CN114625490A - Task allocation method and device based on joint learning - Google Patents

Task allocation method and device based on joint learning Download PDF

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Publication number
CN114625490A
CN114625490A CN202011441307.7A CN202011441307A CN114625490A CN 114625490 A CN114625490 A CN 114625490A CN 202011441307 A CN202011441307 A CN 202011441307A CN 114625490 A CN114625490 A CN 114625490A
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China
Prior art keywords
task
target user
target
request
determining
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CN202011441307.7A
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Chinese (zh)
Inventor
张敏
高庆
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Ennew Digital Technology Co Ltd
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Ennew Digital Technology Co Ltd
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Priority to CN202011441307.7A priority Critical patent/CN114625490A/en
Publication of CN114625490A publication Critical patent/CN114625490A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06F9/4806—Task transfer initiation or dispatching
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals

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  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a task allocation method, a device, a readable medium and electronic equipment based on joint learning, wherein the method comprises the following steps: determining a query task request of a target user based on task allocation qualification of the target user; based on the query task request, screening in a task list of a joint learning engine to determine a target task list; and distributing the target task for the target user based on the selection result of the target user to the target task list. According to the technical scheme, the task query request of the target user is determined, the target task list is further determined, the target task is distributed to the target user according to the selection result of the target user on the target task list, and the task distribution method based on the joint learning is reasonable.

Description

Task allocation method and device based on joint learning
Technical Field
The invention relates to the field of energy, in particular to a task allocation method and device based on joint learning.
Background
With the rapid development of the internet technology, user data becomes more and more important resources, various prediction models can be trained based on the user data, and an accurate prediction result is the basis for efficient operation of an energy system. However, not every energy user can collect massive user data and train an accurate prediction model, which makes joint learning become a trend, and in order to make more and more energy users participate in joint learning, it becomes more and more important to determine a reasonable task allocation method based on joint learning.
Disclosure of Invention
The invention provides a task allocation method, a device, a readable medium and electronic equipment based on joint learning.
In a first aspect, the present invention provides a task allocation method based on joint learning, including:
determining a query task request of a target user based on task allocation qualification of the target user;
based on the query task request, screening in a task list of a joint learning engine to determine a target task list;
and distributing the target task for the target user based on the selection result of the target user to the target task list.
Preferably, the first and second electrodes are formed of a metal,
the determining the query task request of the target user based on the task allocation qualification of the target user comprises the following steps:
judging whether the target user has task allocation qualification;
if the target user has the task allocation qualification, determining a task query request of the target user;
and if the target user does not have the task allocation qualification, reminding the target user to create a task and acquiring the task allocation qualification.
Preferably, the first and second electrodes are formed of a metal,
based on the query task request, screening in a task list of a joint learning engine to determine a target task list, wherein the screening comprises the following steps:
responding to the query task request, and determining a task list of a joint learning engine;
determining current screening conditions;
and based on the current screening conditions, screening in a task list of the joint learning engine to determine a target task list.
Preferably, the first and second electrodes are formed of a metal,
the determining the current screening condition comprises:
receiving a screening request sent by a target user;
and extracting information of the screening request, and determining the current screening condition.
Preferably, the first and second electrodes are formed of a metal,
the determining the current screening condition comprises:
determining a user screening condition sent by a target user;
determining a preset central screening condition corresponding to the joint learning engine;
and determining the current screening condition based on the user screening condition and the central screening condition.
Preferably, the first and second electrodes are formed of a metal,
the current screening conditions include: industry type, joint learning engine support algorithm, task execution state.
In a second aspect, the present invention provides a task allocation apparatus based on joint learning, including:
the request determining module is used for determining a query task request of a target user based on task allocation qualification of the target user;
the list determining module is used for screening in a task list of the joint learning engine based on the query task request to determine a target task list;
and the task allocation module is used for allocating the target task to the target user based on the selection result of the target user to the target task list.
Preferably, the first and second liquid crystal display panels are,
the request determination module includes:
the qualification judging unit is used for judging whether the target user is qualified for task allocation;
the request determining unit is used for determining a task query request of the target user if the target user has the task allocation qualification;
and the qualification acquisition unit is used for reminding the target user to create a task and acquiring the task allocation qualification if the target user does not have the task allocation qualification.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a task allocation method, a device, a readable medium and electronic equipment based on joint learning. According to the technical scheme provided by the invention, the target user is allowed to carry out task query, and the target task list related to the target user in the task list of the joint learning engine is determined, so that the target task distributed to the target user is related to the target user, the task requirement of the target user is met, and the method and the system are reasonable.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a first joint learning-based task allocation method provided in an embodiment of the present invention;
fig. 2 is a schematic flowchart of a second task allocation method based on joint learning according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a third joint learning-based task allocation method according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a task allocation apparatus based on joint learning according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a request determining module in a task allocation apparatus based on joint learning according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a task allocation method based on joint learning, where the method includes:
step 11, determining a query task request of a target user based on task allocation qualification of the target user;
step 12, based on the query task request, screening in a task list of a joint learning engine to determine a target task list;
and step 13, distributing the target tasks for the target users based on the selection results of the target users to the target task list.
In the above embodiment, a task allocation qualification of a target user is determined, where the task allocation qualification is an authority that can perform task query, the task query request is an instruction for triggering task query, and the task query request may carry identification information or requirement information of the target user; and then, according to the query task request, screening in a task list of the joint learning engine to determine a target task list, wherein the target task list is a list corresponding to the target task selected from the task list, for example, task information in the task list is determined, the target task of which the task information meets identification information or requirement information carried by the query task request is selected, and the list formed by the selected target task is the target task list. And sending the target task list to a target user, so that the target user selects the target task in the target task list, and distributing the target task to the target user based on the selection result of the target user to the target task list. According to the technical scheme provided by the embodiment, the target user is allowed to perform task query, and the target task list related to the target user in the task list of the joint learning engine is determined, so that the target task allocated to the target user is related to the target user, the task requirement of the target user can be met, and the method and the device have reasonableness.
Specifically, the target tasks allocated to the target user may be training tasks of a load prediction model, training tasks of a fault prediction model, and training tasks of an operation and maintenance management model, that is, after the tasks are completed, the target user may obtain the corresponding load prediction model, fault prediction model, and operation and maintenance management model, and may also include training tasks of other models, and the user may adjust the models according to actual application scenarios.
As shown in FIG. 2, in one embodiment of the present invention, the step 11 of determining the query task request of the target user based on the task allocation qualification of the target user includes:
step 111, judging whether the target user has task allocation qualification;
step 112, if the target user has the task allocation qualification, determining a query task request of the target user;
and 113, if the target user does not have the task allocation qualification, reminding the target user to create a task and acquiring the task allocation qualification.
In the embodiment, whether the target user has the task allocation qualification is judged, and if the target user has the task allocation qualification, the task query request of the target user is determined; if the user target user does not have the task allocation qualification, the target user is reminded to create the task, and the task allocation qualification can be obtained after the task is created. In a possible implementation manner, a target user sends a task creating request, the task creating request contains required parameter information of the target user, wherein the required parameter information comprises information such as an algorithm, an arithmetic capability and metadata required by the target user, the joint learning engine receives the task creating request and judges whether the required parameter information of the target user can be met, if so, the task creating is successful, the task allocation qualification is obtained, otherwise, the task creating is failed.
As shown in fig. 3, in an embodiment of the present invention, the step 12 performs screening in a task list of a joint learning engine based on the query task request, and determines a target task list, including:
step 121, responding to the query task request, and determining a task list of a joint learning engine;
step 122, determining current screening conditions;
and 123, screening in the task list of the joint learning engine based on the current screening condition to determine a target task list.
In the above embodiment, after receiving the query task request, determining a task list of the joint learning engine in response to the query task request, where the task list includes a task being executed and a task that has ended, and determining a current screening condition, where the current screening condition refers to a screening condition corresponding to a current time, and according to the determined current screening condition, screening in the task list of the joint learning engine to determine a target task list. Specifically, the current screening conditions include: industry type, joint learning engine support algorithm, task execution state.
In a possible implementation manner, the current screening condition is determined by the target user, and the target user sends the screening request, where the screening request carries the current screening condition, so that after receiving the screening request, the information of the screening request is extracted to determine the current screening condition. In another possible implementation manner, the screening conditions may be determined by the target user and the joint learning engine center together, that is, the user screening conditions sent by the target user are received, and the current screening conditions are determined together by combining preset center screening conditions corresponding to the joint learning engine. For example, the user screening condition only screens the joint learning engine support algorithm, the screening algorithm is a, the preset central screening condition not only sets the screening of the joint learning engine support algorithm, the screening algorithms are a and B, but also sets the screening of the industry type and the task execution state, and at the moment, the current screening condition can be determined to be the joint learning engine support algorithm, the screening algorithm is a, and the industry type and the task execution state are simultaneously screened by combining the user screening condition of the target user and the preset central screening condition. And when the range of the user screening condition is different from the range of the preset central screening condition, determining the current screening condition mainly by using the user screening condition. Certainly, in a possible implementation manner, the current screening condition is a preset central screening condition, and after the query task request is obtained, the joint learning engine automatically screens according to the preset central screening condition.
Based on the same inventive concept as the method described above, as shown in fig. 4, an embodiment of the present invention provides a task allocation apparatus based on joint learning, including:
a request determining module 41, configured to determine a query task request of a target user based on task allocation qualifications of the target user;
a list determining module 42, configured to perform screening in a task list of the joint learning engine based on the query task request, and determine a target task list;
and the task allocation module 43 is configured to allocate a target task to the target user based on a selection result of the target user on the target task list.
As shown in fig. 5, in one embodiment of the invention,
the request determining module 41 includes:
a qualification judging unit 411 for judging whether the target user qualifies for task allocation;
a request determining unit 412, configured to determine a query task request of a target user if the target user qualifies for task allocation;
and the qualification acquiring unit 413 is used for reminding the target user of task creation and acquiring the task allocation qualification if the target user does not have the task allocation qualification.
For convenience of description, the above device embodiments are described with functions divided into various units or modules, and the functions of the units or modules may be implemented in one or more software and/or hardware when implementing the present invention.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 601 and a memory 602 storing executable instructions, and optionally further includes an internal bus 603 and a network interface 604. The Memory 602 may include a Memory 6021, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory 6022 (e.g., at least 1 disk Memory); the processor 601, the network interface 604, and the memory 602 may be connected to each other by an internal bus 603, and the internal bus 603 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like; the internal bus 603 may be divided into an address bus, a data bus, a control bus, etc., which is indicated by only one double-headed arrow in fig. 6 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 601 executes execution instructions stored by the memory 602, the processor 601 performs a method in any of the embodiments of the present invention and at least for performing the method as shown in fig. 1-3.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the corresponding execution instruction, and can also obtain the corresponding execution instruction from other equipment, so as to form a task distribution device based on joint learning on a logic level. The processor executes the execution instructions stored in the memory, so that the joint learning-based task allocation method provided by any embodiment of the invention is realized through the executed execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any embodiment of the present invention. The electronic device may specifically be the electronic device shown in fig. 6; the execution instruction is a computer program corresponding to the task allocation device based on joint learning.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method 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.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or boiler that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or boiler. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or boiler that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (10)

1. A task allocation method based on joint learning is characterized by comprising the following steps:
determining a query task request of a target user based on task allocation qualification of the target user;
based on the query task request, screening in a task list of a joint learning engine to determine a target task list;
and distributing the target task for the target user based on the selection result of the target user to the target task list.
2. The task allocation method based on joint learning according to claim 1, wherein the determining the query task request of the target user based on the task allocation qualification of the target user comprises:
judging whether the target user has task allocation qualification;
if the target user has the task allocation qualification, determining a task query request of the target user;
and if the target user does not have the task allocation qualification, reminding the target user to create a task and acquiring the task allocation qualification.
3. The joint learning-based task allocation method according to claim 1, wherein the step of performing screening in a task list of a joint learning engine based on the query task request to determine a target task list comprises:
responding to the query task request, and determining a task list of a joint learning engine;
determining current screening conditions;
and based on the current screening conditions, screening in a task list of the joint learning engine to determine a target task list.
4. The joint learning-based task allocation method according to claim 3, wherein the determining of the current screening condition includes:
receiving a screening request sent by a target user;
and extracting information of the screening request, and determining the current screening condition.
5. The joint learning-based task allocation method according to claim 3, wherein the determining of the current screening condition includes:
determining a user screening condition sent by a target user;
determining a preset central screening condition corresponding to the joint learning engine;
and determining the current screening condition based on the user screening condition and the central screening condition.
6. The joint learning-based task allocation method according to claim 3, wherein the current screening condition includes: industry type, joint learning engine support algorithm, task execution state.
7. A task assigning apparatus based on joint learning, comprising:
the request determining module is used for determining a query task request of a target user based on task allocation qualification of the target user;
the list determining module is used for screening in a task list of a joint learning engine based on the query task request to determine a target task list;
and the task allocation module is used for allocating the target task to the target user based on the selection result of the target user to the target task list.
8. The joint learning-based task allocation method according to claim 1, wherein the request determination module comprises:
the qualification judging unit is used for judging whether the target user is qualified for task allocation;
the request determining unit is used for determining a task query request of the target user if the target user has the task allocation qualification;
and the qualification acquiring unit is used for reminding the target user to create the task and acquiring the task allocation qualification if the target user does not have the task allocation qualification.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 6.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-6 when the processor executes the execution instructions stored by the memory.
CN202011441307.7A 2020-12-08 2020-12-08 Task allocation method and device based on joint learning Pending CN114625490A (en)

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Application Number Priority Date Filing Date Title
CN202011441307.7A CN114625490A (en) 2020-12-08 2020-12-08 Task allocation method and device based on joint learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011441307.7A CN114625490A (en) 2020-12-08 2020-12-08 Task allocation method and device based on joint learning

Publications (1)

Publication Number Publication Date
CN114625490A true CN114625490A (en) 2022-06-14

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