CN110598908B - Path planning method based on multiple tasks and multiple vehicles - Google Patents
Path planning method based on multiple tasks and multiple vehicles Download PDFInfo
- Publication number
- CN110598908B CN110598908B CN201910769709.0A CN201910769709A CN110598908B CN 110598908 B CN110598908 B CN 110598908B CN 201910769709 A CN201910769709 A CN 201910769709A CN 110598908 B CN110598908 B CN 110598908B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- empty
- task
- optimal
- path
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012163 sequencing technique Methods 0.000 claims abstract description 16
- 238000005192 partition Methods 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 6
- 238000003012 network analysis Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of vehicle travel, in particular to a path planning method based on multiple tasks and multiple vehicles, which comprises the following steps: s1, receiving required tasks in a certain current time period, and classifying each task in a partition mode according to the task where the task is located; s2, planning a path from the starting place to the destination of each task, and selecting an optimal path from the planned paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station; s3, searching empty vehicle information in the partition with the task to be distributed, and acquiring vehicle condition information of the empty vehicle; s4, sequencing the empty vehicles to form an empty vehicle sequence; s5, matching empty vehicles and tasks in the sequence; and S6, assigning a task according to the matching result, and pushing the optimal path to the vehicle assigned by the task. The invention provides a path planning method combining order information and vehicle condition states of a vehicle during dispatching, and solves the problem that a network appointment vehicle dispatching system cannot reasonably dispatch the order according to the current power state of the vehicle.
Description
Technical Field
The invention relates to the technical field of vehicle traveling, in particular to a path planning method based on multiple tasks and multiple vehicles.
Background
With the continuous development of network technology, more and more people go out and like to reserve vehicles by using taxi taking software. When a passenger needs to take a car, the information of car taking time, a car type expected to be taken, a place and a destination of getting on the car and the like can be selected through car taking software and submitted to the software system platform, the software system platform can form an order and send the order to a driver user registered on the software system platform, and the driver user screens the order at the car terminal and receives the order, so that convenience is brought to people for going out to a certain extent.
The vehicle path planning is an important part of the network car booking platform, the performance of the vehicle path planning directly influences the efficiency of network car booking operation and the trip experience of travelers on the network car booking, and the vehicle path planning is effectively planned, so that the economic benefit of a network car booking driver can be improved. The vehicle path planning is how to obtain an optimal vehicle path planning scheme under the constraint conditions that the user requirements are met, the distance is shortest, the cost is minimum or the consumed time is shortest, and the quality of the vehicle path planning scheme directly influences the operation efficiency of the network car reservation. Therefore, the appropriate vehicle path planning method can provide a reasonable planning scheme for the user, so that the response speed to the user demand is accelerated, the service quality is improved, and the satisfaction degree of the user on the network car booking is increased.
The invention with patent application publication number CN109506668A discloses a path planning method, a device, a computer device and a storage medium for an electric vehicle, wherein the path planning method for the electric vehicle comprises the following steps: obtaining departure place information, destination information and effective driving mileage information of the electric automobile; acquiring position information and state information of at least one charging station of a path from a departure place to a destination, and planning a navigation route of the destination from the departure place; and generating a navigation page and displaying the navigation route on the navigation page. According to the route planning method of the electric automobile, the route which can be charged in time and has the shortest distance/shortest consumed time in the planned way before the electric automobile runs can be planned according to the position and state information of the charging station, so that the electric automobile can finish long-distance running, and the problem of power shortage in the running way is solved.
The invention with the patent application publication number of CN109086915A discloses a method A1 for network appointment order taking and path planning based on GIS, and the method is used for acquiring the information of passenger request order taking; a2, classifying the passengers needing to receive orders according to destinations; a3, calculating the average real distance, the travel cost and the emergency degree of each group of travel; a4, carrying out normalization processing on each group of passengers according to the starting point distance, the travel cost and the urgency; a5, carrying out normalization processing on each group of strokes, namely calculating a priority quantization standard according to a weight proportion; a6, sorting according to the priority quantization standard, wherein the front journey is prior to the rear journey; a7, sequentially importing the sequenced journey groups and the corresponding destination position information into ArcMap according to the priority order; a8, creating a Network analysis layer by using VRP in Network analysis Tool in ArcMap. The invention can determine a sequencing method of the forming sequence of the priority pairs according to the distance between the passenger and the vehicle owner starting point, the travel cost and the urgency degree, combines the GIS, plans the travel route based on the actual road information, and improves the order receiving efficiency and the travel efficiency of the network appointment vehicle.
Although the first method for planning the path of the electric vehicle can solve the problem that a driver is in power shortage during traveling, the first method is not suitable for network reservation and cannot plan the path according to order information and the power state of the vehicle; the second network appointment order taking and path planning method based on the GIS performs ordering according to order information of passengers and plans a route based on actual road information, but does not consider the power state condition of the vehicle in path planning, and moreover, when an owner selects an order, the owner can only reorder and continue to wait for a proper order by considering that the power state of the current vehicle is not a proper order. At present, no method or system for planning and dispatching routes according to the dynamic state of the vehicle and the information of orders exists in the prior art.
Disclosure of Invention
The invention aims to overcome at least one defect (deficiency) in the prior art, and provides a path planning method based on multiple tasks and multiple vehicles, which carries out path planning according to the dynamic state information of the vehicles and the condition of orders, can carry out sequencing and reasonable dispatching according to the current vehicle state of empty vehicles, and solves the problem that a network appointment vehicle dispatching system can not reasonably dispatch the orders according to the current dynamic state of the vehicles.
The invention achieves its object by the following scheme.
The invention provides a path planning method based on multiple tasks and multiple vehicles, which comprises the following steps:
s1, receiving required tasks in a certain current time period, and classifying each task in a partitioned mode according to the position of the task;
s2, planning a path from the starting place to the destination of each task, and selecting an optimal path from the planned paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station;
s3, searching empty vehicle information in the partition with the allocation task, and acquiring vehicle condition information of the empty vehicle;
s4, sequencing the empty vehicles to form an empty vehicle sequence: calculating the time and/or distance to a task starting place according to the current position information of the vehicle, and sequencing the current empty vehicles according to the power state increasing prompt and the time and/or distance;
s5, matching empty vehicles and tasks in the sequence:
s51, subtracting the distance from the current position of the empty vehicle to the task starting place from the vehicle driving mileage;
s52, judging whether the current empty vehicle has a prompt of increasing the power state, and if so, comparing the vehicle driving mileage with the optimal path distance of the power station of the task; if not, comparing the vehicle driving mileage with the optimal path length corresponding to the time, and obtaining a matching result of the tasks and the empty vehicles according to the comparison result;
and S6, assigning a task according to the matching result, and pushing the optimal path to the vehicle assigned by the task.
The method comprises the steps of planning paths according to the conditions of tasks, selecting an optimal path from the paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station, sequencing empty vehicles according to the current state of the vehicles, matching the empty vehicles in the sequence with the tasks, and assigning the tasks according to the matching result, so that a driver user can receive appropriate tasks and receive the optimal running path under the condition of needing to increase power, and the order receiving efficiency and the travel efficiency are improved.
Preferably, the route planning method based on multiple tasks and multiple vehicles further comprises the step of sequencing each task in each partition according to time, and the tasks are sequentially matched with empty vehicles according to the time sequence. The tasks are classified in a subarea mode, so that the tasks sent to the driver users can be prevented from being too far away from the current vehicle position to cause loss of profits of the driver users; sequencing each task according to time can ensure that the tasks which send the requests firstly are dispatched, reduce the waiting time of the passenger user for order receiving, and improve the travel experience.
Preferably, the optimal path of the power station in step S2 is based on the path planning performed by the power station within a certain distance around the destination of the task. This arrangement ensures, to a certain extent, that the destination of the task is not far from the power station.
Preferably, the optimal path of the power station is a path from the starting point of the task to the power station after the starting point passes through the destination.
Preferably, the vehicle condition information in step S3 includes: vehicle driving mileage, vehicle current position information and power state increasing prompt.
Preferably, in the step S52, the power increase status of the currently empty vehicle is prompted, a redundancy of the vehicle driving range is set according to the power loss condition and the smooth road condition during the actual driving process, and the final vehicle driving range is the actual vehicle driving range plus the redundancy.
Preferably, if the final vehicle driving range is less than the optimal path distance of the power station of the task, and the task is matched with the next empty vehicle if the matching fails; and if the final vehicle endurance mileage is greater than the optimal path distance of the power station of the task, and if the matching is successful, comparing the final vehicle endurance mileage with the optimal path length of the task time:
the vehicle endurance mileage is larger than the optimal time path length of the task, the path corresponding to the optimal time is used as the optimal planning path pushed to the empty vehicle, and the task is assigned to the empty vehicle;
and the final vehicle driving mileage is smaller than the optimal path length of the task in time, the path corresponding to the optimal distance is used as the optimal planning path pushed to the empty vehicle, and the task is assigned to the empty vehicle.
Under the prompt of the state that the vehicle has increased power, the final vehicle endurance state is compared and matched with the optimal path distance of the power station, and the fact that the driver user can not be influenced by the task received by the driver user to go to the power station is guaranteed.
Preferably, in the step S52, if the power state prompt is not added to the empty vehicle at present, and the vehicle driving range is greater than the optimal path length corresponding to the optimal time, if the matching is successful, the path corresponding to the optimal time is used as the optimal planned path to be pushed to the empty vehicle, and the task is assigned to the empty vehicle; and if the vehicle driving mileage is less than the optimal path length corresponding to the time, matching the task with the next empty vehicle and matching the empty vehicle with the next task if the matching fails.
Preferably, each task corresponds to one empty vehicle sequence, and each empty vehicle can belong to empty vehicle sequences of a plurality of orders.
Preferably, the empty sequence of the tasks is updated according to a set time threshold,
the updating method comprises the following steps: and after the time threshold value, eliminating the empty vehicles which are successfully matched with other tasks, eliminating the empty vehicles which are failed to be matched with the tasks, and adding new empty vehicles.
Preferably, empty vehicles which are matched with the tasks for many times and fail to be matched are marked in the updating, and when the marked empty vehicles enter an empty vehicle sequence of a new task, the marked empty vehicles are prioritized according to the number of times of matching failure of the marked empty vehicles.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method can plan paths according to the starting place and the destination of each order, selects an optimal path from the paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station, matches and compares the current vehicle condition of the empty vehicles in the sequence with the orders after the empty vehicles are sequenced, assigns the orders to the empty vehicles after the matching is successful, and takes the path corresponding to the comparison result as the optimal planning path for pushing the empty vehicles. As the orders assigned to the driver users are screened and matched and reasonable path planning is carried out, the empty vehicles can receive proper orders under the prompt of increasing the power state, and the income of order taking is improved.
(2) According to the invention, the driving mileage of the vehicle is matched with the order form, and the driving mileage is compared with the optimal path, the optimal time and the optimal distance of the power station, so that the order form and the path which are most suitable for the empty vehicle are selected and pushed to the empty vehicle, and the travel efficiency of the vehicle is improved.
(3) The invention also marks the empty vehicles which are failed to be matched with the orders for many times, and when the marked empty vehicles enter the empty vehicle sequence of a new order, the marked empty vehicles are prioritized according to the size of the matching failure times of the marked empty vehicles, and the marked empty vehicles are dispatched preferentially.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
fig. 2 is a flow chart of task and empty vehicle matching.
Detailed Description
The drawings are only for purposes of illustration and are not to be construed as limiting the invention. For a better understanding of the following embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The present invention will be described in further detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the present embodiment provides a route planning method based on multitask and multiple vehicles, including the following steps:
s1, receiving required tasks in a certain current time period, and classifying each task in a partition mode according to the position of the task;
s2, planning paths from the starting place to the destination of each task, and selecting an optimal path from the planned paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station;
s3, searching empty vehicle information in the partition with the task to be distributed, and acquiring vehicle condition information of the empty vehicle;
s4, sequencing the empty vehicles to form an empty vehicle sequence: calculating the time and/or distance to a task starting place according to the current position information of the vehicle, and sequencing the current empty vehicles according to the power state increasing prompt and the time and/or distance;
s5, matching empty vehicles and tasks in the sequence:
s51, subtracting the distance from the current position of the empty vehicle to the task starting place from the vehicle driving mileage;
s52, judging whether the current empty vehicle has a prompt of increasing the power state, and if so, comparing the vehicle driving mileage with the optimal path distance of the power station of the task; if not, comparing the vehicle endurance mileage with the optimal path length corresponding to the time, and obtaining a matching result of the task and the empty vehicle according to the comparison result;
and S6, assigning a task according to the matching result, and pushing the optimal path to the vehicle assigned by the task.
In the specific implementation process of the embodiment, the demand task is an order sent to the system platform by a passenger, after the system platform receives the demand order of the passenger, the system platform performs partition classification according to the position information of each order, then performs path planning according to the starting place to the destination of each order, and selects an optimal path from all planned paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station. The system platform searches the empty vehicle information of each subarea, after the empty vehicle condition information is obtained, the time and/or the distance of arriving at the order starting place is calculated according to the current position information of the empty vehicle, and then the current empty vehicle is sequenced according to the power increasing prompt state, the time and/or the distance, wherein the sequencing mode is as follows: and the empty vehicles with the power state increasing prompt are prioritized, then are sorted according to the time and/or distance, and finally form an empty vehicle sequence. And matching the empty vehicles in the sequence with the order, firstly subtracting the distance from the current position of the empty vehicle to the starting place of the order from the mileage of the vehicle, then judging whether the current empty vehicle has a prompt for increasing the power state, if so, comparing the mileage of the vehicle with the optimal path distance of the power station of the order, and if not, comparing the mileage of the vehicle with the optimal path length of the time of the order. And finally, the system platform can dispatch the current empty vehicle and push the optimal path according to the matching result and the comparison result or match the current empty vehicle with the next order and match the order with the next empty vehicle. The system platform comprises the optimal path of the power station in the path planning of each order, judges whether the current empty vehicle has a prompt of increasing the power state or not in the process of matching the current empty vehicle with the order, and compares the vehicle driving range of the current empty vehicle with the prompt of increasing the power state with the optimal path distance of the power station, so that a driver user can receive the order not only in a normal state, but also can receive a proper order under the condition that the vehicle needs to increase the power, and the received order and the optimal path are matched and compared through the system platform, thereby improving the order receiving efficiency and the travel efficiency.
In this embodiment, the method further includes sequencing each task in each partition according to time, and matching the tasks with empty vehicles in sequence according to the time sequence. The order is classified in a subarea mode, so that the order sent to the driver user can be prevented from being too far away from the current vehicle position to cause loss of income of the driver user; sequencing each order according to time can ensure that the order which sends the request first is dispatched, reduce the waiting time of the passenger user for receiving the order and improve the travel experience.
In this embodiment, the optimal path of the power station in step S2 is a path that is planned based on power stations within a certain distance around the destination of the task, and the optimal path of the power station is a path that reaches the power station after the start of the task passes through the destination. This arrangement ensures, to some extent, that the destination of the order is not far from the power station.
In this embodiment, the vehicle condition information in step S3 includes: vehicle driving mileage, vehicle current position information and power state increasing prompt.
In this embodiment, when matching the current empty car with the task, as shown in fig. 2, a flowchart of matching the current empty car with the task is shown. If the power state increasing prompt exists in the current empty vehicle, the redundancy of the vehicle endurance mileage is set according to the power loss condition and the smooth road condition in the actual driving process, and the final vehicle endurance mileage is the actual vehicle endurance mileage plus the redundancy. Specifically, if the final vehicle driving range is less than the optimal path distance of the power station of the task, matching the task with the next empty vehicle if matching fails; if the final vehicle endurance mileage is greater than the optimal path distance of the power station of the task, comparing the final vehicle endurance mileage with the optimal path length of the task in time for successful matching: if the final vehicle driving mileage is larger than the optimal path length of the task, taking the path corresponding to the optimal time as an optimal planning path pushed to the empty vehicle, and assigning the task to the empty vehicle; and if the final vehicle driving mileage is less than the optimal path length of the task time, taking the path corresponding to the optimal distance as the planned path of the empty vehicle, and distributing the task to the empty vehicle. If the power state prompt is not added to the current empty vehicle, and the vehicle driving range is longer than the path length corresponding to the optimal time, the path corresponding to the optimal time is used as the optimal planning path pushed to the empty vehicle for successful matching, and the task is assigned to the empty vehicle; and if the vehicle driving mileage is less than the optimal path length corresponding to the time, matching the task with the next empty vehicle and matching the empty vehicle with the next task if the matching fails. Under the condition that the vehicle is prompted to increase the power state, the final vehicle driving mileage is compared and matched with the optimal path distance of the power station, and the fact that a task received by a driver user does not influence the driver user to go to the power station is guaranteed.
In this embodiment, each order corresponds to an empty sequence, each empty sequence of the order can belong to the empty sequences of the multiple orders, the empty sequence of the order is updated to set a certain time threshold, the update is dynamic update, after the certain time threshold, an empty that has been successfully matched with other orders is excluded, an empty that has failed to be matched is excluded, and a new empty is added. And before the next update comes, if the empty vehicle matched with the current order is successfully matched with other orders, prompting that the matching fails, and matching the current order with the next empty vehicle in the sequence. In addition, empty vehicles which are matched with the orders for many times in a failed mode are marked, when the empty vehicles are contained in an empty vehicle sequence of a new order, priority ordering is carried out according to the number of times of the empty vehicle matching failure, and the empty vehicle sequence ordering mode of the new order is as follows: sorting according to the marked matching failure times of the empty vehicles, then sorting the empty vehicles with the prompt of increasing the power state, and finally sorting the empty vehicles according to the time and/or distance. And marking the empty vehicles which are failed to be matched for many times and preferentially sequencing in the new order empty vehicle sequence, so as to prevent the situation that the marked empty vehicles are not subjected to proper orders all the time because the marked empty vehicles are sequenced later in the new order empty vehicle sequence and the orders which are matched with the marked empty vehicles are successfully matched with the empty vehicles which are sequenced earlier.
In the specific implementation process of this embodiment, the power increase state prompt is a charging prompt, and the power station is a charging station. When the electric automobile needs to be charged, a driver user sends a charging prompt to the system platform, and after the system platform receives the charging prompt and acquires the current automobile condition information, the automobile is prioritized in an empty sequence of a new order. When the order is matched with the electric automobile, subtracting the distance from the current position to the order starting place from the driving range of the electric automobile, after the system platform judges that the electric automobile has a charging prompt, setting a redundancy quantity of the driving range of the electric automobile, wherein the final driving range is the actual driving range plus the redundancy quantity, comparing the final driving range with the optimal path distance of the charging station, if the final driving range is greater than the optimal path distance of the charging station, the matching is successful, then comparing the driving range with the optimal path length, if the driving range is greater than the optimal path length of the charging station, assigning the order to the electric automobile, and pushing the path corresponding to the optimal time to a vehicle terminal as the optimal planned path of the electric automobile. And otherwise, pushing the path corresponding to the optimal distance to a vehicle terminal as the optimal planning path of the electric vehicle. Through the implementation mode, the system platform reasonably plans the path of the order and reasonably dispatches the order according to the order demand information and the condition that the electric automobile needs to be charged in combination, and pushes the appropriate order and the optimal path corresponding to the order to the vehicle terminal, so that the order receiving efficiency and the travel efficiency are improved, and the income of a driver user is also improved.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention claims should be included in the protection scope of the present invention claims.
Claims (10)
1. A path planning method based on multiple tasks and multiple vehicles is characterized by comprising the following steps:
s1, receiving required tasks in a certain current time period, and classifying each task in a partition mode according to the position of the task;
s2, planning a path from the starting place to the destination of each task, and selecting an optimal path from the planned paths, wherein the optimal path comprises optimal time, optimal distance and optimal power station;
s3, searching empty vehicle information in the partition with the task to be distributed, and acquiring vehicle condition information of the empty vehicle;
s4, sequencing the empty vehicles to form an empty vehicle sequence: calculating the time and/or distance to a task starting place according to the current position information of the vehicle, and sequencing the current empty vehicles according to the power state increasing prompt and the time and/or distance;
s5, matching empty vehicles and tasks in the sequence:
s51, subtracting the distance from the current position of the empty vehicle to the task starting place from the vehicle driving mileage;
s52, judging whether the current empty vehicle has a prompt of increasing the power state, and if so, comparing the vehicle driving mileage with the optimal path distance of the power station of the task; if not, comparing the vehicle endurance mileage with the optimal path length corresponding to the time, and obtaining a matching result of the task and the empty vehicle according to the comparison result;
and S6, assigning a task according to the matching result, and pushing the optimal path to the vehicle assigned by the task.
2. The multitask and multi-vehicle based path planning method according to claim 1, further comprising time-sorting each task in each zone, wherein the tasks are sequentially matched with empty vehicles according to the time sequence.
3. The multitask and vehicle-based path planning method according to claim 1, wherein the optimal path of the power station in step S2 is based on the path planning performed by power stations within a certain distance around the destination of the mission.
4. The multitask and vehicle-based path planning method according to claim 3, wherein the optimal path of the power station is a path from a task starting place to the power station after the task starting place passes through a destination.
5. The multitask and vehicle-based path planning method according to claim 1, wherein the vehicle condition information in step S3 includes: vehicle driving mileage, vehicle current position information and power state increasing prompt.
6. The method as claimed in claim 1, wherein in step S52, the current empty vehicle has a prompt for a power-added status, a redundancy of a vehicle driving range is set according to a power loss condition and a smooth road condition during an actual driving process, and the final vehicle driving range is the actual vehicle driving range plus the redundancy.
7. The multitask and vehicle-based path planning method according to claim 6, wherein if the final vehicle driving range is less than the optimal path distance of the power station of the mission, the mission is matched with the next empty vehicle for failure of matching;
and if the final vehicle endurance mileage is greater than the optimal path distance of the power station of the task, and if the matching is successful, comparing the final vehicle endurance mileage with the optimal path length of the task time:
the final vehicle driving mileage is larger than the optimal time path length of the order, the path corresponding to the optimal time is used as the optimal planning path pushed to the empty vehicle, and the task is assigned to the empty vehicle;
and the final vehicle driving mileage is smaller than the optimal path length of the task in time, the path corresponding to the optimal distance is used as the optimal planning path pushed to the empty vehicle, and the task is assigned to the empty vehicle.
8. The multitask and multi-vehicle-based path planning method according to claim 1, wherein in step S52, when the power state prompt is not added to the currently empty vehicle, and the vehicle mileage is greater than the optimal time-corresponding path length, if the matching is successful, the optimal time-corresponding path is used as the optimal planned path to be pushed to the empty vehicle, and the task is assigned to the empty vehicle; and if the vehicle driving mileage is less than the optimal path length corresponding to the time, matching the task with the next empty vehicle and matching the empty vehicle with the next task if the matching fails.
9. The multitask and multi-vehicle-based path planning method according to claim 1, wherein each task corresponds to one empty vehicle sequence, and each empty vehicle can belong to the empty vehicle sequences of multiple tasks.
10. The multitask and vehicle-based path planning method according to claim 9, wherein the empty sequence of tasks is updated according to a set time threshold,
the updating method comprises the following steps: and after the time threshold value, eliminating the empty vehicles which are successfully matched with other tasks, eliminating the empty vehicles which are failed to be matched with the tasks, and adding new empty vehicles.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769709.0A CN110598908B (en) | 2019-08-20 | 2019-08-20 | Path planning method based on multiple tasks and multiple vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910769709.0A CN110598908B (en) | 2019-08-20 | 2019-08-20 | Path planning method based on multiple tasks and multiple vehicles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110598908A CN110598908A (en) | 2019-12-20 |
| CN110598908B true CN110598908B (en) | 2022-07-08 |
Family
ID=68854734
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910769709.0A Active CN110598908B (en) | 2019-08-20 | 2019-08-20 | Path planning method based on multiple tasks and multiple vehicles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110598908B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114402344A (en) * | 2019-12-31 | 2022-04-26 | 华为技术有限公司 | Order management method and device applied to electric automobile |
| CN111798172B (en) * | 2020-05-26 | 2023-07-18 | 嘉兴亚航信息技术有限公司 | Dangerous chemical transportation method based on field-contraindicated horse racing concept |
| CN111694333A (en) * | 2020-06-10 | 2020-09-22 | 中国联合网络通信集团有限公司 | AGV (automatic guided vehicle) cooperative management method and device |
| CN111824657B (en) * | 2020-07-08 | 2022-02-01 | 北京极智嘉科技股份有限公司 | Goods picking scheduling system, method and device |
| CN113077106B (en) * | 2021-04-16 | 2024-06-18 | 北京京东振世信息技术有限公司 | Article transportation method and device based on time window |
| CN113253722B (en) * | 2021-04-30 | 2023-05-02 | 浙江吉利控股集团有限公司 | Method, device and system for planning charging path of electric vehicle |
| CN113361916A (en) * | 2021-06-04 | 2021-09-07 | 付鑫 | Multi-mode sharing travel fusion scheduling optimization system considering single-cut scene |
| CN115082151A (en) * | 2022-06-19 | 2022-09-20 | 詹华 | Path planning method for taking pet for walk |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103935259A (en) * | 2014-03-31 | 2014-07-23 | 同济大学 | Electric automobile optimal path finding method based on power consumption |
| CN107748941A (en) * | 2017-11-23 | 2018-03-02 | 苏州亿科赛卓电子科技有限公司 | A kind of order allocation method and device based on electric automobile |
| CN109658203A (en) * | 2018-12-28 | 2019-04-19 | 永安行科技股份有限公司 | Order allocation method, distribution system and computer readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130158868A1 (en) * | 2011-12-14 | 2013-06-20 | Automotive Research & Testing Center | Recursive route planning method for electric vehicle |
-
2019
- 2019-08-20 CN CN201910769709.0A patent/CN110598908B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103935259A (en) * | 2014-03-31 | 2014-07-23 | 同济大学 | Electric automobile optimal path finding method based on power consumption |
| CN107748941A (en) * | 2017-11-23 | 2018-03-02 | 苏州亿科赛卓电子科技有限公司 | A kind of order allocation method and device based on electric automobile |
| CN109658203A (en) * | 2018-12-28 | 2019-04-19 | 永安行科技股份有限公司 | Order allocation method, distribution system and computer readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110598908A (en) | 2019-12-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110598908B (en) | Path planning method based on multiple tasks and multiple vehicles | |
| CN105070044B (en) | Dynamic scheduling method for customized buses and car pooling based on passenger appointments | |
| JP7226439B2 (en) | Vehicle allocation device, vehicle allocation method, computer program, and computer-readable recording medium | |
| US20190108468A1 (en) | Method and apparatus to operate smart mass transit systems with on-demand rides, dynamic routes and coordinated transfers | |
| JP7008802B2 (en) | Vehicle allocation management device and vehicle allocation management method | |
| CN111882107B (en) | Driver and passenger matching method based on automatic driving shared taxi system | |
| CN112262418B (en) | Vehicle management system and vehicle management method | |
| US20200210905A1 (en) | Systems and Methods for Managing Networked Vehicle Resources | |
| CN111868654A (en) | Method and apparatus for route planning | |
| CN108510730A (en) | Vehicle dispatching method and system between shared automobile site | |
| CN108921762B (en) | Vehicle hybrid scheduling method, device and equipment | |
| US11705001B2 (en) | Device and computer program product for route planning for a vehicle | |
| CN112381472A (en) | Subway connection bus route optimization method and device and storage medium | |
| WO2019225046A1 (en) | Vehicle dispatch device, vehicle dispatch method, computer program, and computer-readable recording medium | |
| JP2020013374A (en) | Vehicle maintenance management system and vehicle maintenance management method | |
| Bischoff et al. | A framework for agent based simulation of demand responsive transport systems | |
| CN114118868A (en) | Scheduling system and method | |
| CN113344336A (en) | Vehicle scheduling method and device and storage medium | |
| CN110598985A (en) | Vehicle path planning method | |
| JP2019133356A (en) | Transfer support system, transfer support method, transfer support program, and mobile body | |
| CN110347937B (en) | Intelligent passenger searching method for taxi | |
| WO2024048231A1 (en) | Vehicle dispatch management device and vehicle dispatch management method | |
| WO2024034540A1 (en) | Vehicle dispatch management device and vehicle dispatch management method | |
| CN115841287B (en) | Hybrid fleet dynamic path planning method and device | |
| CN117391399B (en) | Method, system and storage medium for spelling and outputting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |