9点10分等。例如，车辆在时刻T1从地点A启动，在时刻T2到达地点B，则车辆从地点A到地点B的预计到达时间是T2。不同车辆在同一时刻启动，在运营区域内的一个地点A到另一个地点C的预计到达时间可以相同也可以不同。在一些实施例中，一个车辆在同一时刻从一个地点A出发到另一个地点C的预计到达时间可以有一个，也可以有两个或更多个。如一个车辆在9点1刻从地点A经过地点B再到达地点C的预计到达时间是9点30分，从地点A经过地点B和地点D再达到地点C的预计到达时间是9点50分，则这辆车从地点A到地点C的预计到达时间包括9点30分和9点50分。车辆的预计到达时间根据车辆行驶的历史数据统计得到。可以理解地，车辆的预计到达时间也可以是根据地点之间的距离和该车辆的车速计算得到。在一些实施例中，车辆的预计到达时间还考虑天气状况。例如，车辆在8点1刻从地点A出发到地点E，在晴朗天气的预计到达时间是8点30分；在大雾天气从地点A到地点E的预计到达时间是8点40分，在雪天从从地点A到地点E的预计到达时间是8点50分。
在一些实施例中，车辆属性还包括车辆的容量、车辆的载重和车辆能耗中的至少一种。车辆的容量包括可以车辆的总容量和剩余容量，如车辆的总容量是7人，已载2人，剩余容量是5人。在车辆空载的情况下，车辆的剩余容量等于车辆的总容量。同理，车辆的载重包括车辆最大装载重量和可用装载重量，如车辆最大装载重量是1.8吨，已载0.5吨，可用装载重量1.3吨。在车辆空载的情况下，车辆的最大装载重量等于车辆的可用装载重量。车辆能耗包括车辆每公里消耗的能量和剩余能量。例如，车辆的容量包括车辆的总可载体积和剩余可载体积。车辆是汽油车，则该车辆的车辆能耗包括该车辆 每公里消耗的汽油量和剩余的汽油量；车辆是柴油车，则该车辆的车辆能耗包括该车辆每公里消耗的柴油量和剩余的柴油量；车辆是电动车，则该车辆的车辆能耗包括该车辆每公里消耗的电量和剩余的电量；车辆是LNG(Liquefied Natural Gas，液化天然气)汽车，则该车辆的车辆能耗包括该车辆每公里消耗的LNG量和剩余的LNG量。
在一些实施例中，调度条件包括车辆总行驶路程最短、车辆完成运输任务总时间最短、车辆总能耗最小和车辆调用数最少中的至少一种。可以理解地，车辆的总行驶路程最短，即完成一项运输任务时所用车辆的总行驶路程最短。车辆完成运输任务总时间最短即完成一项运输任务时所用车辆从启动到停止所经历的总时间最短。车辆调用数最少是指完成一项运输任务所用车辆的数目最少。可以理解地，调度条件可以根据运输任务的需要变化，以上仅是对调度条件的示例性说明，调度条件可以是符合运输任务需要的其他合适的条件。
调度模块320，用于基于所述调度参数确定调度结果；在一些实施例中，调度结果包括：运输任务与车辆的多对一、一对多或者多对多映射。
运输任务与车辆的多对一映射，是指多项运输任务由一个车辆完成；运输任务与车辆的一对多映射是指一项运输任务由多个车辆完成；运输任务与车辆的多对多映射是指多项运输任务由多个车辆完成。在一些实施例中，运输任务可以包括一个或多个运输子任务，例如一个运输子任务是将W由地点A运输到地点B，另一个运输子任务是将Y由地点C运输到地点B；例如，一个运输任务是将2M吨货物从地点A运输到地点B，一个车辆的最大载重是M吨，则需要2个车辆共同完成这一项运输任务。调度结果中的车辆预计到达时间是车辆为完成运输任务的预计到达时间。
在一些实施例中，所述调度模块可以基于静态调度或动态的确定调度结果。其中，所述静态调度是指基于精度较高的元启发式算法确定调度结果，所述动态调度是指基于实时性较强的启发式算法确定调度结果。
基于静态调度，调度模块采用元启发式算法。其中所述元启发式是指随机算法与局部搜索算法相结合，通过不断的迭代在搜索空间中进行探索。元启发式算法通常采用启发式算法产生一个或者一组初始解，然后对初始解进行随机变换生成新解，接下来对新解进行评估与原解进行比较，通过一定的规则接收新解作为下一次迭代的起始，以此通过不断地迭代获得最优/近似最优解。在一些实施例中，所述元启发式算法包括但不限于遗传算法、模拟退火算法、禁忌搜索、粒子群算法、蚁群算法等。。
基于动态调度，调度模块采用启发式算法。启发式算法是基于直观或者经验构造的 算法，在可接受的条件下给出优化问题的可行解。在一些实施例中，所述启发式算法包括但不限于节约法、扫描法和插入法等。
调度结果中的车辆预计到达时间，可以包括一个或多个车辆预计到达时间。例如，调度任务是将W由地点A运输到地点B，地点A到地点B的道路途经地点F，则相应的调度结果中的车辆预计到达时间包括车辆从地点A到地点B的预计到达时间，还可以包括车辆从地点A到地点F的预计到达时间和车辆从地点F到地点B的预计到达时间。
协同模块330，用于基于所述调度结果确定协同区域的协同影响值。
协同影响值指当存在多个车辆完成运输任务时相互之间所造成的对预计到达时间的影响。在一些实施例中，协同影响值包括预计到达时间影响值或协同调度影响因子。预计到达时间影响值是指在需要协同的区域内对于各个车辆的预计到达时间的影响。所述协同模块330根据所述调度结果确定需要协同区域及协同车辆，并确定所述协同区域内各个协同车辆的放行顺序和等待时间，并基于所述放行顺序和调度时间确定各个车辆的协同后预计到达时间，基于协同后预计到达时间和协同前预计到达时间确定影响值。
所述预计到达时间影响值是指车辆协同后预计到达时间和对应车辆协同前预计到达时间的差值。对于车辆102-8，其在驶出协同区域后，到达后续一个地点G的预计到达时间影响值是指车辆102-8到地点G的协同后预计到达时间和协同前预计到达时间的差值。对于车辆102-8，其预计到达时间影响值是指车辆102-8在驶出协同区域后到达后续每个地点的预计到达时间影响值之和。因多车协同会产生部分车辆等待的现象，这些车辆到达部分任务点的预计达到时间会变大，预计到达时间影响值即为协同之后车辆为完成运输任务到达各个地点的协同后预计到达时间减去协同之前的协同前预计到达时间的差值之和，预计到达时间影响值是一个正数。在一些实施例中，完成一项运输任务，分配给车辆102-1的任务是从地点A到地点B，再从地点C到地点D，车辆102-2的任务是从地点E到地点F，在协同前，车辆102-1在8点1刻从地点A出发，完成所有任务用时25分钟，到达运输任务终点D的预计达到时间是8点40分；在协同前，车辆102-2在8点10分从地点E出发，完成运输任务用时30分钟，达到运输任务终点F的预计到达时间是8点40分。协同后，车辆102-1在到达地点B的途中，在与车辆102-2的交叉区域等待5分钟，车辆102-2不需等待。则协同后，车辆102-1到达地点B的预计到达时间增加5分钟，到达地点C的预计到达时间增加5分钟，到达地点D的预计到达时间增加5分钟，车辆102-1完成运输任务的预计到达时间影响值为15分钟，车辆102-2的预计到达时间不变，预计到达时间影响值为0。协同区域的预计到达时间影响值是指该区域内所有车辆的预计到达时间影响值之和。
在一些实施例中，基于所述协同影响值为预计到达时间影响值，所述协同模块330确定所述协同影响值包括：首先确定协同区域；确定协同区域内各个车辆的放行顺序和各个车辆的等待时间；基于所述各个车辆的放行顺序和各个辆车的等待时间确定协同区域的预计到达时间影响值。
在一些实施例中，协同调度影响因子是一个车辆完成运输任务的预计到达时间影响值和协同前完成运输任务的总时长的比值。在一些实施例中，完成一项运输任务，分配给车辆102-1的任务是从地点A到地点B，再从地点C到地点D，车辆102-2的任务是从地点E到地点F，在协同前，车辆102-1在8点1刻从地点A出发，完成所有任务用时25分钟，到达运输任务终点D的预计达到时间是8点40分；在协同前，车辆102-2在8点10分从地点E出发，完成运输任务用时30分钟，达到运输任务终点F的预计到达时间是8点40分。协同后，车辆102-1在到达地点B的途中，在与车辆102-2的交叉区域等待5分钟，车辆102-2不需等待。则协同后，车辆102-1到达地点B的预计到达时间增加5分钟，到达地点C的预计到达时间增加5分钟，到达地点D的预计到达时间增加5分钟，车辆102-1完成运输任务的预计到达时间影响值为15分钟，车辆102-1的协同调度影响因子是0.6(15/25＝0.6)，车辆102-2的预计到达时间不变，预计到达时间影响值为0，车辆102-2的协同调度影响因子是0(0/30＝0)。协同前其中，协同区域的协同调度影响因子是指该区域内所有车辆的协同调度影响因子之和。
在一些实施例中，基于所述协同影响值为协同调度影响因子，所述协同模块330确定所述协同影响值包括：确定协同区域；确定协同区域内各个车辆的放行顺序和各个辆车的等待时间；基于所述各个车辆的放行顺序和各个辆车的等待时间确定协同区域的预计到达时间影响值；基于预计到达时间影响值和协同前预计达到时间确定协同区域的协同调度因子。
修正模块340，用于基于所述协同区域的协同影响值修正调度参数。在一些实施例中，所述修正模块可以基于所述协同区域的协同影响值修正调度参数。在一些实施例中，所述修正模块340可以通过调整车辆的协同后预计到达时间修正调度参数；在一些实施例中，所述调整车辆的协同后预计到达时间可以是调整车辆完成运输任务的协同后预计到达时间，从而降低协同影响值。
在一些实施例中，所述调整车辆的协同后预计到达时间是将协同的影响转化到新的调度参数中协同后预计到达时间协同后预计到达时间。在一些实施例中，对于所有涉及 到该协同区域需要等待的车辆，到达该协同区域之前的车辆顺序保持不变，重新调度到达协同区域之后的运输任务。
在一些实施例中，所述修正模块340可以通过调整车辆的行驶路线从而修正调度参数。
所述修正模块340判断所述协同区域的协同影响值是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值。调整协同影响值最大区域内的预计到达时间是调整车辆是调整车辆驶出所述协同区域后到后续任务起点和终点的协同后预计达到时间。
这将带来包括但不限于以下结果中的一种或多种：改变在协同影响值最大区域外的车辆放行顺序、改变车辆的行驶路线、改变车辆的等待时间等。
调整车辆的行驶路线，这将带来包括但不限于以下结果中的一种或多种：车辆改变行驶路线，不驶入该协同影响值最大的区域，进而降低多车协同带来的影响；在该协同影响值最大区域内的车辆的数量减少，进而降低多车协同带来的影响。
输出模块350，用于基于所述判断结果为小于预设阈值时，输出调度结果。
输出模块350接收修正模块的调度结果，并将所述调度结果发送给车辆。
本领域技术人员能够理解，本申请所披露的内容可以出现多种变形和改进。例如，以上所描述的不同功能组件都是通过硬件设备所实现的，但是也可能只通过软件的解决方案得以实现。例如：在现有的服务器上安装系统。这里所披露的内容可能是通过一个固件、固件/软件的组合、固件/硬件的组合或硬件/固件/软件的组合得以实现。例如，所述协同模块和所述修正模块可以是一体的，统一完成车辆的协同调度。再例如，所述修正模块和所述输出模块可以是一体的，统一完成车辆的协同调度。
另一个方面，本公开实施例提供一种电子设备，如图3所示，其包括：处理器、存储器和I/O接口。
存储器存储用于实现根据本公开实施例的车辆调度系统中的相应模块。
处理器用于运行所述存储器中存储的模块，以执行根据本公开实施例的车辆调度系统。
其中，处理器为具有数据处理能力的器件，其包括但不限于中央处理器(CPU)等；存储器为具有数据存储能力的器件，其包括但不限于随机存取存储器(RAM，更具体如SDRAM、DDR等)、只读存储器(ROM)、带电可擦可编程只读存储器(EEPROM)、闪存 (FLASH)。在一些实施例中，处理器可以是一个服务器，也可以是一个服务器群组。服务器群组可以是集中式的，也可以是分布式的。在一些实施例中，服务器可以是本地的或远程的。
I/O接口(读写接口)连接在处理器于存储器间，用于实现存储器与处理器的信息交互，其包括但不限于数据总线(Bus)等。
另一方面，本公开实施例提供一种基于多车协同的车辆调度方法。本实施提供的车辆调度方法的步骤分别由上文中的车辆调度系统各个模块所执行。以下仅对车辆调度方法的主要步骤进行描述，而省略以上已经描述过的细节内容。参照图4，本公开实施例的车辆调度方法包括：
在步骤410中，所述服务器获取调度参数。
在一些实施例中，调度参数包括运输任务属性和调度条件。
运输任务属性包括运输任务的起点及终点。在一些实施例中任务属性还包括运输任务的运输量、运输任务最早开始时间、运输任务的最晚的开始时间、运输任务的最早结束时间和运输任务的最晚结束时间中的至少一种。
在一些实施例中，所述调度参数还包括车辆属性。在一些实施例中，车辆属性包括车辆的容量、车辆的载重和车辆能耗中的至少一种。
在一些实施例中，车辆属性包括运输区域内的车辆的预计到达时间，其中预计到达时间包括每个车辆在运行区域内各地点之间的车辆预计到达时间；车辆预计到达时间是根据历史数据统计的车辆从一个地点行驶到另一个地点所需的时间。为了便于区分，将车辆属性中的预计到达时间称为协同前预计到达时间。
在一些实施例中，车辆属性包括车辆的容量、车辆的载重和车辆能耗中的至少一种。
在一些实施例中，调度条件包括车辆总行驶路程最短、车辆完成运输任务总时间最短、车辆总能耗最小和车辆调用数最少中的至少一种。
在步骤420中，所述服务器基于所述调度参数确定调度结果。
在一些实施例中，基于调度参数确定调度结果包括：基于静态调度，采用元启发式算法确定调度结果；基于动态调度，采用启发式算法确定调度结果。
在一些实施例中，调度结果包括：运输任务与车辆的多对一、一对多或者多对多映射；车辆预计到达时间。基于调度结果确定车辆的出发顺序、出发时间、完成时间中的一种或多种。
运输任务与车辆的多对一映射，是指多项运输任务由一个车辆完成；运输任务与车辆的一对多映射是指一项运输任务由多个车辆完成；运输任务与车辆的多对多映射是指多项运输任务由多个车辆完成。
在步骤430中，所述服务器基于所述调度结果确定协同影响值。
协同影响值指当存在多个车辆完成运输任务时相互之间所造成的对预计到达时间的影响。在一些实施例中，协同影响值包括预计到达时间影响值或协同调度影响因子。预计到达时间影响值是指在需要协同的区域内对于各个车辆的预计到达时间的影响。所述协同模块330根据所述调度结果确定需要协同区域及协同车辆，并确定所述协同区域内各个协同车辆的放行顺序和等待时间，并基于所述放行顺序和调度时间确定各个车辆的协同后预计到达时间，基于协同后预计到达时间和协同前预计到达时间确定影响值。
所述预计到达时间影响值是指车辆协同后预计到达时间和对应车辆协同前预计到达时间的差值；因多车协同会产生部分车辆等待的现象，这些车辆到达部分任务点的预计达到时间会变大，预计到达时间影响值即为协同之后计算所得的协同后预计到达时间减去协同之前的协同前预计到达时间，预计到达时间影响值是一个正数。
协同区域的预计到达时间影响值是指该区域内所有车辆的预计到达时间影响值之和。
在一些实施例中，协同调度影响因子是一个车辆完成运输任务的预计到达时间影响值和协同前预计达到时间的比值。协同前预计到达时间是指协同之前该车辆完成运输任务所需总时间。
协同区域的协同调度影响因子是指该区域内所有车辆的协同调度影响因子之和。
在一些实施例中，协同影响值是预计到达时间影响值，确定协同影响值包括：确定协同区域；确定协同区域内各个车辆的放行顺序和各个辆车的等待时间；确定协同区域的预计到达时间影响值。
在一些实施例中，协同影响值是协同调度影响因子，确定协同影响值包括：确定协同区域；确定协同区域内各个车辆的放行顺序和各个辆车的等待时间；确定协同区域内各个车辆的预计到达时间影响值；基于预计到达时间影响值和协同前预计达到时间确定协同区域的协同调度因子。
在步骤440中，服务器判断所述协同影响值是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确 定调度结果和协同影响值。
在一些实施例中，修正所述调度参数至少包括以下一种：调整协同影响值最大区域内的预计到达时间；调整车辆完成运输任务的预计到达时间；调整车辆的行驶路线。
调整协同影响值最大区域内的预计到达时间是调整车辆驶出所述协同区域后到后续任务起点和终点的协同后预计达到时间。
在步骤450中，服务器基于所述判断结果为小于预设阈值时，输出所述调度结果。
综上所述，本公开实施例的车辆调度方法可用于对多个车辆进行调度。能够有效解决多个车辆在调度时的先后顺序及任务安排问题，完善并解决了多车运营所造成多车冲突、拥堵、安排不合理等问题，有效地提高运输任务的完成效率。
另一个方面，本公开实施例提供一种计算机可读介质，其上存储有计算机程序或指令，程序被处理器执行时实现本公开实施例任意一项的车辆调度方法。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同系统来实现所描述的功能，但是这种实现不应认为超出本公开的范围。
类似地，应当理解，为了精简本公开并帮助理解各个发明方面中的一个或多个，在对本公开的示例性实施例的描述中，本公开的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该本公开的系统解释成反映如下意图：即所要求保护的本公开要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如相应的权利要求书所反映的那样，其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本公开的单独实施例。
应该注意的是上述实施例对本公开进行说明而不是对本公开进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本公开可以借助于包括有若干不同元件的硬件以及借助于适当编程的 计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述，仅为本公开的具体实施方式或对具体实施方式的说明，本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本公开的保护范围之内。本公开的保护范围应以权利要求的保护范围为准。

Claims (24)

  1. 一种基于多车协同的车辆调度系统，其特征在于，包括，
    参数模块，用于确定调度参数；
    调度模块，用于基于所述调度参数确定调度结果；
    协同模块，用于基于所述调度结果确定协同影响值；
    修正模块，用于基于所述协同影响值修正调度参数；其中，所述修正模块判断所述协同影响值是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值；
    输出模块，用于基于所述判断结果为小于预设阈值时，输出所述调度结果。
  2. 根据权利要求1所述的系统，其特征在于，所述调度参数包括运输任务属性、调度条件，所述运输任务属性包括运输任务的起点及终点。
  3. 根据权利要求2所述的系统，其特征在于，所述调度参数包括还包括车辆属性。
  4. 根据权利要求2所述的系统，其特征在于，所述运输任务属性还包括运输任务的运输量、运输任务最早开始时间、运输任务的最晚的开始时间、运输任务的最早结束时间和运输任务的最晚结束时间中的至少一种。
  5. 根据权利要求3所述的系统，其特征在于，所述车辆属性包括车辆协同前预计到达时间，其中
    所述预计到达时间集是指多个车辆在运行区域内各地点之间的车辆协同前预计到达时间；
    所述车辆协同前预计到达时间是根据历史数据统计的车辆从一个地点行驶到另一个地点所需的时间。
  6. 根据权利要求5所述的系统，其特征在于，所述车辆属性还包括车辆的容量、车辆的载重和车辆能耗中的至少一种。
  7. 根据权利要求2所述的系统，其特征在于，所述调度条件包括车辆总行驶路程最短、车辆完成运输任务总时间最短、车辆总能耗最小和车辆调用数最少中的至少一种。
  8. 根据权利要求1所述的系统，其特征在于，所述基于所述调度参数确定调度结果包括：
    基于静态调度，采用元启发式算法确定调度结果；
    基于动态调度，采用启发式算法确定调度结果。
  9. 根据权利要求1所述的系统，其特征在于，所述调度结果包括：
    运输任务与车辆的多对一、一对多或者多对多映射；
  10. 根据权利要求9所述的系统，其特征在于，基于所述调度结果确定车辆协同后预计到达时间；以及
    车辆的出发顺序、出发时间、完成时间中的一种或多种。
  11. 根据权利要求1所述的系统，其特征在于，所述协同影响值包括预计到达时间影响值或协同调度影响因子，其中
    所述预计到达时间影响值是指协同前协同之后车辆为完成运输任务到达各个地点的协同后预计到达时间减去协同之前的协同前预计到达时间的差值之和；
    所述协同调度影响因子是预计到达时间影响值和协同前完成运输任务的总时长的比值协同前。
  12. 根据权利要求11所述的系统，所述协同影响值是预计到达时间影响值，其特征在于，所述确定协同影响值包括：
    确定协同区域；
    确定所述协同区域内各个车辆的放行顺序和各个辆车的等待时间；
    基于所述放行顺序、所述等待时间和所述调度结果确定协同区域的协同影响值。
  13. 根据权利要求11所述的系统，所述协同影响值是协同调度影响因子，其特征在于，所述确定协同影响值包括：
    确定协同区域；
    确定所述协同区域内各个车辆的放行顺序和各个辆车的等待时间；
    基于所述放行顺序、所述等待时间和所述调度结果确定协同区域的协同影响值；
    基于所述预计到达时间影响值和协同前预计到达时间确定协同区域的协同调度因子。
  14. 根据权利要求1所述的系统，其特征在于，所述修正所述调度参数至少包括以下一种：
    调整车辆的协同后预计到达时间；
    调整车辆的行驶路线。
  15. 根据权利要求14所述的系统，其特征在于，所述调整协同影响值最大区域内的预计到达时间是调整车辆驶出所述协同区域后到后续任务起点和终点的协同后预计达到时间。
  16. 一种基于多车协同的车辆调度的方法，其特征在于，包括：
    获取调度参数；
    基于所述调度参数确定调度结果；
    基于所述调度结果确定协同影响值；
    判断所述协同影响值是否不小于预设阈值，基于判断结果为所述协同影响值不小于预设阈值，修正所述调度参数，并基于修正后的调度参数重新确定调度结果和协同影响值；
    基于所述判断结果为小于预设阈值时，输出所述调度结果。
  17. 根据权利要求16所述的方法，其特征在于，所述调度参数包括运输任务属性、车辆属性和调度条件，其中，
    所述运输任务属性包括运输任务的起点及终点，还包括运输任务的运输量、运输任务最早开始时间、运输任务的最晚的开始、运输任务的最早结束时间和运输任务的最晚结束时间中的至少一种；
    所述车辆属性包括车辆的容量、车辆的载重和车辆能耗中的至少一种；
    所述调度条件包括车辆总行驶路程最短、车辆完成运输任务总时间最短、车辆总能耗最小和车辆调用数最少中的至少一种。
  18. 根据权利要求17所述的方法，其特征在于，所述车辆属性还包括车辆协同前预计到达时间集，其中
    所述协同前预计到达时间集是指多个车辆在运行区域内各地点之间的车辆预计到达时间；
    所述协同前车辆预计到达时间是根据历史数据统计的车辆从一个地点行驶到另一个地点所需的时间。
  19. 根据权利要求16所述的方法，其特征在于，所述调度结果包括：
    运输任务与车辆的多对一、一对多或者多对多映射。
  20. 根据权利要求16所述的方法，其特征在于，所述协同影响值是预计到达时间影响值，其特征在于，所述确定协同影响值包括：
    确定协同区域；
    确定所述协同区域内各个车辆的放行顺序和各个辆车的等待时间；
    基于所述放行顺序、所述等待时间和所述调度结果确定协同区域的协同影响值。
  21. 根据权利要求16所述的方法，其特征在于，所述协同影响值是协同调度影响因子，其特征在于，所述确定协同影响值包括：
    确定协同区域；
    确定所述协同区域内各个车辆的放行顺序和各个辆车的等待时间；
    基于所述放行顺序、所述等待时间和所述调度结果确定协同影响值；
    基于所述预计到达时间影响值和协同前预计到达时间确定协同区域的协同调度因子。
  22. 根据权利要求16所述的方法，其特征在于，所述修正所述调度参数包括调整协同影响值最大区域内的预计到达时间，
    所述调整协同影响值最大区域内的预计到达时间是调整车辆驶入所述协同影响值最大区域至驶出所述协同影响值最大区域的所需时间。
  23. 一种电子设备，其特征在于，包括：处理器、存储器和I/O接口；I/O接口连接 所述处理器和所述存储器，用于实现所述存储器与所述处理器的信息交互；所述存储器存储用于实现所述车辆调度系统中的相应模块，所述处理器用于运行所述存储器中存储的模块，以执行所述车辆调度系统。
  24. 一种计算机可读存储介质，其特征在于，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如权利要求16至22任一项所述方法的步骤。
PCT/CN2020/080052 2020-03-18 2020-03-18 基于多车协同的车辆调度系统、方法、电子设备及存储介质 WO2021184265A1 (zh)

Priority Applications (6)

Application Number Priority Date Filing Date Title
EP20925452.3A EP4120156A4 (en) 2020-03-18 2020-03-18 VEHICLE PLANNING SYSTEM BASED ON THE COORDINATION OF MULTIPLE VEHICLES, METHOD, ELECTRONIC DEVICE AND STORAGE MEDIA
KR1020227032898A KR102537002B1 (ko) 2020-03-18 2020-03-18 다중 차량 조정 기반 차량 스케줄링 시스템, 방법, 전자 장치 및 저장 매체
US17/912,533 US12106242B2 (en) 2020-03-18 2020-03-18 Multi-vehicle coordination-based vehicle scheduling system and method, electronic apparatus, and storage medium
PCT/CN2020/080052 WO2021184265A1 (zh) 2020-03-18 2020-03-18 基于多车协同的车辆调度系统、方法、电子设备及存储介质
JP2022556642A JP7474525B2 (ja) 2020-03-18 2020-03-18 複数の車両協調に基づく車両スケジューリングシステム、方法、電子機器および記憶媒体
CN202080047407.3A CN114008647A (zh) 2020-03-18 2020-03-18 基于多车协同的车辆调度系统、方法、电子设备及存储介质

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2020/080052 WO2021184265A1 (zh) 2020-03-18 2020-03-18 基于多车协同的车辆调度系统、方法、电子设备及存储介质

Publications (1)

Publication Number Publication Date
WO2021184265A1 true WO2021184265A1 (zh) 2021-09-23

Family

ID=77771624

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/080052 WO2021184265A1 (zh) 2020-03-18 2020-03-18 基于多车协同的车辆调度系统、方法、电子设备及存储介质

Country Status (6)

Country Link
US (1) US12106242B2 (zh)
EP (1) EP4120156A4 (zh)
JP (1) JP7474525B2 (zh)
KR (1) KR102537002B1 (zh)
CN (1) CN114008647A (zh)
WO (1) WO2021184265A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113807796A (zh) * 2021-10-21 2021-12-17 广西盖德科技有限公司 建立协同等待区优化2点间不同运输路线的协同方法
CN114067601A (zh) * 2021-11-15 2022-02-18 北京汇通天下物联科技有限公司 一种停车调度方法及系统
CN115841305A (zh) * 2022-11-25 2023-03-24 山重建机有限公司 一种提高工程机械协同作业效率的方法和系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114331225B (zh) * 2022-03-07 2022-06-10 北京骑胜科技有限公司 一种车辆资源调度方法、装置、电子设备及存储介质
CN118627850A (zh) * 2024-08-08 2024-09-10 泉州市路桥工程建设有限公司 一种机械装置调度系统及其调度方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104809549A (zh) * 2015-04-02 2015-07-29 常州奥迈信息技术有限公司 一种货运车辆计划行驶路线的调度方法
CN108171428A (zh) * 2017-12-29 2018-06-15 首汽租赁有限责任公司 企业车队管理系统及其方法
US20180341918A1 (en) * 2017-05-24 2018-11-29 Tata Consultancy Services Limited System and method for dynamic fleet management
CN109215333A (zh) * 2017-07-07 2019-01-15 杭州中策车空间汽车服务有限公司 调度配置方法和系统
CN110109448A (zh) * 2018-02-01 2019-08-09 通用汽车环球科技运作有限责任公司 形成车队和在车队中定位车辆的系统和方法

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100913837B1 (ko) * 2006-01-10 2009-08-26 주식회사 엘지화학 다수의 차량에 대한 최적 배차 방법 및 이를 위한 시스템
JP5338305B2 (ja) * 2008-12-26 2013-11-13 Ｊｆｅスチール株式会社 車両運行計画作成方法及び装置
US11164141B1 (en) * 2013-02-07 2021-11-02 United Parcel Service Of America, Inc. Systems and methods for synchronized delivery
KR102500445B1 (ko) * 2015-06-04 2023-02-17 (주)이아이랩 운행 일지 자동 생성 기능을 갖는 운행 기록 분석 시스템 및 방법
US9786187B1 (en) * 2015-06-09 2017-10-10 Amazon Technologies, Inc. Transportation network utilizing autonomous vehicles for transporting items
US20170046653A1 (en) * 2015-08-14 2017-02-16 Amazon Technologies, Inc. Planning of transportation requests
WO2017197468A1 (en) * 2016-05-20 2017-11-23 R K Urban Apps Pty Ltd A method and system for facilitating the delivery of goods
US10930157B2 (en) * 2017-04-26 2021-02-23 Dropoff, Inc. Systems and methods for automated real-time and advisory routing within a fleet of geographically distributed drivers
US10636297B2 (en) * 2017-08-11 2020-04-28 Fujitsu Limited Cooperative autonomous driving for traffic congestion avoidance
US10787315B2 (en) * 2017-08-28 2020-09-29 Google Llc Dynamic truck route planning between automated facilities
WO2019084794A1 (en) * 2017-10-31 2019-05-09 Beijing Didi Infinity Technology And Development Co., Ltd. Methods and systems for carpool services
WO2020055770A1 (en) * 2018-09-11 2020-03-19 Zume, Inc. Automated food preparation coordination with customer activity

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104809549A (zh) * 2015-04-02 2015-07-29 常州奥迈信息技术有限公司 一种货运车辆计划行驶路线的调度方法
US20180341918A1 (en) * 2017-05-24 2018-11-29 Tata Consultancy Services Limited System and method for dynamic fleet management
CN109215333A (zh) * 2017-07-07 2019-01-15 杭州中策车空间汽车服务有限公司 调度配置方法和系统
CN108171428A (zh) * 2017-12-29 2018-06-15 首汽租赁有限责任公司 企业车队管理系统及其方法
CN110109448A (zh) * 2018-02-01 2019-08-09 通用汽车环球科技运作有限责任公司 形成车队和在车队中定位车辆的系统和方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4120156A4 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113807796A (zh) * 2021-10-21 2021-12-17 广西盖德科技有限公司 建立协同等待区优化2点间不同运输路线的协同方法
CN113807796B (zh) * 2021-10-21 2024-02-02 广西盖德科技有限公司 建立协同等待区优化2点间不同运输路线的协同方法
CN114067601A (zh) * 2021-11-15 2022-02-18 北京汇通天下物联科技有限公司 一种停车调度方法及系统
CN115841305A (zh) * 2022-11-25 2023-03-24 山重建机有限公司 一种提高工程机械协同作业效率的方法和系统
CN115841305B (zh) * 2022-11-25 2023-08-01 山重建机有限公司 一种提高工程机械协同作业效率的方法和系统

Also Published As

Publication number Publication date
KR102537002B1 (ko) 2023-05-26
US20230136829A1 (en) 2023-05-04
KR20220144849A (ko) 2022-10-27
EP4120156A1 (en) 2023-01-18
JP2023518545A (ja) 2023-05-02
US12106242B2 (en) 2024-10-01
CN114008647A (zh) 2022-02-01
JP7474525B2 (ja) 2024-04-25
EP4120156A4 (en) 2023-05-10

Similar Documents

Publication Publication Date Title
WO2021184265A1 (zh) 基于多车协同的车辆调度系统、方法、电子设备及存储介质
US10936992B1 (en) Logistical management system
US20140046585A1 (en) Real-time computation of vehicle service routes
CN107145980A (zh) 无人车配送方法、系统和控制服务器
CN109657820B (zh) 一种可预约的出租车匹配方法
CN112183932A (zh) 一种机器人配送任务调度方法及系统
AU2017423439A1 (en) Improved routing system
WO2022120935A1 (zh) 一种车辆系统的调度控制方法，装置及系统
US20190114595A1 (en) Systems and Methods for Joint Control of Multi-Modal Transportation Networks
WO2020055770A1 (en) Automated food preparation coordination with customer activity
CN113988770B (zh) 运输车辆在途排队控制方法、装置以及电子设备
CN116187706A (zh) 行李分拣amr的配置方法、装置、计算机设备和存储介质
JP2018180652A (ja) 交通システム
WO2023050946A1 (zh) 工程运输车辆的调度方法和装置、工程运输车辆和电子设备
CN117217481B (zh) 一种叉车调度方法、装置、电子设备及存储介质
CN116757585B (zh) 一种基于移动边缘计算的无人机和无人车协同配送方法
US20220343227A1 (en) Freight optimization
WO2022173376A1 (en) Systems and methods for scalable, decentralized and coordinated logistics schedule planning
JPH0516047B2 (zh)
CN118822182A (zh) 一种智能运输方案匹配方法
CN115481773A (zh) 车辆配置信息的生成方法、装置、计算机设备和存储介质
CN118154063A (zh) 一种低空空域融合运行的无人机调度方法
CN117649094A (zh) 无人驾驶矿车调度系统及方法
CN114596023A (zh) 订单分配方法和装置、电子设备及计算机可读存储介质
CN114193605A (zh) 混凝土生产运输方法和装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20925452

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2022556642

Country of ref document: JP

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 20227032898

Country of ref document: KR

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2020925452

Country of ref document: EP

Effective date: 20221014