CN110712206A - Multitask allocation method, multitask allocation device, multitask allocation equipment and storage medium of intelligent robot - Google Patents
Multitask allocation method, multitask allocation device, multitask allocation equipment and storage medium of intelligent robot Download PDFInfo
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- CN110712206A CN110712206A CN201910982353.9A CN201910982353A CN110712206A CN 110712206 A CN110712206 A CN 110712206A CN 201910982353 A CN201910982353 A CN 201910982353A CN 110712206 A CN110712206 A CN 110712206A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1682—Dual arm manipulator; Coordination of several manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
Abstract
The invention discloses a multitask allocation method, a multitask allocation device, computer equipment and a storage medium of an intelligent robot.
Description
Technical Field
The invention relates to the technical field of task allocation, in particular to a multitask allocation method and device of an intelligent robot, computer equipment and a storage medium.
Background
With the development of social economy and the progress of robotics, more and more hotels start to purchase intelligent hotel robots so that hotel services are more intelligent and safe. The intelligent robot is applied to the hotel industry, not only conforms to the intelligent trend, but also better accords with the intelligent consumption viewpoint of people. Currently, the intelligent robot used in the hotel industry is usually requested by a user, and the intelligent robot processes related tasks indicated by the user. Because the hotel has many users and a large task amount, in the prior art, after a request is received each time, a current idle intelligent robot is arranged to serve, and the problems of low utilization rate of the intelligent robot, high task time cost and the like are not considered.
Disclosure of Invention
The invention aims to solve the problems of low utilization rate and high cost of an intelligent robot in the prior art, and provides a multitask allocation method and device of the intelligent robot, computer equipment and a storage medium.
In one aspect of the present invention, a multitask allocation method for an intelligent robot is provided, including:
receiving task requests sent by users to the intelligent robot, wherein each user sends at least one task request;
determining the total number of tasks corresponding to the task request, and determining the time consumed by the intelligent robot to execute each task;
acquiring the total number of currently available intelligent robots;
calculating an intelligent robot allocation strategy under the current decision time by using a preset target algorithm by taking the total number of the tasks, the consumed time of each task and the total number of the currently available intelligent robots as input parameters, wherein the allocation strategy is used for allocating the intelligent robots for processing the tasks for different users;
and controlling the intelligent robot to process the task of the corresponding user based on the allocation strategy.
Optionally, the calculating, by using a preset target algorithm, an allocation policy of the intelligent robot at the current decision time includes:
calling a preset target function of the target algorithm;
determining constraints of the target algorithm;
and solving the optimal solution of the book searching objective function by utilizing a particle swarm intelligent algorithm to obtain the distribution strategy.
Optionally, the objective function is:
wherein F is an objective function value of the target algorithm, T is a time to be decided by the target algorithm, and the time belongs to a decision time set T, omega1A time cost weight, ω, of the time required for task completion in the target algorithm2Economic cost weight, omega, for an intelligent robot for task completion in the target algorithm3Is a utilization factor weight, Z, of the intelligent robot utilization in the target algorithmB(j)The total number of tasks of the jth user is b (j) {1, …, b }j，…，ZB(j)}，ZM(t)For the total number of intelligent robots available in the t-period, the set of intelligent robots in the t-period is set to M ═ 1, …, i, …, ZM(t)]And the set of users needing service by the user intelligent robot in the t period is set as N ═ 1, …, j, …, ZN(t)]，C(bj) Is the b thjThe time consumption of an individual task; x is the number ofijIs a decision variable of the target algorithm.
Optionally, the xijDetermined by the following equation:
optionally, the constraint condition includes: in unit decision time, each intelligent robot is only used for serving one user, and each user is only allocated with one intelligent robot for serving in unit decision time.
Optionally, the constraint condition is:
in the formula, M is a set of available intelligent robots in the t period, N is a set of users needing services of the intelligent robots in the hotel in the t period, and xijIndicating whether the j user's task is served by the i intelligent robot.
Optionally, after controlling the intelligent robot to process the task of the corresponding user, the method further includes:
and after the intelligent robot finishes the service of the user, controlling the intelligent robot to carry out charging and maintenance.
In another aspect of the embodiments of the present invention, a multitask allocation device for an intelligent robot is provided, including:
the system comprises a receiving module, a task processing module and a task processing module, wherein the receiving module is used for receiving task requests of the intelligent robot sent by users, and each user sends at least one task request;
the determining module is used for determining the total number of tasks corresponding to the task request and determining the time consumed by the intelligent robot to execute each task;
the acquisition module is used for acquiring the total number of the currently available intelligent robots;
the computing module is used for computing an intelligent robot allocation strategy at the current decision time by using a preset target algorithm by taking the total number of the tasks, the consumed time of each task and the total number of the currently available intelligent robots as input parameters, wherein the allocation strategy is used for allocating the intelligent robots for processing the tasks for different users;
and the control module is used for controlling the intelligent robot to process the task corresponding to the user based on the distribution strategy.
In another aspect of the embodiments of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the method when executing the computer program.
In another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program: the computer program realizes the steps of the method when executed by a processor.
According to the embodiment of the invention, the total number of the currently available intelligent robots is obtained by receiving the task requests sent by the users, determining the total number of the tasks of all the users and the completion time of each task based on the task requests, then calculating the optimal allocation strategy for the intelligent robots with the minimum task completion time by using a target algorithm, and then controlling the corresponding intelligent robots to process the corresponding tasks, thereby achieving the technical effects of improving the utilization rate of the intelligent robots and reducing the cost of the task completion time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a multitask allocation method for an intelligent robot according to an embodiment of the present invention;
FIG. 2 is a diagram of a message queue according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multitask allocation device of an intelligent robot in an embodiment of the invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
An embodiment of the present invention provides a multitask allocation system for an intelligent robot, and as shown in fig. 1, the system mainly includes a control center server and an intelligent robot. For different application scenarios, the tasks that the intelligent robot can accomplish may be different, for example, the intelligent robot used in a hotel may accomplish tasks of a user, such as identification, welcome, room inquiry and reservation, location navigation, check-in and check-out, check-out and check-in, meal ordering, goods transportation, and baggage handling. The method aims at the problem of task allocation in a static environment in which multiple tasks correspond to multiple intelligent robot services in a hotel.
Each user has a single or multiple tasks, each task can be performed on multiple intelligent robots. The tasks have a precedence order and need to be serviced strictly in accordance with the task order. The task allocation optimization method provided by the embodiment of the invention has the main purposes of reasonably allocating each task and the corresponding intelligent robot, so that the purposes of minimizing the time for completing the tasks by a user, minimizing the cost, maximizing the utilization rate of the intelligent robot in the hotel and the like are achieved.
The user sends a task request to the control center server through a terminal or other ways, the control center server performs allocation strategy optimization based on the task request to generate an allocation strategy, and then the intelligent robot is controlled to process the corresponding task of the user based on the strategy. The system can be used in service places such as hotels and the like, and the application places of the system are not limited by the invention.
The embodiment of the invention provides a multitask allocation method of an intelligent robot, which can be executed by a control center server used for managing the intelligent robot by a background. As shown in fig. 2, the method includes:
step S201, receiving task requests for the intelligent robot sent by each user, wherein each user sends at least one task request. As described above, the number of users may be multiple, each user has one or more task requests, and each task request corresponds to one task, that is, a service item required by the user.
Step S202, determining the total number of tasks corresponding to the task request, and determining the time consumed by the intelligent robot to execute each task.
And step S203, acquiring the total number of the currently available intelligent robots. The available intelligent robot refers to the intelligent robot which is in an idle standby state at present. The intelligent robot in a charging or maintenance state or in an operating state does not belong to the available intelligent robots.
The total number of tasks may be determined for each user and then summed, and since the task category is fixed in each scenario, the completion time of each task may be predetermined, such as a refuge task, and the time for completion of the process may be determined to be 5 minutes after a plurality of trials (the average time of a plurality of tasks is taken as the time for completing the task). This allows the completion time of each task to be determined. Since the request of the user is persistent, the task request, the total number of tasks, and the total number of available intelligent robots in the embodiment of the present invention all refer to the number in the current decision time period. Each of the above quantities is a parameter for calculating the allocation policy of the current intelligent robot.
And step S204, taking the total number of tasks, the consumed time of each task and the total number of the currently available intelligent robots as input parameters, and calculating an intelligent robot allocation strategy at the current decision time by using a preset target algorithm, wherein the allocation strategy is used for allocating the intelligent robots for processing the tasks for different users.
Since each task corresponds to one user, the total number of tasks is determined, that is, the number of given users can be determined. In the embodiment of the invention, the total number of tasks, the consumed time of each task and the total number of available intelligent robots are used as input parameters, and the allocation strategy of the intelligent robots is calculated by using a preset target algorithm, so that the optimal allocation strategy of the intelligent robots is realized.
And S205, controlling the intelligent robot to process the task of the corresponding user based on the allocation strategy.
After the allocation strategy of the intelligent robot is calculated, the intelligent robot is controlled to complete the corresponding task of the corresponding user based on the allocation strategy, and quick and high-quality service is provided for the user.
According to the embodiment of the invention, the total number of the currently available intelligent robots is obtained by receiving the task requests sent by the users, determining the total number of the tasks of all the users and the completion time of each task based on the task requests, then calculating the optimal allocation strategy for the intelligent robots with the minimum task completion time by using a target algorithm, and then controlling the corresponding intelligent robots to process the corresponding tasks, thereby achieving the technical effects of improving the utilization rate of the intelligent robots and reducing the cost of the task completion time.
As an optional implementation manner, in the embodiment of the present invention, relevant parameters for target computation may be set, which specifically include:
the method comprises the steps of optimizing decision total duration (set to H) by the intelligent robot, optimizing decision unit duration (set to H) by the intelligent robot, and setting decision time set (set to T ═ 1, …, T, … and Z)tWherein H ═ ZtH), a set of smart robots available for a period of t (set to M ═ 1, …, i, …, Z)M(t)]Wherein the total number of the intelligent robots available in the t period is ZM(t)) The set of users (set to N ═ 1, …, j, …, Z) that the intelligent robot needs to serve during the t periodN(t)]Wherein the total number of users in the t period is ZN(t)) A set of tasks that each user needs to complete (a set of the number of tasks of the jth user is set as b (j) ═ 1, …, b)j，…，ZB(j)J, wherein the total number of tasks of the j-th user is ZB(j)) The set of task elapsed times that each user needs to complete (the set of task elapsed times for the jth user is set to C (j) ═ C (1), …, C (b)j)，…，c(ZB(j)) Wherein b isjThe time consumed for each task is c (b)j))。
As an optional implementation manner, in step S204, the calculating, by using a preset target algorithm, an intelligent robot allocation policy at the current decision time includes: calling a preset target function of the target algorithm; determining constraints of the target algorithm; and solving the optimal solution of the book searching objective function by utilizing a particle swarm intelligent algorithm to obtain the distribution strategy.
The objective function is a function for solving the optimal allocation policy, and an optional objective function in the embodiment of the present invention is:
wherein F is an objective function value of the target algorithm, T is a time to be decided by the target algorithm, and the time belongs to a decision time set T, omega1A time cost weight, ω, of the time required for task completion in the target algorithm2Economic cost weight, omega, for an intelligent robot for task completion in the target algorithm3Is a utilization factor weight, Z, of the intelligent robot utilization in the target algorithmB(j)The total number of tasks of the jth user is b (j) {1, …, b }j，…，ZB(j)}，ZM(t)For the total number of intelligent robots available in the t-period, the set of intelligent robots in the t-period is set to M ═ 1, …, i, …, ZM(t)]And the set of users needing service by the user intelligent robot in the t period is set as N ═ 1, …, j, …, ZN(t)]，C(bj) Is the b thjThe time consumption of an individual task; x is the number ofijIs a decision variable of the target algorithm.
In the embodiment of the invention, the objective function is adopted to reasonably distribute each task and the corresponding intelligent robot, so that the time for completing the task is minimum, the cost is minimum, and the utilization rate of the intelligent robot is maximum.
It should be noted that the objective function is only an alternative to the embodiment of the present invention, and those skilled in the art can adjust the objective function according to actual needs, for example, ω of the above-mentioned objective function can be adjusted when economic cost of only a robot is not considered, and ω of the above-mentioned objective function can be adjusted according to actual needs2The corresponding variable is removed, and the weight value is assigned to other quantities, so that the following objective function is obtained:
further, x isijThe value is determined by the following formula, and is a decision variable of the algorithm, and the value is zero-one variable to indicate whether the task of the jth user is served by the ith intelligent robot.
C(bj) Is the b thjTime of individual task, ZB(j)Total number of tasks for jth user, ZM(t)The total number of smart robots available for the t period,
the number of intelligent robots used in the period of t, then
Is the utilization rate of the intelligent robot in the time period t.
In each short time interval, namely the period t, the charging, maintenance and other work of the robot are considered, each robot only serves one user in each unit optimization decision stage, the charging and maintenance can be carried out after the service task is completed, and whether the robot can be used as an available intelligent robot in the next optimization decision stage again can be judged according to the electric quantity state and the equipment state after the charging and maintenance work is finished. That is to say, in order to improve the overall operation efficiency of the robot only, after controlling the intelligent robot to process the task of the corresponding user, the embodiment of the present invention further includes: and after the intelligent robot finishes the service of the user, controlling the intelligent robot to carry out charging and maintenance.
Meanwhile, in consideration of user experience, in each short time interval, namely the period t, after the task of one user is served by one intelligent robot, the task is not required to be switched and then undertaken by other robots, and the user can be served by the intelligent robot or other intelligent robots unless the period is finished and the next optimization period is started, and the task is not completed. In summary, each intelligent robot is only used for servicing one user in a unit decision time, and each user is only allocated to one intelligent robot for servicing in a unit decision time. From the above conditions, constraint conditions expressed by the following formula in the algorithm are set:
in the formula, M is a set of available intelligent robots in the t period, N is a set of users needing services of the intelligent robots in the hotel in the t period, and xijIndicating whether the j user's task is served by the i intelligent robot.
In the embodiment of the invention, in the calculation process of the optimal solution of the objective function, a particle swarm intelligent algorithm can be adopted for solving,
the characteristics of the intelligent particle swarm algorithm comprise: particle Swarm Optimization (PSO) is an evolutionary computing technique. The algorithm is derived from behavioral studies on the predation of a flock of birds. The basic idea of the particle swarm optimization algorithm is as follows: the optimal solution is found through cooperation and information sharing among individuals in a group. The specific flow of the particle swarm optimization algorithm is as follows:
step 1, initializing an algorithm, wherein relevant parameters including the maximum iteration times of the particle swarm intelligent algorithm and the dimensionality of a decision variable are initialized, the particle swarm scale of each time is set, the particle updating speed interval is the maximum speed and the minimum speed, the maximum value and the minimum value of a weighting coefficient of the speed updating are set, the initial particle updating speed is randomly generated in the speed interval, and a group of initial solutions are randomly generated according to the constraint condition of the set algorithm;
and Step 2, calculating an individual extreme value and a global optimal solution, taking an optimized objective function as a fitness function of the particle swarm intelligent algorithm, calculating a fitness function value of each particle of the particle swarm, namely each feasible solution, and finding an optimal solution in the initial solution group. After the particle position and speed are updated, that is, after the iteration times are not the first time, the individual extremum is the optimal solution in the particle group in the current iteration times, and the global optimal solution at this time is the corresponding solution with the optimal fitness function value, which is found after all the calculated fitness functions of all the solutions are compared.
And Step 3, updating the speed V of the particles according to the following formula, adjusting the updated speed according to the speed interval of particle updating, enabling the updated speed to be equal to the fixed maximum speed if the updated speed is greater than the set maximum speed, and enabling the updated speed to be equal to the fixed minimum speed if the updated speed is less than the set minimum speed.
Vi′＝ωVi+c1·rand(0，1)(Pi-Xi)+c2·rand(0，1)(Gb-Xi)
In the formula Vi' updated speed, ViFor pre-update speed, ω is the inertial weight, c1And c2For the learning factor, rand (0, 1) is a random number between the (0, 1) range, PiGb indicates the global optimum of the particle population in order to indicate the individual extremum searched so far for the ith particle. Omega is a weighting coefficient, the weighting coefficient omega is used for controlling the influence of the particle update speed at the previous moment on the particle update speed at the current moment, the motion inertia of the particles can be kept, the particles are promoted to have enough capacity to explore a new space, the omega value is reduced along with the particle update speed in the continuous iteration process of the algorithm, the individual extreme value and the global value are continuously updated, the global optimum is finally achieved, and the expression of the weighting coefficient is as follows:
ω＝ωmax-Niter(ωmax-ωmin)/Niter，max
where ω is a weighting factor for controlling the influence of the particle velocity at the previous time on the velocity at the current time, which decreases as the number of iterations increases, and NiterFor the current update iteration algebra, Niter，maxFor maximum update iteration algebra, omegamaxIs a preset maximum weighting coefficient, omegaminIs a preset minimum weighting factor, ωmaxAnd omegaminAre all real numbers between the (0, 1) range.
Step 4, update the particles according to the following equation.
x′i＝xi+v′i
X 'in the formula'iFor renewed particles, xiIs speed before update, v'iIs the updated velocity of the particle.
Step 5, judging a termination condition, and if the algorithm reaches the maximum iteration times or the calculated fitness value reaches a certain precision requirement, ending the algorithm; otherwise go to Step 2.
And Step 6, outputting an optimal result after the particle swarm algorithm is finished, and determining which intelligent robot serves the task of each user, namely an optimal task allocation scheme.
In summary, the embodiment of the present invention aims at the task allocation problem of multi-user multi-task multi-intelligent robot, and determines the allocation scheme between each task and the corresponding intelligent robot with the minimum time for task completion, the lowest cost, and the maximum utilization rate of the intelligent robot in the hotel. According to the technical scheme provided by the invention, the intelligent robot for the hotel can quickly meet the user requirements, and the utilization rate of the intelligent robot is effectively improved.
An embodiment of the present invention further provides a multitask allocation device for an intelligent robot, as shown in fig. 3, the device includes:
a receiving module 301, configured to receive task requests for the intelligent robot sent by users, where each user sends at least one task request;
a determining module 302, configured to determine a total number of tasks corresponding to the task request, and determine time consumed for the intelligent robot to execute each task;
an obtaining module 303, configured to obtain a total number of currently available intelligent robots;
a calculating module 304, configured to calculate, by using the total number of tasks, the consumed time of each task, and the total number of currently available intelligent robots as input parameters, an intelligent robot allocation policy at the current decision time by using a preset target algorithm, where the allocation policy is used to allocate intelligent robots for processing tasks to different users;
and the control module 305 is used for controlling the intelligent robot to process the task corresponding to the user based on the distribution strategy.
According to the embodiment of the invention, the total number of the currently available intelligent robots is obtained by receiving the task requests sent by the users, determining the total number of the tasks of all the users and the completion time of each task based on the task requests, then calculating the optimal allocation strategy for the intelligent robots with the minimum task completion time by using a target algorithm, and then controlling the corresponding intelligent robots to process the corresponding tasks, thereby achieving the technical effects of improving the utilization rate of the intelligent robots and reducing the cost of the task completion time.
For specific description, reference is made to the above method embodiments, which are not described herein again.
The present embodiment also provides a computer device, such as a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including an independent server or a server cluster composed of multiple servers) capable of executing programs. The computer device 20 of the present embodiment includes at least, but is not limited to: a memory 21, a processor 22, which may be communicatively coupled to each other via a system bus, as shown in FIG. 4. It is noted that fig. 4 only shows the computer device 20 with components 21-22, but it is to be understood that not all shown components are required to be implemented, and that more or fewer components may be implemented instead.
In the present embodiment, the memory 21 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 20, such as a hard disk or a memory of the computer device 20. In other embodiments, the memory 21 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 20. Of course, the memory 21 may also include both internal and external storage devices of the computer device 20. In this embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 20 and various types of application software, such as program codes of the multitask allocation device of the intelligent robot described in the embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to execute the program codes stored in the memory 21 or process data, for example, execute a multitask allocation device of the intelligent robot, so as to implement the multitask allocation method of the intelligent robot according to the embodiment.
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer-readable storage medium of the embodiment is used for storing a multitask allocation device of the intelligent robot, and when the multitask allocation device is executed by a processor, the multitask allocation method of the intelligent robot of the embodiment is realized.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of this invention are intended to be covered by the present application.
Claims (10)
1. A multitask allocation method of an intelligent robot is characterized by comprising the following steps:
receiving task requests sent by users to the intelligent robot, wherein each user sends at least one task request;
determining the total number of tasks corresponding to the task request, and determining the time consumed by the intelligent robot to execute each task;
acquiring the total number of currently available intelligent robots;
calculating an intelligent robot allocation strategy under the current decision time by using a preset target algorithm by taking the total number of the tasks, the consumed time of each task and the total number of the currently available intelligent robots as input parameters, wherein the allocation strategy is used for allocating the intelligent robots for processing the tasks for different users;
and controlling the intelligent robot to process the task of the corresponding user based on the allocation strategy.
2. The multitask allocation method for the intelligent robot according to claim 1, wherein the calculating of the intelligent robot allocation strategy at the current decision time by using a preset target algorithm includes:
calling a preset target function of the target algorithm;
determining constraints of the target algorithm;
and solving the optimal solution of the book searching objective function by utilizing a particle swarm intelligent algorithm to obtain the distribution strategy.
3. The intelligent robot multitask allocation method according to claim 2, wherein said objective function is:
wherein F is an objective function value of the target algorithm, T is a time to be decided by the target algorithm, and the time belongs to a decision time set T, omega1A time cost weight, ω, of the time required for task completion in the target algorithm2Economic cost weight, omega, for an intelligent robot for task completion in the target algorithm3Is a utilization factor weight, Z, of the intelligent robot utilization in the target algorithmB(j)The total number of tasks of the jth user is b (j) {1, …, b }j，…，ZB(j)}，ZM(t)For the total number of intelligent robots available in the t-period, the set of intelligent robots in the t-period is set to M ═ 1, …, i, …, ZM(t)]And the set of users needing service by the user intelligent robot in the t period is set as N ═ 1, …, j, …, ZN(t)]，C(bj) Is the b thjThe time consumption of an individual task; x is the number ofijIs a decision variable of the target algorithm.
4. The method of claim 3, wherein x is the number of tasks assigned to the robotijDetermined by the following equation:
5. the intelligent robot multitask allocation method according to any one of claims 2 to 4, wherein the constraint condition includes: in unit decision time, each intelligent robot is only used for serving one user, and each user is only allocated with one intelligent robot for serving in unit decision time.
6. The multitask allocation method for the intelligent robot according to claim 4, wherein the constraint condition is:
in the formula, M is a set of available intelligent robots in the t period, N is a set of users needing services of the intelligent robots in the hotel in the t period, and xijIndicating whether the j user's task is served by the i intelligent robot.
7. The method of claim 1, further comprising, after controlling the intelligent robot to process the task of the corresponding user:
and after the intelligent robot finishes the service of the user, controlling the intelligent robot to carry out charging and maintenance.
8. A multitask allocation device of an intelligent robot is characterized by comprising:
the system comprises a receiving module, a task processing module and a task processing module, wherein the receiving module is used for receiving task requests of the intelligent robot sent by users, and each user sends at least one task request;
the determining module is used for determining the total number of tasks corresponding to the task request and determining the time consumed by the intelligent robot to execute each task;
the acquisition module is used for acquiring the total number of the currently available intelligent robots;
the computing module is used for computing an intelligent robot allocation strategy at the current decision time by using a preset target algorithm by taking the total number of the tasks, the consumed time of each task and the total number of the currently available intelligent robots as input parameters, wherein the allocation strategy is used for allocating the intelligent robots for processing the tasks for different users;
and the control module is used for controlling the intelligent robot to process the task corresponding to the user based on the distribution strategy.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7.
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| CN114860416A (en) * | 2022-06-06 | 2022-08-05 | 清华大学 | Distributed multi-agent detection task allocation method and device in confrontation scene |
| CN114860416B (en) * | 2022-06-06 | 2024-04-09 | 清华大学 | Distributed multi-agent detection task allocation method and device in countermeasure scene |
| CN116205023A (en) * | 2022-07-12 | 2023-06-02 | 上海奔曜科技有限公司 | Design method, device, equipment and storage medium of intelligent robot laboratory |
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