CN114037293A - Task allocation method, device, computer system and medium - Google Patents
Task allocation method, device, computer system and medium Download PDFInfo
- Publication number
- CN114037293A CN114037293A CN202111335865.XA CN202111335865A CN114037293A CN 114037293 A CN114037293 A CN 114037293A CN 202111335865 A CN202111335865 A CN 202111335865A CN 114037293 A CN114037293 A CN 114037293A
- Authority
- CN
- China
- Prior art keywords
- task
- operator
- information
- attribute information
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000012549 training Methods 0.000 claims abstract description 27
- 238000004590 computer program Methods 0.000 claims abstract description 25
- 230000015654 memory Effects 0.000 claims description 11
- 230000002123 temporal effect Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000010586 diagram Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000013136 deep learning model Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004806 packaging method and process Methods 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/06314—Calendaring for a resource
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The disclosure provides a task allocation method, a device, a computer system, a readable storage medium and a computer program product. The present disclosure relates to the field of big data technology, and may be used in the field of financial technology. The task allocation method comprises the following steps: according to a task allocation rule, acquiring a first task from a task pool and determining a worker for executing the first task from a worker queue; inputting the task attribute information of the first task and the attribute information of the operator into a time prediction model obtained by pre-training, and outputting the predicted completion time of the operator for completing the first task; and distributing the second task in the task pool to the operator within a preset time before the predicted completion time.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a task allocation method, an apparatus, a computer system, a readable storage medium, and a computer program product.
Background
The intensive operation process can improve the working efficiency and efficiency by intensively and reasonably applying the modern management technology, and is widely applied to various basic technical fields.
In the process of intensive operation, a serial task allocation method is adopted in the current task allocation process, namely after the current task is submitted by an operator, the next task is allocated, however, waiting time exists between the completion of the current task and the execution of the next task by the operator, and the overall efficiency of the intensive operation is reduced due to the waiting time.
Disclosure of Invention
In view of the above, the present disclosure provides a task allocation method, a task allocation apparatus, a computer system, a readable storage medium, and a computer program product.
One aspect of the present disclosure provides a task allocation method, including:
according to a task allocation rule, acquiring a first task from a task pool and determining a worker for executing the first task from a worker queue;
inputting the task attribute information of the first task and the attribute information of the operator into a time prediction model obtained by pre-training, and outputting the predicted completion time of the operator for completing the first task; and
and distributing the second task in the task pool to the operator within a preset time length before the predicted completion time.
According to the embodiment of the disclosure, the task attribute information includes task type information, task quantity information, task quality information, and task start time information; the attribute information of the operator comprises identity information of the operator, quality information of the historical task completed by the operator and time information of the historical task completed by the operator; wherein the historical task is the same as the task type information of the first task.
According to an embodiment of the present disclosure, the task allocation rule includes a rule for selecting a target task according to a task execution order and a rule for determining a worker for executing the target task according to a task type, where the obtaining a first task from a task pool and determining a worker for executing the first task from a worker queue according to the task allocation rule includes:
acquiring a first task from a task pool according to the task execution sequence;
determining a target operator capable of executing the first task according to the task type of the first task; and
and determining the target operator from the operator queue.
According to an embodiment of the present disclosure, inputting task attribute information of the first task and attribute information of the operator into a time prediction model obtained by training in advance, and outputting a predicted completion time for the operator to complete the first task includes:
inputting the task attribute information of the first task and the attribute information of the operator into a time prediction model obtained by pre-training, and outputting the predicted completion time length of the operator for completing the first task;
and determining and outputting the predicted completion time of the operator for completing the first task according to the task start time information of the first task and the predicted completion time of the operator for completing the first task.
According to the embodiment of the disclosure, the time prediction model is obtained by pre-training through the following operations:
acquiring a plurality of pieces of sample information, wherein each piece of sample information comprises task attribute information of a historical task sample and attribute information of an operator who finishes the historical task sample;
and inputting each piece of sample information into an initial model for training to obtain the time prediction model.
According to an embodiment of the present disclosure, the task allocation method further includes:
when the worker completes the first task, task attribute information of the first task and feedback information of the worker are input to a historical task operation database for training the time prediction model.
According to an embodiment of the present disclosure, the feedback information of the operator includes the identity information of the operator, the quality information of the first task actually completed by the operator, and the time information of the first task actually completed by the operator.
Another method of the present disclosure also discloses a task allocation apparatus, including: the device comprises a determining module, an output module and an allocation module. The system comprises a determining module, a task pool processing module and a task distribution module, wherein the determining module is used for acquiring a first task from the task pool and determining a worker for executing the first task from a worker queue according to a task distribution rule; an output module, configured to input the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model, and output an estimated completion time for the operator to complete the first task; and the distribution module is used for distributing the second task in the task pool to the operator within a preset time length before the predicted completion time.
According to an embodiment of the present disclosure, the determining module includes an obtaining unit, a first determining unit, and a second determining unit. The acquiring unit is used for acquiring the first task from the task pool according to the task execution sequence. A first determination unit configured to determine a target operator who can execute the first task according to a task type of the first task. And a second determination unit configured to determine the target worker from the worker queue.
According to an embodiment of the present disclosure, the output module includes a first output unit and a second output unit. The first output unit is configured to input the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model, and output an expected completion time length for the operator to complete the first task. And a second output unit, configured to determine and output an expected completion time for the operator to complete the first task according to the task start time information of the first task and the expected completion time for the operator to complete the first task.
According to the embodiment of the disclosure, the time prediction model includes an obtaining sub-module and a training sub-module. The acquisition submodule is used for acquiring a plurality of pieces of sample information, wherein each piece of sample information comprises task attribute information of a historical task sample and attribute information of an operator who finishes the historical task sample. And the training submodule is used for inputting each piece of sample information into an initial model for training so as to obtain the time prediction model.
According to an embodiment of the present disclosure, the task assigning apparatus further includes a feedback module, configured to input task attribute information of the first task and feedback information of the operator into a historical task operation database when the operator completes the first task, and configured to train the time prediction model. The feedback information of the operator includes the identity information of the operator, the quality information of the first task actually completed by the operator, and the time information of the first task actually completed by the operator.
Yet another aspect of the present disclosure provides a computer system comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method described above.
Yet another aspect of the present disclosure provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method described above.
Yet another aspect of the present disclosure provides a computer program product comprising:
computer-executable instructions that, when executed, cause the method described above to be performed.
According to the embodiment of the disclosure, the first task is acquired from the task pool and the operator for executing the first task is determined from the staff queue according to the task allocation rule, then the task attribute information of the first task and the attribute information of the operator for executing the first task are input into the time prediction model obtained by pre-training, the predicted completion time of the operator for completing the first task is output, the time of the operator for completing the first task can be accurately predicted, and finally the second task in the task pool is allocated to the operator within a certain time before the predicted completion time, so that the operator can acquire the allocated second task before completing the first task, the time of the operator waiting for the second task after completing the first task is reduced, and the overall operation efficiency is improved.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture to which the task allocation method of the present disclosure may be applied;
FIG. 2 schematically illustrates a flow chart of a task assignment method of an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining a worker performing a first task in an embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of a method of determining an expected completion time for a first task in an embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart of a method of training a temporal prediction model in an embodiment of the present disclosure;
FIG. 6 is a block diagram schematically illustrating a task assigning apparatus according to an embodiment of the present disclosure;
FIG. 7 is a system block diagram schematically illustrating a task assignment method according to an embodiment of the present disclosure;
FIG. 8 schematically illustrates a block diagram of a computer system suitable for implementing the methods described above.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
It should be noted that the task allocation method and apparatus disclosed in the present disclosure relate to the field of big data technology, and may be used in the field of financial technology, and may also be used in any field other than the field of big data technology and the field of financial technology.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the information of the related operators are all in accordance with the regulations of related laws and regulations, and necessary confidentiality measures are taken without violating the good customs of the public order.
In the current serial task allocation method adopted in the intensive operation process, the operation efficiency is reduced due to the time for waiting task allocation by operators. In view of this, the embodiments of the present disclosure provide the following task allocation method.
Fig. 1 schematically illustrates an exemplary system architecture 100 to which the task allocation method and apparatus may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as: data processing type applications, social platform software applications, and the like (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received task data to be distributed, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to a user request) to the terminal device.
It should be noted that the task allocation method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the task allocation apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The task allocation method provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the task allocation apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
For example, the task attribute information and the worker attribute information may be input through any one of the terminal devices 101, 102, or 103 (for example, the terminal device 101 is not limited thereto), and then the terminal device 101 may transmit the task attribute information to be allocated to another server or a server cluster, and the other server or the server cluster that receives the task attribute information to be allocated performs the task allocation method provided by the embodiment of the present disclosure.
It should be understood that the number of terminal devices, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, and servers, as desired for implementation.
Fig. 2 schematically shows a flowchart of a task allocation method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S201 to S203.
In operation S201, a first task is obtained from a task pool and a worker for performing the first task is determined from a worker queue according to a task allocation rule.
In operation S202, task attribute information of the first task and attribute information of the operator are input to a time prediction model trained in advance, and a predicted completion time for the operator to complete the first task is output.
In operation S203, a second task in the task pool is assigned to the worker within a preset time period before the predicted completion time.
According to the embodiment of the disclosure, the preset time length before the predicted completion time can be set according to the requirement of task allocation efficiency, for example, the preset time length can be set to 5s, and then the second task in the task pool is allocated to the operator within 5s before the predicted completion time of the first task is reached.
In the embodiment of the disclosure, according to the task allocation rule, a first task is acquired from the task pool and an operator for executing the first task is determined from the staff queue, then the task attribute information of the first task and the attribute information of the operator for executing the first task are input into a time prediction model obtained by pre-training, the predicted completion time of the operator for completing the first task is output, the time of the operator for completing the first task can be accurately predicted, and finally, a second task in the task pool is allocated to the operator within a certain time before the predicted completion time, so that the operator can acquire the allocated second task before completing the first task, the time of waiting for the second task after the operator completes the first task is reduced, and the overall operation efficiency is improved.
According to the embodiment of the disclosure, the task attribute information includes task type information, task quantity information, task quality information, and task start time information. The task type information can be divided according to different service types, such as a savings task, a credit task, an insurance task, a payment task, and the like. The task quality information can comprise quality standard information or quality requirement information of the client to the task, and can also comprise an industry quality standard of a business type to which the task belongs.
According to the embodiment of the disclosure, the attribute information of the operator comprises identity information of the operator, quality information of the historical task completed by the operator and time information of the historical task completed by the operator; the identity information of the operator can include the name, the working age and the professional field of the operator. The quality information of the worker completing the historical task and the time information of the worker completing the historical task may include an average quality and an average time the worker completed the same type of historical task.
In the embodiment of the disclosure, the predicted time for the task operator to complete the task is predicted according to the task attribute information and the attribute information of the task operator, so that on one hand, the predicted time for the task operator to complete the task can be accurately and efficiently predicted, and on the other hand, the task type information, the task quantity information, the task quality information, the task starting time information, the quality information of the historical task completed by the task operator and the time information of the historical task completed by the task operator are selected in a targeted manner as basic parameters for predicting the task completion time, so that the data running calculation speed in the prediction time model equipment can be increased, and the consumption of invalid data on equipment resources is reduced while the prediction accuracy is ensured.
According to the embodiment of the disclosure, the task allocation rule may include a rule for selecting a target task in the task execution order and a rule for determining a worker for executing the target task in the task type.
Fig. 3 schematically illustrates a flowchart of a method for determining a worker performing a first task in an embodiment of the disclosure.
As shown in fig. 3, the method includes operations S301 to S303.
In operation S301, a first task is acquired from a task pool in a task execution order.
In operation S302, a target worker capable of executing the first task is determined according to the task type of the first task.
In operation S303, a target worker is determined from the worker queue.
According to the embodiment of the disclosure, for example, a personal credit approval task is acquired from a task pool, since the type of the credit approval task belongs to personal credit approval, the task needs to be allocated to a credit worker with personal credit approval qualification in accordance with a task allocation rule, and the credit worker with personal credit approval qualification in a worker queue is used as a target worker of the task to perform task allocation.
In the embodiment of the disclosure, the target operator capable of executing the task is determined through the task allocation rule, so that the automatic matching process between the operator and the task to be executed according to the task type can be realized, and the allocation efficiency of the task allocation system is improved.
Fig. 4 schematically illustrates a flowchart of a method for determining an expected completion time of a first task in an embodiment of the disclosure.
As shown in fig. 4, the method includes operations S401 to S402.
In operation S401, the task attribute information of the first task and the attribute information of the operator are input into a pre-trained time prediction model, and a predicted completion time of the operator to complete the first task is output.
In operation S402, an expected completion time for the worker to complete the first task is determined and output according to the task start time information of the first task and the expected completion time for the worker to complete the first task.
According to the embodiment of the disclosure, for example, for a saving task, an operator in charge of executing the saving task needs 0.5 hour to complete the saving task of the same type, after the saving task and attribute information of an operator in charge of completing the saving task are input into the time prediction model, the time prediction model predicts that the predicted completion time of the operator completing the saving task is 0.5 hour, and then according to the task start time included in the task attribute information: 14 o 'clock and 15 o' clock at 8 month and 5 days in 2020, the time prediction model outputs that the completion time of the operator for completing the saving task is 14 o 'clock and 45 o' clock at 8 month and 5 days in 2020.
In the embodiment of the disclosure, since the task attribute information includes the time when the task starts, the time prediction model may predict the predicted time when the operator changes into the task according to the quality information of the historical task of the same type as the task and the time information of the historical task of the same type as the task, and add the predicted time when the task is completed on the basis of the time when the task starts, the predicted completion time when the operator completes the task may be obtained, so as to accurately predict the task completion time, so as to allocate the next task.
FIG. 5 is a flow chart illustrating a method for training a temporal prediction model in an embodiment of the present disclosure.
As shown in fig. 5, the method includes operations S501 to S502.
In operation S501, a plurality of pieces of sample information are acquired, wherein each piece of sample information includes task attribute information of a historical task sample and worker attribute information of a completed historical task sample.
According to the embodiment of the disclosure, each piece of sample information comprises task type information, task quality information, task quantity information, task starting time information of a historical task sample, identity information of an operator who completes the historical task sample, quality information of the operator who completes the historical task sample, and time information of the operator who completes the historical task sample, so that the time length required by the operator to complete the historical task sample can be obtained according to the sample information.
In operation S502, each piece of sample information is input into the initial model for training to obtain a temporal prediction model.
According to the embodiment of the disclosure, the initial model may adopt various machine learning models and deep learning models, for example, a logistic regression model, a factorization model, a gradient boosting decision tree model, etc. may be selected based on the machine learning model. A convolutional neural network model, a cyclic neural network model, or the like may be selected based on the deep learning model.
In the embodiment of the disclosure, historical data including task type information, task quality information, task starting time information, task quantity information, identity information of an operator who completes the historical task, time information of the operator who completes the historical task, and quality information of the operator who completes the historical task is used as sample information of the training initial model, so that the prediction effect of the trained time prediction model can be more accurate, and the task allocation efficiency can be improved.
According to an embodiment of the present disclosure, the task allocation method provided by the present disclosure further includes: and when the operator finishes the first task, inputting task attribute information of the first task and feedback information of the operator into a historical task operation database for training a time prediction model.
According to the embodiment of the disclosure, for tasks or operators to be distributed without historical operation data, reference data or average data of tasks of the same type or similar types can be used as historical operation sample information training time prediction models, quality information and time information of actual tasks completed by actual operators are input into a historical operation database, and the time prediction models are iteratively trained.
According to the embodiment of the disclosure, the feedback information of the operator includes the identity information of the operator, the quality information of the first task actually completed by the operator, and the time information of the first task actually completed by the operator.
In the embodiment of the disclosure, the quality information and the time information of the task actually completed by the operator who executes the task are fed back to the historical task operation database and used for training the time prediction model, and the time prediction model can be continuously and iteratively trained, so that the accuracy of the predicted completion time when the time prediction model predicts the task executed by the operator is improved.
Fig. 6 schematically shows a block diagram of a task assigning apparatus in an embodiment of the present disclosure.
As shown in fig. 6, the task assigning apparatus 600 includes: a determination module 601, an output module 602, and an assignment module 603.
The determining module 601 is configured to obtain a first task from a task pool and determine a worker for executing the first task from a worker queue according to a task allocation rule.
An output module 602, configured to input the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model, and output a predicted completion time for the operator to complete the first task.
An allocating module 603, configured to allocate a second task in the task pool to the operator within a preset time period before the predicted completion time.
According to the embodiment of the disclosure, when a task needs to be allocated, a determining module 601 acquires a first task from a task pool and determines an operator for executing the first task from a staff queue, then an output module 602 inputs task attribute information of the first task and attribute information of the operator into a pre-trained time prediction model, outputs an estimated completion time for the operator to complete the first task, and finally, an allocating module 603 allocates a second task in the task pool to the operator within a preset time period before the estimated completion time. The method can combine the attribute information of the task and the attribute information of the operator, efficiently and accurately predict the task before completion, and distribute the next task before the task is completed, thereby reducing the time for the operator to wait for task distribution and improving the task distribution efficiency.
According to an embodiment of the present disclosure, the determining module includes an obtaining unit, a first determining unit, and a second determining unit. The acquiring unit is used for acquiring the first task from the task pool according to the task execution sequence. A first determination unit configured to determine a target operator who can execute the first task according to a task type of the first task. And a second determination unit configured to determine the target worker from the worker queue.
According to an embodiment of the present disclosure, the output module includes a first output unit and a second output unit. The first output unit is configured to input the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model, and output an expected completion time length for the operator to complete the first task. And a second output unit, configured to determine and output an expected completion time for the operator to complete the first task according to the task start time information of the first task and the expected completion time for the operator to complete the first task.
According to the embodiment of the disclosure, the time prediction model includes an obtaining sub-module and a training sub-module. The acquisition submodule is used for acquiring a plurality of pieces of sample information, wherein each piece of sample information comprises task attribute information of a historical task sample and attribute information of an operator who finishes the historical task sample. And the training submodule is used for inputting each piece of sample information into an initial model for training so as to obtain the time prediction model.
According to an embodiment of the present disclosure, the task assigning apparatus further includes a feedback module, configured to input task attribute information of the first task and feedback information of the operator into a historical task operation database when the operator completes the first task, and configured to train the time prediction model. The feedback information of the operator includes the identity information of the operator, the quality information of the first task actually completed by the operator, and the time information of the first task actually completed by the operator.
Any of the modules, units, or at least part of the functionality of any of them according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules and units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, units according to the embodiments of the present disclosure may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by any other reasonable means of hardware or firmware by integrating or packaging the circuits, or in any one of three implementations of software, hardware and firmware, or in any suitable combination of any of them. Alternatively, one or more of the modules, units according to embodiments of the present disclosure may be implemented at least partly as computer program modules, which, when executed, may perform the respective functions.
For example, any number of the determining module 601, the outputting module 602 and the allocating module 603 may be combined and implemented in one module/unit/sub-unit, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the present disclosure, at least one of the determining module 601, the outputting module 602, and the allocating module 603 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the determining module 601, the outputting module 602 and the assigning module 603 may be at least partly implemented as a computer program module, which when executed may perform a corresponding function.
Fig. 7 schematically shows a system block diagram to which the task allocation method in the embodiment of the present disclosure is applied.
As shown in fig. 7, the system includes a task pool 701, a person queue 702, a task assigning device 703, a terminal device 704 of a worker, a time prediction model 705, and a historical task job database 706.
According to the embodiment of the present disclosure, the task assigning apparatus 703 acquires the first task from the task pool 701, matches a target worker capable of executing the first task from the worker queue 702 according to the task assignment rule, and assigns the first task to the worker 704. Meanwhile, the time prediction model 705 acquires task attribute information of the first task and attribute information of the worker 704 from the historical task job database 706, wherein the attribute information of the worker 704 includes quality information and time information of completing the same type of historical task as the first task, and identity information. The time prediction model 705 predicts the completion time of the worker 704 to complete the first task based on the task attribute information of the first task and the attribute information of the worker 704, and the task assigning device 703 acquires the predicted completion time of the worker 704 to complete the first task from the time prediction model 705 and assigns the second task to the worker 704 within a preset time period before the predicted completion time.
When the worker 704 completes the first task, the time at which the worker 704 actually completes the first task, the attribute information of the first task, and the attribute information of the worker 704 are transmitted from the worker's terminal device 704 to the historical task job database 706 for training the time prediction model 705.
FIG. 8 schematically shows a block diagram of a computer system for implementing a task allocation method according to an embodiment of the present disclosure. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are stored. The processor 801, the ROM 802, and the RAM803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM 802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: 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), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 802 and/or RAM803 described above and/or one or more memories other than the ROM 802 and RAM 803.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method provided by the embodiments of the present disclosure, when the computer program product is run on an electronic device, the program code being configured to cause the electronic device to implement the project template generation method provided by the embodiments of the present disclosure.
The computer program, when executed by the processor 801, performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via communication section 809, and/or installed from removable media 811. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (11)
1. A task allocation method, comprising:
according to a task allocation rule, acquiring a first task from a task pool and determining a worker for executing the first task from a worker queue;
inputting the task attribute information of the first task and the attribute information of the operator into a time prediction model obtained by pre-training, and outputting the predicted completion time of the operator for completing the first task; and
and distributing the second task in the task pool to the operator within a preset time length before the predicted completion time.
2. The method of claim 1, wherein,
the task attribute information comprises task type information, task quantity information, task quality information and task starting time information;
the attribute information of the operating personnel comprises identity information of the operating personnel, quality information of the historical tasks completed by the operating personnel and time information of the historical tasks completed by the operating personnel;
wherein the historical task is the same as the task type information of the first task.
3. The method of claim 1, wherein the task allocation rules include a rule for selecting a target task in order of task execution and a rule for determining a worker for executing the target task by task type, wherein the retrieving a first task from a task pool and determining a worker for executing the first task from a worker queue based on task allocation rules comprises:
acquiring a first task from a task pool according to the task execution sequence;
determining a target operator capable of executing the first task according to the task type of the first task; and
and determining the target operator from the personnel queue.
4. The method according to claim 1 or 2, wherein inputting the task attribute information of the first task and the attribute information of the worker into a pre-trained time prediction model and outputting a predicted completion time of the worker for completing the first task comprises:
inputting the task attribute information of the first task and the attribute information of the operator into a time prediction model obtained by pre-training, and outputting the predicted completion time of the operator for completing the first task;
and determining and outputting the predicted completion time of the operator for completing the first task according to the task start time information of the first task and the predicted completion time of the operator for completing the first task.
5. The method of claim 1 or 2, wherein the temporal prediction model is pre-trained by:
acquiring a plurality of pieces of sample information, wherein each piece of sample information comprises task attribute information of a historical task sample and attribute information of an operator who finishes the historical task sample;
and inputting each piece of sample information into an initial model for training to obtain the time prediction model.
6. The method of claim 1, further comprising:
and when the operator finishes the first task, inputting task attribute information of the first task and feedback information of the operator into a historical task operation database for training the time prediction model.
7. The method of claim 6, wherein,
the feedback information of the operator comprises the identity information of the operator, the quality information of the first task actually completed by the operator and the time information of the first task actually completed by the operator.
8. A task assigning apparatus comprising:
the determining module is used for acquiring a first task from a task pool and determining a worker for executing the first task from a worker queue according to a task allocation rule;
the output module is used for inputting the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model and outputting the predicted completion time of the operator for completing the first task;
and the distribution module is used for distributing the second task in the task pool to the operator within a preset time length before the predicted completion time.
9. A computer system, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 7.
11. A computer program product, comprising:
computer executable instructions for use when executed to implement the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111335865.XA CN114037293A (en) | 2021-11-11 | 2021-11-11 | Task allocation method, device, computer system and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111335865.XA CN114037293A (en) | 2021-11-11 | 2021-11-11 | Task allocation method, device, computer system and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114037293A true CN114037293A (en) | 2022-02-11 |
Family
ID=80144156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111335865.XA Pending CN114037293A (en) | 2021-11-11 | 2021-11-11 | Task allocation method, device, computer system and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114037293A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114662888A (en) * | 2022-03-16 | 2022-06-24 | 惠州市索沃科技有限公司 | Job management method, job management apparatus, electronic device, and storage medium |
| CN115881288A (en) * | 2023-03-03 | 2023-03-31 | 四川省肿瘤医院 | Vein catheterization management system |
| CN116909744A (en) * | 2023-07-20 | 2023-10-20 | 之江实验室 | Thread pool parameter adjusting method and device, storage medium and electronic equipment |
| CN117130873A (en) * | 2023-10-26 | 2023-11-28 | 中国证券登记结算有限责任公司 | Task monitoring method and device |
| CN117764343A (en) * | 2023-12-22 | 2024-03-26 | 北京小趣智品科技有限公司 | Task assignment method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228323A (en) * | 2016-12-14 | 2018-06-29 | 龙芯中科技术有限公司 | Hadoop method for scheduling task and device based on data locality |
| CN110648047A (en) * | 2019-08-16 | 2020-01-03 | 深圳市轱辘汽车维修技术有限公司 | Task scheduling method, device, system and storage medium |
| WO2021139438A1 (en) * | 2020-01-07 | 2021-07-15 | 平安科技（深圳）有限公司 | Big data resource processing method and apparatus, and terminal and storage medium |
-
2021
- 2021-11-11 CN CN202111335865.XA patent/CN114037293A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228323A (en) * | 2016-12-14 | 2018-06-29 | 龙芯中科技术有限公司 | Hadoop method for scheduling task and device based on data locality |
| CN110648047A (en) * | 2019-08-16 | 2020-01-03 | 深圳市轱辘汽车维修技术有限公司 | Task scheduling method, device, system and storage medium |
| WO2021139438A1 (en) * | 2020-01-07 | 2021-07-15 | 平安科技（深圳）有限公司 | Big data resource processing method and apparatus, and terminal and storage medium |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114662888A (en) * | 2022-03-16 | 2022-06-24 | 惠州市索沃科技有限公司 | Job management method, job management apparatus, electronic device, and storage medium |
| CN115881288A (en) * | 2023-03-03 | 2023-03-31 | 四川省肿瘤医院 | Vein catheterization management system |
| CN115881288B (en) * | 2023-03-03 | 2023-05-12 | 四川省肿瘤医院 | Vein catheterization management system |
| CN116909744A (en) * | 2023-07-20 | 2023-10-20 | 之江实验室 | Thread pool parameter adjusting method and device, storage medium and electronic equipment |
| CN117130873A (en) * | 2023-10-26 | 2023-11-28 | 中国证券登记结算有限责任公司 | Task monitoring method and device |
| CN117130873B (en) * | 2023-10-26 | 2024-01-26 | 中国证券登记结算有限责任公司 | Task monitoring method and device |
| CN117764343A (en) * | 2023-12-22 | 2024-03-26 | 北京小趣智品科技有限公司 | Task assignment method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220027202A1 (en) | Stream-based accelerator processing of computational graphs | |
| US11087216B2 (en) | Modifying computational graphs | |
| CN114037293A (en) | Task allocation method, device, computer system and medium | |
| CN108885571B (en) | Input of batch processing machine learning model | |
| US11238409B2 (en) | Techniques for extraction and valuation of proficiencies for gap detection and remediation | |
| US9423957B2 (en) | Adaptive system provisioning | |
| US11763146B1 (en) | Processing loops in computational graphs | |
| US10635492B2 (en) | Leveraging shared work to enhance job performance across analytics platforms | |
| CN113076224B (en) | Data backup method, data backup system, electronic device and readable storage medium | |
| US11755954B2 (en) | Scheduled federated learning for enhanced search | |
| US10789559B2 (en) | Virtually assisted task generation | |
| US20230177425A1 (en) | System and method for resource allocation optimization for task execution | |
| CN115048561A (en) | Recommendation information determination method and device, electronic equipment and readable storage medium | |
| US11789774B2 (en) | Optimization of workload scheduling in a distributed shared resource environment | |
| CN112860344A (en) | Component processing method and device, electronic equipment and storage medium | |
| US20140114719A1 (en) | Allocating Service Consumers into Compatible Resource Pools | |
| CN116860796A (en) | Data matching method and device, storage medium and electronic equipment | |
| CN115689211A (en) | Service scheduling method, device, electronic equipment, medium and product | |
| CN115586959A (en) | Resource allocation method, device, electronic equipment and storage medium | |
| CN118153959A (en) | Risk identification method, apparatus, device and storage medium | |
| CN116089094A (en) | Thread pool allocation method and device | |
| CN115470505A (en) | Authority management method, device, electronic equipment and storage medium | |
| CN113379236A (en) | Data processing method, device and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |