Scheduling methods, devices, and cloud control platforms for large-scale unmanned vehicle swarms

Abstract

This disclosure provides a scheduling method, apparatus, and cloud control platform for large-scale unmanned vehicle (UAV) swarms, comprising: acquiring operational status information of the UAV swarm scheduled within a preset operating area; determining a first area range where a traffic congestion event has occurred based on the operational status information; determining a second area range based on the first area range; and controlling UAVs operating within the second area to switch from a first operating mode to a second operating mode, wherein, under the same operating conditions, the energy consumption of UAVs in the second operating mode is lower than that in the first operating mode. This disclosure improves operational efficiency while reducing the load on congested areas.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to scheduling methods, devices and cloud control platforms for large-scale unmanned vehicle swarms. Background Technology

[0002] During large-scale unmanned vehicle cluster production operations in mining areas, traffic congestion often occurs in certain areas. Traditionally, to alleviate congestion, the frequency of vehicle departures from departure points is reduced—for example, by decreasing the loading frequency of excavators to reduce departures from loading areas. However, this approach leads to a significant decrease in operational efficiency and may trigger new congestion in other areas. No effective solution has yet been proposed to address these issues. Summary of the Invention

[0003] This disclosure provides a scheduling method, apparatus, and cloud control platform for large-scale unmanned vehicle clusters to address problems existing in related technologies.

[0004] In view of the above problems, firstly, this disclosure provides a scheduling method for large-scale unmanned vehicle clusters, including:

[0005] Obtain the operational status information of the dispatched unmanned vehicle cluster within the preset work area;

[0006] If a traffic congestion event is determined based on the aforementioned operational status information, the first area where the traffic congestion event occurred is determined.

[0007] The second area is determined based on the first area range, and the unmanned vehicle traveling within the second area range is controlled to switch from the first working mode to the second working mode. Under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode.

[0008] In conjunction with the first aspect, in one possible implementation, determining the second region range based on the first region range includes:

[0009] Determine the area category corresponding to the preset work area;

[0010] Based on the area category corresponding to the preset work area, determine the corresponding preset energy-saving strategy;

[0011] The second area is determined based on the first area range and the predetermined energy-saving strategy.

[0012] In conjunction with the first aspect, in one possible implementation, the area category corresponding to the preset work area includes at least one of the following: local road driving area, loading and unloading operation area, and unloading operation area.

[0013] In conjunction with the first aspect, in one possible implementation, when the area category is a local road travel area, the first area range is the road range occupied by the congested vehicle queue.

[0014] Determining the range of the second region based on the range of the first region includes:

[0015] The first starting position of the congestion location is determined based on the road area occupied by the congested vehicle queue.

[0016] The second region range is determined based on the first starting position and the first preset energy-saving region range;

[0017] The first preset energy-saving area is a predefined area extending upstream from a traffic congestion location by a first preset distance.

[0018] In conjunction with the first aspect, in one possible implementation, when the area category is a mining and loading operation area, the first area range is the mining and loading operation area;

[0019] Determining the range of the second region based on the range of the first region includes:

[0020] The second area is determined based on the mining and loading operation area and the working status of the unmanned vehicles outside the mining and loading operation area.

[0021] In conjunction with the first aspect, in one possible implementation, determining the range of the second area based on the mining and loading operation area and the operating status of the unmanned vehicle outside the mining and loading operation area includes:

[0022] The area where the unmanned vehicles operating outside the mining and loading area are located is defined as the second area.

[0023] In conjunction with the first aspect, in one possible implementation, the loading and unloading operation area includes: the topology of all paths leading from the downstream intersection before the branching point on the path from the unloading operation area to the loading area to all loading areas, and all loading areas.

[0024] In conjunction with the first aspect, in one possible implementation, when the area category is an unloading operation area, the first area range includes the unloading operation area, or the entrance area of ​​the unloading operation area;

[0025] Determining the range of the second region based on the range of the first region includes:

[0026] The entrance to the unloading operation area is determined as the second starting point of the congestion location;

[0027] The second area range is determined based on the second starting position and the second preset energy-saving area range;

[0028] The second preset energy-saving area is a predefined area that extends upstream from the entrance of the unloading operation area by a second preset distance.

[0029] In conjunction with the first aspect, in one possible implementation, when the area category corresponding to the preset operating area is a local road driving area, the operating status information includes: current location information and current vehicle speed information; determining that a traffic congestion event has occurred based on the operating status information includes: based on the current location information and the current vehicle speed information, if there are at least two unmanned vehicles with zero speed and the distance between the two unmanned vehicles is not greater than a preset following distance, then a traffic congestion event is determined to have occurred; or,

[0030] When the preset work area corresponds to an unloading work area, the operating status information includes: current vehicle speed information and real-time work information; determining a traffic congestion event based on the operating status information includes: based on the current vehicle speed information and real-time work information, if there are at least two unmanned vehicles in the unloading work area, satisfying the conditions that their speed is zero and they are not currently performing unloading operations, determining that a traffic congestion event has occurred; or...

[0031] When the area category corresponding to the preset work area is a mining and loading work area, the operating status information includes the current load information; determining the occurrence of a traffic congestion event based on the operating status information includes: determining a first number of unmanned vehicles operating without load in the mining and loading work area based on the current load information; determining that a traffic congestion event has occurred if the first number is greater than or equal to a preset saturation value; and / or determining a second number of unmanned vehicles operating without load per excavator in the mining and loading work area based on the current load information; determining that a traffic congestion event has occurred if the second number is greater than or equal to a preset saturation value for excavators.

[0032] In conjunction with the first aspect, in one possible implementation, it further includes:

[0033] Mark the map area corresponding to the second region on the map; and

[0034] The marked map is sent to the autonomous vehicle so that the autonomous vehicle can determine the working mode to be adopted based on the marked map and its own position. The working mode includes a first working mode or a second working mode.

[0035] Secondly, a scheduling device for large-scale unmanned vehicle swarms is provided, comprising:

[0036] The acquisition module is used to acquire the operating status information of the dispatched unmanned vehicle cluster within the preset operating area;

[0037] The congestion determination module is used to determine the first area range where a traffic congestion event has occurred, based on the operational status information.

[0038] The mode switching module is used to determine the second area range based on the first area range, and control the unmanned vehicle traveling within the second area range to switch from the first working mode to the second working mode, wherein, under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode.

[0039] Thirdly, a cloud control platform is provided, including: a scheduling device as described in the second aspect for use in large-scale unmanned vehicle clusters.

[0040] The beneficial effects of the embodiments disclosed herein include:

[0041] This disclosure provides a scheduling method, apparatus, and cloud control platform for large-scale unmanned vehicle (UAV) clusters, comprising: acquiring operational status information of the UAV clusters scheduled within a preset work area; determining a first area range where a traffic congestion event occurs based on the operational status information; determining a second area range based on the first area range; and controlling UAVs operating within the second area to switch from a first working mode to a second working mode, wherein, under the same operating conditions, the energy consumption of UAVs in the second working mode is lower than that in the first working mode. The scheduling method for large-scale UAV clusters provided in this disclosure acquires real-time operational status information of UAVs within each preset work area, determines the first area range where a traffic congestion event occurs, and controls UAVs in the relevant area to operate in an energy-saving manner without affecting operational efficiency. This achieves energy-saving operation of UAVs in congested scenarios, reducing energy consumption, alleviating the load on congested areas, and improving operational efficiency, providing technical support for achieving a green and intelligent mining operation environment. Attached Figure Description

[0042] Figure 1 A flowchart illustrating a scheduling method for a large-scale unmanned vehicle cluster provided in this embodiment of the disclosure;

[0043] Figure 2 This is one of the schematic diagrams of road topology provided in the embodiments of this disclosure;

[0044] Figure 3 This is the second schematic diagram of road topology provided in the embodiments of this disclosure;

[0045] Figure 4 This is the third schematic diagram of road topology provided in the embodiments of this disclosure;

[0046] Figure 5 This is the fourth schematic diagram of road topology provided in the embodiments of this disclosure;

[0047] Figure 6 This is the fifth schematic diagram of road topology provided in the embodiments of this disclosure;

[0048] Figure 7 This is the sixth schematic diagram of road topology provided in the embodiments of this disclosure;

[0049] Figure 8 This is a schematic diagram of the scheduling device structure for a large-scale unmanned vehicle cluster provided in an embodiment of this disclosure. Detailed Implementation

[0050] This disclosure provides a scheduling method, apparatus, and cloud control platform for large-scale unmanned vehicle swarms. Preferred embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of this disclosure. Furthermore, the embodiments and features described herein can be combined with each other unless otherwise specified.

[0051] This disclosure provides a scheduling method for large-scale unmanned vehicle clusters, such as... Figure 1 As shown, it includes:

[0052] S101. Obtain the operating status information of the unmanned vehicle clusters scheduled within the preset operating area;

[0053] S102. If a traffic congestion event is determined based on the operational status information, the first area where the traffic congestion event occurred shall be determined.

[0054] S103. Determine the second area range based on the first area range, and control the unmanned vehicle traveling within the second area range to switch from the first working mode to the second working mode, wherein, under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode.

[0055] In this embodiment of the disclosure, the executing entity can be a server or a terminal. Optionally, the executing entity can be a cloud control platform. The cloud control platform can provide detailed map information within a preset work area through the production map management module, including road conditions and work locations; it can communicate with unmanned vehicles through the production scheduling module to control the working mode and task allocation of a large-scale unmanned vehicle cluster, thereby completing the transportation operation task.

[0056] Unmanned vehicles can be mining vehicles equipped with driverless transportation capabilities in mines. They can obtain their own operating status information through a combination of various devices and sensors. For example, they can be equipped with integrated navigation equipment as a positioning module to obtain real-time location information within the preset working area. In addition, they can be equipped with various sensors, such as weight sensors installed on the vehicle's suspension system or axles, which can measure the vehicle's load in real time. These sensors can accurately measure the weight difference between the vehicle under heavy load and unload conditions, thereby providing load information. For example, sensors such as cameras, lidar, and millimeter-wave radar can be used to identify traffic signs, vehicles, and obstacles on the road ahead.

[0057] However, due to the complex environment of mining areas, traffic congestion often occurs due to special circumstances, such as falling rocks and scattered materials. When encountering obstacles ahead, unmanned vehicles usually slow down or stop. As the scale of the unmanned vehicle cluster increases, traffic congestion on work roads or work sites is inevitable.

[0058] In this embodiment of the disclosure, real-time operational status information of the autonomous vehicle cluster dispatched within a preset operating area is obtained through communication with the cluster. Based on this operational status information, it can be determined whether a traffic congestion event has occurred within the preset operating area, and then, if a traffic congestion event occurs, the first area range of the traffic congestion event is determined. For example, as... Figure 2 As shown, within the preset operating area, the cloud control platform communicates with the unmanned vehicle cluster to obtain the operating status information of unmanned vehicles a and b traveling in the x direction, determines that a traffic congestion event has occurred in the area where unmanned vehicles a and b are located, and then determines the first area AB where the traffic congestion event occurred.

[0059] Furthermore, such as Figure 2 As shown, the cloud control platform determines a second area BC upstream of the first area AB based on the first area range AB, and controls the unmanned vehicle c traveling within the second area BC to switch from a first working mode to a second working mode. Under the same operating conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode, thereby achieving energy-saving operation of the unmanned vehicle within the second area without affecting operational efficiency. Furthermore, after the traffic congestion event ends, the platform controls the unmanned vehicle within the second area to switch back to the first working mode. For example, after a special situation is handled, such as after the auxiliary vehicle clears the obstacle, unmanned vehicles a and b resume transportation operations. The intelligent cloud control platform, based on the acquired operating status information of unmanned vehicles a and b, determines that the traffic congestion event has ended, deletes the second area BC, and controls unmanned vehicle c to switch back to the first working mode.

[0060] In this embodiment, by acquiring real-time operational status information of a cluster of unmanned vehicles dispatched within a preset work area, the initial area of ​​traffic congestion can be accurately located. Then, the unmanned vehicles upstream of the congestion area are controlled to operate in the most energy-efficient mode without reducing work efficiency. This not only reduces energy consumption but also alleviates pressure on congested areas, improves overall operational efficiency, and contributes to building a more environmentally friendly and intelligent mining environment.

[0061] In another embodiment of this disclosure, step S103 above, determining the range of the second region based on the range of the first region, includes:

[0062] Step 1: Determine the area category corresponding to the preset work area;

[0063] Step 2: Determine the corresponding preset energy-saving strategy based on the area category corresponding to the preset work area;

[0064] Step 3: Determine the scope of the second area based on the first area and the predetermined energy-saving strategy.

[0065] In this embodiment of the disclosure, during the planning and construction of the mining area, the pre-defined work area can be divided into different area categories based on the actual environment. Unmanned vehicles perform different work tasks within each area category. For step 1, the area category corresponding to the pre-defined work area is first determined. For step 2, a pre-defined energy-saving strategy is pre-set for each area category, and then the corresponding pre-defined energy-saving strategy is determined based on the determined area category of the pre-defined work area. For step 3, the second area range is finally determined based on the first area range where traffic congestion occurs and the determined corresponding pre-defined energy-saving strategy. By refining the classification of the pre-defined work areas and corresponding pre-defined energy-saving strategies, the first area range where traffic congestion occurs and the corresponding energy-saving second area range can be more accurately identified.

[0066] In another embodiment of this disclosure, the area category corresponding to the preset work area includes at least one of the following: local road driving area, loading and unloading operation area, and unloading operation area.

[0067] In this embodiment of the disclosure, the area categories divided according to the functions of the pre-set work areas within the mine include at least one of the following: a local road driving area, a mining and loading operation area, and an unloading operation area. For example, the mining and loading operation area mainly includes large excavating equipment, such as excavators and loaders, performing mining operations and loading materials such as ore into unmanned vehicles for transportation; the unloading operation area is the location where unmanned vehicles unload the ore and other materials transported from the mining and loading operation area, and is also an important connection point between mining and subsequent processing stages. It typically includes multiple unloading points to meet the storage and handling needs of different materials; the local road driving area can be a road connecting the mining and loading operation area and the unloading operation area for unmanned vehicle cluster transportation operations. By rationally dividing the pre-set work areas of the mine, mutual interference between the various work areas is prevented, workflow optimization is facilitated, and unmanned vehicle scheduling is made easier, which can improve the safety and efficiency of mining operations to a certain extent.

[0068] In another embodiment of this disclosure, when the area category is a local road driving area, the first area range is the road range occupied by the congested vehicle queue.

[0069] In step S103 above, determining the range of the second region based on the range of the first region includes:

[0070] Step 1: Determine the first starting position of the congestion location based on the road area occupied by the congested vehicle queue;

[0071] Step 2: Determine the second area range based on the first starting position and the first preset energy-saving area range; wherein, the first preset energy-saving area range is a predefined area range extending upstream from the traffic congestion location by a first preset distance.

[0072] In this embodiment of the disclosure, when the area category is a local road driving area, the road range occupied by the congested vehicle queue is determined as the first area range where the traffic congestion event occurs, thereby accurately locating the initial area range where the traffic congestion event occurs. In one possible implementation, the local road can be a road between various working areas in the mining area, such as a road connecting the loading / unloading area and the unloading area. For example, as shown... Figure 3 As shown, the cloud control platform obtains that unmanned vehicles a and b are traveling on a local road area from the unloading area to the loading area in the x direction, and determines that a traffic congestion event has occurred. Then, the road area AB occupied by the congested vehicle queue unmanned vehicles a and b is determined as the first area.

[0073] Regarding step 1, based on the road area occupied by the congested vehicle queue, determine the first starting position of the congestion. The first starting position can be the position of the first vehicle in the congested queue, the position of the last vehicle in the congested queue, or any position within the road area occupied by the congested vehicle queue; for example, such as... Figure 3 As shown, the road area AB occupied by the congested vehicle queue is used to determine the first starting position of the congestion. This can be the position of the first vehicle (A), the position of the last vehicle (B), or any position within the road area AB occupied by the congested vehicle queue.

[0074] For step 2, a first preset distance is first defined. Then, starting from the location of traffic congestion, the area extending upstream by the first preset distance is defined as the first preset energy-saving area. If the road within the first area can cover the first preset energy-saving area, the first preset energy-saving area is determined as the second area. For example, as shown... Figure 3 As shown, starting from the last vehicle in the vehicle queue within the first area AB (taking the congested location as the last vehicle as an example), the area BC extending upstream by a first preset distance is defined as the first preset energy-saving area. If the road containing the first area AB is long enough, and the first preset energy-saving area BC is still within the road range of the first area AB, then the first preset energy-saving area BC is defined as the second area.

[0075] If the road within the first energy-saving zone cannot cover the first preset energy-saving zone (e.g., if there is an intersection within the first preset energy-saving zone), then it is determined whether there is a road, other than the road where the traffic congestion event occurred, that can reach the downstream preset work area from the intersection without experiencing a traffic congestion event; for example, such as... Figure 4 As shown, if the first area AB where unmanned vehicles a and b are located is congested, and the vehicles are traveling in the x direction from the unloading area to the loading area, based on the road topology, it can be determined whether there is a road other than the road in the first area AB that can reach the loading area from the intersection to the downstream loading area, and whether this road is free from traffic congestion.

[0076] If it exists, for example, such as Figure 4 As shown, if road FG provides access to the second loading area and there is no traffic congestion on this road, then the road BE between the starting point B of the first preset energy-saving area and the intersection is defined as the second area. In this case, the length of the second area is less than or equal to the length of the first preset distance. This ensures that the unmanned vehicle c traveling on the road from the intersection to the upstream road continues to operate in the first working mode when heading to the second loading area, guaranteeing operational efficiency.

[0077] If it does not exist, for example, such as Figure 4 As shown, if a traffic congestion event occurs on road FG, or if there is no road other than the road within the first area AB that can reach the loading area, then the first distance range BE from the starting point B of the first preset energy-saving area to the intersection is determined. For each upstream road entering the intersection, a second distance range is extended upstream from the intersection entrance, as shown below. Figure 4 As shown, the second distance range is CD and C'D'. Finally, the first distance range BE and the second distance ranges CD and C'D' of each upstream road are jointly determined as the second area range. Furthermore, the sum of the lengths of the first distance range and each of the second distance ranges can be greater than or equal to the length of the first preset energy-saving area range.

[0078] In another embodiment of this disclosure, when the area category is a mining and loading operation area, the first area range is the mining and loading operation area.

[0079] In step S103 above, determining the range of the second region based on the range of the first region includes:

[0080] The scope of the second area is determined based on the working status of the mining and loading operation area and the unmanned vehicles outside the mining and loading operation area.

[0081] In this embodiment of the disclosure, when the area category is a mining and loading operation area, the mining and loading operation area is identified as the first area where traffic congestion occurs. This reduces the impact on other areas, maintains overall production order, and allows the cloud control platform to quickly dispatch resources, facilitating timely handling of congestion events in the mining and loading operation area.

[0082] The second area is determined based on the operational status of the mining and loading area and the unmanned vehicles outside the mining and loading area. For example, the cloud control platform can obtain the operational status of the unmanned vehicle cluster outside the mining and loading area, and then determine the second area based on the operational status.

[0083] In another embodiment of this disclosure, the range of the second area is determined based on the mining and loading operation area and the working status of the unmanned vehicle outside the mining and loading operation area, including:

[0084] The area where unmanned vehicles operating outside the mining and loading area are located is designated as the second area.

[0085] In this embodiment of the disclosure, the second area is determined based on the working status of the mining and loading operation area and the unmanned vehicles outside the mining and loading operation area. This includes defining the area where the unmanned vehicles outside the mining and loading operation area are located as the second area. For example, the cloud control platform can obtain the load information and working mode of the unmanned vehicles. When a traffic congestion event occurs in the mining and loading operation area, the number of unmanned vehicles in that area is already saturated, and there is no need for unmanned vehicles from other areas to quickly proceed to the mining and loading operation area. Therefore, the area where the unmanned vehicles outside the mining and loading operation area are located is defined as the second area. If the unmanned vehicles within the second area are in the first working mode, the cloud control platform controls the unmanned vehicles to switch to the second working mode.

[0086] In another embodiment of this disclosure, the loading and unloading operation area includes: the topology of all paths leading from the downstream intersection before the branching point on the path from the unloading operation area to the loading area to all loading areas, and all loading areas.

[0087] In this embodiment of the disclosure, the loading and unloading operation area includes: the topology of all paths leading from the downstream intersection before the branching point on the path from the unloading operation area to the loading area, and all loading areas. For example, as shown... Figure 5 As shown, the unmanned vehicle travels from the unloading area to the loading area, passing through intersections A and B. Intersection A connects to the first loading area, and intersection B connects the second and third loading areas. The unmanned vehicle traveling along the x-direction first passes through intersection A, thus intersection A is determined to be the downstream intersection before the diversion on the path from the unloading area to the loading area. The topology of all paths from intersection A to all loading areas, as well as all loading areas, together constitute the loading and unloading operation area 100.

[0088] In another embodiment of this disclosure, when the area category is an unloading operation area, the first area range includes the unloading operation area or the entrance area of ​​the unloading operation area.

[0089] In step S103 above, determining the range of the second region based on the range of the first region includes:

[0090] Step 1: Determine the entrance to the unloading area as the second starting point of the congestion location;

[0091] Step 2: Determine the second area range based on the second starting position and the second preset energy-saving area range; wherein, the second preset energy-saving area range is a predefined area range extending upstream from the entrance of the unloading operation area by a second preset distance.

[0092] In this embodiment of the disclosure, when the area category is an unloading operation area, the unloading operation area or the entrance area of ​​the unloading operation area is determined as the first area range where a traffic congestion event occurs. Here, the entrance area of ​​the unloading operation area includes a certain range, which can be defined as a range of X meters from the entrance within the unloading operation area. X can be set as a default distance, the distance from the nearest available unloading position to the entrance of the unloading operation area, or the maximum value of the two.

[0093] Regarding step 1, if there is traffic congestion within a certain range of the entrance to the unloading operation area or if there is a traffic congestion event within the unloading operation area, the entrance to the unloading operation area can be determined as the second starting point of the congestion location.

[0094] Regarding step 2, a second preset distance is first defined. Then, starting from the entrance of the unloading operation area, the area extending upstream by the second preset distance is defined as the second preset energy-saving area. If the road at the entrance of the unloading operation area can cover the second preset energy-saving area, then the second preset energy-saving area is determined as the second area. For example, as shown... Figure 6 As shown, the area AB extending upstream from the entrance of the unloading operation area by a second preset distance is the second preset energy-saving area. If the road where the entrance of the unloading operation area is located is long enough, and the second preset energy-saving area AB is still within the road where the entrance of the unloading operation area is located, then the second preset energy-saving area AB is determined as the second area.

[0095] If the road leading to the unloading area cannot cover the second preset energy-saving area (e.g., if there is an intersection within the second preset energy-saving area), then it is determined whether there is a road, other than the road where the traffic congestion event occurred, that leads from the intersection to the downstream preset operation area without any traffic congestion event. For example, such as... Figure 7 As shown, congestion occurs in the first unloading operation area. The vehicle travels in the y direction from the loading area to the unloading operation area. Based on the road topology, it can be determined whether there is a road from the intersection to the downstream unloading operation area other than the road where the entrance of the first unloading operation area is located that can reach the unloading operation area, and whether there is no traffic congestion event in the unloading operation area on this road.

[0096] If it exists, for example, such as Figure 7As shown, if road CD provides access to the second unloading area and no traffic congestion occurs in the second unloading area, then road AE between the starting point A of the second preset energy-saving area and the intersection is defined as the second area. In this case, the length of the second area is less than or equal to the length of the first preset distance. This ensures that the unmanned vehicle a traveling on the road from the intersection to the upstream road continues to operate in the first working mode when heading to the second unloading area, guaranteeing operational efficiency.

[0097] If it does not exist, for example, such as Figure 7 As shown, if a traffic congestion event occurs in the second unloading operation area, or if there is no road other than the road where the entrance to the first unloading operation area is located that can reach the unloading operation area, then a third distance range AE from the starting point A of the second preset energy-saving area to the intersection is determined. For each upstream road entering the intersection, a fourth distance range is extended upstream from the intersection, such as... Figure 7 As shown, the fourth distance range is BF and B'F'. Finally, the third distance range AE and the fourth distance ranges BF and B'F' of each upstream road are collectively defined as the second region range. Furthermore, the sum of the lengths of the third distance range and each of the fourth distance ranges can be greater than or equal to the length of the second preset energy-saving region range.

[0098] In another embodiment of this disclosure, when the preset operating area corresponds to a local road driving area, the operating status information includes: current location information and current vehicle speed information; in step S102 above, determining that a traffic congestion event has occurred based on the operating status information includes: based on the current location information and current vehicle speed information, if there are at least two unmanned vehicles with zero speed and the distance between the two unmanned vehicles is not greater than a preset following distance, then a traffic congestion event is determined to have occurred; or,

[0099] When the preset work area corresponds to an unloading work area, the operational status information includes: current vehicle speed information and real-time work information; in step S102 above, determining a traffic congestion event based on the operational status information includes: based on the current vehicle speed information and real-time work information, if there are at least two unmanned vehicles in the unloading work area or its entrance area, satisfying the conditions that the vehicle speed is zero and no unloading operation is currently being performed, then a traffic congestion event is determined to have occurred; or...

[0100] When the preset work area corresponds to the area category of a mining and loading work area, the operating status information includes the current load information; in step S102 above, determining the occurrence of a traffic congestion event based on the operating status information includes: determining the first number of unmanned vehicles operating empty in the mining and loading work area based on the current load information; determining that a traffic congestion event has occurred if the first number is greater than or equal to a preset saturation value; assuming that the first number of unmanned vehicles operating empty in the entire mining and loading work area is currently 20, and the preset saturation value for the entire mining and loading work area to accommodate unmanned vehicles operating empty is 15, then a traffic congestion event is determined to have occurred; and / or

[0101] Based on the current load information, determine the second number of unmanned vehicles operating on average per excavator in the mining and loading operation area; if the second number is greater than or equal to the excavator's preset saturation value, a traffic congestion event is determined to have occurred; assuming that the first number of unmanned vehicles operating on average per excavator in the entire mining and loading operation area is 20, the number of excavators is 5, and the preset saturation value for each excavator is 3 unmanned vehicles, then the current second number of unmanned vehicles operating on average per excavator is 4, which is greater than the excavator's preset saturation value, so a traffic congestion event is determined to have occurred.

[0102] In this embodiment of the disclosure, when the preset operating area corresponds to a local road driving area, the current location information and current speed information of the unmanned vehicle cluster within the local road driving area are first obtained. Then, based on the aforementioned current location information and current speed information, a judgment is made. If at least two unmanned vehicles within the local road driving area have a speed of zero and a distance between them not greater than a preset following distance, then a traffic congestion event is determined to have occurred. For example, such as... Figure 2 As shown, autonomous vehicles a, b, and c are driving within a local road driving area. After obtaining the current location and speed information of autonomous vehicles a, b, and c, the cloud control platform makes a judgment. Autonomous vehicles a and b meet the conditions that their speed is zero and the distance between them is no greater than the preset following distance. Therefore, it is confirmed that a traffic congestion event has occurred on the road where autonomous vehicles a and b are located; or,

[0103] When the preset work area corresponds to an unloading work area, the system first acquires the current speed information and real-time work information of the unmanned vehicles within that area. For example, the cloud control platform can obtain the real-time work information of the unmanned vehicles through the production scheduling module; this information displays the current work status of the unmanned vehicles, such as whether they are unloading or loading. Then, based on the current speed information and real-time work information, a judgment is made: if at least two unmanned vehicles within the unloading work area have a speed of zero and are not currently performing unloading operations, a traffic congestion event is determined to have occurred; or...

[0104] When the preset work area corresponds to the mining and loading work area, the current load information of the unmanned vehicles (UAVs) within the mining and loading work area is first obtained. For example, the UAVs can obtain their current load information through weight sensors or load sensing devices such as lidar. Based on the current load information of the UAVs within the mining and loading work area, a first number of unmanned vehicles operating without load is determined. If the first number is greater than or equal to a preset saturation value, it indicates that the number of unmanned vehicles operating without load has reached saturation, and a traffic congestion event is determined. And / or, based on the current load information of the UAVs within the mining and loading work area, the first number of unmanned vehicles operating without load is determined, and the number of excavators within the mining and loading work area is obtained, thereby determining a second number of unmanned vehicles operating without load per excavator (i.e., the average number of unmanned vehicles operating without load per excavator). Based on the second number, if the second number is greater than or equal to a preset saturation value for excavators, it indicates that the number of unmanned vehicles operating without load has reached saturation, and a traffic congestion event is determined.

[0105] In another embodiment of this disclosure, the method further includes: marking the map area corresponding to the second area on the map; and sending the marked map to the unmanned vehicle so that the unmanned vehicle can determine the working mode to be adopted based on the marked map and its own position. The working mode includes a first working mode or a second working mode.

[0106] In this embodiment of the disclosure, for example, the production map management module of the cloud control platform marks the map area corresponding to the second area range on the map; and through communication with the unmanned vehicle, it sends the marked map to the unmanned vehicle so that the unmanned vehicle can determine the working mode to be adopted based on the marked map and its own position. In this case, the working mode of the unmanned vehicle can be controlled by the cloud control platform or by the unmanned vehicle itself.

[0107] Furthermore, the operating mode can include either a first operating mode or a second operating mode. In the second operating mode, the vehicle's speed is lower than in the first operating mode. This allows the vehicle to operate more energy-efficiently and reach congested areas more quickly, thus reducing the load on those areas. And / or, the braking distance in the second operating mode is greater than in the first operating mode. The vehicle uses less braking force in the second operating mode, relying more on coasting for deceleration, thereby reducing energy loss due to braking. And / or, the acceleration during acceleration in the second operating mode is less than in the first operating mode; slower acceleration reduces energy consumption, thus saving energy. Additionally, the vehicle uses the second operating mode when operating outside intersections within the second area, and reverts to the first operating mode when operating within intersections. This avoids interfering with the normal passage of other vehicles, ensuring the safe passage of vehicles through intersections.

[0108] Optionally, in any of the above embodiments, the first preset distance and / or the second preset distance are related to environmental information, including either slippery conditions or dust conditions. The first preset distance and / or the second preset distance are positively correlated with the severity of slippery conditions or the severity of dust conditions. Extensive experimental verification shows that this embodiment can further improve energy-saving effects and operational efficiency.

[0109] Optionally, when any vehicle is in the second operating mode, it can broadcast information to other vehicles within a specified distance range. This broadcast information includes its own vehicle driving parameters in the second operating mode. After receiving the broadcast information, other vehicles parse the information, obtain its vehicle driving parameters, and adjust their own vehicle driving parameters accordingly. Optionally, the broadcast information may also include the driving length or coordinates of the energy-saving road segment corresponding to the vehicle in the second operating mode (e.g., the aforementioned first preset distance and / or second preset distance). Other vehicles obtain its vehicle driving parameters and the corresponding driving length or coordinates of the energy-saving road segment, and adjust their own vehicle driving parameters accordingly. This embodiment can, to some extent, operate independently of the cloud platform, especially in areas with poor network signals, effectively improving data processing speed and effectiveness.

[0110] Optionally, after the target vehicle receives broadcast information from other vehicles, including vehicle driving parameters and vehicle environmental attributes, it adjusts its own target vehicle driving parameters based on the target vehicle's current driving parameters and environmental attribute information. Specifically, the target vehicle collects its current perception data and / or combines it with map information to obtain current environmental attribute information. It compares the current environmental attributes with those sent by other vehicles. If the rating of the current environmental attributes is not lower than that of the other vehicles, it directly uses the driving parameters of the other vehicles as its own target vehicle driving parameters. If the rating of the current environmental attributes is lower than that of the other vehicles, it determines its own target vehicle driving parameters based on the target vehicle's current environmental attributes, vehicle model, the driving parameters of the other vehicles, and the vehicle models of the other vehicles. For example, this can be achieved by looking up a table, or by pre-training a deep learning model. This embodiment links the energy-saving adjustment methods of each vehicle together, enabling more effective energy-saving scheduling of the entire vehicle cluster.

[0111] Based on the same inventive concept, this disclosure also provides a scheduling device and cloud control platform for large-scale unmanned vehicle clusters. Since the principle of solving the problem by these devices and cloud control platforms is similar to the aforementioned scheduling method for large-scale unmanned vehicle clusters, the implementation of the devices and cloud control platforms can refer to the implementation of the aforementioned methods, and the repeated parts will not be described again.

[0112] This disclosure provides a scheduling device for large-scale unmanned vehicle clusters, such as... Figure 8 As shown, it includes:

[0113] The acquisition module 801 is used to acquire the operating status information of the dispatched unmanned vehicle cluster within the preset operating area;

[0114] The congestion determination module 802 is used to determine the first area range of the traffic congestion event when it is determined that a traffic congestion event has occurred based on the operation status information.

[0115] The mode switching module 803 is used to determine the second area range based on the first area range, and control the unmanned vehicle traveling within the second area range to switch from the first working mode to the second working mode, wherein, under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode.

[0116] In another embodiment of this disclosure, the mode switching module 803 is used to determine the region category corresponding to the preset work area; determine the corresponding preset energy-saving strategy according to the region category corresponding to the preset work area; and determine the second region range according to the first region range and the determined preset energy-saving strategy.

[0117] In another embodiment of this disclosure, the area category corresponding to the preset work area includes at least one of the following: local road driving area, loading and unloading operation area, and unloading operation area.

[0118] In another embodiment of this disclosure, when the area category is a local road driving area, the first area range is the road range occupied by the congested vehicle queue.

[0119] The mode switching module 803 is used to determine a first starting position of the congestion location based on the road range occupied by the congested vehicle queue; and to determine a second area range based on the first starting position and a first preset energy-saving area range; wherein the first preset energy-saving area range is a predefined area range extending upstream from the traffic congestion location by a first preset distance.

[0120] In another embodiment of this disclosure, when the area category is a mining and loading operation area, the first area range is the mining and loading operation area;

[0121] The mode switching module 803 is used to determine the range of the second area based on the mining and loading operation area and the working status of the unmanned vehicle outside the mining and loading operation area.

[0122] In another embodiment of this disclosure, the mode switching module 803 is used to determine the area where the unmanned vehicle is located outside the mining and loading operation area as the second area range.

[0123] In another embodiment of this disclosure, the loading and unloading operation area includes: the topology of all paths leading from the downstream intersection before the branching point on the path from the unloading operation area to the loading area to all loading areas, and all loading areas.

[0124] In another embodiment of this disclosure, when the area category is an unloading operation area, the first area range includes the unloading operation area or the entrance area of ​​the unloading operation area;

[0125] The mode switching module 803 is used to determine the entrance of the unloading operation area as the second starting position of the congestion location; and to determine the second area range based on the second starting position and the second preset energy-saving area range; wherein, the second preset energy-saving area range is a predefined area range extending upstream from the entrance of the unloading operation area by a second preset distance.

[0126] In another embodiment of this disclosure, when the area category corresponding to the preset operating area is a local road driving area, the operating status information includes: current location information and current vehicle speed information; the congestion determination module 802 is used to determine that a traffic congestion event has occurred if, based on the current location information and the current vehicle speed information, there are at least two unmanned vehicles with a speed of zero and a distance between the two unmanned vehicles not greater than a preset following distance; or,

[0127] When the preset work area corresponds to an unloading work area, the operating status information includes: current vehicle speed information and real-time work information; the congestion determination module 802 is used to determine a traffic congestion event based on the current vehicle speed information and real-time work information, if there are at least two unmanned vehicles in the unloading work area, satisfying the conditions that the vehicle speed is zero and no unloading operation is currently being performed; or...

[0128] When the area category corresponding to the preset work area is a mining and loading work area, the operating status information includes current load information; the congestion determination module 802 is used to determine a first number of unmanned vehicles operating without load in the mining and loading work area based on the current load information; if the first number is greater than or equal to a preset saturation value, a traffic congestion event is determined; and / or based on the current load information, determine a second number of unmanned vehicles operating without load per excavator in the mining and loading work area; if the second number is greater than or equal to a preset saturation value for excavators, a traffic congestion event is determined.

[0129] In another embodiment of this disclosure, the mode switching module 803 is further configured to mark the map area corresponding to the second area range on the map; and send the marked map to the unmanned vehicle so that the unmanned vehicle can determine the working mode to be adopted based on the marked map and its own position, the working mode including a first working mode or a second working mode.

[0130] This disclosure provides a cloud control platform, including: a scheduling device for large-scale unmanned vehicle clusters as described in any of the above embodiments.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of this disclosure can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0132] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the drawings are not necessarily essential for implementing this disclosure.

[0133] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0134] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0135] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

Claims

1. A scheduling method for large-scale unmanned vehicle clusters, characterized in that, include: Obtain the operational status information of the dispatched unmanned vehicle cluster within a preset work area; wherein, the area category corresponding to the preset work area includes local road driving area, loading and unloading operation area and / or unloading operation area; when the area category of the preset work area is local road driving area, the operational status information includes: current location information and current vehicle speed information; when the area category of the preset work area is unloading operation area, the operational status information includes: current vehicle speed information and real-time operation information; when the area category corresponding to the preset work area is loading and unloading operation area, the operational status information includes current load information; If a traffic congestion event is determined based on the aforementioned operational status information, the first area where the traffic congestion event occurred is determined. The second area is determined based on the first area range, and the unmanned vehicle traveling within the second area range is controlled to switch from the first working mode to the second working mode. Under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode. Determining the second area range based on the first area range includes: determining the area category corresponding to the preset work area; determining the corresponding preset energy-saving strategy based on the area category corresponding to the preset work area; and determining the second area range based on the first area range and the determined preset energy-saving strategy.

2. The method as described in claim 1, characterized in that, When the area category is a local road travel area, the first area range is the road range occupied by the congested vehicle queue; Determining the range of the second region based on the range of the first region includes: The first starting position of the congestion location is determined based on the road area occupied by the congested vehicle queue. The second region range is determined based on the first starting position and the first preset energy-saving region range; The first preset energy-saving area is a predefined area extending upstream from a traffic congestion location by a first preset distance.

3. The method as described in claim 1, characterized in that, When the area category is a mining and loading operation area, the first area range is the mining and loading operation area; Determining the range of the second region based on the range of the first region includes: The second area is determined based on the mining and loading operation area and the working status of the unmanned vehicles outside the mining and loading operation area.

4. The method as described in claim 3, characterized in that, The step of determining the range of the second area based on the mining and loading operation area and the working status of the unmanned vehicles outside the mining and loading operation area includes: The area where the unmanned vehicles operating outside the mining and loading area are located is defined as the second area.

5. The method as described in claim 1, characterized in that, The loading and unloading operation area includes: the topology of all paths leading from the downstream intersection before the branching point on the path from the unloading operation area to the loading area, and all loading areas.

6. The method as described in claim 1, characterized in that, When the area category is an unloading operation area, the first area range includes the unloading operation area, or the entrance area of ​​the unloading operation area; Determining the range of the second region based on the range of the first region includes: The entrance to the unloading operation area is determined as the second starting point of the congestion location; The second area range is determined based on the second starting position and the second preset energy-saving area range; The second preset energy-saving area is a predefined area that extends upstream from the entrance of the unloading operation area by a second preset distance.

7. The method as described in claim 1, characterized in that, When the area category corresponding to the preset operating area is a local road driving area, determining that a traffic congestion event has occurred based on the operating status information includes: based on the current location information and the current vehicle speed information, if there are at least two unmanned vehicles with zero speed and the distance between the two unmanned vehicles is not greater than a preset following distance, then a traffic congestion event is determined to have occurred; or, When the preset work area corresponds to an unloading work area, determining a traffic congestion event based on the operating status information includes: based on the current vehicle speed information and real-time work information, if there are at least two unmanned vehicles in the unloading work area, and their speeds are zero and unloading operations are not currently being performed, determining that a traffic congestion event has occurred; or... When the area category corresponding to the preset work area is a mining and loading work area, determining the occurrence of a traffic congestion event based on the operating status information includes: determining a first number of unmanned vehicles operating without load in the mining and loading work area based on the current load information; determining that a traffic congestion event has occurred if the first number is greater than or equal to a preset saturation value; and / or determining a second number of unmanned vehicles operating without load per excavator in the mining and loading work area based on the current load information; determining that a traffic congestion event has occurred if the second number is greater than or equal to a preset saturation value for excavators.

8. The method as described in claim 1, characterized in that, Also includes: Mark the map area corresponding to the second area range on the map; and The marked map is sent to the autonomous vehicle so that the autonomous vehicle can determine the working mode to be adopted based on the marked map and its own position. The working mode includes a first working mode or a second working mode.

9. A scheduling device for large-scale unmanned vehicle swarms, characterized in that, include: The acquisition module is used to acquire the operational status information of the dispatched unmanned vehicle cluster within a preset work area. The preset work area includes categories such as local road driving areas, loading / unloading operation areas, and / or unloading operation areas. When the preset work area is a local road driving area, the operational status information includes current location information and current vehicle speed information. When the preset work area is an unloading operation area, the operational status information includes current vehicle speed information and real-time operation information. When the preset work area is a loading / unloading operation area, the operational status information includes current load information. The congestion determination module is used to determine the first area range where a traffic congestion event has occurred, based on the operational status information. The mode switching module is used to determine the second area range based on the first area range, and control the unmanned vehicle driving in the second area range to switch from the first working mode to the second working mode, wherein, under the same working conditions, the energy consumption of the unmanned vehicle in the second working mode is lower than that in the first working mode. Determining the second area range based on the first area range includes: determining the area category corresponding to the preset work area; determining the corresponding preset energy-saving strategy based on the area category corresponding to the preset work area; and determining the second area range based on the first area range and the determined preset energy-saving strategy.

10. A cloud control platform, characterized in that, include: A scheduling device for large-scale unmanned vehicle clusters as described in claim 9.

Images