CN111645069A - Cloud platform scheduling system and method for high-intelligence robot - Google Patents
Cloud platform scheduling system and method for high-intelligence robot Download PDFInfo
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- CN111645069A CN111645069A CN202010415343.XA CN202010415343A CN111645069A CN 111645069 A CN111645069 A CN 111645069A CN 202010415343 A CN202010415343 A CN 202010415343A CN 111645069 A CN111645069 A CN 111645069A
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- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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Abstract
The invention provides a cloud platform scheduling system of a high intelligent robot and a scheduling method thereof, wherein the cloud platform scheduling system is connected with a plurality of high intelligent robots, and each high intelligent robot independently executes a corresponding first task; the cloud platform scheduling system comprises: the robot information acquisition module is used for acquiring real-time state information of each high-intelligent robot; the emergency task acquisition module is used for acquiring an emergency task instruction issued by a user; the scheduling module is used for selecting the optimal high-intelligent robot to issue an emergency task instruction according to the real-time state information; and the task adjusting module is used for putting a first task originally executed by the high-intelligent robot executing the emergency task into a message queue for waiting to be redistributed. The invention has the beneficial effects that: by remote scheduling of the cloud platform, cooperative scheduling and emergency task processing of a plurality of high-intelligent robots can be realized, and the application range of cooperative work of the high-intelligent robots and an actual scene is expanded.
Description
Technical Field
The invention relates to the technical field of high-intelligence robots, in particular to a cloud platform scheduling system and a scheduling method of a high-intelligence robot.
Background
The current high-intelligent robot can execute a corresponding task according to an instruction issued by a cloud, for example, when the cloud sends an instruction corresponding to a task to the high-intelligent robot, the high-intelligent robot moves to the task according to the instruction and automatically executes the task, so that a scheduling mode is needed to distribute the task to the corresponding high-intelligent robot for execution, and the purposes of saving the completion time, improving the efficiency of the high-intelligent robot and the like are achieved.
At present, a scheduling system in the prior art does not process emergent emergency tasks, but directly uses all the emergency tasks as common tasks to wait for being distributed to idle high-intelligent robots for processing, so that the prior art does not process the emergency tasks in time, and the waiting time of the emergency tasks is prolonged. Therefore, when an emergency task is suddenly performed, how to shorten the waiting time of the emergency task becomes an important problem to be solved by the invention.
Disclosure of Invention
Aiming at the problems in the prior art, a cloud platform scheduling system of a high-intelligent robot and a scheduling method thereof are provided, wherein the cloud platform scheduling system aims at improving the processing efficiency of a first task on the basis of improving the processing efficiency of an emergency task.
The specific technical scheme is as follows:
a cloud platform scheduling system of a high intelligent robot is connected with a plurality of high intelligent robots, and each high intelligent robot executes a corresponding first task;
the cloud platform scheduling system comprises:
the robot information acquisition module is used for acquiring real-time state information of each high-intelligent robot;
the emergency task acquisition module is used for acquiring an emergency task instruction issued by a user;
the dispatching module is respectively connected with the robot information acquisition module and the emergency task acquisition module and used for selecting the optimal high-intelligent robot to issue an emergency task instruction according to the state information so that the corresponding high-intelligent robot executes an emergency task according to the emergency instruction;
and the task adjusting module is respectively connected with the robot information acquiring module and the scheduling module and is used for putting a first task originally executed by the high-intelligent robot executing the emergency task into a message queue for waiting for redistribution.
Preferably, the cloud platform scheduling system, wherein the real-time status information includes real-time location information and real-time task execution information of the highly intelligent robot.
Preferably, the cloud platform scheduling system, wherein the real-time task execution information includes: the navigation process stage, the task execution stage and the task completion standby stage.
Preferably, the cloud platform scheduling system, wherein the scheduling module includes:
the first selection unit is used for selecting the high-intelligent robot of which the real-time task execution information is not in the task execution stage as a first high-intelligent execution robot;
the second selection unit is connected with the first selection unit and selects the first execution high-intelligence robot with the shortest relative distance with the execution position of the emergency task instruction from the first execution high-intelligence robots as a second execution high-intelligence robot;
and the scheduling unit is connected with the second selection unit and used for issuing an emergency instruction to the second execution high-intelligent robot so that the second execution high-intelligent robot executes an emergency task according to the emergency instruction.
Preferably, the cloud platform scheduling system, wherein the task adjusting module includes:
a message queue;
the first task acquisition unit is connected with the message queue and used for acquiring and putting a first task originally executed by the high-intelligent robot executing the emergency task into the message queue;
and the task allocation unit is connected with the message queue and used for allocating the first task in the message queue to the high-intelligent robot with the real-time task execution information as a task completion standby stage.
A scheduling method of an emergency task of a high intelligent robot is applied to any one of the cloud platform scheduling systems, the cloud platform scheduling system is connected with a plurality of high intelligent robots, and each high intelligent robot executes a corresponding first task;
the scheduling method comprises the following steps:
step S1, acquiring real-time state information of each high-intelligent robot; and
acquiring an emergency task instruction;
step S2, selecting the optimal high intelligent robot to issue an emergency instruction according to the real-time state information, so that the corresponding high intelligent robot executes an emergency task according to the emergency instruction;
step S3, the first task originally executed by the highly intelligent robot executing the emergency task is placed in a message queue for waiting for reallocation.
Preferably, the scheduling method, wherein the status information includes real-time location information and real-time task execution information of the highly intelligent robot.
Preferably, the scheduling method, wherein the real-time task execution information includes: the navigation process stage, the task execution stage and the task completion standby stage.
The technical scheme has the following advantages or beneficial effects: by remote scheduling of the cloud platform, cooperative scheduling and emergency task processing of a plurality of high-intelligent robots can be realized, and the application range of cooperative work of the high-intelligent robots and an actual scene is expanded.
Drawings
Embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings. The drawings are, however, to be regarded as illustrative and explanatory only and are not restrictive of the scope of the invention.
FIG. 1 is a schematic block diagram of an embodiment of a cloud platform scheduling system of a highly intelligent robot according to the present invention;
FIG. 2 is a schematic block diagram of a scheduling module of an embodiment of a cloud platform scheduling system of a highly intelligent robot according to the present invention;
fig. 3 is a schematic block diagram of a task adjustment module in an embodiment of the cloud platform scheduling system of the highly intelligent robot according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
The invention comprises a cloud platform scheduling system of a high intelligent robot, which is applied to the cloud platform scheduling system and can issue instructions corresponding to tasks to the high intelligent robot so that the high intelligent robot can execute the steps of avoiding obstacles to reach task places and executing corresponding tasks (wherein, the high intelligent robot is the prior art and is not elaborated herein in detail), the cloud platform scheduling system is connected with a plurality of high intelligent robots, and each high intelligent robot executes a corresponding first task; as shown in fig. 1, the cloud platform scheduling system includes:
the robot information acquisition module 1 is used for acquiring the state information of each high-intelligent robot;
the emergency task obtaining module 2 is used for obtaining the execution position of the emergency task;
the scheduling module 3 is respectively connected with the robot information acquisition module 1 and the emergency task acquisition module 2 and is used for selecting the corresponding high-intelligent robot to issue an emergency instruction according to the state information and the execution position so that the corresponding high-intelligent robot executes an emergency task according to the emergency instruction;
and the task adjusting module 4 is respectively connected with the robot information acquiring module 1 and the scheduling module 3, and is used for placing a first task originally executed by the high-intelligent robot executing the emergency task into a message queue 41 to wait for reallocation.
In the above embodiment, the scheduling module 3 selects the high-intelligence robot which is closest to the execution position of the emergency task and can preferentially execute the emergency task according to the state information and the execution position to issue the emergency instruction, so that the high-intelligence robot can quickly suspend the first task executed originally according to the emergency instruction and move to the execution position of the emergency task, and execute the emergency task after the high-intelligence robot reaches the execution position of the emergency task, thereby shortening the waiting time of the emergency task and improving the processing efficiency of the emergency task.
In the above embodiment, the first task originally executed by the high-intelligent robot executing the emergency task is placed in the message queue 41 to wait for reallocation, that is, the original high-intelligent robot is not required to continue executing the first task after the emergency task is executed, so that the waiting time of the first task originally executed is shortened.
Further, in the above-described embodiment, the state information includes real-time position information and real-time task execution information of the highly intelligent robot.
In the above embodiment, the highly intelligent robot closest to the relative distance to the execution position of the emergency task may be found from the real-time position information of the highly intelligent robot.
Further, in the above embodiment, the real-time task execution information includes: the navigation process stage, the task execution stage and the task completion standby stage.
In the above embodiment, when the real-time task execution information is in the navigation traveling stage, the representative high-intelligence robot does not start executing the task, but only the representative high-intelligence robot receives the task instruction issued by the cloud and is in the starting place of the previous task, so that the high-intelligence robot is not in an idle state at this time, but can suspend traveling to the starting place of the task to execute the emergency task;
when the real-time task execution information is in a task execution stage, the real-time task execution information represents the current task execution stage of the high-intelligent robot and cannot execute an emergency task;
the real-time task execution information represents that the high-intelligent robot has completed the currently executed task when the task is completed in the standby stage, namely, the high-intelligent robot is in an idle state and can execute an emergency task.
Further, in the above embodiment, as shown in fig. 2, the scheduling module 3 includes:
the first selection unit 31 selects a high-intelligence robot of which the real-time task execution information is not in the task execution stage as a first execution high-intelligence robot;
a second selecting unit 32 connected to the first selecting unit 31, for selecting, as a second execution high-intelligence robot, the first execution high-intelligence robot having the shortest relative distance to the execution position among the first execution high-intelligence robots;
and the scheduling unit 33 is connected with the second selecting unit 32, and issues an emergency instruction to the second execution high-intelligence robot, so that the second execution high-intelligence robot executes an emergency task according to the emergency instruction.
In the above embodiment, the high-intelligence robot whose real-time task execution information is not in the task execution phase within the preset range is first selected as the first high-intelligence robot, that is, the real-time task execution information of the first high-intelligence robot may be in the navigation progress phase or the task completion standby phase;
then, the first execution high-intelligence robot with the shortest relative distance to the execution position of the emergency task is selected as the second execution high-intelligence robot from among the first execution high-intelligence robots by the second selection unit 32;
finally, an emergency instruction is issued to the second execution high-intelligent robot through the scheduling unit 33, so that the second execution high-intelligent robot executes an emergency task according to the emergency instruction.
In the above embodiment, the highly intelligent robot that cannot execute the emergency task is excluded to obtain the highly intelligent robot that can execute the emergency task, and then the nearest highly intelligent robot is selected from the highly intelligent robots that can execute the emergency task to execute the emergency task, so that the waiting time of the emergency task is shortened, and the execution accuracy of the first task originally executed by the highly intelligent robot is not affected.
As a preferred embodiment, the preset range may be a circular area formed according to a preset radius with the execution position of the emergency task as a center, the preset radius may be 20 meters, and the specific preset radius may be set according to a requirement.
Further, in the above-described embodiment, as shown in fig. 3, the task adjustment module 4 includes:
a message queue 41;
a first task obtaining unit 42, connected to the message queue 41, for obtaining and placing a first task originally executed by the highly intelligent robot executing the emergency task into the message queue 41;
and the task allocation unit 43 is connected with the message queue 41 and is used for allocating the first task in the message queue 41 to the high-intelligent robot with the real-time task execution information as a task completion standby stage.
In the above embodiment, the first task originally executed by the high intelligent robot executing the emergency task is placed in the message queue 41 to wait for the idle high intelligent robot to execute, so that the execution efficiency of the first task is improved, and the processing efficiency of the first task is improved on the basis of improving the processing efficiency of the emergency task.
The high-intelligent robot system comprises a cloud platform scheduling system, a plurality of high-intelligent robots and a plurality of intelligent robots, wherein the cloud platform scheduling system can issue instructions corresponding to tasks to the high-intelligent robots, so that the high-intelligent robots can execute the instructions to avoid obstacles to reach task places and execute the corresponding tasks (the high-intelligent robots are the prior art and are not described in detail herein);
the scheduling method comprises the following steps:
step S1, acquiring state information of each high-intelligent robot; and
acquiring an execution position of an emergency task;
step S2, selecting the corresponding high intelligent robot to issue an emergency instruction according to the state information and the execution position, so that the corresponding high intelligent robot executes an emergency task according to the emergency instruction;
in step S3, the first task originally performed by the highly intelligent robot performing the emergency task is placed in a message queue 41 to wait for reallocation.
In the embodiment, firstly, the state information of each high-intelligent robot and the execution position of the emergency task are acquired;
then, according to the state information and the execution position, selecting a high-intelligent robot which is closest to the execution position of the emergency task in relative distance and can preferentially execute the emergency task to issue an emergency instruction, so that the high-intelligent robot can quickly suspend the originally executed first task according to the emergency instruction and move to the execution position of the emergency task, and execute the emergency task after the high-intelligent robot reaches the execution position of the emergency task, thereby shortening the waiting time of the emergency task and improving the processing efficiency of the emergency task;
finally, the first task originally executed by the highly intelligent robot executing the emergency task is placed in a message queue 41 to wait for reallocation, so that the waiting time of the originally executed first task can be shortened.
Further, in the above-described embodiment, the state information includes real-time position information and real-time task execution information of the highly intelligent robot.
Further, in the above embodiment, the real-time task execution information includes: the navigation process stage, the task execution stage and the task completion standby stage.
Further, in the above embodiment, step S2 specifically includes the following steps:
step S21, selecting a high intelligent robot with real-time task execution information not in a task execution stage as a first high intelligent robot to execute;
step S22, selecting the first execution high-intelligence robot with the shortest relative distance with the execution position as a second execution high-intelligence robot in the first execution high-intelligence robots;
and step S23, issuing an emergency instruction to the second execution high-intelligent robot so that the second execution high-intelligent robot executes an emergency task according to the emergency instruction.
In the above embodiment, first, in step S21, a high-intelligence robot whose real-time task execution information in a preset range is not in the task execution stage may be selected as the first high-intelligence robot;
then, in step S22, the first execution high-intelligence robot having the shortest relative distance to the execution position of the emergency task is selected as the second execution high-intelligence robot;
finally, in step S23, an emergency instruction is issued to the second execution high-intelligence robot, so that the second execution high-intelligence robot executes an emergency task according to the emergency instruction.
In the above embodiment, the highly intelligent robots incapable of performing the emergency task are first excluded to obtain the highly intelligent robots capable of performing the emergency task (i.e. the real-time task execution information is the navigation progress phase and the task completion standby phase), and then the nearest highly intelligent robot is selected from the highly intelligent robots capable of performing the emergency task to perform the emergency task, so that the waiting time of the emergency task is shortened, and the execution accuracy of the first task originally performed by the highly intelligent robots is not affected.
Further, in the above embodiment, the step S3 includes the steps of:
step S31, putting the first task originally executed by the highly intelligent robot executing the emergency task into the message queue 41;
in step S32, the first task in the message queue 41 is assigned to the high-intelligent robot whose real-time task execution information is the task completion standby stage.
In the above embodiment, the first task originally executed by the high intelligent robot executing the emergency task is placed in the message queue 41 to wait for the idle high intelligent robot to execute, so that the execution efficiency of the first task is improved, and the processing efficiency of the first task is improved on the basis of improving the processing efficiency of the emergency task.
In conclusion, through remote scheduling of the cloud platform, cooperative scheduling and emergency task processing of a plurality of high-intelligent robots can be achieved, and the application range of cooperative work of the high-intelligent robots and the actual scene is expanded.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (8)
1. The cloud platform scheduling system of the high-intelligent robot is characterized in that the cloud platform scheduling system is connected with a plurality of high-intelligent robots, and each high-intelligent robot executes a corresponding first task;
the cloud platform scheduling system comprises:
the robot information acquisition module is used for acquiring the real-time state information of each high-intelligent robot;
the emergency task acquisition module is used for acquiring an emergency task instruction issued by a user;
the dispatching module is respectively connected with the robot information acquisition module and the emergency task acquisition module and is used for selecting the optimal high-intelligent robot to issue the emergency task instruction according to the state information so that the corresponding high-intelligent robot executes an emergency task according to the emergency instruction;
and the task adjusting module is respectively connected with the robot information acquiring module and the scheduling module and is used for putting the first task which is originally executed by the high-intelligent robot executing the emergency task into a message queue for waiting for reallocation.
2. The cloud platform scheduling system of claim 1 wherein the real-time status information comprises real-time location information and real-time task execution information of the high-intelligence robot.
3. The cloud platform scheduling system of claim 2, wherein the real-time task execution information comprises: the navigation process stage, the task execution stage and the task completion standby stage.
4. The cloud platform scheduling system of claim 3, wherein the scheduling module comprises:
the first selection unit is used for selecting the high-intelligent robot of which the real-time task execution information is not in the task execution stage as a first high-intelligent robot to execute;
a second selection unit connected to the first selection unit, and configured to select, as a second execution highly intelligent robot, the first execution highly intelligent robot having the shortest relative distance to the execution position of the emergency task instruction from among the first execution highly intelligent robots;
and the scheduling unit is connected with the second selection unit and used for issuing the emergency instruction to the second execution high-intelligent robot so that the second execution high-intelligent robot executes the emergency task according to the emergency instruction.
5. The cloud platform scheduling system of claim 3, wherein the task adjustment module comprises:
the message queue;
a first task obtaining unit, connected to the message queue, configured to obtain and place the first task originally executed by the highly intelligent robot executing the emergency task into the message queue;
and the task allocation unit is connected with the message queue and used for allocating the first task in the message queue to the high-intelligent robot with the real-time task execution information in the task completion standby stage.
6. The method for scheduling the emergency task of the high-intelligence robot is applied to the cloud platform scheduling system according to any one of claims 1 to 5, wherein the cloud platform scheduling system is connected with a plurality of high-intelligence robots, and each high-intelligence robot executes a corresponding first task;
the scheduling method comprises the following steps:
step S1, acquiring real-time state information of each high-intelligent robot; and
acquiring an emergency task instruction;
step S2, selecting the optimal high intelligent robot to issue the emergency instruction according to the real-time state information, so that the corresponding high intelligent robot executes the emergency task according to the emergency instruction;
step S3, the first task originally executed by the high-intelligent robot executing the emergency task is placed in a message queue for waiting for reallocation.
7. The method of scheduling an emergency task of claim 6, wherein the status information includes real-time location information and real-time task execution information of the high intelligent robot.
8. The method for scheduling an emergency task according to claim 7, wherein the real-time task execution information includes: the navigation process stage, the task execution stage and the task completion standby stage.
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Application publication date: 20200911 |