CN116362482A - Automatic task dispatch system based on robot community service operation big data - Google Patents

Automatic task dispatch system based on robot community service operation big data Download PDF

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Publication number
CN116362482A
CN116362482A CN202310181634.0A CN202310181634A CN116362482A CN 116362482 A CN116362482 A CN 116362482A CN 202310181634 A CN202310181634 A CN 202310181634A CN 116362482 A CN116362482 A CN 116362482A
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China
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task
robot
target
service
community
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Chinese (zh)
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吴季泳
朱悦
李蒙
张莉
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Tami Intelligence Technology Beijing Co ltd
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Tami Intelligence Technology Beijing Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention discloses a task automatic dispatch system based on robot community service operation big data, which is deployed on a cloud server, wherein the cloud server is in communication connection with the robot community, the robot community consists of a plurality of service robots of different types, and the system performs the following operations: acquiring real-time state information and target task types of at least part of service robots in the robot community; determining a task order according to the target task type; determining a target service robot according to the task order and the real-time state information; the task order is dispatched to the target robot to execute the target task. By utilizing the method, the corresponding tasks are automatically issued to the robot communities based on the real-time state information and task types of the service robots, so that the autonomous service of the robot communities is realized, and the service efficiency of the robot communities is improved.

Description

Automatic task dispatch system based on robot community service operation big data
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a system, a device, equipment and a storage medium for automatically distributing tasks based on robot community service operation big data.
Background
With the development of scientific technology, the artificial intelligence technology is mature gradually, intelligent robots are widely applied in a plurality of industries, a great amount of labor cost is saved, a great number of human errors can be avoided, the working efficiency of people is greatly improved, and the intelligent robots are widely applied to more application places in the future.
Most of the robots at present are basically single-type robots or independently provide single-type service work, for example, in some hospitals, a welcome guide robot, a medical waste collection robot, an article delivery robot, a spray disinfection robot, a security patrol robot and the like are deployed at the same time, the object groups of the services of the robots are different, the provided services are also different, under the background, the different types of service robots form communities, the robots are subjected to unified operation data collection based on the communities of the robots, cloud service management is performed, and data basis is improved for automatic task assignment, so that the intelligent performance and the working efficiency of the robots are further improved.
Disclosure of Invention
The embodiment of the invention provides a task automatic dispatch system, device, equipment and storage medium based on robot community service operation big data, which can realize task demand modeling, automatically dispatch corresponding tasks to a robot community by the system, realize autonomous service of the robot community and improve the service efficiency of the robot community.
In a first aspect, an embodiment of the present invention provides an automatic task serving system based on big data of a robot community service, where the system is deployed on a cloud server, and the cloud server is in communication connection with the robot community, where the robot community is composed of a plurality of service robots of different types, and the system performs the following operations:
acquiring real-time state information and target task types of at least part of service robots in the robot community;
determining a task order according to the target task type;
determining a target service robot according to the task order and the real-time state information;
the task order is dispatched to the target robot to execute the target task.
In a second aspect, an embodiment of the present invention further provides a task automatic dispatch device based on big data of a robot community service, where the device is disposed in the task automatic dispatch system based on big data of a robot community service, and includes:
the information acquisition module is used for acquiring real-time state information and target task types of at least part of service robots in the robot community;
the service request distribution module is used for determining a task order according to the target task type;
the robot determining module is used for determining a target service robot according to the task order and the real-time state information;
and the order dispatch module is used for dispatching the task order to the target robot so as to execute the target task.
In a third aspect, embodiments of the present disclosure further provide an electronic device, including:
one or more processors;
storage means for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement operations performed by the task automatic dispatch system based on the robot community service operation big data provided by the embodiments of the present disclosure.
In a fourth aspect, the disclosed embodiments also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing operations performed by a task automatic dispatch system implementing big data based on robotic community service provided by the disclosed embodiments.
The invention discloses a task automatic dispatch system, device, equipment and storage medium based on robot community service operation big data, wherein the system is deployed on a cloud server, the cloud server is in communication connection with the robot community, the robot community consists of a plurality of service robots of different types, and the system executes the following operations: acquiring real-time state information and target task types of at least part of service robots in the robot community; determining a task order according to the target task type; determining a target service robot according to the task order and the real-time state information; the task order is dispatched to the target robot to execute the target task. By utilizing the method, the corresponding tasks are automatically issued to the robot communities based on the real-time state information and task types of the service robots, so that the autonomous service of the robot communities is realized, and the service efficiency of the robot communities is improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flowchart of operations performed by a task automatic dispatch system based on robot community service operation big data according to an embodiment of the present disclosure;
FIG. 2 is a diagram of an example of a task automatic dispatch system based on robot community service operation big data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a task automatic dispatch device based on robot community service operation big data according to an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.
As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.
It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a flowchart of an operation performed by a task automatic dispatch system based on big data of a robot community service provided in an embodiment of the present disclosure, where the embodiment of the present disclosure is applicable to a situation of task automatic dispatch based on big data of a robot community service, the method may be performed by an operation device performed by a task automatic dispatch system based on big data of a robot community service, and the device may be implemented in a form of software and/or hardware, optionally, may be implemented by an electronic device, and the electronic device may be a mobile terminal, a PC side, a server, or the like.
Fig. 2 is a diagram illustrating an example of a task automatic dispatch system based on robot community service operation big data according to an embodiment of the present disclosure. As shown in fig. 2, in the task distribution system for service robot community service operation big data designed in the embodiment of the present disclosure, a system platform is deployed on a cloud server, which may also be referred to as a cloud brain, and meanwhile, the system uses a cache service and accelerates data interaction. In addition, all different types of service robots make up a robot community that acts as an end node. The service robots can comprise security robots, welcome robots, logistics robots, vending robots and the like, and all robots are connected with the cloud brain in real time in a network communication mode.
As shown in fig. 1, the operation performed by the task automatic dispatch system based on the robot community service operation big data according to the embodiment of the present disclosure may specifically include the following steps:
s110, acquiring real-time state information and target task types of at least part of service robots in the robot community.
Wherein the real-time status information includes at least one of: remaining working time, working state, charging state, fault state and robot position; the working state comprises an idle state and a busy state, and the idle state further comprises an idle duration.
Specifically, the real-time status information is status information of the service robot body, where the remaining working time length, the working state being in an idle state or a busy state, and the idle time length being in an idle state, whether the service robot body is in a charging state, whether the service robot body is in a fault state, a robot position, and the like may be included.
All robots in the robot community are connected with the cloud server in real time in a network communication mode, and the system acquires real-time state information of part of service robots or all service robots in the robot community and target task types required in places served by the robot community in real time. The target task type may include a type of each type of task, a execution time period, and the like.
S120, determining a task order according to the target task type.
Specifically, the obtained target task type can be input into a task prediction model with modeling completed, and the execution period of various tasks is predicted to obtain a task order.
Optionally, the method for determining the task order according to the target task type may be: inputting the target task type into a task prediction model to obtain a task order; wherein the task order includes a task execution period.
Specifically, the obtained target task type is input into a task prediction model with modeling completed, execution time periods of various tasks are predicted, and task orders are obtained. Wherein the task order may include a category of the task and an execution period of the task.
Optionally, the task prediction model may be constructed in the following manner: acquiring historical operation data of at least part of service robots in a robot community; the historical operation data comprise task execution time periods and task types; constructing a task prediction model based on historical operation data; the task prediction model characterizes the relation between the task type and the task execution time; the task execution period includes a task start time and a task end time.
Specifically, historical operation data of service robots in a robot community is obtained, wherein the historical operation data comprises task execution time periods, task types and the like. According to the historical operation data, task types of a plurality of tasks are used as input, task execution periods of certain tasks are used as output to perform modeling, and task prediction models of various tasks are obtained. The task prediction model is used for representing the relation between the task type and the task execution time. The task execution period may include a task start time and a task end time.
Exemplary tasks include medical waste collection tasks, medical waste transportation tasks, and disinfection tasks, among which certain correlations exist. The task prediction model is built for representing the relation between the task type and the task execution time, and modeling is referred to according to the collected historical operation data of the service robot.
S130, determining the target service robot according to the task order and the real-time state information.
In this embodiment, according to acquiring a task order and real-time status information of service robots in a robot community, determining a target service robot type required by the task order, determining which of such service robots can complete the task order according to the real-time status information of the service robots, determining candidate service robots, and finally determining a target robot from the candidate robots based on the real-time status information and a task execution period.
Optionally, the method for determining the task order according to the target task type may be: acquiring a task type, a robot type and a task execution period in a task order; determining candidate service robots according to the task types and the robot types; a target robot is determined from the candidate robots based on the real-time status information and the task execution period.
Specifically, according to the task type, the robot type and the task execution period in the task order and the real-time state information of the service robots in the robot community, the target service robot type required by the task order is determined, according to the remaining working time in the real-time state information of the service robots, the task order is supported to be completed, the working state is an idle state, meanwhile, the service robots which can not be in a charging state or a fault state in the service robots are determined, the service robots which can meet the condition and complete the task order in the service robots are determined to be candidate service robots, and meanwhile, the number of the candidate service robots can be reduced according to the fact that the distance between the robots and the task starting point is smaller than a certain set threshold value
And finally determining the target robot from the candidate robots according to the automatic dispatch task setting conditions based on the real-time state information and the task execution period.
Optionally, the method for determining the target service robot according to the task order and the real-time status information may be: if two or more target robots are determined, determining a final target robot according to the distance between the task starting point and the target robot and/or the idle time of the target robot.
Specifically, if the candidate robots include two or more target robots, the selected service robot is determined as the target robot according to the distance between the task starting point and the target robot and according to the set distance dispatch, or the selected service robot is determined as the target robot according to the waiting dispatch time and according to the idle duration in the service robot. The specific setting rules can be set alternatively according to actual conditions or set simultaneously according to the set priority.
Optionally, the method for determining the final target robot according to the distance between the task starting point and the target robot and/or the idle time length of the target robot may be: the target robot closest to the task starting point is determined as the final target robot, and/or the target robot with the longest idle duration is determined as the final target robot.
Specifically, according to the distance between the task starting point and the target robot, the service robot closest to the target robot is determined according to the rule of setting the nearby distance dispatch, or the service robot with the longest idle duration in the service robot is determined as the target robot according to the rule of waiting for dispatch time to be longest. The specific setting rules can be set alternatively according to actual conditions or set simultaneously according to the set priority.
For example, if two or more target robots are included in the candidate robots, the service robot waiting for the dispatch time to be longest may be selected as the target robot. If two or more target robots are included in the candidate robots and waiting for dispatch times are similar or equal, a robot with the closest distance between the task starting point and the target robot can be selected as the target robot.
S140, distributing the task order to the target robot to execute the target task.
In this embodiment, this step is to dispatch a task order to the target robot to execute the target task.
The embodiment of the disclosure provides a task automatic dispatch system based on robot community service operation big data, the system is deployed on a cloud server, the cloud server is in communication connection with a robot community, wherein the robot community is composed of a plurality of service robots of different types, and the system executes the following operations: acquiring real-time state information and target task types of at least part of service robots in a robot community; determining a task order according to the target task type; determining a target service robot according to the task order and the real-time state information; the task order is dispatched to the target robot to execute the target task. By utilizing the method, based on the real-time state information and task type of the service robot, task demand modeling is realized, the system automatically issues corresponding tasks to the robot community, autonomous service of the robot community is realized, and the service efficiency of the robot community is improved.
Example two
Fig. 3 is a schematic structural diagram of a task automatic dispatch device based on robot community service operation big data, where in fig. 3, the device includes: an information acquisition module 210, a business request distribution module 220, a robot determination module 230, and an order dispatch module 240.
An information obtaining module 210, configured to obtain real-time status information and a target task type of at least a part of service robots in the robot community;
a service request allocation module 220, configured to determine a task order according to the target task type;
a robot determining module 230, configured to determine a target service robot according to the task order and the real-time status information;
an order dispatch module 240, configured to dispatch the task order to the target robot to execute the target task.
According to the technical scheme provided by the embodiment of the disclosure, the corresponding tasks are automatically issued to the robot communities based on the real-time state information and task types of the service robots, so that the robot communities are automatically served, and the service efficiency of the robot communities is improved.
Further, the information acquisition module 210 may be configured to:
the real-time status information includes at least one of: remaining working time, working state, charging state, fault state and robot position; the working state comprises an idle state and a busy state, and the idle state further comprises an idle duration.
Further, the service request allocation module 220 may be further configured to:
inputting the target task type into a task prediction model to obtain a task order; wherein the task order includes a task execution period.
Further, the service request allocation module 220 may be configured to:
acquiring historical operation data of at least part of service robots in the robot community; the historical operation data comprise task execution time periods and task types;
constructing a task prediction model based on the historical operation data; the task prediction model characterizes the relation between the task type and the task execution time period; the task execution period includes a task start time and a task end time.
Further, the robot determination module 230 may be configured to:
acquiring a task type, a robot type and a task execution period in the task order;
determining candidate service robots according to the task types and the robot types;
a target robot is determined from the candidate robots based on the real-time status information and the task execution period.
Further, the robot determination module 230 may be configured to:
if two or more target robots are determined, determining a final target robot according to the distance between the task starting point and the target robot and/or the idle time length of the target robot.
Further, the robot determination module 230 may be configured to:
determining the target robot closest to the task starting point as a final target robot, and/or determining the target robot with the longest idle duration as the final target robot;
the device can execute the method provided by all the embodiments of the invention, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in this embodiment can be found in the methods provided in all the foregoing embodiments of the invention.
Example III
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as operations performed by the task automatic dispatch system that operates big data based on the robot community service.
In some embodiments, the operations performed by the automated task serving system that operates big data based on the robotic community service may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM13 and executed by the processor 11, one or more steps of the operations performed by the task automatic dispatch system described above that operates big data based on the robot community service may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform the operations performed by the automated task serving system that operates big data based on the robotic community service.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. The automatic task dispatch system based on the robot community service operation big data is characterized in that the system is deployed on a cloud server, the cloud server is in communication connection with the robot community, wherein the robot community is composed of a plurality of service robots of different types, and the system performs the following operations:
acquiring real-time state information and target task types of at least part of service robots in the robot community;
determining a task order according to the target task type;
determining a target service robot according to the task order and the real-time state information;
the task order is dispatched to the target robot to execute the target task.
2. The system of claim 1, wherein the real-time status information comprises at least one of: remaining working time, working state, charging state, fault state and robot position; the working state comprises an idle state and a busy state, and the idle state further comprises an idle duration.
3. The system of claim 1, wherein determining a task order based on the target task type comprises:
inputting the target task type into a task prediction model to obtain a task order; wherein the task order includes a task execution period.
4. The system according to claim 1, wherein the task prediction model is constructed by:
acquiring historical operation data of at least part of service robots in the robot community; the historical operation data comprise task execution time periods and task types;
constructing a task prediction model based on the historical operation data; the task prediction model characterizes the relation between the task type and the task execution time period; the task execution period includes a task start time and a task end time.
5. The system of claim 1, wherein determining a target service robot from the task order and the real-time status information comprises:
acquiring a task type, a robot type and a task execution period in the task order;
determining candidate service robots according to the task types and the robot types;
a target robot is determined from the candidate robots based on the real-time status information and the task execution period.
6. The method of claim 2, wherein determining a target service robot from the task order and the real-time status information comprises:
if two or more target robots are determined, determining a final target robot according to the distance between the task starting point and the target robot and/or the idle time length of the target robot.
7. The system of claim 6, wherein determining a final target robot based on a distance of a task starting point from the target robot and/or an idle duration of the target robot comprises:
the target robot closest to the task starting point is determined as the final target robot, and/or the target robot with the longest idle duration is determined as the final target robot.
8. The utility model provides a device is dispatched automatically to task based on big data of robot community service operation, this device set up in the automatic dispatch system of task based on big data of robot community service operation, characterized in that includes:
the information acquisition module is used for acquiring real-time state information and target task types of at least part of service robots in the robot community;
the service request distribution module is used for determining a task order according to the target task type;
the robot determining module is used for determining a target service robot according to the task order and the real-time state information;
and the order dispatch module is used for dispatching the task order to the target robot so as to execute the target task.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the operations performed by the automated task serving system based on robotic community service operation big data of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the operations performed by the automated task serving system for big data based on the robotic community service of any of claims 1-7 when executed.
CN202310181634.0A 2023-02-20 2023-02-20 Automatic task dispatch system based on robot community service operation big data Pending CN116362482A (en)

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Application Number Priority Date Filing Date Title
CN202310181634.0A CN116362482A (en) 2023-02-20 2023-02-20 Automatic task dispatch system based on robot community service operation big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310181634.0A CN116362482A (en) 2023-02-20 2023-02-20 Automatic task dispatch system based on robot community service operation big data

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Publication Number Publication Date
CN116362482A true CN116362482A (en) 2023-06-30

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Country Link
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