CN107103388B - Robot scheduling system and method based on demand prediction - Google Patents
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Abstract
The invention discloses a robot scheduling system and method based on demand prediction, the scheme schedules a robot in place before a user sends a use request according to user use data and prior knowledge, and the reservation and waiting time required by the user to use the robot is shortened; meanwhile, the robot distribution is dynamically dispatched according to the prediction of the user demand in real time, the condition that the robot blindly waits for service reservation is reduced, and the service efficiency of the robot is improved.
Description
Technical Field
The invention relates to the technical field of robot scheduling, in particular to a robot scheduling system based on demand prediction.
Background
With the development of technology and the change of population structure, the robot gradually enters the daily life of people. For a robot facing public services, a scheduling system is needed to schedule the trips and tasks of the robot. Conventional robot scheduling systems often rely on service requests (reservations) sent by users or fixed operating plans for scheduling.
However, a scheduling system based on a user request (reservation) can schedule a robot only after a user demand is issued, resulting in a time process for the user to wait for the robot to be in place or to "look for" the robot. This approach can result in untimely service or require a large number of robots for intensive deployment, resulting in wasted resources.
Disclosure of Invention
The invention aims to provide a demand prediction-based robot scheduling system and method, which can schedule a robot to a target place required by a user by predicting the demand of the robot used by the user and prior to the request of the user, meet the instant demand of the user on robot service and realize robots 'and the like'.
The purpose of the invention is realized by the following technical scheme:
a demand forecast based robot scheduling system comprising: the scheduling analysis module, the scheduling execution module and the scheduling learning module; wherein:
the scheduling analysis module is used for generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request and forming a new scheduling plan in real time according to the scheduling result fed back by the scheduling execution module;
the scheduling execution module is used for dynamically scheduling the robot according to the received scheduling plan and feeding back a scheduling result to the scheduling analysis module and the scheduling learning module;
and the scheduling learning module is used for analyzing according to the scheduling result and the actual utilization rate of the robot, excavating a scheduling method for improving the actual utilization rate of the robot and a robot distribution scheme, and further optimizing the scheduling analysis module.
The working process of the scheduling analysis module is as follows:
robot distribution initialization: establishing the initialization distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation;
generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests: a. adjusting robot distribution according to the planned events: when a scheduling analysis module receives a planning event which possibly influences the use of the robot, according to the number of participators in the planning event and the prediction of the number of the robots in use, the robot with the prediction use rate lower than a first threshold value area is allocated to an area required by the planning event in advance to form a corresponding first scheduling plan; b. adjusting robot distribution according to the density of served target users: when the probability that the user uses the robot can be predicted by using historical data, the robot in the region with the use probability lower than the second threshold value is dispatched to the region with the use probability higher than the third threshold value, and the use probability density of the user in the dispatched region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; forming a corresponding second dispatching plan according to the mode; c. adjusting robot distribution according to a reservation request of a user: when the scheduling analysis module receives a robot reservation request of a user, nearby robots are scheduled to a reservation destination of the user, and at the moment, a corresponding third scheduling plan is formed according to the user request;
and (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
The robot is an electromechanical system with autonomous movement capability, comprising: robot, unmanned car, unmanned aerial vehicle and unmanned ship.
A robot scheduling method based on demand prediction is realized based on the system and comprises the following steps:
scheduling analysis and scheduling plan generation: generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request, and forming a new scheduling plan in real time according to the fed-back scheduling result;
scheduling and executing: dynamically scheduling the robot according to the received scheduling plan, and outputting a scheduling result outwards;
scheduling learning: and analyzing according to the scheduling result and the actual utilization rate of the robot, and mining a scheduling method and a robot distribution scheme for improving the actual utilization rate of the robot so as to optimize the scheduling analysis and scheduling plan generation process.
The working process of the scheduling analysis and the scheduling plan generation is as follows:
robot distribution initialization: establishing the initialization distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation;
generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests: a. adjusting robot distribution according to the planned events: when a scheduling analysis module receives a planning event which possibly influences the use of the robot, according to the number of participators in the planning event and the prediction of the number of the robots in use, the robot with the prediction use rate lower than a first threshold value area is allocated to an area required by the planning event in advance to form a corresponding first scheduling plan; b. adjusting robot distribution according to the density of served target users: when the probability that the user uses the robot can be predicted by using historical data, the robot in the region with the use probability lower than the second threshold value is dispatched to the region with the use probability higher than the third threshold value, and the use probability density of the user in the dispatched region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; forming a corresponding second dispatching plan according to the mode; c. adjusting robot distribution according to a reservation request of a user: when the scheduling analysis module receives a robot reservation request of a user, nearby robots are scheduled to a reservation destination of the user, and at the moment, a corresponding third scheduling plan is formed according to the user request;
and (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
The robot is an electromechanical system with autonomous movement capability, comprising: robot, unmanned car, unmanned aerial vehicle and unmanned ship.
According to the technical scheme provided by the invention, the robot is scheduled in place before the user sends the use request according to the user use data and the prior knowledge, so that the reservation and waiting time required by the user to use the robot are shortened; meanwhile, the robot distribution is dynamically dispatched according to the prediction of the user demand in real time, the condition that the robot blindly waits for service reservation is reduced, and the service efficiency of the robot is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic diagram of a demand prediction-based robot scheduling system according to an embodiment of the present invention;
FIG. 2 is a flowchart of a robot scheduling system based on demand forecasting according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a work flow of a scheduling analysis module according to an embodiment of the present invention;
fig. 4 is a flowchart of a robot scheduling method based on demand prediction according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are 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 embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic diagram of a demand prediction-based robot scheduling system according to an embodiment of the present invention. As shown in fig. 1, it mainly includes: the scheduling analysis module, the scheduling execution module and the scheduling learning module.
As shown in fig. 2, the functions and working processes of the various modules of the system are as follows:
1. and a scheduling analysis module. And generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request, and forming a new scheduling plan in real time according to the scheduling result fed back by the scheduling execution module.
2. And scheduling the execution module. And dynamically scheduling the robot according to the received scheduling plan, and feeding back a scheduling result to the scheduling analysis module and the scheduling learning module.
In the embodiment of the present invention, the robot includes, but is not limited to, any electromechanical system with autonomous moving capability, such as a robot, an unmanned vehicle, an unmanned aerial vehicle, an unmanned ship, and the like.
3. And a scheduling learning module. And analyzing according to the scheduling result and the actual utilization rate of the robot, and mining a scheduling method and a robot distribution scheme for improving the actual utilization rate of the robot so as to optimize a scheduling analysis module. The optimized dispatch analysis module herein may include an initialized profile that improves the robot for use by the dispatch analysis module.
In the embodiment of the invention, the scheduling analysis module and the scheduling execution module can perform analysis and execution operation in real time, that is, the distribution condition of the robot is adjusted in real time, so that the condition that the robot waits for service reservation blindly is reduced, and the service efficiency of the robot is improved.
In the embodiment of the present invention, the working process of the scheduling analysis module is as shown in fig. 3, and mainly includes the following parts:
1. robot distribution initialization: and establishing the initialized distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation.
2. Generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests:
a. and (4) event pre-judgment adjustment.
Adjusting robot distribution according to the planned events: when the dispatching analysis module receives a planning event which may affect the use of the robot, the robot with the estimated use rate lower than a first threshold value is dispatched to an area required by the planning event in advance according to the number of the participators in the planning event and the estimation of the number of the robots to use, and a corresponding first dispatching plan is formed.
b. And adjusting the user density.
Adjusting robot distribution according to the density of served target users: when the probability of the robot used by the user can be predicted by using historical data (for example, prediction is performed by registering the use habit and other historical data of the user), the robot in the region with the use probability lower than the second threshold value is scheduled to the region with the use probability higher than the third threshold value, and the use probability density of the user in the scheduled region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; according to the above manner, a corresponding second scheduling plan is formed.
c. And adjusting the reservation request.
Adjusting robot distribution according to a reservation request of a user: and when the scheduling analysis module receives a robot reservation request of the user, scheduling the nearby robot to a reservation destination of the user, and forming a corresponding third scheduling plan according to the user request.
Those skilled in the art will appreciate that the first threshold, the second threshold, and the third threshold may be set according to requirements, and the first threshold, the second threshold, and the third threshold before the dispatch plan are only used for identifying different dispatch plans, and are not limited. In addition, the above lists three scheduling plans, and in practical cases, only one of the three scheduling plans may appear, and of course, the three scheduling plans may also appear simultaneously.
3. And (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
According to the system provided by the embodiment of the invention, the robot is scheduled in place before the user sends the use request according to the user use data and the prior knowledge, so that the reservation and waiting time required by the user to use the robot is shortened; meanwhile, the robot distribution is dynamically dispatched according to the prediction of the user demand in real time, the condition that the robot blindly waits for service reservation is reduced, and the service efficiency of the robot is improved.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to perform all or part of the above described functions.
Another embodiment of the present invention further provides a demand prediction-based robot scheduling method, which is implemented based on the system provided in the foregoing embodiment, as shown in fig. 4, and mainly includes the following steps:
scheduling analysis and scheduling plan generation: generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request, and forming a new scheduling plan in real time according to the fed-back scheduling result;
scheduling and executing: dynamically scheduling the robot according to the received scheduling plan, and outputting a scheduling result outwards;
scheduling learning: and analyzing according to the scheduling result and the actual utilization rate of the robot, and mining a scheduling method and a robot distribution scheme for improving the actual utilization rate of the robot so as to optimize the scheduling analysis and scheduling plan generation process.
In the embodiment of the present invention, the working process of the scheduling analysis and the scheduling plan generation is as follows:
robot distribution initialization: establishing the initialization distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation;
generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests: a. adjusting robot distribution according to the planned events: when a scheduling analysis module receives a planning event which possibly influences the use of the robot, according to the number of participators in the planning event and the prediction of the number of the robots in use, the robot with the prediction use rate lower than a first threshold value area is allocated to an area required by the planning event in advance to form a corresponding first scheduling plan; b. adjusting robot distribution according to the density of served target users: when the probability that the user uses the robot can be predicted by using historical data, the robot in the region with the use probability lower than the second threshold value is dispatched to the region with the use probability higher than the third threshold value, and the use probability density of the user in the dispatched region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; forming a corresponding second dispatching plan according to the mode; c. adjusting robot distribution according to a reservation request of a user: when the scheduling analysis module receives a robot reservation request of a user, nearby robots are scheduled to a reservation destination of the user, and at the moment, a corresponding third scheduling plan is formed according to the user request;
and (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
It should be noted that, the specific implementation manner of the above method has been described in detail in the foregoing system embodiment, and therefore, is not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A demand forecast based robot scheduling system comprising: the scheduling analysis module, the scheduling execution module and the scheduling learning module; wherein:
the scheduling analysis module is used for generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request and forming a new scheduling plan in real time according to the scheduling result fed back by the scheduling execution module;
the scheduling execution module is used for dynamically scheduling the robot according to the received scheduling plan and feeding back a scheduling result to the scheduling analysis module and the scheduling learning module;
the dispatching learning module is used for analyzing according to a dispatching result and the actual utilization rate of the robot, excavating a dispatching method for improving the actual utilization rate of the robot and a robot distribution scheme, and further optimizing the dispatching analysis module;
the working process of the scheduling analysis module is as follows:
robot distribution initialization: establishing the initialization distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation;
generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests: a. adjusting robot distribution according to the planned events: when a scheduling analysis module receives a planning event which possibly influences the use of the robot, according to the number of participators in the planning event and the prediction of the number of the robots in use, the robot with the prediction use rate lower than a first threshold value area is allocated to an area required by the planning event in advance to form a corresponding first scheduling plan; b. adjusting robot distribution according to the density of served target users: when the probability that the user uses the robot can be predicted by using historical data, the robot in the region with the use probability lower than the second threshold value is dispatched to the region with the use probability higher than the third threshold value, and the use probability density of the user in the dispatched region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; adjusting the robot distribution according to the density of the served target users to form a corresponding second scheduling plan; c. adjusting robot distribution according to a reservation request of a user: when the scheduling analysis module receives a robot reservation request of a user, nearby robots are scheduled to a reservation destination of the user, and at the moment, a corresponding third scheduling plan is formed according to the user request;
and (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
2. The demand forecast based robot scheduling system of claim 1 wherein said robot is an electromechanical system with autonomous mobility, comprising: robot, unmanned car, unmanned aerial vehicle and unmanned ship.
3. A demand forecast based robot scheduling method, implemented based on the system of claim 1 or 2, comprising:
scheduling analysis and scheduling plan generation: generating a corresponding scheduling plan according to the plan event, the user density and/or the reservation request, and forming a new scheduling plan in real time according to the fed-back scheduling result;
scheduling and executing: dynamically scheduling the robot according to the received scheduling plan, and outputting a scheduling result outwards;
scheduling learning: analyzing according to the scheduling result and the actual utilization rate of the robot, and mining a scheduling method and a robot distribution scheme for improving the actual utilization rate of the robot so as to optimize the scheduling analysis and scheduling plan generation process;
the working process of the scheduling analysis and the scheduling plan generation is as follows:
robot distribution initialization: establishing the initialization distribution of the robot in space and time according to the historical data and the prior knowledge of the robot operation;
generating a corresponding dispatch plan based on the plan events, the user density, and/or the reservation requests: a. adjusting robot distribution according to the planned events: when a scheduling analysis module receives a planning event which possibly influences the use of the robot, according to the number of participators in the planning event and the prediction of the number of the robots in use, the robot with the prediction use rate lower than a first threshold value area is allocated to an area required by the planning event in advance to form a corresponding first scheduling plan; b. adjusting robot distribution according to the density of served target users: when the probability that the user uses the robot can be predicted by using historical data, the robot in the region with the use probability lower than the second threshold value is dispatched to the region with the use probability higher than the third threshold value, and the use probability density of the user in the dispatched region is matched with the robot density; when the probability that the robot is used by the user cannot be predicted, the probability that the robot is used by each user is equal by default, the user use probability density is equal to the user crowd density, the robot in the area with the use probability lower than the second threshold value is dispatched to the area with the use probability higher than the third threshold value, and the user use probability density in the dispatched area is matched with the robot density; adjusting the robot distribution according to the density of the served target users to form a corresponding second scheduling plan; c. adjusting robot distribution according to a reservation request of a user: when the scheduling analysis module receives a robot reservation request of a user, nearby robots are scheduled to a reservation destination of the user, and at the moment, a corresponding third scheduling plan is formed according to the user request;
and (3) global evaluation: forming expected distribution of the robot according to the first dispatching plan, the second dispatching plan and/or the third dispatching plan; and performing global robot distribution evaluation, calculating the ratio of the user use probability density of each region to the robot density, performing dynamic adjustment to enable the ratio of each region to be in a target interval, and forming a final scheduling plan to be output to a scheduling execution module.
4. The demand forecasting-based robot scheduling method according to claim 3, wherein the robot is an electromechanical system with autonomous moving capability, and the method comprises the following steps: robot, unmanned car, unmanned aerial vehicle and unmanned ship.
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| CN109686087B (en) * | 2018-12-28 | 2021-02-02 | 西安艾润物联网技术服务有限责任公司 | Management method and device for patrol robot |
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| CN107103388A (en) | 2017-08-29 |
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