Task allocation method, device, computer system and medium

Abstract

The present disclosure provides a task allocation method, device, computer system, readable storage medium and computer program product. The present disclosure relates to the field of big data technology and can be used in the field of financial technology. The task allocation method of the present disclosure includes: according to the task allocation rule, obtaining a first task from a task pool and determining an operator for performing the first task from a personnel queue; inputting the task attribute information of the first task and the attribute information of the operator into a pre-trained time prediction model, outputting the estimated completion time of the operator to complete the first task; and within a preset time before the estimated completion time, allocating the second task in the task pool to the operator.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and more specifically, to a task allocation method, device, computer system, readable storage medium, and computer program product. Background Art

[0002] The intensive operation process is widely used in various basic technical fields because it can improve work benefits and efficiency through the centralized and rational use of modern management technology.

[0003] In the intensive operation process, the current task allocation process adopts a serial task allocation method, that is, the next task is allocated after the operator submits the current task. However, there is a waiting time between the operator completing the current task and before proceeding to the next task. The waiting time will reduce the overall efficiency of the intensive operation. Summary of the invention

[0004] In view of this, the present disclosure provides a task allocation method, device, computer system, readable storage medium and computer program product.

[0005] One aspect of the present disclosure provides a task allocation method, comprising:

[0006] According to the task allocation rule, a first task is obtained from the task pool and a worker for executing the first task is determined from the worker queue;

[0007] 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 an estimated completion time of the operator to complete the first task; and

[0008] Within a preset time before the estimated completion time, the second task in the task pool is assigned to the operator.

[0009] According to an embodiment of the present disclosure, the above-mentioned task attribute information includes task type information, task quantity information, task quality information, and task start time information; the above-mentioned operator attribute information includes operator identity information, quality information of the above-mentioned operator completing historical tasks, and time information of the operator completing the above-mentioned historical tasks; wherein the above-mentioned historical tasks are the same as the task type information of the above-mentioned first task.

[0010] 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 an operator for executing the target task according to a task type, wherein the step of obtaining a first task from a task pool and determining an operator for executing the first task from a personnel queue according to the task allocation rule includes:

[0011] Obtain the first task from the task pool according to the above task execution order;

[0012] Determining a target operator who can perform the first task according to the task type of the first task; and

[0013] Determine the above-mentioned target operating personnel from the above-mentioned personnel queue.

[0014] According to an embodiment of the present disclosure, 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 the estimated completion time of the operator to complete the first task is output, including:

[0015] 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 estimated completion time of the operator to complete the first task;

[0016] According to the task start time information of the first task and the estimated completion time for the operator to complete the first task, the estimated completion time for the operator to complete the first task is determined and output.

[0017] According to the embodiment of the present disclosure, the above time prediction model is pre-trained by the following operations:

[0018] Acquire multiple pieces of sample information, wherein each piece of the sample information includes task attribute information of a historical task sample and attribute information of a worker who completed the historical task sample;

[0019] Each of the above sample information is input into the initial model for training to obtain the above time prediction model.

[0020] According to an embodiment of the present disclosure, the above task allocation method further includes:

[0021] When the operator completes the first task, the task attribute information of the first task and the feedback information of the operator are input into the historical task operation database for training the time prediction model.

[0022] According to an embodiment of the present disclosure, the feedback information of the operator includes the identity information of the operator, quality information of the operator actually completing the first task, and time information of the operator actually completing the first task.

[0023] Another method disclosed in the present invention also discloses a task allocation device, including: a determination module, an output module and an allocation module. The determination module is used to obtain a first task from a task pool and determine a worker for performing the first task from a worker queue according to a task allocation rule; the output module is used to input the task attribute information of the first task and the attribute information of the worker into a pre-trained time prediction model, and output the estimated completion time of the worker to complete the first task; the allocation module is used to allocate the second task in the task pool to the worker within a preset time before the estimated completion time.

[0024] According to an embodiment of the present disclosure, the above-mentioned determination module includes an acquisition unit, a first determination unit, and a second determination unit. Among them, the acquisition unit is used to acquire the first task from the task pool according to the above-mentioned task execution order. The first determination unit is used to determine the target operator who can perform the above-mentioned first task according to the task type of the above-mentioned first task. The second determination unit is used to determine the above-mentioned target operator from the above-mentioned personnel queue.

[0025] 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 used 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 the estimated completion time for the operator to complete the first task. The second output unit is used to determine and output the estimated completion time for the operator to complete the first task based on the task start time information of the first task and the estimated completion time for the operator to complete the first task.

[0026] According to the embodiment of the present disclosure, the time prediction model includes an acquisition submodule and a training submodule. The acquisition submodule is used to acquire multiple pieces of sample information, each of which includes task attribute information of a historical task sample and attribute information of a worker who completed the historical task sample. The training submodule is used to input each piece of sample information into the initial model for training to obtain the time prediction model.

[0027] According to an embodiment of the present disclosure, the task allocation device further includes a feedback module, which is used to input the task attribute information of the first task and the feedback information of the operator into the historical task operation database when the operator completes the first task, so as to train the time prediction model. The feedback information of the operator includes the identity information of the operator, the quality information of the operator actually completing the first task, and the time information of the operator actually completing the first task.

[0028] Another aspect of the present disclosure provides a computer system, comprising:

[0029] one or more processors;

[0030] a memory for storing one or more programs,

[0031] When the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0032] Another aspect of the present disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the above method.

[0033] Another aspect of the present disclosure provides a computer program product, comprising:

[0034] Computer executable instructions, when executed, are used to implement the above method.

[0035] According to an embodiment of the present disclosure, by obtaining the first task from the task pool and determining the operator for performing the first task from the personnel queue according to the task allocation rule, and then inputting the task attribute information of the first task and the attribute information of the operator who performs the first task into a pre-trained time prediction model, and outputting the estimated completion time of the operator to complete the first task, the time when the operator completes the first task can be accurately predicted. Finally, within a certain time before the estimated completion time, the second task in the task pool is allocated to the operator, so that the operator can obtain the assigned second task before completing the first task, thereby reducing the time the operator waits for the second task after completing the first task, thereby improving the overall work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0037] Figure 1 An exemplary system architecture to which the task allocation method disclosed in the present invention can be applied is schematically shown;

[0038] Figure 2 A flowchart schematically shows a task allocation method according to an embodiment of the present disclosure;

[0039] Figure 3 A flowchart of a method for determining an operator to perform a first task in an embodiment of the present disclosure is schematically shown;

[0040] Figure 4A flowchart of a method for determining an estimated completion time of a first task in an embodiment of the present disclosure is schematically shown;

[0041] Figure 5 A flowchart of a method for training a time prediction model in an embodiment of the present disclosure is schematically shown;

[0042] Figure 6 A block diagram of a task allocation device in an embodiment of the present disclosure is schematically shown;

[0043] Figure 7 A system block diagram schematically shows the application of the task allocation method in the embodiment of the present disclosure;

[0044] Figure 8 A block diagram of a computer system suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0045] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0046] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0047] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0048] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0049] It should be noted that the task allocation method and device disclosed in the present invention relate to the field of big data technology, and can be used in the field of financial technology, and can also be used in any field other than the fields of big data technology and financial technology. The present invention does not limit the application field of the task allocation system method and device.

[0050] In the technical solution disclosed in the present invention, the acquisition, storage and application of the operating personnel information involved are in compliance with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and do not violate public order and good morals.

[0051] The serial task allocation method currently used in the intensive operation process reduces the operation efficiency due to the time that operators have to wait for task allocation. In view of this, the embodiment of the present disclosure provides the following task allocation method.

[0052] Figure 1 The exemplary system architecture 100 to which the task allocation method and apparatus according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0053] like Figure 1 As shown, 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 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0054] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as data processing applications, social platform software applications, etc. (only for example).

[0055] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0056] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received task data to be assigned, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0057] It should be noted that the task allocation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the task allocation device provided in the embodiment of the present disclosure can generally be set in the server 105. The task allocation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the task allocation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0058] For example, task attribute information and operator attribute information can be input through any one of the terminal devices 101, 102, or 103 (for example, terminal device 101, but not limited to this), and then, the terminal device 101 can send the task attribute information to be assigned to other servers or server clusters, and the other servers or server clusters that receive the task attribute information to be assigned can execute the task assignment method provided by the embodiment of the present disclosure.

[0059] It should be understood that Figure 1 The number of terminal devices and servers in the embodiment is only for illustration. Any number of terminal devices and servers may be provided according to the implementation requirements.

[0060] Figure 2 The flowchart of the task allocation method according to the embodiment of the present disclosure is schematically shown.

[0061] like Figure 2As shown, the method includes operations S201 to S203.

[0062] In operation S201, according to a task allocation rule, a first task is acquired from a task pool and a worker for executing the first task is determined from a worker queue.

[0063] In operation S202, 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 the estimated completion time of the operator to complete the first task is output.

[0064] In operation S203, the second task in the task pool is assigned to the operator within a preset time period before the expected completion time.

[0065] According to an embodiment of the present disclosure, the preset time before the expected completion time can be set according to the requirements of task allocation efficiency. For example, the preset time can be set to 5s, and the second task in the task pool will be assigned to the operator within 5s before the expected completion time of the first task.

[0066] In the embodiment of the present disclosure, by obtaining the first task from the task pool and determining the operator for performing the first task from the personnel queue according to the task allocation rule, and then inputting the task attribute information of the first task and the attribute information of the operator who performs the first task into the pre-trained time prediction model, the estimated completion time of the operator to complete the first task is output, and the time when the operator completes the first task can be accurately predicted. Finally, within a certain time before the estimated completion time, the second task in the task pool is allocated to the operator, so that the operator can obtain the assigned second task before completing the first task, thereby reducing the time the operator waits for the second task after completing the first task, thereby improving the overall work efficiency.

[0067] According to the embodiment of the present 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 business types, such as savings tasks, credit tasks, insurance tasks, payment tasks, etc. The task quality information can include the customer's quality standard information or quality requirement information for the task, and can also include the industry quality standard of the business type to which the task belongs.

[0068] According to the embodiment of the present disclosure, the attribute information of the operator includes the identity information of the operator, the quality information of the operator's completion of historical tasks, and the time information of the operator's completion of historical tasks; wherein the identity information of the operator may include the name, years of work, and professional field of the operator. The quality information of the operator's completion of historical tasks and the time information of the operator's completion of historical tasks may include the average quality and average time of the operator completing historical tasks of the same type.

[0069] In the disclosed embodiments, by predicting the estimated time for a worker who performs a task to complete the task based on the task attribute information and the worker's attribute information, on the one hand, the estimated time for the worker to complete the task can be accurately and efficiently predicted; on the other hand, by specifically selecting task type information, task quantity information, task quality information, task start time information, as well as quality information of the worker's completion of historical tasks and time information of the worker's completion of historical tasks as basic parameters for predicting the task completion time, the speed of data operation and calculation in the prediction time model device can be improved, thereby ensuring the accuracy of the prediction and reducing the consumption of device resources by invalid data.

[0070] According to an embodiment of the present disclosure, the task allocation rules may include 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.

[0071] Figure 3 A flowchart of a method for determining a worker to perform a first task in an embodiment of the present disclosure is schematically shown.

[0072] like Figure 3 As shown, the method includes operations S301 to S303.

[0073] In operation S301 , a first task is obtained from a task pool according to a task execution order.

[0074] In operation S302 , a target operator who can perform the first task is determined according to the task type of the first task.

[0075] In operation S303, a target operator is determined from the operator queue.

[0076] According to the embodiment of the present disclosure, for example, a personal credit approval task is obtained from the task pool. Since the type of the credit approval task belongs to personal credit approval, according to the task assignment rules, the task needs to be assigned to a credit personnel with personal credit approval qualifications. The credit personnel with personal credit approval qualifications in the personnel queue are used as the target operators of the task for task assignment.

[0077] In the embodiment of the present disclosure, by determining the target operator who can perform the task through the task allocation rules, an automatic matching process between the operator and the task to be performed according to the task type can be implemented, thereby improving the allocation efficiency of the task allocation system.

[0078] Figure 4 A flowchart of a method for determining an estimated completion time of a first task in an embodiment of the present disclosure is schematically shown.

[0079] like Figure 4 As shown, the method includes operations S401-S402.

[0080] In operation S401, task attribute information of the first task and attribute information of the operator are input into a pre-trained time prediction model, and an estimated completion time for the operator to complete the first task is output.

[0081] In operation S402 , an estimated completion time for the operator to complete the first task is determined and output according to the task start time information of the first task and the estimated completion time for the operator to complete the first task.

[0082] According to an embodiment of the present disclosure, for example, for a savings task, the operator responsible for performing the task needs 0.5 hours to complete the same type of savings task. After the task and the attribute information of the operator responsible for completing the task are input into the time prediction model, the time prediction model predicts that the estimated completion time for the operator to complete the task is 0.5 hours. Based on the task start time included in the task attribute information: 14:15 on August 5, 2020, the time prediction model outputs that the completion time for the operator to complete the savings task is 14:45 on August 5, 2020.

[0083] In the disclosed embodiment, since the task attribute information includes the start time of the task, the time prediction model can predict the estimated time it will take for the operator to complete the task based on the quality information of the historical tasks of the same type as the task completed by the operator and the time information of the historical tasks of the same type as the task completed by the operator. By adding the estimated time to complete the task on the basis of the start time of the task, the estimated completion time for the operator to complete the task can be obtained, thereby accurately predicting the task completion time so as to assign the next task.

[0084] Figure 5 A flowchart of a method for training a time prediction model in an embodiment of the present disclosure is schematically shown.

[0085] like Figure 5 As shown, the method includes operations S501-S502.

[0086] 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 attribute information of a worker who completes the historical task sample.

[0087] According to an embodiment of the present disclosure, each piece of sample information includes task type information, task quality information, task quantity information and task start time information of the historical task sample, as well as identity information of the operator who completed the historical task sample, quality information of the operator completing the historical task sample, and time information of the operator completing the historical task sample. Therefore, the time required for the operator to complete the historical task sample can be obtained based on the sample information.

[0088] In operation S502, each piece of sample information is input into an initial model for training to obtain a time prediction model.

[0089] According to the embodiments of the present disclosure, the initial model can adopt a variety of machine learning models and deep learning models. For example, based on the machine learning model, a logistic regression model, a factor decomposition model, a gradient boosting decision tree model, etc. can be selected. Based on the deep learning model, a convolutional neural network model, a recurrent neural network model, etc. can be selected.

[0090] In the disclosed embodiment, by using historical data containing task type information, task quality information, task start time information, task quantity information, identity information of the operator who completed the historical task, time information when the operator completed the historical task, and quality information when the operator completed the historical task as sample information for training the initial model, the prediction effect of the trained time prediction model can be made more accurate, thereby improving the efficiency of task allocation.

[0091] According to an embodiment of the present disclosure, the task allocation method provided by the present disclosure also includes: when the operator completes the first task, inputting the task attribute information of the first task and the operator's feedback information into a historical task operation database for training a time prediction model.

[0092] According to the embodiments of the present disclosure, for tasks to be assigned or operators without historical work data, the time prediction model can be trained based on reference data or average data of tasks of the same or similar types as historical work sample information, and the quality information and time information of actual tasks completed by actual operators can be input into the historical work database, and the time prediction model can be iteratively trained.

[0093] 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 operator actually completing the first task, and the time information of the operator actually completing the first task.

[0094] In the disclosed embodiment, the quality information and time information of the operator who actually completes the task are fed back into the historical task job database for training the time prediction model. The time prediction model can be continuously iterated to improve the accuracy of the time prediction model in predicting the estimated completion time when the operator performs this type of task.

[0095] Figure 6 A block diagram of a task allocation device in an embodiment of the present disclosure is schematically shown.

[0096] like Figure 6 As shown, the task allocation device 600 includes: a determination module 601 , an output module 602 and an allocation module 603 .

[0097] The determination module 601 is used 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.

[0098] The output module 602 is used to input the task attribute information of the above-mentioned first task and the attribute information of the above-mentioned operator into the pre-trained time prediction model, and output the estimated completion time of the above-mentioned operator to complete the above-mentioned first task.

[0099] The allocation module 603 is used to allocate the second task in the task pool to the operator within a preset time before the estimated completion time.

[0100] According to the embodiment of the present disclosure, when it is necessary to assign tasks, the first task is obtained from the task pool and the operator for performing the first task is determined from the personnel queue through the determination module 601, and then the task attribute information of the first task and the attribute information of the operator are input into the pre-trained time prediction model through the output module 602, and the estimated completion time of the operator to complete the first task is output, and finally, the assignment module 603 assigns the second task in the task pool to the operator within a preset time before the estimated completion time. The attribute information of the task and the attribute information of the operator can be combined to efficiently and accurately predict the time before the task is completed, and the next task can be assigned before the task is completed, thereby reducing the time the operator waits for task assignment and improving the efficiency of task assignment.

[0101] According to an embodiment of the present disclosure, the above-mentioned determination module includes an acquisition unit, a first determination unit, and a second determination unit. Among them, the acquisition unit is used to acquire the first task from the task pool according to the above-mentioned task execution order. The first determination unit is used to determine the target operator who can perform the above-mentioned first task according to the task type of the above-mentioned first task. The second determination unit is used to determine the above-mentioned target operator from the above-mentioned personnel queue.

[0102] 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 used 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 the estimated completion time for the operator to complete the first task. The second output unit is used to determine and output the estimated completion time for the operator to complete the first task based on the task start time information of the first task and the estimated completion time for the operator to complete the first task.

[0103] According to the embodiment of the present disclosure, the time prediction model includes an acquisition submodule and a training submodule. The acquisition submodule is used to acquire multiple pieces of sample information, each of which includes task attribute information of a historical task sample and attribute information of a worker who completed the historical task sample. The training submodule is used to input each piece of sample information into the initial model for training to obtain the time prediction model.

[0104] According to an embodiment of the present disclosure, the task allocation device further includes a feedback module, which is used to input the task attribute information of the first task and the feedback information of the operator into the historical task operation database when the operator completes the first task, so as to train the time prediction model. The feedback information of the operator includes the identity information of the operator, the quality information of the operator actually completing the first task, and the time information of the operator actually completing the first task.

[0105] According to the modules and units of the embodiments of the present disclosure, any multiple or at least part of the functions of any multiple thereof can be implemented in one module. According to the modules and units of the embodiments of the present disclosure, any one or more can be split into multiple modules for implementation. According to the modules and units of the embodiments of the present disclosure, any one or more can 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 can be implemented by hardware or firmware of any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, according to the modules and units of the embodiments of the present disclosure, one or more can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0106] For example, any multiple of the determination module 601, the output module 602, and the allocation module 603 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the determination module 601, the output module 602, and the allocation module 603 can 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 can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in a suitable combination of any of them. Alternatively, at least one of the determination module 601, the output module 602 and the allocation module 603 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0107] Figure 7 A system block diagram schematically illustrates the application of the task allocation method in the embodiment of the present disclosure.

[0108] like Figure 7 As shown, the system includes a task pool 701, a personnel queue 702, a task allocation device 703, a terminal device 704 of the operator, a time prediction model 705, and a historical task operation database 706.

[0109] According to the embodiment of the present disclosure, the task allocation device 703 obtains the first task from the task pool 701, and matches the target operator who can perform the first task from the personnel queue 702 according to the task allocation rule, and allocates the first task to the operator 704. At the same time, the time prediction model 705 obtains the task attribute information of the first task and the attribute information of the operator 704 from the historical task operation database 706, wherein the attribute information of the operator 704 includes the quality information and time information of completing the historical tasks of the same type as the first task, and the identity information. The time prediction model 705 predicts the completion time of the operator 704 to complete the first task according to the task attribute information of the first task and the attribute information of the operator 704, and the task allocation device 703 obtains the estimated completion time of the operator 704 to complete the first task from the time prediction model 705, and allocates the second task to the operator 704 within the preset time before the estimated completion time.

[0110] When the operator 704 completes the first task, the time when the operator 704 actually completes the first task, the attribute information of the first task and the attribute information of the operator 704 are transmitted from the operator's terminal device 704 to the historical task operation database 706 for training the time prediction model 705.

[0111] Figure 8 A block diagram of a computer system for implementing a task allocation method according to an embodiment of the present disclosure is schematically shown. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0112] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which 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 part 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 a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an 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 flow according to an embodiment of the present disclosure.

[0113] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0114] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The system 800 may further include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage section 808 as needed.

[0115] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0116] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0117] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0118] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or the RAM 803 described above and / or one or more memories other than the ROM 802 and the RAM 803 .

[0119] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the project template generation method provided by the embodiment of the present disclosure.

[0120] When the computer program is executed by the processor 801, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0121] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0122] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0124] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A task allocation method, comprising: According to the task allocation rule, a first task is obtained from the task pool and a worker for executing the first task is determined from the worker queue; 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 estimated completion time of the operator to complete the first task; Allocating the second task in the task pool to the operator within a preset time before the expected completion time; 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 includes the identity information of the operator, the quality information of the historical tasks completed by the operator, and the time information of the historical tasks completed by the operator; The historical task has the same task type information as the first task; The time prediction model is pre-trained by the following operations: Acquire multiple pieces of sample information, wherein each piece of the sample information includes task attribute information of a historical task sample and attribute information of a worker who completed the historical task sample; Each piece of the sample information is input into the initial model for training to obtain the time prediction model.

2. The method according to claim 1, wherein: 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, wherein 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: Obtaining a first task from the task pool according to the task execution order; Determining a target operator who can perform the first task according to the task type of the first task; and The target operating personnel is determined from the personnel queue.

3. The method according to claim 1 or 2, wherein: 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 estimated completion time of the operator to complete the first task, including: 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 estimated completion time of the operator to complete the first task; According to the task start time information of the first task and the estimated time required for the operator to complete the first task, the estimated time required for the operator to complete the first task is determined and output.

4. The method according to claim 1, further comprising: When the operator completes the first task, the task attribute information of the first task and the feedback information of the operator are input into the historical task operation database for training the time prediction model.

5. The method according to claim 4, wherein: The feedback information of the operator includes the identity information of the operator, the quality information of the operator actually completing the first task, and the time information of the operator actually completing the first task.

6. A task allocation device, comprising: A determination module, 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; An output module, used 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; An allocating module, configured to allocate the second task in the task pool to the operator within a preset time before the estimated completion time; 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 includes the identity information of the operator, the quality information of the historical tasks completed by the operator, and the time information of the historical tasks completed by the operator; The historical task has the same task type information as the first task; The time prediction model is pre-trained by the following operations: Acquire multiple pieces of sample information, wherein each piece of the sample information includes task attribute information of a historical task sample and attribute information of a worker who completed the historical task sample; Each piece of the sample information is input into the initial model for training to obtain the time prediction model.

7. A computer system comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 5.

9. A computer program product comprising: Computer executable instructions, when executed, are used to implement the method according to any one of claims 1 to 5.

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