Task distribution method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a task distribution method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of determining a target object set in response to triggering of a task distribution event; for each target object in the target object set, acquiring historical task operation information of the target object, and determining operation characteristics of the target object for various tasks based on the historical task operation information; determining a distribution mapping relationship between the tasks and the target objects according to the job characteristics of each target object in the target object set for each type of tasks; and distributing tasks to each target object based on the distribution mapping relationship. According to the scheme, the matching degree between the target object and the correspondingly distributed task is effectively improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer, and particularly, to a task distribution method and device, electronic device, storage medium and program product. BACKGROUND

[0002] The application scenarios of target task distribution are increasingly wide, for example, in a data labeling scenario, a process of adding labels and annotations to unprocessed raw data (such as pictures, texts, speeches, videos, etc.) to convert into computer device recognizable information. A data demander can publish a labeling task on a data labeling platform, and a labeler can take the labeling task and perform data labeling through the data labeling platform; the data labeling platform is a job platform providing data labeling capability. In an education scenario, a question task is also distributed to a question job provider. However, at present, the matching of tasks and distribution objects is complex and costly when the task is distributed. SUMMARY

[0003] The present disclosure provides a task distribution method, device, electronic device, storage medium and program product, which effectively improves the matching degree of target objects and corresponding distributed tasks.

[0004] In a first aspect, the embodiments of the present disclosure provide a task distribution method, comprising:

[0005] In response to a task distribution event being triggered, a target object set is determined; wherein the target object set contains at least two target objects;

[0006] For each target object, historical task job information of the target object is obtained, and job characteristics of the target object for various types of tasks are determined based on the historical task job information;

[0007] According to the job characteristics of each target object in the target object set for various types of tasks, a distribution mapping relationship between tasks and target objects is determined; wherein the distribution mapping relationship contains a task type and a task quantity corresponding to each target object;

[0008] Tasks are distributed to each target object based on the distribution mapping relationship.

[0009] In a second aspect, the embodiments of the present disclosure also provide a task distribution device, comprising:

[0010] A target object set determination module is configured to determine a target object set in response to a task distribution event being triggered; wherein the target object set contains at least two target objects;

[0011] An operation feature determination module is used to obtain, for each target object, the historical task operation information of the target object, and determine the operation features of the target object for various tasks based on the historical task operation information;

[0012] a distribution mapping relationship determination module, configured to determine a distribution mapping relationship between tasks and target objects based on the operation characteristics of each target object in the target object set for each type of task; wherein the distribution mapping relationship includes the type and quantity of tasks corresponding to each target object;

[0013] The task distribution module is used to distribute tasks to each of the target objects based on the distribution mapping relationship.

[0014] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0015] one or more processors;

[0016] a storage device for storing one or more programs,

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the task distribution method as described in any one of the embodiments of the present disclosure.

[0018] In a fourth aspect, an embodiment of the present disclosure further provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the task distribution method as described in any one of the embodiments of the present disclosure.

[0019] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the task distribution method as described in any one of the embodiments of the present disclosure.

[0020] In response to a task distribution event being triggered, embodiments of the present disclosure determine a target object set, wherein the target object set contains at least two target objects; for each target object, the historical task job information of the target object is obtained, and the job characteristics of the target object for various types of tasks are determined based on the historical task job information; according to the job characteristics of each target object in the target object set for various types of tasks, a distribution mapping relationship between tasks and target objects is determined, wherein the distribution mapping relationship contains the type and quantity of tasks distributed to each target object; and tasks are distributed to each target object based on the distribution mapping relationship. Through the technical solutions provided by the embodiments of the present disclosure, the matching degree of target objects and corresponding distributed tasks is effectively improved, for example, the situation that a target object picks up a task that he is not good at, or a task that a target object is best at is occupied by a sub-optimal target object can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other features, advantages, and aspects of the present disclosure will become more apparent by describing in detail the embodiments thereof with reference to the attached drawings. Throughout the drawings, the same or similar reference numerals can refer to the same or similar elements. It should be understood that the drawings are schematic, and the sizes of the components and elements are not necessarily drawn to scale.

[0022] Figure 1 A flowchart of a task distribution method provided by an embodiment of the present disclosure;

[0023] Figure 2 A flowchart of another task distribution method provided by an embodiment of the present disclosure;

[0024] Figure 3 A structural diagram of a task distribution device provided by an embodiment of the present disclosure;

[0025] Figure 4 A structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that each step recited in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit performing the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "comprising" and variations thereof as used herein are open-ended, that is, "comprising but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment." The term "another embodiment" means "at least one additional embodiment." The term "some embodiments" means "at least some embodiments." Related terms have analogous meanings.

[0029] It should be noted that the terms "first", "second", and the like in the present disclosure are merely used to distinguish different devices, modules or units, and do not imply the order or interdependence of the functions performed by these devices, modules or units.

[0030] It should be noted that the terms "one", "multiple" in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that "one or more" should be understood unless otherwise explicitly indicated in the context.

[0031] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0032] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained in a proper manner according to relevant laws and regulations.

[0033] For example, in response to receiving the active request of the user, prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide the personal information to the software or hardware such as electronic device, application program, server or storage medium, etc. performing the operation of the technical solutions of the present disclosure according to the prompt information.

[0034] As an optional but not limited implementation manner, in response to receiving the active request of the user, the manner of sending prompt information to the user may, for example, be a pop-up window manner, in which the prompt information can be presented in the form of text. In addition, the pop-up window can also carry selection controls for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0035] It can be understood that the above notification and user authorization process is only illustrative and does not limit the implementation of the present disclosure, and other methods that meet relevant laws and regulations can also be applied to the implementation of the present disclosure.

[0036] It can be understood that the data involved in the technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of relevant laws and regulations and relevant provisions.

[0037] Figure 1 A flowchart of a task distribution method provided by the embodiments of the present disclosure is shown. The embodiments of the present disclosure are applicable to the case of constructing a page. The method can be executed by a task distribution device. The device can be implemented in the form of software and / or hardware. Optionally, the device can be implemented by an electronic device, which can be a mobile terminal, a PC terminal, or a server, etc.

[0038] As shown in Figure 1 , the method comprises:

[0039] S110, in response to a task distribution event being triggered, determining a target object set; wherein the target object set contains at least two target objects.

[0040] In the embodiments of the present disclosure, when a task distribution request is detected, it is determined that a task distribution event is triggered. The task distribution request can be understood as a request for distributing tasks through a task processing platform. Optionally, the task distribution request can be generated in at least one of the following ways: a control trigger operation of a task distribution control; receiving preset audio information through an electronic device; reaching a pre-set task distribution time.

[0041] In response to the task distribution event being triggered, a target object set is determined, wherein the target object set contains at least two target objects. Optionally, the target object can be any object that can perform task work through the task processing platform, or an object that can perform a task (i.e., can work on the task) within a preset time period after the current time. The current time is the time when the task distribution event is triggered. For example, in a data labeling scenario, the task distribution event is a labeling task distribution event, and the target object can be any labeling party that can perform data labeling through a data labeling platform, or a labeling party that can perform a labeling task (i.e., can work on the labeling task) within a preset time period after the current time. Another example is in an education scenario, the task distribution event is a question task distribution event, and the target object can be any object that can perform question work through a question work platform, or an object that can work on a question task within a preset time period after the current time.

[0042] In the embodiments of the present disclosure, the task distribution is taken as an example for illustration. Optionally, the target object set is determined, including: obtaining historical work hour reporting data of each labeling object in a labeling object library; filtering at least two target objects from the labeling object library according to the historical work hour reporting data, and forming a target object set based on the at least two target objects; wherein the target object is a labeling object that can perform a labeling task in a next time period of a triggering time of the task distribution event. In this way, the benefit is that the labeling object that can perform the labeling task in the next time period of the triggering time of the task distribution event can be effectively predicted through the historical work hour reporting data, thereby helping the labeling task to be completed in time after the labeling task is distributed.

[0043] The labeling object library is a database containing all labeling objects that perform data labeling through a data labeling platform. Since different labeling objects may have different time periods for performing labeling tasks through the data labeling platform, the historical work hour reporting data of each labeling object in the labeling object library is obtained, wherein the historical work hour reporting data includes historical dates and historical time periods of the labeling object performing data labeling through the data labeling platform. For example, the work logs generated by each labeling object in the labeling object library when performing data labeling through the data labeling platform in the historical time period are obtained respectively, and the work logs are analyzed to determine the historical work hour reporting data of the corresponding labeling object. According to the historical work hour reporting data, at least two target objects are filtered from the labeling object library according to a preset filtering strategy, and a target object set is formed based on the at least two target objects. The target object is a labeling object that can perform a labeling task in a next time period of a triggering time of the task distribution event. It can be understood that the target object is a labeling object that can perform a labeling task in a future time period predicted by the historical work hour reporting data.

[0044] Exemplarily, 24 hours of a whole day can be divided into different time periods, such as a first time period 0:00-8:00, a second time period 8:00-12:00, a third time period 12:00-14:00, a fourth time period 14:00-18:00, and a fifth time period 18:00-24:00. A time period in which the trigger time t of the task distribution event is located is determined, and a preset number of target objects whose historical work reporting data meet preset conditions are screened according to the time period in which the trigger time t is located. For example, if the time period in which the trigger time t is located is the first time period 0:00-8:00, then the target objects are determined according to the historical work reporting data to be the labeled objects that perform tasks in the time period [t-1h, t+1h] of the previous working day; if the time period in which the trigger time t is located is the second time period 8:00-12:00, the third time period 12:00-14:00, or the fourth time period 14:00-18:00, then the target objects are determined according to the historical work reporting data to be the labeled objects that perform tasks in the time period [t-2h, t] of the same day as the trigger time; if the time period in which the trigger time t is located is the fifth time period 18:00-24:00, then the target objects are determined according to the historical work reporting data to be the labeled objects that perform tasks in the time period [t-4h, t] of the same day as the trigger time. Exemplarily, the labeled objects that perform tasks in a preset time period (such as 1 hour before the trigger time t) before the trigger time t of the task distribution event are screened according to the historical work reporting data, and it is determined whether the number of the screened labeled objects reaches a preset number threshold. If yes, the labeled objects screened in the preset time period are taken as the target objects. If no, the preset time period is increased, such as from 1 hour before the trigger time t to 2 hours before the trigger time t, and it is continuously determined whether the number of the labeled objects screened in the increased preset time period reaches the preset number threshold, until the number of the screened labeled objects reaches the preset number threshold.

[0045] In S120, historical task operation information of each target object is obtained, and operation characteristics of the target object for each type of task are determined based on the historical task operation information.

[0046] In the embodiments of the present disclosure, for each target object in the target object set, historical task job information of the target object is obtained, where the historical task job information is related data of the target object performing tasks in a historical time period. For example, the historical task job information can include the types and quantities of tasks distributed to the target object, the types and quantities of tasks completed by the target object, the difficulty rates of the target object performing various types of tasks, and related data such as job characteristics and ability characteristics of the target object. The job characteristics of the target object for various types of tasks are determined based on the historical task job information, where the job characteristics can include a first completion rate of the target object for various types of tasks, a total quantity of each type of task distributed to the target object, a completion quantity of the target object for various types of tasks, a completion time consumption, and a difficulty rate of the target object for various types of tasks, and the like. The types of tasks can include creative tasks, knowledge question and answer tasks, mathematical logic tasks, and the like. It should be noted that the types of tasks are not limited in the embodiments of the present disclosure, and the granularity of the types of tasks can be divided according to business requirements.

[0047] Optionally, the job characteristics include the first completion rate; and determining the job characteristics of the target object for various types of tasks based on the historical task job information includes: analyzing the historical task job information to respectively determine a total quantity of each type of task distributed to the target object and a first completion quantity of the target object for various types of tasks; and respectively determining a first completion rate of the target object for each type of task according to the total quantity of each type of task distributed to the target object and the corresponding first completion quantity. This setting has the advantage that the first completion rate of the target object for each type of task can be quickly determined. For example, the historical task job information is analyzed to respectively determine a total quantity of each type of task distributed to the target object and a first completion quantity of the target object for various types of tasks in a historical time period, where the first completion quantity can be a successful job completion quantity of the target object for various types of tasks, or a correct job completion quantity of the target object for various types of tasks. For each type of task, a ratio of the first completion quantity of the target object for the type of task to the total quantity of the type of task distributed to the target object is taken as the first completion rate of the target object for the type of task.

[0048] Optionally, determining the job characteristics of the target object for various types of tasks based on the historical task job information includes: inputting the historical task job information into a pre-trained job characteristic prediction model, and determining the job characteristics of the target object for various types of tasks according to an output result of the job characteristic prediction model. This setting has the advantage that the job characteristics of the target object for each type of task can be accurately and quickly determined.

[0049] In the embodiments of the present disclosure, the historical task job information of the target object can include a task type of a historical job, historical task job characteristics, and characteristic information of the target object. The task type of the historical job can be understood as a type of task processed by the target object in a historical time period. The historical task job characteristics can include a job difficulty rate of each type of task, a job time length, and an overall job difficulty rate of all tasks of the target object in the historical time period, and the like. The job difficulty rate of each type of task can be a ratio of a number of tasks marked as difficult by the target object to a total number of tasks of the type distributed to the target object in the historical time period. The overall job difficulty rate can be a ratio of a number of all tasks marked as difficult by the target object to a total number of all tasks distributed to the target object in the historical time period. The characteristic information of the target object can include job characteristics and ability characteristics of the target object, and the like. The historical task job information, such as the task type of the historical job, the historical task job characteristics, and the characteristic information of the target object, is input into a pre-trained job characteristic prediction model, and the job characteristics of the target object for each type of task are determined according to an output result of the job characteristic prediction model. The job characteristic prediction model can be understood as a machine learning model that is pre-trained and can quickly determine the job characteristics of the target object for each type of task.

[0050] Optionally, the construction method of the job characteristic prediction model can include: obtaining a training sample set, wherein the training sample set includes historical task job data of a large number of task processing objects (such as 10,000 task processing objects). The historical task job data can include historical job task types, historical task job characteristics, and characteristic information of the task processing objects of each task processing object, and can also include job characteristics (such as average job time length and average difficulty rate) corresponding to each type of task. The pre-set machine learning model is trained based on the training sample set until a pre-set convergence condition is reached, and the job characteristic prediction model is generated.

[0051] In S130, a distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set for each type of task. The distribution mapping relationship includes a type of task and a number of tasks distributed to each target object.

[0052] In the embodiments of the present disclosure, the distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task. The distribution mapping relationship includes the type and the number of tasks distributed to each target object. For example, because the number of job tasks of each target object is limited within a certain period of time, and the task pool contains thousands of tasks, the target number of tasks to be distributed can be determined according to the number of target objects in the target object set, and the target number of tasks with the highest priority (i.e., the highest degree of urgent processing) is selected from the task pool to generate a task list. The distribution mapping relationship between each task in the task list and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task. For example, the job characteristics are the first completion rate, and the first completion rate of the tasks distributed to all target objects in the target object set is maximized as a global target, and the distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task.

[0053] Optionally, the distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task, including: on the basis of satisfying a preset constraint condition, the distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task; wherein the preset constraint condition includes at least one of the number of tasks distributed to each target object being greater than a first preset number threshold, the number of tasks distributed to each target object being less than a second preset number threshold, and the number of different target objects to which the same task is distributed being less than a third preset number threshold. The advantage of this setting is that the balance of task distribution can be effectively guaranteed.

[0054] Because the first completion rates of the target objects to each type of task are different, and the job capabilities of each target object to the tasks also have certain differences, in order to avoid the situation that the target objects with strong job capabilities are distributed too many tasks, and the target objects with weak job capabilities are distributed too few tasks, at least one of the number of tasks distributed to each target object being greater than a first preset number threshold m, the number of tasks distributed to each target object being less than a second preset number threshold n, and the number of different target objects to which the same task is distributed being less than a third preset number threshold p is used as a constraint condition, and the distribution mapping relationship between the tasks and the target objects is determined according to the job characteristics of each target object in the target object set to each type of task. For example, on the basis of satisfying the above-mentioned preset constraint condition, the distribution mapping relationship between the tasks and the target objects is determined according to the first completion rate of each target object in the target object set to each type of task, so that the first completion rate of the tasks distributed to all target objects in the target object set is maximized.

[0055] S140, distribute tasks to each of the target objects based on the distribution mapping relationship.

[0056] In the embodiments of the present disclosure, according to the task types and the number of tasks corresponding to each target object in the distribution mapping relationship, corresponding tasks are retrieved from the task pool according to the task types and the task priority, and the retrieved tasks are distributed to the corresponding target objects. Among them, the tasks can be distributed to the target object corresponding client, so that the target object performs the corresponding tasks on the task processing platform based on the client. Optionally, when the tasks distributed to the target object are all completed by the target object, other tasks in the task pool can be continuously distributed to the target object according to the work characteristics of the target object for each type of task, for example, the task corresponding to the task type with the highest first completion rate of the target object in the task pool is distributed to the target object.

[0057] In the embodiments of the present disclosure, in response to the task distribution event being triggered, a target object set is determined; wherein the target object set contains at least two target objects; for each of the target objects, the historical task work information of the target object is obtained, and the work characteristics of the target object for each type of task are determined based on the historical task work information; according to the work characteristics of each of the target objects in the target object set for each type of task, a distribution mapping relationship between tasks and target objects is determined; wherein the distribution mapping relationship contains the task types and the number of tasks corresponding to each of the target objects; and tasks are distributed to each of the target objects based on the distribution mapping relationship. Through the technical scheme provided by the embodiments of the present disclosure, the matching degree of the target object and the corresponding distributed task is effectively improved, for example, the situation that the target object takes the task that is not good at, or the target object's best task is occupied by a sub-optimal target object can be reduced.

[0058] Figure 2Another flowchart of a task distribution method provided by the embodiments of the present disclosure is shown. The technical solution of the embodiments is based on the above-mentioned embodiments, and the determination manner of the first completion rate is optimized. Optionally, the first completion rate of each type of task of the target object is determined according to the total number of each type of task distributed to the target object and the corresponding first completion number, including: determining the corresponding completion rate confidence according to the total number of each type of task distributed to the target object; determining the first completion rate of each type of task of the target object according to the total number of each type of task distributed to the target object, the corresponding first completion number and the completion rate confidence. Optionally, after determining the first completion rate of each type of task of the target object, the method further includes: obtaining part of the task job information in the historical task job information; wherein the part of the task job information is the task job information in a historical time period of a preset time length traced back from the trigger time of the task distribution event as the starting time; the preset time length is less than the time length of the historical time period involved in the historical task job information; determining the second completion number and the second completion rate of each type of task of the target object according to the part of the task job information; when the second completion number, the first completion rate and the second completion rate meet the preset compensation condition, compensating the first completion rate based on the preset compensation strategy. The specific implementation can be referred to the description of the embodiments. Wherein, the same or similar technical features as the foregoing embodiments are not described here. As shown in Figure 2 The method of the embodiments can specifically include:

[0059] S210, in response to a task distribution event being triggered, determining a target object set; wherein the target object set contains at least two target objects.

[0060] S220, for each target object, obtaining the historical task job information of the target object, and analyzing the historical task job information to determine the total number of each type of task distributed to the target object and the first completion number of each type of task of the target object.

[0061] S230, determining the corresponding completion rate confidence according to the total number of each type of task distributed to the target object.

[0062] S240, determining the first completion rate of each type of task of the target object according to the total number of each type of task distributed to the target object, the corresponding first completion number and the completion rate confidence.

[0063] In the embodiments of the present disclosure, when the first completion rate of the target object for each type of task is determined according to the historical task operation information, the greater the total number of each type of task distributed to the target object, the more it can reflect the real operation level of the target object for each type of task. For example, the total number of a certain type of task distributed to the target object is 100, and the first completion number of the target object for the type of task is 90, then the first completion rate of the target object for the type of task is 90%; if the total number of a certain type of task distributed to the target object is 2, and the first completion number of the target object for the type of task is 2, then the first completion rate of the target object for the type of task is 100%. Obviously, when the total number of each type of task of the target object is small, it cannot truly reflect the real operation level of the target object for the type of task. Therefore, the corresponding completion rate confidence is determined according to the total number of each type of task distributed to the target object, wherein the completion rate confidence can be represented as k=(total_cnt / (total_cnt+a)), wherein total_cnt represents the total number of each type of task distributed to the target object, and a represents a preset constant, such as a=10.

[0064] The first completion rate of the target object for each type of task is determined according to the total number of each type of task distributed to the target object, the corresponding first completion number and the completion rate confidence, respectively. For example, the first completion rate of the target object for each type of task can be calculated according to the following formula: answer_rate=(answer_cnt / total_cnt)*(total_cnt / (total_cnt+a)), wherein answer_rate represents the first completion rate, and answer_cnt represents the first completion number. In the embodiments of the present disclosure, by setting the completion rate confidence, the reliability of the determined first completion rate of the target object for each type of task can be effectively improved.

[0065] S250, obtain part of the task operation information in the historical task operation information; wherein the part of the task operation information is the task operation information in a historical time period of a preset time length traced back from the trigger time of the task distribution event as a starting time; and the preset time length is less than the time length of the historical time period involved in the historical task operation information.

[0066] In the embodiments of the present disclosure, part of the task operation information is extracted from the historical task operation information, where the part of the task operation information is the task operation information in a historical time period that is traced back from a trigger time of the task distribution event as a starting time by a preset time length. For example, if the historical task operation information is the task operation information in a first historical time period that is traced back from the trigger time of the task distribution event as a starting time by a first preset time length, the part of the task operation information is the task operation information in a second historical time period that is traced back from the trigger time of the task distribution event as a starting time by a second preset time length, where the second preset time length is less than the first preset time length, for example, the first preset time length is 6 months and the second preset time length is 1 month. It can be understood that the part of the task operation information is the task operation information in a historical time period that is relatively close to the trigger time of the task distribution event, and can accurately reflect the operation level of the target object on the task.

[0067] In S260, the second completion quantity and the second completion rate of each type of task of the target object are determined according to the part of the task operation information.

[0068] In the embodiments of the present disclosure, the second completion quantity and the second completion rate of each type of task of the target object are determined according to the part of the task operation information, where the determination manner of the second completion rate is similar to the determination manner of the first completion rate, and will not be described herein.

[0069] In S270, when the second completion quantity, the first completion rate and the second completion rate satisfy a preset compensation condition, the first completion rate is compensated based on a preset compensation strategy.

[0070] Optionally, when the second completion quantity, the first completion rate and the second completion rate satisfy a preset compensation condition, compensating the first completion rate based on a preset compensation strategy, including: when the second completion quantity is greater than a preset completion quantity, the second completion rate is greater than a first preset completion rate and the second completion rate is greater than the first completion rate, increasing the first completion rate based on a first preset compensation strategy; when the second completion quantity is greater than the preset completion quantity, the second completion rate is less than a second preset completion rate and the second completion rate is less than the first completion rate, decreasing the first completion rate based on a second preset compensation strategy, wherein the second preset completion rate is less than the first preset completion rate. For example, when the second completion quantity is greater than a preset completion quantity (e.g., the preset completion quantity is 10), the second completion rate is greater than a first preset completion rate (e.g., the first preset completion rate is 0.8) and the second completion rate is greater than the first completion rate, the first completion rate is increased based on a first preset compensation strategy. It can be understood that, if the second completion quantity is greater than the preset completion quantity and the second completion rate is greater than the first preset completion rate, it indicates that the task operation information in the second historical time period is of high reliability, and the target object has a high operation level for the task. If the second completion rate is further greater than the first completion rate, in order to make the distributed task more matched with the operation level of the target object for the task, the first completion rate is increased. For example, the first completion rate can be increased by a preset constant under the premise that the adjusted first completion rate is less than or equal to 1. Optionally, the first completion rate can also be increased according to the following formula: answer_rate1+0.5*(answer_rate2-answer_rate1), wherein answer_rate1 represents the first completion rate, and answer_rate2 represents the second completion rate. When the second completion quantity is greater than the preset completion quantity (e.g., the preset completion quantity is 10), the second completion rate is less than the second preset completion rate (e.g., the second preset completion rate is 0.3) and the second completion rate is less than the first completion rate, the first completion rate is decreased based on the second preset compensation strategy. It can be understood that, if the second completion quantity is greater than the preset completion quantity and the second completion rate is less than the second preset completion rate, it indicates that the task operation information in the second historical time period is of high reliability, and the target object has a low operation level for the task. If the second completion rate is further less than the first completion rate, in order to make the distributed task more matched with the operation level of the target object for the task, the first completion rate is decreased. For example, the first completion rate can be decreased by a preset constant under the premise that the adjusted first completion rate is greater than 0. Optionally, the first completion rate can also be decreased according to the following formula: answer_rate1-0.5*(answer_rate1-answer_rate2).

[0071] S280, determine a distribution mapping relationship between tasks and target objects based on the first completion rate of each target object in the target object set for each type of task, wherein the distribution mapping relationship includes the type and number of tasks distributed to each target object; and the preset constraint condition includes at least one of the number of tasks distributed to each target object being greater than a first preset number threshold, the number of tasks distributed to each target object being less than a second preset number threshold, and the number of different target objects to which a same task is distributed being less than a third preset number threshold.

[0072] S290, distribute tasks to each target object based on the distribution mapping relationship.

[0073] In the embodiment of the present disclosure, the completion rate confidence is introduced when determining the first completion rate of each type of task of the target object, which can effectively improve the reliability of the determined first completion rate, and the first completion rate is compensated based on the task operation information in the historical time period close to the triggering time of the task distribution event, so that the determined first completion rate can better reflect the real operation level of the target object for the task, thereby further improving the matching degree of the target object and the corresponding distributed task, for example, reducing the situation that the target object takes the task that is not good at, or the target object that is best at the task is occupied by a sub-optimal target object, and effectively ensuring the balance of task distribution.

[0074] Figure 3 A structural schematic diagram of a task distribution device provided by an embodiment of the present disclosure is shown in FIG. 3. Figure 3 As shown in FIG. 3, the device includes a target object set determination module 310, a job feature determination module 320, a distribution mapping relationship determination module 330, and a task distribution module 340. The target object set determination module 310 is configured to determine a target object set in response to a task distribution event being triggered, wherein the target object set includes at least two target objects. The job feature determination module 320 is configured to obtain historical task operation information of each target object, and determine the job feature of each target object for each type of task based on the historical task operation information. The distribution mapping relationship determination module 330 is configured to determine a distribution mapping relationship between tasks and target objects based on the job feature of each target object in the target object set for each type of task, wherein the distribution mapping relationship includes the type and number of tasks distributed to each target object. The task distribution module 340 is configured to distribute tasks to each target object based on the distribution mapping relationship.

[0075] The technical scheme provided in the embodiments of the present disclosure comprises: a target object set determination module 310 determines a target object set in response to a task distribution event being triggered; wherein the target object set comprises at least two target objects; a job feature determination module 320 acquires historical task job information of each target object and determines job features of each target object for various types of tasks based on the historical task job information; a distribution mapping relationship determination module 330 determines a distribution mapping relationship between tasks and target objects according to the job features of each target object for various types of tasks in the target object set; wherein the distribution mapping relationship comprises a type of task and a number of tasks corresponding to each target object; and a task distribution module 340 distributes tasks to each target object based on the distribution mapping relationship. The technical scheme provided in the embodiments of the present disclosure effectively improves the matching degree between target objects and corresponding distributed tasks, for example, can reduce the situation that a target object picks up a task that the target object is not good at, or a task that a target object is best at is preempted by a suboptimal target object.

[0076] On the basis of any optional technical scheme in the embodiments of the present disclosure, optionally, the job feature comprises a first completion rate; the job feature determination module comprises:

[0077] a first completion number determination unit configured to analyze the historical task job information and determine a total number of various types of tasks distributed to the target object and a first completion number of the target object for various types of tasks;

[0078] a first completion rate determination unit configured to determine a first completion rate of the target object for each type of task according to the total number of each type of task distributed to the target object and the corresponding first completion number.

[0079] On the basis of any optional technical scheme in the embodiments of the present disclosure, optionally, the first completion rate determination unit is configured to:

[0080] determine a completion rate confidence degree according to the total number of each type of task distributed to the target object;

[0081] determine a first completion rate of the target object for each type of task according to the total number of each type of task distributed to the target object, the corresponding first completion number, and the completion rate confidence degree.

[0082] On the basis of any optional technical scheme in the embodiments of the present disclosure, optionally, the technical scheme further comprises:

[0083] The partial task operation information acquisition module is configured to, after determining the first completion rate of each type of task by the target object, acquire partial task operation information in the historical task operation information, wherein the partial task operation information is task operation information in a historical time period that is traced back from a trigger time of the task distribution event as a starting time by a preset time length, and the preset time length is less than a time length of the historical time period involved in the historical task operation information.

[0084] The second completion rate determination module is configured to determine a second completion quantity and a second completion rate of each type of task by the target object according to the partial task operation information.

[0085] The first completion rate compensation module is configured to, when the second completion quantity, the first completion rate and the second completion rate satisfy a preset compensation condition, compensate the first completion rate based on a preset compensation strategy.

[0086] In any optional technical solution in the embodiments of the present disclosure, optionally, the first completion rate compensation module is configured to:

[0087] When the second completion quantity is greater than a preset completion quantity, the second completion rate is greater than a first preset completion rate and the second completion rate is greater than the first completion rate, the first completion rate is increased based on a first preset compensation strategy.

[0088] When the second completion quantity is greater than the preset completion quantity, the second completion rate is less than a second preset completion rate and the second completion rate is less than the first completion rate, the first completion rate is decreased based on a second preset compensation strategy, wherein the second preset completion rate is less than the first preset completion rate.

[0089] In any optional technical solution in the embodiments of the present disclosure, optionally, the operation feature determination module is configured to:

[0090] The historical task operation information is input into a pre-trained operation feature prediction model, and the operation feature of each type of task by the target object is determined according to an output result of the operation feature prediction model.

[0091] In any optional technical solution in the embodiments of the present disclosure, optionally, the distribution mapping relationship determination module is configured to:

[0092] On the basis of meeting preset constraint conditions, a distribution mapping relationship between tasks and target objects is determined according to a job characteristic of each target object in the target object set for each type of task; wherein the preset constraint conditions include at least one of a number of tasks distributed to each target object being greater than a first preset number threshold, a number of tasks distributed to each target object being less than a second preset number threshold, and a number of different target objects to which a same task is distributed being less than a third preset number threshold.

[0093] The task distribution apparatus provided by the embodiments of the present disclosure can perform the task distribution method provided by any of the embodiments of the present disclosure, and has the corresponding function modules and beneficial effects of performing the method.

[0094] It should be noted that each unit and module included in the above apparatus is only divided according to function logic, but is not limited to the above division, as long as the corresponding function can be implemented; in addition, the specific name of each functional unit is only for convenient mutual distinction, and does not serve to limit the protection scope of the embodiments of the present disclosure.

[0095] Figure 4 A structural schematic diagram of an electronic device provided by the embodiments of the present disclosure is provided. The following refers to Figure 4 which shows a structural schematic diagram of an electronic device (for example Figure 4 , a terminal device or a server) 400 suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 4 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0096] As shown in Figure 4 , the electronic device 400 can include a processing apparatus (for example, a central processor, a graphics processor, and the like) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or loaded into a random access memory (RAM) 403 from a storage apparatus 408. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing apparatus 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An edit / output (I / O) interface 405 is also connected to the bus 404.

[0097] In general, the following devices can be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 408 including, for example, a magnetic tape, a hard disk, and the like; and communication devices 409. The communication devices 409 can allow the electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it is understood that all of the illustrated devices are not required to be implemented or present. More or fewer devices can alternatively be implemented or present.

[0098] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 409, or installed from the storage devices 408, or installed from the ROM 402. When the computer program is executed by the processing devices 401, the above-described functions defined in the methods of the embodiments of the present disclosure are performed.

[0099] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0100] The electronic device provided by the embodiments of the present disclosure and the task distribution method provided by the above-described embodiments belong to the same inventive concept, and the technical details not described in detail in the present embodiments can be referred to the above-described embodiments, and the present embodiments have the same beneficial effects as the above-described embodiments.

[0101] The embodiments of the present disclosure provide a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the task distribution method provided by the above-described embodiments.

[0102] It should be noted that the computer readable medium in the above disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0103] According to one or more embodiments of the present disclosure, example one provides a task distribution method, comprising: in response to a task distribution event being triggered, determining a target object set; wherein the target object set contains at least two target objects; for each target object, obtaining the historical task job information of the target object, and determining the job characteristics of the target object for each type of task based on the historical task job information; determining the distribution mapping relationship between tasks and target objects according to the job characteristics of each target object in the target object set for each type of task; wherein the distribution mapping relationship contains the type and number of tasks distributed to each target object; distributing tasks to each target object based on the distribution mapping relationship.

[0104] According to one or more embodiments of the present disclosure, Example Two provides the method of Example One, further comprising: optionally, the job characteristics include a first completion rate; determining the job characteristics of each type of task of the target object based on the historical task job information, comprising: analyzing the historical task job information to determine the total number of each type of task distributed to the target object and the first completion number of each type of task of the target object; and determining the first completion rate of each type of task of the target object according to the total number of each type of task distributed to the target object and the corresponding first completion number.

[0105] According to one or more embodiments of the present disclosure, Example Three provides the method of Example Two, further comprising: optionally, determining the first completion rate of each type of task of the target object according to the total number of each type of task distributed to the target object and the corresponding first completion number, comprising: determining the corresponding completion rate confidence according to the total number of each type of task distributed to the target object; and determining the first completion rate of each type of task of the target object according to the total number of each type of task distributed to the target object, the corresponding first completion number, and the completion rate confidence.

[0106] According to one or more embodiments of the present disclosure, Example Four provides the method of Example Two or Example Three, further comprising: optionally, after determining the first completion rate of each type of task of the target object, further comprising: obtaining part of the task job information in the historical task job information; wherein the part of the task job information is the task job information in a historical time period that is traced back from the trigger time of the task distribution event as the starting time by a preset time length; the preset time length is less than the time length of the historical time period involved in the historical task job information; determining the second completion number and the second completion rate of each type of task of the target object according to the part of the task job information; and compensating the first completion rate based on a preset compensation strategy when the second completion number, the first completion rate, and the second completion rate meet a preset compensation condition.

[0107] According to one or more embodiments of the present disclosure, Example Five provides the method of Example Four, further comprising: optionally, when the second completion quantity, the first completion rate and the second completion rate satisfy a preset compensation condition, compensating the first completion rate based on a preset compensation strategy, including: when the second completion quantity is greater than a preset completion quantity, the second completion rate is greater than a first preset completion rate, and the second completion rate is greater than the first completion rate, increasing the first completion rate based on a first preset compensation strategy; when the second completion quantity is greater than the preset completion quantity, the second completion rate is less than a second preset completion rate, and the second completion rate is less than the first completion rate, decreasing the first completion rate based on a second preset compensation strategy, wherein the second preset completion rate is less than the first preset completion rate.

[0108] According to one or more embodiments of the present disclosure, Example Six provides the method of Example One, further comprising: optionally, determining the work characteristics of each type of task of the target object based on the historical task work information, including: inputting the historical task work information into a pre-trained work characteristic prediction model, and determining the work characteristics of each type of task of the target object according to the output result of the work characteristic prediction model.

[0109] According to one or more embodiments of the present disclosure, Example Seven provides the method of Example One, further comprising: optionally, determining a distribution mapping relationship between tasks and target objects according to the work characteristics of each type of task of each target object in the target object set, including: on the basis of satisfying a preset constraint condition, determining a distribution mapping relationship between tasks and target objects according to the work characteristics of each type of task of each target object in the target object set; wherein the preset constraint condition includes at least one of the number of tasks distributed to each target object being greater than a first preset number threshold, the number of tasks distributed to each target object being less than a second preset number threshold, and the number of different target objects to which the same task is distributed being less than a third preset number threshold.

[0110] According to one or more embodiments of the present disclosure, Example Eight provides a task distribution apparatus, comprising: a target object set determination module configured to determine a target object set in response to a task distribution event being triggered; wherein the target object set comprises at least two target objects; a job characteristic determination module configured to, for each target object, acquire historical task job information of the target object, and determine job characteristics of the target object for various types of tasks based on the historical task job information; a distribution mapping relationship determination module configured to determine a distribution mapping relationship between tasks and target objects according to the job characteristics of each target object in the target object set for various types of tasks; wherein the distribution mapping relationship comprises a type of task and a number of tasks corresponding to each target object; and a task distribution module configured to distribute tasks to each target object based on the distribution mapping relationship.

[0111] In some embodiments, the client, server can communicate using any currently known or future developed network protocols, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communications (e.g., communications networks) of any form or medium, such as a local area network ("LAN"), a wide area network ("WAN"), the Internet, and a peer-to-peer network (e.g., ad hoc peer-to-peer network), and any currently known or future developed networks.

[0112] The computer readable medium described above can be included in the electronic device described above; or can exist separately, and not be assembled into the electronic device.

[0113] The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to: determine a target object set in response to a task distribution event being triggered; wherein the target object set comprises at least two target objects; for each target object, acquire historical task job information of the target object, and determine job characteristics of the target object for various types of tasks based on the historical task job information; determine a distribution mapping relationship between tasks and target objects according to the job characteristics of each target object in the target object set for various types of tasks; wherein the distribution mapping relationship comprises a type of task and a number of tasks corresponding to each target object; and distribute tasks to each target object based on the distribution mapping relationship.

[0114] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0115] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0116] The units described in the embodiments of the present disclosure can be implemented by hardware, software, or a combination thereof. In some cases, the names of the units do not constitute a limitation on the units themselves. For example, the first obtaining unit can also be described as a unit that obtains at least two Internet protocol addresses.

[0117] The functions described in this specification can be performed by one or more hardware logic components. For example, non-limiting examples of hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0118] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include a lined- up electrical connection, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] The above description is only preferred embodiments of the present disclosure and a description of principles of applied technology. It should be understood by those skilled in the art that the disclosed scope of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and also covers other technical solutions formed by the combinations of the above technical features or equivalent features without departing from the above disclosed concept. For example, the technical solutions formed by the mutual replacement of the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions.

[0120] In addition, although each operation is described in a particular order, this should not be understood as requiring the operations to be performed in the specific order shown or in a sequential order. In certain circumstances, multitasking and parallel processing can be advantageous. Similarly, although several implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be combined in a single embodiment. Conversely, various features described in the context of a single embodiment can also be separated and implemented in multiple embodiments.

[0121] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A task distribution method, characterized in that: include: In response to a task distribution event being triggered, determining a target object set; wherein the target object set includes at least two target objects; For each target object, obtaining historical task operation information of the target object, and determining the operation characteristics of the target object for various tasks based on the historical task operation information; Determine a distribution mapping relationship between tasks and target objects based on the operation characteristics of each target object in the target object set for each type of task; wherein the distribution mapping relationship includes the type and quantity of tasks corresponding to each target object; Distribute tasks to each of the target objects based on the distribution mapping relationship.

2. The method according to claim 1, characterized in that The operation characteristics include a first completion rate; and determining the operation characteristics of the target object for each type of task based on the historical task operation information includes: Analyze the historical task operation information to determine the total number of each type of tasks distributed to the target object and the first completion number of each type of tasks by the target object; A first completion rate of each type of task by the target object is determined based on the total number of each type of tasks distributed to the target object and the corresponding first completion number.

3. The method according to claim 2, characterized in that Determining a first completion rate of each type of task by the target object based on the total number of each type of tasks distributed to the target object and the corresponding first completion number includes: Determining the corresponding completion rate confidence level according to the total number of each type of tasks distributed to the target object; A first completion rate of each type of task for the target object is determined based on the total number of each type of tasks distributed to the target object, the corresponding first completion number, and the completion rate confidence level.

4. The method according to claim 2 or 3, characterized in that After determining the first completion rate of each type of task by the target object, the method further includes: Obtaining partial task operation information from the historical task operation information; wherein the partial task operation information is task operation information within a historical time period of a preset length starting from the triggering time of the task distribution event; the preset length is less than the length of the historical time period involved in the historical task operation information; Determining a second completion quantity and a second completion rate of each type of task by the target object according to the partial task operation information; When the second completion quantity, the first completion rate, and the second completion rate meet a preset compensation condition, the first completion rate is compensated based on a preset compensation strategy.

5. The method according to claim 4, characterized in that When the second completion quantity, the first completion rate, and the second completion rate meet a preset compensation condition, compensating the first completion rate based on a preset compensation strategy includes: When the second completion quantity is greater than a preset completion quantity, the second completion rate is greater than a first preset completion rate, and the second completion rate is greater than the first completion rate, increasing the first completion rate based on a first preset compensation strategy; When the second completion quantity is greater than the preset completion quantity, the second completion rate is less than the second preset completion rate, and the second completion rate is less than the first completion rate, the first completion rate is reduced based on a second preset compensation strategy, wherein the second preset completion rate is less than the first preset completion rate.

6. The method according to claim 1, characterized in that Determining the target object's operation characteristics for various tasks based on the historical task operation information includes: The historical task operation information is input into a pre-trained operation feature prediction model, and the operation features of the target object for various tasks are determined according to the output results of the operation feature prediction model.

7. The method according to claim 1, characterized in that Determining a distribution mapping relationship between tasks and target objects based on the operation characteristics of each target object in the target object set for various tasks includes: On the basis of satisfying the preset constraints, the distribution mapping relationship between tasks and target objects is determined according to the operating characteristics of each target object in the target object set for various types of tasks; wherein, the preset constraints include at least one of the number of tasks distributed to each target object is greater than a first preset number threshold, the number of tasks distributed to each target object is less than a second preset number threshold, and the number of the same task distributed to different target objects is less than a third preset number threshold.

8. A task distribution device, characterized in that: include: A target object set determining module is configured to determine a target object set in response to a task distribution event being triggered; wherein the target object set includes at least two target objects; An operation feature determination module is used to obtain, for each target object, the historical task operation information of the target object, and determine the operation features of the target object for various tasks based on the historical task operation information; a distribution mapping relationship determination module, configured to determine a distribution mapping relationship between tasks and target objects based on the operation characteristics of each target object in the target object set for each type of task; wherein the distribution mapping relationship includes the type and quantity of tasks corresponding to each target object; The task distribution module is used to distribute tasks to each of the target objects based on the distribution mapping relationship.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device 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 task distribution method according to any one of claims 1 to 7.

10. A storage medium comprising computer-executable instructions, wherein the computer-executable instructions are used to perform the task distribution method according to any one of claims 1 to 7 when executed by a computer processor.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the task distribution method according to any one of claims 1 to 7.

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