Unmanned sweeper task real-time distribution system based on cloud platform

Abstract

The invention discloses an unmanned sweeper task real-time distribution system based on a cloud platform, and the system comprises a task distribution module which is used for screening a to-be-distributed vehicle from vehicles capable of executing tasks according to a vehicle distribution rule, distributing an operation region for the to-be-distributed vehicle, and generating vehicle distribution information; and the path planning module is used for calculating the optimal driving path of the distributed vehicles according to the vehicle distribution information and the task area traffic map information. According to the invention, the to-be-distributed vehicles meeting the requirements can be screened out, and automatic distribution of the operation areas is carried out on the to-be-distributed vehicles according to the requirements, so that the vehicle distribution efficiency is effectively improved, and the accuracy is high; and meanwhile, the optimal driving path can be planned for the allocated vehicle, so that the operation task can be completed in the shortest time and at the fastest speed, and the operation efficiency is high.

Description

Technical Field

[0001] This invention relates to the field of unmanned sweeping vehicles, and in particular to a real-time task allocation system for unmanned sweeping vehicles based on a cloud platform. Background Technology

[0002] With the acceleration of urbanization, environmental sanitation issues are receiving increasing attention. As a highly efficient and intelligent cleaning device, driverless sweepers have shown great potential in improving cleaning efficiency and reducing labor costs, and are therefore being widely deployed in the sanitation industry. However, existing driverless sweepers still have some shortcomings in task allocation.

[0003] In existing technologies, the common methods for assigning cleaning tasks to unmanned sweepers mainly include the following:

[0004] (1) Fixed route mode: The unmanned sweeper performs the sweeping task according to the pre-set route. The advantage of this method is that it is simple and easy to implement, but the disadvantage is that it lacks flexibility and cannot dynamically adjust the sweeping task according to the actual situation.

[0005] (2) Zone division mode: The cleaning area is divided into several sub-areas, and each driverless sweeper is assigned to a fixed sub-area for cleaning work. This method improves cleaning efficiency to a certain extent, but it is still difficult to cope with sudden increases in garbage and vehicle malfunctions.

[0006] (3) Manual dispatch mode: The unmanned sweeper is assigned cleaning tasks by manual monitoring and dispatch based on the real-time situation. Although this method can allocate cleaning tasks according to the actual situation, it requires a large number of personnel, resulting in high costs and low dispatch efficiency, which can easily lead to problems such as uneven distribution or omission of cleaning tasks.

[0007] Furthermore, to address the aforementioned issues, some companies and research institutions are exploring more intelligent task allocation methods. For example, some employ task allocation strategies based on simple rules or preset thresholds. However, these methods often fail to fully utilize real-time vehicle information (such as the autonomous vehicle's battery level and the amount of waste) and road conditions (such as traffic jams, road repairs, and obstacles), leading to inefficient task allocation, unreasonable allocation, and low vehicle utilization. Additionally, after being assigned tasks, vehicles directly travel along designated routes without adjusting their paths in a timely manner based on real-time road conditions (such as traffic congestion on the designated route), affecting their speed and normal operation, resulting in low operational efficiency.

[0008] Therefore, existing technologies still need to be improved. Summary of the Invention

[0009] In view of the shortcomings of the prior art, the purpose of this invention is to provide a real-time task allocation system for unmanned sweeping vehicles based on a cloud platform, which aims to solve the problems of low vehicle allocation efficiency and unreasonable allocation in the prior art; at the same time, it also solves the problems of fixed driving paths after vehicle allocation, low flexibility, and low operation efficiency in special situations such as traffic jams.

[0010] The technical solution of the present invention is as follows: A real-time task allocation system for unmanned sweeping vehicles based on a cloud platform, comprising:

[0011] A task allocation module is used to filter out vehicles to be allocated from the available vehicles based on vehicle allocation rules, assign work areas to the vehicles to be allocated, and generate vehicle allocation information. The available vehicles include: all idle vehicles within the scope of the system of this invention, and vehicles currently performing work tasks.

[0012] A path planning module used to calculate the optimal driving route for assigned vehicles based on vehicle allocation information and traffic map information of the task area.

[0013] Obviously, the cloud-based unmanned sweeper task real-time allocation system of this invention filters out vehicles capable of performing tasks through a task allocation module, selecting those that meet the requirements for allocation, rather than directly allocating tasks to all vehicles capable of performing tasks. This ensures that the vehicles ultimately performing the tasks are those that meet the requirements, reducing the risk of task interruptions and unexpected situations (such as vehicles running out of power midway), and effectively improving operational efficiency. Furthermore, after selecting vehicles for allocation, the system further matches them with their work areas, thereby achieving rapid allocation of tasks to the vehicles. After allocation, the path planning module plans the travel path of the allocated vehicles during their work tasks to ensure they travel on the optimal route, quickly completing the sweeping requirements of their work areas and guaranteeing high operational efficiency.

[0014] In one embodiment, the process by which the task allocation module selects vehicles to be allocated based on vehicle allocation rules is as follows:

[0015] Determine whether the remaining battery power of the vehicle capable of performing the task meets the requirements of the battery power threshold and the amount of garbage stored meets the requirements of the garbage amount threshold; vehicles capable of performing the task that meet both the battery power threshold and the garbage amount threshold are vehicles to be assigned.

[0016] In one embodiment, the vehicle allocation information includes: the allocated vehicle number and the work area assigned to the allocated vehicle. The allocated vehicle number can be a number within the unified numbering system for all vehicles under the jurisdiction of this invention, or it can be its license plate number.

[0017] In one embodiment, the work area assigned to the assigned vehicle is either the closest work area to the assigned vehicle or a work area whose task priority is higher than the task priority of the previously performed work by the assigned vehicle, and is the closest work area to the assigned vehicle. This ensures that the assigned vehicle arrives at its work area in the shortest possible time to perform the cleaning operation, saving time and energy.

[0018] In one embodiment, the traffic map information for the task area includes: real-time traffic information and historical traffic information. Specifically, the real-time traffic information includes: traffic flow, congestion, and speed limits on each drivable road within the task area corresponding to the assigned vehicle; while the historical traffic information includes: historical traffic flow and historical congestion on each drivable road within the task area corresponding to the assigned vehicle.

[0019] In one embodiment, the path planning module plans the optimal driving path for the assigned vehicles based on the principle that the assigned vehicles complete their assigned work areas in the shortest possible time and / or with the lowest energy consumption. Therefore, this ensures that the assigned vehicles complete the cleaning tasks as quickly as possible, effectively improving cleaning efficiency.

[0020] In one embodiment, the cloud-based unmanned sweeper task real-time allocation system further includes a vehicle status monitoring module for monitoring vehicle status information, vehicle location information, and vehicle operating status information of the vehicles capable of performing tasks. The vehicle status information includes: real-time vehicle battery level, real-time amount of garbage stored in the vehicle, and real-time water level in the vehicle's water tank; the vehicle location information includes: the vehicle's real-time location; and the vehicle operating status information includes: whether it is running, idle, or experiencing a malfunction.

[0021] In one embodiment, the vehicle status monitoring module is further configured to send the vehicle status information, vehicle location information, and vehicle operating status information to the task allocation module.

[0022] In another embodiment, the cloud-based unmanned sweeper task real-time allocation system further includes a task management module for receiving and managing sweeping task information. The sweeping task information includes: the work area (including all sweeping work areas within its managed scope), sweeping requirements, task priority, etc.

[0023] In one embodiment, the traffic map information for the task area is provided by map software and / or a city traffic management system. The traffic map information for the task area is widely available, providing a comprehensive understanding of the real-time traffic conditions along the current path of the autonomous vehicle. For roads with poor conditions, the vehicle can be avoided in a timely manner, preventing congestion and other problems, shortening cleaning time, and improving overall operational efficiency.

[0024] In summary, the cloud-based real-time task allocation system for unmanned sweepers proposed in this paper has the following beneficial effects:

[0025] 1. It can screen vehicles capable of performing tasks to select those that meet the requirements for assignment, instead of directly assigning tasks to all vehicles capable of performing tasks. This ensures that the vehicles that ultimately perform the tasks are those that meet the requirements, reducing the possibility of task interruption or unexpected situations.

[0026] 2. Based on the principle of proximity, vehicles to be assigned can be placed in nearby work areas to improve work efficiency and vehicle operating efficiency.

[0027] 3. At the same time, no manual allocation is required, and the work tasks of vehicles to be assigned can be automatically and quickly allocated, ensuring high allocation efficiency and strong allocation rationality.

[0028] 4. It can quickly and automatically plan the driving path of the assigned vehicles during the operation process to ensure that they travel on the optimal route, effectively avoid traffic jams and other problems, and ensure that the cleaning task is completed quickly and with high operation efficiency. Attached Figure Description

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0030] Figure 1 This is a schematic diagram of the principle of the present invention.

[0031] The module consists of: Task Allocation Module 1, Path Planning Module 2, Vehicle Status Monitoring Module 3, and Task Management Module 4. Detailed Implementation

[0032] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The embodiments of the invention are described below in conjunction with the accompanying drawings.

[0033] Please refer to Figure 1A cloud-based real-time task allocation system for unmanned sweeping vehicles includes: a task allocation module 1, a path planning module 2, a vehicle status monitoring module 3, and a task management module 4. This invention uses the task allocation module 1 to screen vehicles capable of performing tasks, selecting those that meet the requirements for allocation, rather than directly assigning tasks to all capable vehicles. This ensures that the vehicles ultimately performing the tasks are those that meet the requirements, reducing the risk of task interruptions and unexpected situations (such as vehicle battery depletion or garbage bins becoming full), effectively improving operational efficiency. Simultaneously, after selecting vehicles for allocation, the system further matches them with their work areas, enabling rapid allocation of tasks. After allocation, the path planning module 2 plans the travel path for the assigned vehicles to complete their tasks, ensuring they travel on the optimal route and quickly fulfill the cleaning requirements of their work areas, guaranteeing high operational efficiency.

[0034] Specifically, in this embodiment, the task allocation module 1 is used to filter out vehicles to be allocated from the available task vehicles according to the vehicle allocation rules, allocate work areas to the vehicles to be allocated, and generate vehicle allocation information. The available task vehicles include: idle vehicles within the jurisdiction of the system of this invention, and vehicles currently performing work tasks; generally, work tasks are divided into low priority, medium priority, and high priority, therefore, available task vehicles are generally divided into: idle vehicles, vehicles performing low-priority tasks, vehicles performing medium-priority tasks, and vehicles performing high-priority tasks. Obviously, compared to the manual scheduling and allocation method in the prior art, the task allocation module 1 of this invention can allocate work tasks to vehicles to be allocated more quickly and accurately, avoiding the inefficiency and delays of manual scheduling.

[0035] Specifically, in this embodiment, the process by which the task allocation module 1 selects vehicles to be allocated based on vehicle allocation rules is as follows:

[0036] The system determines whether the remaining battery power of the vehicle capable of performing the task meets the battery power threshold and whether the amount of garbage stored meets the garbage quantity threshold. In this embodiment, the battery power threshold is 50% and the garbage quantity threshold is 60%. When the remaining battery power of the vehicle capable of performing the task is greater than 50% and the amount of garbage stored is less than 60%, it meets the requirements of both the battery power threshold and the garbage quantity threshold, and is therefore a vehicle to be assigned. For vehicles with low remaining battery power, they are instructed to go to the nearest charging area for charging. Vehicles with excessive garbage stored first go to the nearest garbage dumping area to dump their garbage. After dumping their garbage, their stored garbage quantity is updated and fed back to the present invention in real time, so they will enter a new round of judgment process, and a task will be assigned to them subsequently. Obviously, by judging the remaining battery power and the amount of garbage stored of the vehicles capable of performing the task, vehicles with relatively sufficient battery power and low garbage storage can be selected to ensure that they can complete the task normally without interruption due to the need for charging, which would require reassigning vehicles to continue the task. Frequent garbage dumping will also prevent the task progress from being affected, ensuring a high task completion rate and effectively improving the user experience. In addition, it should be noted that the power threshold and waste volume threshold can be set according to specific requirements. For example, if the distance between the work area and the vehicle capable of performing the task is relatively close and the work area is not large, the remaining power requirement for the vehicle capable of performing the task can be reduced. Therefore, it can be adjusted according to the actual situation to meet different needs.

[0037] Specifically, in this embodiment, the path planning module 2 is used to calculate the optimal driving path for the assigned vehicles based on vehicle allocation information and task area traffic map information. Specifically, in this embodiment, the vehicle allocation information includes: the assigned vehicle number and the work area assigned to the assigned vehicle. The assigned vehicle number can be a unified number among all vehicles within the scope of the system of this invention, or it can be its license plate number. After obtaining the vehicle allocation information, based on the work area assigned to the assigned vehicle and the task area traffic map information, the real-time road conditions of each road in the assigned work area can be understood, including traffic flow speed and congestion. An optimal route can be planned based on the actual situation to ensure the rapid completion of the cleaning task. Instead of simply assigning vehicles to drive along a designated route after allocation, this approach, combined with actual road conditions, effectively improves operational efficiency.

[0038] Specifically, in this embodiment, the work area assigned to the assigned vehicle is either the closest work area to the assigned vehicle or a work area whose priority level is higher than the work level of the previously executed work task of the assigned vehicle, and which is the closest work area to the assigned vehicle. Requiring the assigned vehicle to be closest to the work area ensures that the assigned vehicle reaches its work area in the shortest time to perform cleaning operations, reducing travel time and distance, saving time and energy, and is highly efficient. The situation where the work area's work priority level is higher than the work level of the previously executed work task of the assigned vehicle, and which is the closest work area to the assigned vehicle, generally occurs when there are no available vehicles, but a higher-priority cleaning task needs to be performed. This cleaning task is more urgent than the currently executed task (of course, after reassignment, it belongs to the previously executed task for the assigned vehicle). Therefore, it is necessary to dispatch a vehicle with a lower task priority and the closest proximity to perform the higher-priority task. Generally, the work levels corresponding to low priority, medium priority, and high priority increase sequentially.

[0039] Specifically, in this embodiment, the traffic map information of the task area includes: real-time traffic information and historical traffic information. The real-time traffic information includes: traffic flow, congestion, and speed limits on each drivable road within the task area corresponding to the assigned vehicle; while the historical traffic information includes: historical traffic flow and historical congestion on each drivable road within the task area corresponding to the assigned vehicle. By referring to the real-time and historical traffic information, the road conditions of each road within the task area of ​​the assigned vehicle during the period of its operation can be understood, ensuring that the planned driving path is the optimal route. Specifically, in this embodiment, the principle of the path planning module 2 in planning the optimal driving path for the assigned vehicle is: the assigned vehicle completes its assigned task in the shortest time and / or completes its assigned task with the lowest energy consumption (generally considering power consumption). Therefore, it can be ensured that the assigned vehicle is on the optimal driving path, completing the cleaning task at the fastest speed, achieving high-efficiency and low-energy operation.

[0040] In one embodiment, the cloud-based unmanned sweeper task real-time allocation system further includes a vehicle status monitoring module 3 for monitoring vehicle status information, vehicle location information, and vehicle operating status information of the vehicles capable of performing tasks. The vehicle status information includes: real-time battery level, real-time amount of garbage stored, and real-time water level (in the water tank). The vehicle location information includes: real-time vehicle location, obtained from sensors at various locations on the vehicle. The vehicle operating status information includes: whether the vehicle is in operation, idle, or experiencing a malfunction. The vehicle location information includes: real-time vehicle location, typically obtained by a GPS module or SLAM positioning module on the vehicle and then transmitted to the vehicle status monitoring module 3. Specifically, in this embodiment, the vehicle status monitoring module 3 also transmits the vehicle status information, vehicle location information, and vehicle operating status information to the task allocation module 1. The vehicle status monitoring module 3 is communicatively connected to all vehicles capable of performing tasks, typically via 4G / 5G / WIFI wireless communication. The vehicles capable of performing tasks transmit their vehicle status information, vehicle location information, and vehicle operating status information to the vehicle status monitoring module 3 in real time.

[0041] Specifically, in this embodiment, the cloud-based unmanned sweeper task real-time allocation system further includes: a task management module 4 for receiving and managing sweeping task information; sweeping task information can be manually input by the user or automatically obtained by accessing the corresponding existing urban sweeping task management system; after completing the allocation of work areas, the task management module 4 manages and tracks each work area, generating corresponding work orders (work area name, street and community scope, work duration, and vehicle number of the assigned vehicle working in the work area). The sweeping task information includes: work area (including all sweeping work areas within its management scope), sweeping requirements (e.g., sweeping only, sweeping and watering for dust suppression, multiple sweeping trips, etc.), and task priority (low priority, medium priority, high priority), etc.

[0042] Specifically, in this embodiment, the traffic map information of the task area is provided by map software and / or urban traffic management systems. Specifically, the map software can be Gaode Map, Baidu Map, etc., and it also connects to existing map software and urban traffic management systems (depending on the city where the vehicle is operating, it connects to the local traffic management bureau's urban traffic management system). Clearly, the task area traffic map information of this invention has a wide range of sources, allowing for a comprehensive understanding of the real-time road conditions along the current path of the autonomous vehicle. For roads with poor road conditions, it can be avoided in a timely manner, preventing congestion and other problems, shortening cleaning time, and improving overall operational efficiency.

[0043] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A real-time task allocation system for unmanned sweeping vehicles based on a cloud platform, characterized in that, include: A task allocation module used to filter out vehicles to be allocated from vehicles capable of performing tasks according to vehicle allocation rules, allocate work areas to the vehicles to be allocated, and generate vehicle allocation information. A path planning module used to calculate the optimal driving route for assigned vehicles based on vehicle allocation information and traffic map information of the task area.

2. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 1, characterized in that, The process by which the task allocation module selects vehicles to be allocated based on vehicle allocation rules is as follows: Determine whether the remaining battery power of the vehicle capable of performing the task meets the requirements of the battery power threshold and the amount of garbage stored meets the requirements of the garbage amount threshold; vehicles capable of performing the task that meet both the battery power threshold and the garbage amount threshold are vehicles to be assigned.

3. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 2, characterized in that, The vehicle allocation information includes: the allocated vehicle number and the work area assigned to the allocated vehicle.

4. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 3, characterized in that, The work area assigned to the assigned vehicle is either the work area to be cleaned that is closest to the assigned vehicle, or the work area whose priority level is higher than the work level of the last work task performed by the assigned vehicle, and which is the work area to be cleaned that is closest to the assigned vehicle.

5. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 1, characterized in that, The traffic map information for the task area includes: real-time traffic information and historical traffic information.

6. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 1, characterized in that, The principle of the path planning module in planning the optimal driving path for the assigned vehicles is: the assigned vehicles complete the work tasks in their assigned work areas in the shortest time and / or with the lowest energy consumption.

7. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 1, characterized in that, Also includes: A vehicle status monitoring module for monitoring vehicle status information, vehicle location information, and vehicle operating status information on the vehicle performing the task.

8. The real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 7, characterized in that, The vehicle status monitoring module is also used to send the vehicle status information, vehicle location information, and vehicle operating status information to the task allocation module.

9. A real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 1, characterized in that, Also includes: The task management module is used to receive and manage cleaning task information.

10. A real-time task allocation system for an unmanned sweeper based on a cloud platform according to claim 5, characterized in that, The traffic map information for the task area is provided by map software and / or urban traffic management systems.

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