CN117573329B - Multi-brain collaborative task scheduling method, task scheduling device and storage medium - Google Patents
Multi-brain collaborative task scheduling method, task scheduling device and storage medium Download PDFInfo
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
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- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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Abstract
The invention discloses a multi-brain collaborative task scheduling method, a task scheduling device and a storage medium, wherein the method comprises the following steps: when receiving the multi-task scheduling information, performing task decomposition processing on the multi-task scheduling information based on a script brain to obtain subtasks corresponding to the multi-task scheduling information; the subtasks are sent to the task digestion brain, and the task distribution brain is controlled to distribute the subtasks to the processing brain corresponding to the subtasks; and determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask, and receiving a task scheduling result fed back by each robot software. The tasks are decomposed through the script brain, each decomposed subtask is sent to the corresponding processing brain through the task digestion brain, the corresponding robot software is controlled to execute the scheduling tasks based on the processing brain, the plurality of brains cooperatively process, and the processing efficiency of the tasks is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a task scheduling method, a task scheduling device, and a storage medium for multi-brain cooperation.
Background
In the task scheduling management process of the current virtual robot software, tasks are generally divided in a manual processing mode, and subtasks after division processing are manually issued to the robot software of different modules for execution.
However, the task of manual splitting needs to be understood and judged by a person, and is influenced by subjective factors of the person, so that the effect of task splitting is different. Meanwhile, a great deal of time and effort may be required for manually splitting tasks, and particularly when complex tasks are processed, the task execution efficiency is affected by the manual splitting manner, so that the task execution efficiency is low.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a method for solving the problem of low task execution efficiency caused by manual task splitting in the prior art.
In order to achieve the above purpose, the present invention provides a task scheduling method for multi-brain cooperation, the method comprising the following steps:
when receiving the multi-task scheduling information, performing task decomposition processing on the multi-task scheduling information based on a script brain to obtain subtasks corresponding to the multi-task scheduling information;
The subtasks are sent to a task digestion brain, and a task distribution brain is controlled to distribute the subtasks to processing brains corresponding to the subtasks;
and determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask, and receiving a task scheduling result fed back by each robot software.
Optionally, the step of sending the subtasks to a task digestion brain and controlling a task distribution brain to distribute each subtask to a processing brain corresponding to the subtask includes:
the subtasks are sent to the task digestion brain, and the execution sequence and scene information of the subtasks are determined based on the task digestion brain;
determining a processing node and the processing brain associated with the processing node according to the execution sequence and the scene information;
And controlling the task distribution brain to distribute each subtask to the processing brain corresponding to the subtask.
Optionally, the step of determining a processing node according to the execution order and the scene information, and the processing brain associated with the processing node comprises:
determining task item scheduling, data storage formats, map data storage sources and instance creation information of each subtask according to the scene information, and determining a node processing sequence corresponding to the instance creation information according to the execution sequence;
and taking the nodes corresponding to the node processing sequence as the processing nodes, and determining logic brains, storage brains, map brains and robot instance brains corresponding to the task item scheduling, the data storage format, the map data storage source and the instance creation information, wherein the logic brains, the storage brains, the map brains and the robot instance brains are the processing brains.
Optionally, when the multi-task scheduling information is received, performing task decomposition processing on the multi-task scheduling information based on a script brain, and obtaining the subtasks corresponding to the multi-task scheduling information includes:
When the multitasking scheduling information is received and is a voice instruction, identifying the voice instruction based on a voice identification script of the script brain;
when the recognition result is in accordance with the voice right, acquiring text information corresponding to the voice instruction based on a text extraction script, and disassembling the text information according to the text recognition script to obtain each item to be processed;
and taking the to-be-processed item as the subtask corresponding to the multitask scheduling information.
Optionally, the step of determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask, and receiving the task scheduling result fed back by each robot software includes:
determining robot software corresponding to each processing brain, and forwarding each subtask to Emitter based on central control so that the Emitter forwards the subtask to a server;
The server side is controlled to forward each subtask to each robot software;
and when the robot software receives and executes each subtask, acquiring a processing result fed back by the robot software received by the server.
Optionally, after the step of determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask and receive the task scheduling result fed back by each robot software, the method further includes:
determining a task processing state of the processing brain;
when the task processing state of the target processing brain is empty, controlling the target processing brain to send a task processing request to a central control based on a preset period; or alternatively
When the task processing states of all the processing brains are empty, controlling a task distribution brain to send the task processing request to the central control based on the preset time period; or alternatively
When the processing brain is in the offline state and the task processing state is to be executed, waking up the processing brain in the offline state.
Optionally, after the step of determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask and receive the task scheduling result fed back by each robot software, the method further includes:
carrying out knowledge graph analysis processing on the task scheduling result according to a graph brain, and/or carrying out data correction processing on the task scheduling result according to a data correction script of the script brain to obtain a processing result;
determining redundant data, error data and repeated data corresponding to the processing result according to a data analysis script of the script brain;
and storing the processing results after deleting the redundant data, the error data and the repeated data into a storage brain.
Optionally, when the multi-task scheduling information is received, performing task decomposition processing on the multi-task scheduling information based on a script brain, and after the step of obtaining the subtasks corresponding to the multi-task scheduling information, further including:
The task scheduling result is sent to an information checking interface, and a checking result fed back by the information checking interface is responded;
when the checking result is an abnormal result, skipping to execute the task decomposition processing on the multi-task scheduling information based on the script brain to obtain a subtask corresponding to the multi-task scheduling information;
When the verification result is a result to be corrected, controlling the script brain to correct the task scheduling result based on a correction instruction corresponding to the result to be corrected, and storing the corrected result into a storage brain;
And when the checking result is a normal result, storing the data corresponding to the task scheduling result into the storage brain.
In addition, in order to achieve the above object, the present invention also provides a multi-brain cooperative task scheduling device, where the multi-brain cooperative task scheduling device includes a memory, a processor, and a multi-brain cooperative task scheduler stored in the memory and capable of running on the processor, where the multi-brain cooperative task scheduler implements the steps of the multi-brain cooperative task scheduling method as described above when executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a multi-brain collaborative task scheduler, which when executed by a processor, implements the steps of the multi-brain collaborative task scheduling method as described above.
The embodiment of the invention provides a multi-brain collaborative task scheduling method, a task scheduling device and a storage medium, wherein when multi-task scheduling information is received, task decomposition processing is carried out on the multi-task scheduling information based on a script brain to obtain subtasks corresponding to the multi-task scheduling information, then the subtasks are sent to a task digestion brain, a task distribution brain is controlled to distribute each subtask to a processing brain corresponding to the subtask, finally, robot software corresponding to each processing brain is determined, the robot software is controlled to execute each subtask, and task scheduling results fed back by the robot software are received. It can be seen that the task is disassembled and distributed through different brains, and the task is processed by a plurality of different robot software corresponding to the processing brains, so that the task execution efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of an operating system architecture of a task scheduling method of the present invention for multiple brain collaboration;
FIG. 2 is a functional schematic of the kernel brain of the multi-brain collaborative task scheduling method of the present invention;
FIG. 3 is a flow chart of a first embodiment of a task scheduling method for multi-brain collaboration according to the present invention;
FIG. 4 is a flow chart of a second embodiment of a task scheduling method for multi-brain collaboration according to the present invention;
fig. 5 is a schematic diagram of a terminal hardware structure of each embodiment of the task scheduling method of the multi-brain cooperation of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention is applied to a robot operating system, as an optional implementation, please refer to fig. 1, the operating system comprises Emitter (transmitter) message queues for message forwarding and registration connection, central control for establishing connection with a plurality of kernel brains, issuing corresponding instructions and scheduling tasks, processing service logic message forwarding of all ends and user authorization login authentication, and kernel brains formed by a plurality of brains, wherein the central control can establish socket (a protocol stack) connection of a client and a server with each brain in the kernel brains, and address signals, instruction distribution and scheduling tasks and the like. In the kernel brain, the functions of a plurality of brains are shown in fig. 2, logic brains are used for realizing logic task related scheduling, the atlas brain is used for atlas data access maintenance and providing knowledge atlas related processing functions, the memory brain is used for storing related data, and memory state and persistent storage and random format storage are supported; the robot pool is used for maintaining a robot instance creation record and performing persistence storage; the robot instance brain is used for dynamically creating related instances according to instance creation records; the script brain analyzes the scheduling task through a plurality of script modules of the script brain, so as to obtain a plurality of subtasks of the scheduling task; the task digestion brain is then used to deliver a plurality of subtasks of the scheduled task to the corresponding brain.
Referring to fig. 3, in a first embodiment, the steps of the task scheduling method of the present invention include:
step S10, when multi-task scheduling information is received, task decomposition processing is carried out on the multi-task scheduling information based on a script brain, and subtasks corresponding to the multi-task scheduling information are obtained;
in this embodiment, the multi-task scheduling information is used to obtain data information of multiple different scenarios, for example, when asset information of an intranet needs to be obtained, the script brain decomposes the multi-task scheduling information of the asset information of the intranet, so as to obtain processing tasks corresponding to different brains. That is, the multitasking information may be any form of internet information scheduling task that requires multiple ones of the kernel brains to be done in combination.
As an alternative embodiment, the multitasking scheduling information may be composed of various forms, such as a text command, a voice command, or a pre-stored scheduling task called by a manager from the local or cloud. For example, when the received multitask scheduling information is a voice command, it is required to determine whether the initiator corresponding to the current voice command satisfies the corresponding task issuing authority. At the moment, the voice command can be recognized through the voice recognition script in the script brain to obtain a recognition result, when the recognition result is that the voice command meets the preset authority, the voice command is indicated to meet the preset authority, at the moment, the corresponding text information in the voice command is extracted through the text extraction script of the script brain, and after the text information is extracted, the recognition script is used for disassembling processing to obtain the subtasks corresponding to the to-be-processed matters, namely the multitask scheduling information. Based on the task decomposition processing is carried out on the multi-task scheduling information through each script module in the script brain, so that the task decomposition efficiency is improved, and the task execution efficiency is further improved.
Step S20, the subtasks are sent to a task digestion brain, and a task distribution brain is controlled to distribute the subtasks to processing brains corresponding to the subtasks;
In this embodiment, please continue to refer to fig. 1, the processing brain includes, but is not limited to, logic brain, atlas brain, storage brain and robot instance brain, and after the subtasks are distributed to the corresponding processing brain, the processing brain can perform task data acquisition or calculation processing according to the corresponding task instance or execution sequence.
As an alternative embodiment, the step of distributing the subtasks to the processing brain based on the task digestion brain specifically comprises:
Step S21, the subtasks are sent to the task digestion brain, and the execution sequence and scene information of each subtask are determined based on the task digestion brain;
Step S22, determining a processing node and the processing brain associated with the processing node according to the execution sequence and the scene information;
and step S23, controlling the task distribution brain to distribute each subtask to the processing brain corresponding to the subtask.
In this embodiment, the scenario information includes a simulation scenario, a data processing scenario, a proxy server, and the like, and task item scheduling, a data storage format, a map data storage source, and instance creation information of each subtask may be determined according to the scenario information, and a node processing order corresponding to the instance creation information, for example, a processing order and a processing number between a data storage node, an instance creation node, and an item scheduling node, may be determined according to an execution order. And then, taking the nodes corresponding to the node processing sequence as processing nodes, determining logic brains corresponding to task item scheduling, a storage brain corresponding to a data storage format, a map brain corresponding to a map data storage source, a robot instance brain corresponding to instance creation information and the like, so as to control a task digestion brain to distribute subtasks to the corresponding processing brains, and enabling the processing brains to orderly or simultaneously control corresponding robot software to execute corresponding scheduling tasks according to the subtasks, thereby improving the execution efficiency of the scheduling tasks. It will be appreciated that in this embodiment logic brains, store brains, map brains and the robotic example brains are treatment brains.
And step S30, determining the corresponding robot software of each processing brain, controlling the robot software to execute each subtask, and receiving a task scheduling result fed back by each robot software.
In the present embodiment, the robot software refers to virtual robot software capable of executing intelligent software that extracts data, fills out a form, moves a file, and performs various simulation processes. The task scheduling result may be personnel data information of a certain area, or related network information of each network device in a certain network scene, such as infrastructure information, network service information, application program information, vulnerability information, and the like.
As an alternative embodiment, the robot software corresponding to each processing brain may be determined first, wherein each processing brain corresponds to a unique robot software. Referring to fig. 1, in this process, each subtask may be forwarded to Emitter based on the central control, so that Emitter forwards the subtask to the server, and then the server is controlled to forward the subtask to each of the robot software. Based on the above, after the robot software receives the subtasks, the corresponding tasks can be executed, and when the robot software receives and executes each subtask, the central control of the robot operating system acquires the processing result fed back by the robot software received by the server. After receiving the processing result, the server returns the processing result to Emitter as the task scheduling result based on a Web gateway, so that Emitter returns the task scheduling result to the central control, wherein data corresponding to the task scheduling result can be stored in a storage brain. Based on the above, the current subtasks are processed by the robot software corresponding to each processing brain, so that the task processing efficiency is improved.
In the technical scheme disclosed in the embodiment, after the multi-task scheduling information is received, the task scheduling information is analyzed and processed through a plurality of scripts in a script brain, so that a plurality of subtasks are obtained, meanwhile, the corresponding task execution sequence, scene information and the like in the subtasks are determined according to task digestion brain, the processing brain is selected according to the execution sequence and the scene information, the subtasks are distributed to the processing brain, finally, the subtasks are distributed to the robot software related to the processing brain based on Emitter, and then, the processing result fed back by the robot software is received through a server. Based on the method, the plurality of brains divide, issue and execute the tasks respectively, so that the cooperative processing of the plurality of processing brains is realized, and the processing efficiency of scheduling the tasks is further improved.
Referring to fig. 4, in the second embodiment, after step S30, the method further includes:
step S40, determining a task processing state of the processing brain;
and step S50, when the task processing state of the target processing brain is empty, controlling the target processing brain to send a task processing request to a central control based on a preset period.
In this embodiment, after providing the corresponding robot software and receiving the task scheduling result fed back by the robot software, the processing brain is in an idle state in the task processing state. As an alternative embodiment, when the task processing state of the target processing brain is empty, a task processing request can be sent to the central control based on a preset period, that is, a preset period, so that a worker can issue a corresponding scheduling task to the processing brain based on the request. In addition, the processing brain may send task processing requests according to preset intervals, so that calculation of the preset intervals need not be performed while in an idle state. It will be appreciated that when the processing brain is in a task execution state, no corresponding task request is sent. Meanwhile, when the task processing states of all the processing brains are empty, the task processing requests can be directly sent based on the preset time period.
In another optional embodiment, when there is a processing brain in a offline state, that is, the processing brain is not connected to the central control, and the processing brain corresponding to the processing brain is in a to-be-executed task processing state, the central control may directly loop the processing brain in the offline state, so that the processing brain executes a corresponding scheduling task, thereby improving task processing efficiency.
In an optional implementation manner after the task scheduling result is obtained, analysis processing can be performed on the data corresponding to the task scheduling result, so that the effectiveness of data storage is improved. Specifically, the knowledge graph analysis processing can be performed on the task scheduling result according to the graph brain in the processing brain, and/or the data correction processing can be performed on the task scheduling result according to the data correction script of the script brain, the two processing modes can be executed separately or together, further, the processing result corresponding to the task scheduling result is obtained, then, the redundant data, the error data and the repeated data corresponding to the processing result are determined according to the data analysis script of the script brain, for example, in the processing result, the obtained parameters, the number of times of a person is more than 200 years, and a plurality of pieces of repeated gender information exist, and at the moment, the data analysis script considers that the error data and the repeated data exist, and the marking processing is needed for the data cleaning processing. And deleting the redundant data, the error data and the repeated data after the data analysis script marks all the data, and storing the processing result after the deleting processing into a memory brain. After the task scheduling result is obtained, the data of the scheduling result is cooperatively processed through the script brain and the atlas brain, so that the accuracy of data screening processing in the scheduling task is improved.
In another optional implementation manner after the task scheduling result is obtained, the task scheduling result may be sent to the information verification interface, and the verification result fed back by the operator at the information verification interface may be responded. Specifically, when the verification result is an abnormal result, the data returned by the currently executed scheduling task is abnormal data, at the moment, task decomposition processing is needed to be carried out on the multi-task scheduling information again based on the script brain, after a new subtask is obtained, the scheduling task is executed again, and when the failure times are too many, an operator is needed to maintain the script brain. When the verification result is a result to be corrected, the result comprises an instruction for correcting the data fed back by the task scheduling result, at the moment, the script brain is required to be controlled to correct the task scheduling result based on the correction instruction, the corrected result is stored in the storage brain, and when the verification result is a normal result, the data corresponding to the task scheduling result can be directly stored in the storage brain. Based on the above, stability and accuracy of data storage are improved.
Referring to fig. 5, fig. 5 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 5, the terminal may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a network interface 1003, a memory 1004. Wherein the communication bus 1002 is used to enable connected communication between these components. The network interface 1003 may optionally include a standard wired interface, a wireless interface (e.g., a wireless FIdelity (WI-FI) interface). The Memory 1004 may be a high-speed RAM Memory (Random Access Memory, RAM) or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory 1004 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 5 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 5, an operating system, a data storage module, a network communication module, and a task scheduler for a multi-brain collaboration may be included in the memory 1004, which is a type of computer storage medium.
In the terminal shown in fig. 5, the network interface 1003 is mainly used for connecting to a background server, and performing data communication with the background server; the processor 1001 may call a multi-brain collaborative task scheduler stored in the memory 1004 and perform the following operations:
when receiving the multi-task scheduling information, performing task decomposition processing on the multi-task scheduling information based on a script brain to obtain subtasks corresponding to the multi-task scheduling information;
The subtasks are sent to a task digestion brain, and a task distribution brain is controlled to distribute the subtasks to processing brains corresponding to the subtasks;
and determining the robot software corresponding to each processing brain, controlling the robot software to execute each subtask, and receiving a task scheduling result fed back by each robot software.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
the subtasks are sent to the task digestion brain, and the execution sequence and scene information of the subtasks are determined based on the task digestion brain;
determining a processing node and the processing brain associated with the processing node according to the execution sequence and the scene information;
And controlling the task distribution brain to distribute each subtask to the processing brain corresponding to the subtask.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
determining task item scheduling, data storage formats, map data storage sources and instance creation information of each subtask according to the scene information, and determining a node processing sequence corresponding to the instance creation information according to the execution sequence;
and taking the nodes corresponding to the node processing sequence as the processing nodes, and determining logic brains, storage brains, map brains and robot instance brains corresponding to the task item scheduling, the data storage format, the map data storage source and the instance creation information, wherein the logic brains, the storage brains, the map brains and the robot instance brains are the processing brains.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
When the multitasking scheduling information is received and is a voice instruction, identifying the voice instruction based on a voice identification script of the script brain;
when the recognition result is in accordance with the voice right, acquiring text information corresponding to the voice instruction based on a text extraction script, and disassembling the text information according to the text recognition script to obtain each item to be processed;
and taking the to-be-processed item as the subtask corresponding to the multitask scheduling information.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
determining robot software corresponding to each processing brain, and forwarding each subtask to Emitter based on central control so that the Emitter forwards the subtask to a server;
The server side is controlled to forward each subtask to each robot software;
and when the robot software receives and executes each subtask, acquiring a processing result fed back by the robot software received by the server.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
determining a task processing state of the processing brain;
when the task processing state of the target processing brain is empty, controlling the target processing brain to send a task processing request to a central control based on a preset period; or alternatively
When the task processing states of all the processing brains are empty, controlling a task distribution brain to send the task processing request to the central control based on the preset time period; or alternatively
When the processing brain is in the offline state and the task processing state is to be executed, waking up the processing brain in the offline state.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
carrying out knowledge graph analysis processing on the task scheduling result according to a graph brain, and/or carrying out data correction processing on the task scheduling result according to a data correction script of the script brain to obtain a processing result;
determining redundant data, error data and repeated data corresponding to the processing result according to a data analysis script of the script brain;
and storing the processing results after deleting the redundant data, the error data and the repeated data into a storage brain.
Further, the processor 1001 may call a task scheduler for a multi-brain collaboration stored in the memory 1004, and further perform the following operations:
The task scheduling result is sent to an information checking interface, and a checking result fed back by the information checking interface is responded;
when the checking result is an abnormal result, skipping to execute the task decomposition processing on the multi-task scheduling information based on the script brain to obtain a subtask corresponding to the multi-task scheduling information;
When the verification result is a result to be corrected, controlling the script brain to correct the task scheduling result based on a correction instruction corresponding to the result to be corrected, and storing the corrected result into a storage brain;
And when the checking result is a normal result, storing the data corresponding to the task scheduling result into the storage brain.
Furthermore, it will be appreciated by those of ordinary skill in the art that implementing all or part of the processes in the methods of the above embodiments may be accomplished by computer programs to instruct related hardware. The computer program comprises program instructions, and the computer program may be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the control terminal to carry out the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a computer-readable storage medium storing a multi-brain collaborative task scheduler, which when executed by a processor, implements the steps of the multi-brain collaborative task scheduling method as described in the above embodiments.
It should be noted that, because the storage medium provided in the embodiments of the present application is a storage medium used for implementing the method in the embodiments of the present application, based on the method described in the embodiments of the present application, a person skilled in the art can understand the specific structure and the modification of the storage medium, and therefore, the description thereof is omitted herein. All storage media adopted by the method of the embodiment of the application belong to the scope of protection of the application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. The multi-brain collaborative task scheduling method is characterized by comprising the following steps of:
when the multitask scheduling information is received and is a voice instruction, the voice instruction is identified based on a voice identification script of a script brain; when the recognition result is in accordance with the voice right, acquiring text information corresponding to the voice instruction based on a text extraction script, and disassembling the text information according to the text recognition script to obtain each item to be processed; taking the to-be-processed items as subtasks corresponding to the multitask scheduling information, wherein the multitask scheduling information is an internet information scheduling task;
The subtasks are sent to a task digestion brain, and the execution sequence and scene information of each subtask are determined based on the task digestion brain;
determining task item scheduling, data storage formats, map data storage sources and instance creation information of each subtask according to the scene information, and determining a node processing sequence corresponding to the instance creation information according to the execution sequence;
Taking the nodes corresponding to the node processing sequence as processing nodes, and determining logic brains, storage brains, map brains and robot instance brains corresponding to the task item scheduling, the data storage format, the map data storage source and the instance creation information, wherein the logic brains, the storage brains, the map brains and the robot instance brains are processing brains;
Controlling the task distribution brain to distribute each subtask to the processing brain corresponding to the subtask;
determining robot software corresponding to each processing brain, controlling the robot software to execute each subtask, and receiving a task scheduling result fed back by each robot software, wherein the robot software refers to virtual robot software capable of executing intelligent software for extracting data, filling in forms, moving files and performing various simulation processes;
The task scheduling result is sent to an information checking interface, and a checking result fed back by the information checking interface is responded;
When the checking result is an abnormal result, skipping to execute the task decomposition processing on the multi-task scheduling information based on the script brain to obtain a subtask corresponding to the multi-task scheduling information; when the verification result is a result to be corrected, controlling the script brain to correct the task scheduling result based on a correction instruction corresponding to the result to be corrected, and storing the corrected result into a storage brain; and when the checking result is a normal result, storing the data corresponding to the task scheduling result into the storage brain.
2. The multi-brain collaborative task scheduling method according to claim 1, wherein the step of determining the corresponding robot software of each processing brain, controlling the robot software to execute each subtask, and receiving the task scheduling result fed back by each robot software comprises:
determining robot software corresponding to each processing brain, and forwarding each subtask to Emitter based on central control so that the Emitter forwards the subtask to a server;
The server side is controlled to forward each subtask to each robot software;
and when the robot software receives and executes each subtask, acquiring a processing result fed back by the robot software received by the server.
3. The method for task scheduling according to claim 1, wherein after the step of determining the corresponding robot software of each processing brain, controlling the robot software to execute each subtask and receiving the task scheduling result fed back by each robot software, the method further comprises:
determining a task processing state of the processing brain;
when the task processing state of the target processing brain is empty, controlling the target processing brain to send a task processing request to a central control based on a preset period; or alternatively
When the task processing states of all the processing brains are empty, controlling a task distribution brain to send the task processing request to the central control based on the preset time period; or alternatively
When the processing brain is in the offline state and the task processing state is to be executed, waking up the processing brain in the offline state.
4. The method for task scheduling according to claim 1, wherein after the step of determining the corresponding robot software of each processing brain, controlling the robot software to execute each subtask and receiving the task scheduling result fed back by each robot software, the method further comprises:
carrying out knowledge graph analysis processing on the task scheduling result according to a graph brain, and/or carrying out data correction processing on the task scheduling result according to a data correction script of the script brain to obtain a processing result;
determining redundant data, error data and repeated data corresponding to the processing result according to a data analysis script of the script brain;
and storing the processing results after deleting the redundant data, the error data and the repeated data into a storage brain.
5. A multi-brain cooperative task scheduling device, characterized in that the multi-brain cooperative task scheduling device comprises: a memory, a processor and a multi-brain collaborative task scheduler stored on the memory and operable on the processor, which when executed by the processor, performs the steps of the multi-brain collaborative task scheduling method of any one of claims 1 to 4.
6. A computer readable storage medium, wherein a multi-brain collaborative task scheduler is stored on the computer readable storage medium, which when executed by a processor, implements the steps of the multi-brain collaborative task scheduling method of any one of claims 1-4.
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