Data processing method and device, equipment, storage medium and program product

Abstract

The invention discloses a data processing method and device, equipment, a storage medium and a program product, which are used for enhancing the possibility that each object completes a to-be-executed task by using a non-negative pressure applying mode, thereby improving the completion rate of the to-be-executed task. The data processing method comprises the following steps: dividing a first object set participating in a to-be-executed task into a plurality of groups; for each group, based on first execution information of group members in the group for the to-be-executed task, determining second execution information of the group for the to-be-executed task; and issuing task rights and interests of the to-be-executed task to group members of the group based on the second execution information of the group.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, and particularly relates to a data processing method and device, equipment, storage medium and program product. BACKGROUND

[0002] In the business processing process, a plurality of objects usually need to be allocated to be executed tasks. The completion rate of the to-be-executed tasks will affect the business processing effect. Therefore, a solution for improving the completion rate of the to-be-executed tasks is needed. SUMMARY

[0003] Embodiments of the present application provide a data processing method, device, equipment, storage medium and program product, which are used for enhancing the possibility of completing the to-be-executed tasks of each object in a "non-negative pressure" manner, so as to improve the completion rate of the to-be-executed tasks.

[0004] In order to achieve the above-mentioned purpose, the technical scheme adopted by the embodiments of the present application is as follows:

[0005] In a first aspect, the embodiments of the present application provide a data processing method, comprising:

[0006] dividing a first object set participating in a to-be-executed task into a plurality of groups;

[0007] for each group, determining second execution information of the group for the to-be-executed task based on first execution information of group members in the group for the to-be-executed task;

[0008] and, based on the second execution information of the group, issuing task rights and interests of the to-be-executed task to the group members of the group.

[0009] In a second aspect, the embodiments of the present application provide a data processing device, comprising:

[0010] a grouping unit, configured to divide a first object set participating in a to-be-executed task into a plurality of groups;

[0011] a determining unit, configured to, for each group, determine second execution information of the group for the to-be-executed task based on first execution information of group members in the group for the to-be-executed task;

[0012] an issuing unit, configured to, based on the second execution information of the group, issue task rights and interests of the to-be-executed task to the group members of the group.

[0013] In a third aspect, the embodiments of the present application provide an electronic device, comprising:

[0014] a processor;

[0015] a memory for storing the processor-executable instructions;

[0016] The processor is configured to execute the instructions to implement the data processing method according to the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the electronic device is enabled to perform the data processing method according to the first aspect.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to perform some or all of the steps of the data processing method according to the first aspect.

[0019] The above at least one technical solution adopted by the embodiments of the present application can achieve the following beneficial effects:

[0020] By dividing the first object set participating in the to-be-executed task into multiple groups, and for each group, determining the second execution information of the group for the to-be-executed task based on the first execution information of the group members in the group for the to-be-executed task, and distributing the task rights and interests of the to-be-executed task to the group members of the group based on the second execution information of the group, since the first execution information of any one group member in the group for the to-be-executed task will directly affect the second execution information of the group, and then affect the task rights and interests distributed to all group members, through the above data processing method, the group members in the same group can improve the overall task completion effect of the group by completing the to-be-executed task in time, thereby improving the completion rate of the to-be-executed task. In addition, since the above method is a "non-negative pressure" scheme, it will not bring negative effects to any object, and thus will not excessively limit the objects participating in the to-be-executed task, which is conducive to improving the number of objects participating in the to-be-executed task, thereby improving the business processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of this application, illustrate embodiments of the present application and specific embodiments thereof, and serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0022] Figure 1 A schematic diagram of an implementation environment suitable for the data processing method provided by an embodiment of the present application;

[0023] Figure 2 A flowchart of a data processing method provided by an embodiment of the present application;

[0024] Figure 3 A flowchart of a data processing method provided for another embodiment of the present application

[0025] Figure 4 A structural diagram of a data processing apparatus provided for an embodiment of the present application

[0026] Figure 5 A structural diagram of an electronic device provided for an embodiment of the present application DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in conjunction with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0028] The terms "first", "second", etc. in the specification and claims are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally means that the front and rear associated objects are in an "or" relationship.

[0029] The technical solutions provided by the embodiments of the present application will be described in detail below in conjunction with the drawings.

[0030] In order to facilitate the understanding of the technical solutions provided by the embodiments of the present application, first, the technical solutions provided by the embodiments of the present application will be described in conjunction with Figure 1 The implementation environment to which the technical solutions provided by the embodiments of the present application are applicable will be described. It should be understood that the technical solutions provided by the embodiments of the present application are applicable to Figure 1 The implementation environment shown is only an exemplary description, and should not be understood as a limitation of the implementation environment to which the technical solutions are applicable.

[0031] As Figure 1 shown, an implementation environment to which the technical solutions provided by the embodiments of the present application are applicable includes a business server and n objects, n being an integer greater than 1. The n objects can participate in the business processing of the business server and execute business-related to-be-executed tasks. Since the completion rate of the to-be-executed tasks will affect the business processing effect and the utilization rate of communication resources, it is particularly important to improve the completion rate of the to-be-executed tasks.

[0032] The related art mainly adopts two schemes to improve the completion rate of the to-be-executed task. The first scheme is to set strict task allocation conditions before allocating the to-be-executed task to multiple objects, and only allocate the to-be-executed task to the objects that meet the task allocation conditions. However, this way easily over-restricts the number of objects participating in the to-be-executed task, which is not conducive to improving the business processing efficiency.

[0033] The second scheme is to periodically assess the risk of the object participating in the to-be-executed task after allocating the to-be-executed task to multiple objects, and if it is found that the object has the risk of not completing the to-be-executed task on time, timely measures are taken to improve the method, and in the case that the object does not complete the to-be-executed task on time, a reminder message is pushed to the object to promote the object to complete the to-be-executed task. However, inappropriate preventive measures or message pushing methods can easily have a negative impact on the object, and can easily reduce the number of objects participating in the to-be-executed task, thereby affecting the business processing effect.

[0034] As an example, in a file download scenario, each object is a client for downloading a file from a storage platform, and the business server is a server that provides network resources. Each client and the server communicate data through wired or wireless networks. The client can apply for network resources from the server to improve the file download speed. In this case, the to-be-executed task of the client is to return the applied network resources to the server within the specified time limit of the server, and the completion rate of the to-be-executed task is the resource return rate. Since the resource return rate will affect the operation of the server, it is particularly important to improve the resource return rate.

[0035] As for the first scheme described above, the server will set strict resource application conditions, and the server will only issue network resources to the clients that meet the resource application conditions, which easily over-restricts the number of clients applying for network resources, and is not conducive to the circulation and maximum utilization of the server's resources.

[0036] As for the second scheme described above, the server usually periodically assesses the risk of the client after issuing network resources to the client, and if it is found that the client has the risk of returning the resources overdue, timely preventive measures are taken, such as sending a reminder message to the client, to promote the client to return the network resources on time. However, inappropriate preventive measures have poor effects, and can easily have a negative impact on the client, and can easily reduce the number of clients applying for resources, which is not conducive to the circulation and maximum utilization of the server's resources.

[0037] As another example, in a consumer credit scenario, the object is a credit user. The credit user interacts with the server through the client to apply for credit resources from the server. In this case, the to-be-executed task in which the credit user participates is to repay the credit resources to the server on time, and the completion rate of the to-be-executed task is the credit resource repayment rate. Since the credit resource repayment rate affects the utilization rate of the credit resources of the server, it is particularly important to improve the credit resource repayment rate.

[0038] In view of this, an embodiment of the present application provides a data processing method. The first object set participating in the to-be-executed task is divided into multiple groups, and for each group, the second execution information of the group for the to-be-executed task is determined based on the first execution information of the group members in the group for the to-be-executed task, and the task rights and interests of the to-be-executed task are issued to the group members of the group based on the second execution information of the group. Since the first execution information of any one of the group members in the group for the to-be-executed task will directly affect the second execution information of the group, and then affect the task rights and interests issued to all group members, through the above data processing method, the group members in the same group can improve the overall task completion effect of the group by completing the to-be-executed task in time, thereby improving the completion rate of the to-be-executed task. In addition, since the above method is a "non-negative pressure" scheme, it will not bring negative effects to any object executing the task, and thus will not excessively limit the objects participating in the to-be-executed task, which is beneficial to improve the number of objects participating in the to-be-executed task, thereby improving the business processing efficiency.

[0039] It should be noted that the above application scenarios are only some exemplary descriptions and should not be understood as a limitation on the application scenarios to which the data processing method provided by the embodiments of the present application is applied.

[0040] It should be understood that the data processing method provided by the embodiments of the present application can be executed by an electronic device. The electronic device referred to here can include a server, such as a standalone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. As an example, the above data processing method can be applied to the business server shown in the above Figure 1 , or a data processing server independent of the business server.

[0041] Based on the above implementation environment, an embodiment of the present application provides a data processing method. Please refer to Figure 2 , a flowchart of a data processing method provided by an embodiment of the present application, which includes the following steps:

[0042] S202, the first object set participating in the to-be-executed task is divided into multiple groups.

[0043] The first object set includes some or all objects having a task to be performed. As an example, all objects participating in the task to be performed are added to the first object set. As another example, an invitation message is pushed to the objects having the task to be performed; in response to a confirmation instruction for the invitation message, the objects having the task to be performed are added to the first object set.

[0044] For example, in a file download scenario, after a server issues a network resource to a client, the server sends an invitation message to the client. The client displays the invitation message in a pop-up window, and after receiving a confirmation operation on the invitation message, sends a confirmation instruction to the server. The server receives the confirmation instruction and adds the client to the first object set. In this way, the willingness of the client to return the network resource can be ensured, and the resource return rate can be improved.

[0045] For another example, in a consumer credit scenario, after a server issues a credit resource to a user, the server sends an invitation message to a client logged in by the user. The client displays the invitation message to the user, and after receiving a confirmation operation on the invitation message, sends a confirmation instruction to the server. The server receives the confirmation instruction and adds the user to the first object set. In this way, the interest of the user in participating in a group task can be ensured, so that the credit resource return rate of the user can be increased to some extent.

[0046] In S202 described above, the division of the first object set into multiple groups can be implemented in various ways, which can be selected according to actual needs, and embodiments of the present application do not limit this.

[0047] In an embodiment, the division of the first object set into multiple groups includes the following steps: determining the number of group members included in each group based on the number of objects in the first object set and the number of groups; and then randomly allocating the objects in the first object set to the corresponding groups based on the number of group members included in each group. In actual application, in order to reduce the difference in the number of group members between each group and improve the comparability between different groups, the difference between the number of group members included in each group is less than a preset threshold.

[0048] For example, the first object set includes 30 objects, and if 5 groups are to be divided, the objects in the first object set are randomly allocated to the 5 groups according to the requirement that each group includes 6 group members.

[0049] In another embodiment, the division of the first account set into multiple groups includes the following steps:

[0050] S221, for each object in the first object set, predicting an expected task completion probability of the object based on an object feature of the object.

[0051] The expected task completion probability of an object represents a prior probability that the object completes a task to be performed within a specified time limit.

[0052] The object characteristics of an object may, for example, include but are not limited to historical task information of the object, such as historical tasks participated in, completion of historical tasks, and the like. For example, in a file download scenario, the object characteristics of a client include historical resource application information, historical overdue return information, a quantity of network resources applied for this time, and a resource use time limit, and the like, and the expected task completion probability represents a prior probability that the client returns network resources to the server within a time limit specified by the server. For another example, in a consumer credit scenario, the characteristics of an object include historical loan information, historical overdue repayment information, a loan amount applied for this time, and a loan time limit, and the like, and the expected task completion probability represents a prior probability that a user repays a loan on time.

[0053] The expected task completion probability of an object can be obtained by inputting the object characteristics of the object into a pre-trained classification model for prediction. The classification model can adopt various models having a probability prediction function, for example, a Logistic Regression (LR) linear model, which outputs a value between 0 and 1 as the expected task completion probability of an object.

[0054] S222, dividing the first object set into a plurality of groups based on the expected task completion probability of the objects in the first object set.

[0055] As an example, the distribution characteristics of the expected task completion probability of the group members of each group are similar as a division target, and the objects in the first object set are divided into a plurality of groups.

[0056] As another example, the above S222 includes the following steps: Step A1, selecting a specified number of objects from the first object set to obtain a second object set, wherein the distribution characteristics of the expected task completion probability of the objects in the second object set are similar to the distribution characteristics of the expected task completion probability of the objects in the first object set; and Step A2, taking the objects in the second object set as initial clustering centers, clustering the first object set based on the expected task completion probability of the objects in the first object set to obtain a plurality of groups.

[0057] In Step A1, a specified number of objects can be randomly selected from the first object set to obtain the second object set, so that in Step A2, the distribution characteristics of the expected return probability of the group members of all groups are similar through continuous optimization of the clustering centers.

[0058] Alternatively, a statistical sampling method of stratified sampling is adopted, a specified number of objects are extracted from the first object set to obtain the second object set, not only the distribution characteristics of the expected task completion probability of the objects in the second object set can be ensured to be similar to the distribution characteristics of the expected task completion probability of the objects in the first object set, but also the sampling error can be reduced, the important subsets in the second object set can not be underestimated or overestimated, and objects in each subset can be properly reflected in the second object set; further, when clustering is performed through step A2, only a few times of updating of the cluster centers are required, and the multiple groups similar in the distribution characteristics of the expected task completion probability can be quickly and accurately divided, thereby improving the accuracy and efficiency of group division.

[0059] Specifically, the first object set is divided into multiple subsets based on the expected return probability of the objects in the first object set; the extraction ratio is determined based on the ratio between the specified number and the number of objects in the first object set; the objects are extracted from each subset based on the extraction ratio; and the second object set is determined based on the objects extracted from the multiple subsets.

[0060] For example, the first object set includes 500 objects, and based on the expected task completion probability of each object in the first object set, the first object set can be divided into three subsets, the first subset includes 125 objects, the expected task completion probability of these objects is between 0 and 0.4, the second subset includes 280 objects, the expected task completion probability of these objects is between 0.4 and 0.6, and the third subset includes 95 objects, the expected task completion probability of these objects is more than 0.6. If the specified number is 100, the extraction ratio is determined to be 100 / 500 = 1 / 5. Finally, the number of objects extracted from each subset is: 125 / 5 = 25, 280 / 5 = 56, and 95 / 5 = 19, and then 25 objects are randomly extracted from the first subset, 56 objects are randomly extracted from the second subset, and 19 objects are randomly extracted from the third subset. Thus, based on the objects extracted from each subset, the second object set containing 100 objects is obtained.

[0061] In the step A2, each object in the second object set can be taken as an initial cluster center, the absolute value of the difference between the expected task completion probabilities of different objects can be taken as the distance between different objects, and the remaining objects in the first object set which are not added to the second object set can be added to the group to which the nearest cluster center belongs based on a clustering algorithm such as a K-Nearest Neighbor (KNN) clustering algorithm, to obtain an initial plurality of groups. Then, the initial cluster centers are updated, and the above steps are repeatedly executed until all objects in the first object set have been added to the corresponding group, or the number of remaining unassigned objects is less than a specified number. If the latter, the remaining unassigned objects are randomly and uniformly distributed to the existing groups. At this point, the grouping of the first object set is completed, and a plurality of groups is obtained.

[0062] In the above embodiment, the first object set is divided into a plurality of groups based on the expected task completion probabilities of the objects in the first object set, which can ensure that the difference in expected task completion probabilities between different groups is not large, thereby increasing the comparability between different groups, so that the reason why the overall task completion of a group is good is that the task completion of each group member, rather than the grouping difference. In this way, the influence of the grouping difference on the overall task completion of the group can be eliminated, thereby guiding the group members to perform the to-be-executed task and greatly improving the task completion rate of each group for the to-be-executed task.

[0063] In another embodiment of the present application, in order to better promote the group members of each group to better complete the to-be-executed task and improve the overall task completion of the group, after the first object set is divided into a plurality of groups, the data processing method provided by the embodiment of the present application can further include: step B1, determining the role of the group members in each group; and step B2, pushing incentive information of the to-be-executed task to the group members based on the role of the group members.

[0064] In the above step B1, the role of the group members can be implemented in various ways, which can be selected according to actual needs, and the embodiment of the present application does not limit this.

[0065] In an embodiment, determining the role of the group members includes the following steps: randomly assigning a role to each group member. For example, for each group, the roles it contains can include but are not limited to: role 1, role 2, role 3, role 4, and role 5, etc. A certain group includes 10 objects, i.e., 10 group members, then 4 group members are randomly selected as role 1, role 2, role 3, and role 4, and the roles of the remaining group members are determined as role 5.

[0066] In the above embodiments, the same role is assigned to the group members of the same group, which is not conducive to improving the completion rate of the group for the to-be-executed task. In the above embodiments, different roles are assigned to different group members of the same group, which is conducive to giving full play to the roles of different roles and improving the completion rate of the group for the to-be-executed task.

[0067] In another embodiment, in order to give full play to the roles of different roles and improve the completion rate of the group for the to-be-executed task, the role of the group member includes the following steps: the group members of the group are sorted in descending order of expected task completion probability, and the role of the group member is determined based on the arrangement order of the group member. For example, a group includes 10 objects, i.e. 10 group members. After sorting the 10 group members in descending order of expected task completion probability, the role of the group member ranked first is determined as role 1, the role of the group member ranked second is determined as role 2, the role of the group member ranked third is determined as role 3, the role of the group member ranked fourth is determined as role 4, and the roles of the remaining group members are determined as role 5.

[0068] In the above embodiments, the expected task completion probability of the group member is taken into account when determining the role of the group member. Since the group member with high expected task completion probability is more likely to complete the to-be-executed task, the role of the group member is determined according to the high and low of the expected task completion probability of the group member, which is conducive to using the group member with high expected task completion probability to guide the group member with low expected task completion probability to better complete the to-be-executed task, so as to improve the overall task completion effect of the group.

[0069] In yet another embodiment, determining the role of the group member includes the following steps: determining the role score of the group member based on the expected task completion probability and the historical auxiliary task information of the group member.

[0070] The historical auxiliary task information of the group member includes the execution of the group member for the auxiliary task. As an example, the historical auxiliary task information includes but is not limited to: the historical completion rate of the group member when executing the first role of the historical auxiliary task, and the historical contribution coefficient of the group member to the historical group when executing the second role.

[0071] The first role and the second role can be set according to actual needs, and the embodiments of the present application are not limited thereto. As an example, the first role refers to a role with high importance, such as role 1, and the second role refers to a role with high task completion probability, such as role 4.

[0072] The historical auxiliary task refers to an auxiliary task of a historical task in which a group member participates, and the auxiliary task is used to motivate the group member to perform the historical task. The historical completion rate of the group member performing the historical auxiliary task of the first role refers to a probability that the execution of the historical task by a historical group to which the group member belongs meets an execution requirement when the group member performs the historical auxiliary task of the first role. The execution requirement can be set according to actual needs, for example, the execution requirement can be that the completion rate of the historical task by the historical group exceeds the completion rate of the historical task by other historical groups.

[0073] As an example, the historical completion rate of the group member performing the historical auxiliary task of the first role is a ratio between a number of times that the completion rate of the historical group to which the group member belongs exceeds the completion rate of other historical groups when the group member performs the first role and a number of times that the group member performs the historical auxiliary task of the first role. For example, a certain group member performs the historical auxiliary task of role 1 for 5 times, and among them, there is 1 time when the completion rate of the historical group to which the group member belongs exceeds the completion rate of other historical groups, and the historical completion rate of the group member performing the first role is 1 / 5 = 25%.

[0074] The historical contribution coefficient represents the contribution of the group member performing the historical auxiliary task of the second role to the historical group to which the group member belongs.

[0075] The role score of the group member represents the ability of the group member to undertake important roles. The greater the role score of the group member, the stronger the ability of the group member to undertake important roles; otherwise, the weaker the ability of the group member to undertake important roles.

[0076] As an example, the role score of the group member can be determined by: performing a first exponential operation based on the expected task completion probability of the group member to obtain a first value; performing a second exponential operation based on the historical completion rate of the first historical group to obtain a second value; and determining the role score of the group member based on the first value, the second value, and the historical contribution coefficient of the group member in the second historical group.

[0077] More specifically, assuming that a certain group member has performed n historical auxiliary tasks, the role score of the group member can be determined by the following formula (1):

[0078]

[0079] wherein score represents the role score of the group member, p1 represents the expected task completion probability of the group member, is the first value, p2 represents the historical completion rate of the group member performing the historical auxiliary task of the first role, is the second value, p 3iγ, ω, β are hyperparameters.

[0080] The role score of the group member is determined through the above example. The role score is not only affected by the expected task completion probability of the group member, but also affected by the proportion of the group member performing the historical auxiliary task of the first role and the cumulative contribution coefficient of the group member performing the historical auxiliary task of the second role to the historical group to which the group member belongs. That is, the higher the proportion of the group member performing the historical auxiliary task of the first role, the higher the role score of the group member. The higher the cumulative contribution coefficient of the group member performing the historical auxiliary task of the second role, the higher the role score of the group member. In this way, the role of the group member is determined based on the role score, which can ensure that the group member has a better incentive effect on himself and other group members in the role.

[0081] In the above step B2, the incentive information is used to encourage the group member to perform the to-be-performed task. Different group members in different roles are pushed different incentive information, so as to improve the participation of the group member in the to-be-performed task, promote mutual promotion among different group members in different roles, and better perform the to-be-performed task, thereby improving the task completion rate.

[0082] As an example, the incentive information can be a reminder message, or an auxiliary task of the to-be-performed task, that is, a task for encouraging the group member to complete the to-be-performed task.

[0083] For example, for each group, the roles it contains can include but are not limited to: role 1, role 2, role 3, role 4, and role 5, etc. Among them, the auxiliary tasks pushed to the group members of role 1 include but are not limited to: completing the to-be-performed task on time to improve the overall task completion rate of the group; ensuring the overall task execution rate of the group, striving for a higher ranking, and being issued to a larger number of task benefits; monitoring the task execution situation and overdue risk degree of the group member; according to the task execution situation of the group member with high overdue risk degree, jointly guiding such group member with role 2 to better complete the to-be-performed task; updating the task execution requirements of the group from time to time to adapt to the task change demand; regularly interacting with the group member to promote the group member to complete the to-be-performed task on time, etc.

[0084] The auxiliary tasks pushed to the group members of role 2 include but are not limited to: completing the to-be-performed task on time to improve the overall completion rate of the group; pushing guiding information to the group member with high overdue risk degree to guide such group member to perform the to-be-performed task on time; adjusting the task information of the group member; interacting with other group members to collect the task execution situation of the group member, etc., and sending the summary to the group members of role 1, etc.

[0085] The auxiliary task pushed to the group member of role 3 includes but is not limited to: completing the to-be-executed task on schedule to improve the overall completion rate of the group; improving the interaction between group members to improve the activity of the group; pushing an incentive message to other group members; and pushing information such as the task completion situation and ranking of the group to other group members on a regular basis to improve the possibility of other group members completing the to-be-executed task.

[0086] The auxiliary task pushed to the group member of role 4 includes but is not limited to: completing the to-be-executed task on schedule and guiding other group members to complete the to-be-executed task on schedule.

[0087] The auxiliary task pushed to the role 5 includes but is not limited to: completing the to-be-executed task on schedule.

[0088] By assigning a corresponding role to each group member in the group and pushing corresponding incentive information to the group member based on the role, the group member can be effectively motivated to better execute the to-be-executed task, thereby improving the overall task completion rate of the group.

[0089] In the above embodiment, the incentive information of the to-be-executed task pushed to the group member can be implemented in various ways, which are not limited in the embodiment of the application.

[0090] In an embodiment, in order to enhance the interaction between group members of the same group and guide each other to better execute the to-be-executed task and improve the overall task completion rate of the group, the incentive information of the to-be-executed task pushed to the group member includes: displaying a conversation window to the group member, the conversation window being used for instant messaging between group members of the same group; and displaying the incentive information of the to-be-executed task in the conversation window based on the role of the group member.

[0091] In this way, the group members of the same group can obtain the incentive information of themselves and other group members through the conversation window, not only can supervise each other to better respond to the incentive information, but also can interact with each other through the conversation window to guide each other to better execute the to-be-executed task, thereby improving the overall completion rate of the group.

[0092] In another embodiment of the application, after the conversation window is displayed to the group member, the above data processing method can further include the following steps: determining a response message of a consultation message sent by the group member in the conversation window based on a conversation robot; and displaying the response message in the conversation window.

[0093] The consultation message may, for example, include but is not limited to comparison rules between groups, overall completion situations of to-be-executed tasks of each group, and the like.

[0094] The conversation robot can respond to the consultation messages sent by the group members in the conversation window, thereby reducing the workload of some role group members and assisting these group members in better responding to the incentive information. In addition, the conversation robot can also push the reference response plan for the incentive information to the group members and display the guide message in the conversation window to activate the activity of the group and the attractiveness of the group message, thereby guiding the completion rate of the group members for the to-be-executed task.

[0095] In actual application, the conversation robot can be implemented by a large-scale language model. For example, the conversation robot is built-in with a Retrieve Augmented Generative (RAG) technology framework, which can search in all obtained documents about the group comparison rules to obtain the response message of the consultation message. Secondly, the conversation robot also has a certain planning ability of Agent. By inputting various information such as object features, credit reports, and task execution records of the group members into the conversation robot, the conversation robot can determine the current task execution of the group members and predict the next task execution of the group members. In addition, the conversation robot can also automatically obtain the latest information that the group members are generally interested in on the Internet and push it to the conversation window to improve the activity of the group.

[0096] S204, for each group, determining second task execution information of the group for the to-be-executed task based on first task execution information of the group members in the group for the to-be-executed task.

[0097] The first task execution information of the group members for the to-be-executed task is used to reflect the execution progress of the group members for the to-be-executed task, which may include at least one of the following information, for example, but not limited to: whether the to-be-executed task is completed, the completion time of the to-be-executed task, the overdue time length of the to-be-executed task, etc. The second task execution information of the group for the to-be-executed task is used to reflect the overall completion of the to-be-executed task by the group members, for example, including but not limited to the overall completion rate of the to-be-executed task by the group members, etc.

[0098] S206, based on the second execution information of the group, issuing task rights and interests of the to-be-executed task to the group members of the group.

[0099] The task equity of the to-be-executed task is used to exchange the equity related to the to-be-executed task. As an example, in the file downloading scenario, the task equity may include, for example, but is not limited to, points, a number of deductible resources, a number of resources that need not be returned, and the like. Based on the points, a level can be promoted, thereby applying for a larger number of network resources; based on the number of deductible resources, a part of the network resources that need to be returned can be exempted; and based on the number of resources that need not be returned, a part of the resources that need not be returned can be applied for. As another example, in the consumption credit scenario, the task equity may include, for example, but is not limited to, points, electronic coupons, and the like, the points can be exchanged for goods, promote the credit level of the user, withdraw cash, and the like, and the electronic coupons can be used to purchase goods and the like.

[0100] In an embodiment, S206 includes the following steps:

[0101] S261, based on the second execution information of the group, determines the number of target equities to be distributed to the group.

[0102] The second execution information includes an overall completion rate of the group on the to-be-executed task. The overall completion rate of the group is a ratio between a number of group members in the group who have completed the to-be-executed task and a number of group members included in the group. For example, a certain group includes 10 group members, of which 4 have completed the to-be-executed task, and thus the overall completion rate of the group is 4 / 10=40%. In this way, it is helpful to encourage group members to complete the to-be-executed task to improve the overall completion rate of the group to which the group members belong, thereby improving the ranking of the group to which the group members belong to obtain a larger number of task equities.

[0103] As an example, the plurality of groups are sorted in descending order of the overall return rate to obtain the ranking of each group; then, based on the ranking of each group and the number of available equities, the number of target equities to be distributed to the group is determined. For example, there are 100 groups, and the number of available equities is 100. The number of target equities to be distributed to the group ranked first is 50, the number of target equities to be distributed to the groups ranked 2th to 5th is 30, the number of target equities to be distributed to the groups ranked 6th to 10th is 20, and the number of target equities to be distributed to the groups ranked after the 10th is 0.

[0104] As another example, S261 includes the following steps: step C1, determining a first task unqualified rate of the first object set based on the task overdue duration of the objects in the first object set and the number of objects in the first object set; step C2, determining a second task unqualified rate of the group based on the task overdue duration of the group members of the group and the number of group members included in the group; step C3, determining a distribution coefficient of the group based on the second execution information of the group, the first task unqualified rate, and the second task unqualified rate; and step C4, determining the number of target equities to be distributed to the group based on the distribution coefficient of the group and the number of available equities.

[0105] The first task unqualified rate is used to reflect the proportion of objects in the first object set that fail to complete the to-be-executed task. For example, in the file download scenario, the first task unqualified rate is used to reflect the proportion of resources that are overdue and not returned in the first object set. For another example, in the consumer credit scenario, the first task unqualified rate is also referred to as the bad debt rate of the first object set, and is used to reflect the proportion of the number of loans that are overdue and not repaid in the first object set.

[0106] The second task unqualified rate is used to reflect the proportion of objects in the group that fail to complete the to-be-executed task. For example, in the file download scenario, the second task unqualified rate is used to reflect the proportion of resources that are overdue and not returned in the group. For another example, in the consumer credit scenario, the second task unqualified rate is also referred to as the bad debt rate of the group, and is used to reflect the proportion of the number of loans that are overdue and not repaid in the group.

[0107] Taking the second task unqualified rate in the consumer credit scenario as an example, assuming that the group contains N group members, and each group member needs to return resources in 6 installments within 6 months, the group has a total of 6*N resources to be returned. For each resource, if it is not returned within 1 month, the score is 1*2^0=1; if it is not returned within 1-2 months, the score is 1*2^1=2; if it is not returned within 2-3 months, the score is 1*2^2=4; and if it is not returned for 3 months or more, the score is 1*2^3=8. In this case, the second task unqualified rate of the group is: (the number of loans that are not returned within 1 month*1+the number of loans that are not returned within 1-2 months*2+the number of loans that are not returned within 2-3 months*4+the number of loans that are not returned for 3 months or more*8) / (6*N*2 3 ).

[0108] In the above step C3, the allocation coefficient of the group can be determined by: performing a third exponential operation based on the second task execution information of the group to obtain a third score; determining a difference value between the first task unqualified rate and the second task unqualified rate, and performing a fourth exponential operation based on the difference value to obtain a fourth score; and determining the allocation coefficient of the group based on a ratio between the third score and the fourth score.

[0109] For example, the allocation coefficient of the group can be determined by the following formula (2).

[0110]

[0111] wherein, 1+2 -α*第i个群组的排名 is the third score, 1+e -β*(第一任务不合格率-第i个群组的第二任务不合格率) is the fourth score, and a and b are hyperparameters.

[0112] In the above example, the allocation coefficient of the group is not only affected by the ranking of the group, but also affected by the difference between the second task unqualified rate and the first task unqualified rate of the group, that is, the smaller the difference, the greater the allocation coefficient of the group, and the greater the number of target benefits that the group can allocate. In this way, the group members can be greatly encouraged to better complete the to-be-executed task, reduce the second task unqualified rate of the group, and thus improve the overall task completion rate.

[0113] In the above step C4, the product of the allocation coefficient of the group and the available benefit quantity can be determined as the number of target benefits allocated to the group.

[0114] In the embodiments of the present application, the available benefit quantity can be set according to actual needs. As an example, the available benefit quantity can be a fixed value.

[0115] As another example, the available benefit quantity is related to the first task unqualified rate. Specifically, the available benefit quantity can be determined by: determining a third task unqualified rate of the third object set based on the task overdue duration of the objects in the third object set and the number of objects in the third object set; and determining the available benefit quantity based on the difference between the third task unqualified rate and the first task unqualified rate.

[0116] Among them, the objects in the third object set all have to-be-executed tasks, and the first object set includes objects in the third object set that meet a preset screening condition. As an example, the first object set includes objects in the third object set that have confirmed the invitation message. For example, the third object set includes object 1 to object 10, and objects 1 to 5 have confirmed the invitation message after receiving the invitation message, so the first object set includes objects 1 to 5.

[0117] The third task unqualified rate is used to reflect the proportion of objects in the third object set that do not meet the requirements for completing the to-be-executed task. The specific determination method of the third task unqualified rate is similar to the specific determination method of the second task unqualified rate, which will not be repeated. For example, in the file download scenario, the third task unqualified rate is used to reflect the proportion of overdue resources in the third object set. For another example, in the consumer credit scenario, the third task unqualified rate is also called the bad debt rate of the third object set, and is used to reflect the proportion of overdue loan amounts in the third object set.

[0118] The greater the difference between the first task unqualified rate and the third task unqualified rate, the greater the overall revenue that the group competition brings to the server, and thus the server can increase the available benefit amount; on the contrary, the smaller the overall revenue that the group competition brings to the server, and thus the server can reduce the available benefit amount.

[0119] S262, based on the target benefit quantity, the group members of the group are allocated the task benefits of the to-be-executed task.

[0120] As an example, the target quantity of rights is evenly distributed to the group members of the group, to obtain a quantity of rights of each group member, and then the corresponding task rights are distributed to the distribution group members based on the quantity of rights of each group member.

[0121] As another example, based on the roles of the group members of the group and the target quantity of rights, a quantity of rights allocated to each group member is determined, and then the corresponding task rights are distributed to the distribution group members based on the quantity of rights of each group member. For example, the more important the role of the group member is, the larger the quantity of rights allocated to the group member is, such as the quantity of rights allocated to the group member with role 1 is larger than the quantity of rights allocated to the group member with role 4.

[0122] As yet another example, in the case that the group member receives the incentive information of the task to be executed, based on the response information of the group member to the incentive information, a distribution coefficient of the group member is determined; and based on the distribution coefficient of the group member and the target quantity of rights, the task rights of the task to be executed are distributed to the group member.

[0123] Specifically, the distribution coefficient of the group member can be determined by the following way: based on the role of the group member, a level coefficient of the group member is determined; based on the response information, a contribution coefficient of the group member is determined; and based on the product of the level coefficient and the contribution coefficient, the distribution coefficient of the group member is determined.

[0124] The more important the role of the group member is, the larger the level coefficient of the group member is. For example, the level coefficient of role 5 is 1.0, the level coefficient of role 4 is 1.1, the level coefficients of role 43 and role 2 are both 1.2, and the level coefficient of role 1 is 1.3.

[0125] The higher the response degree of the group member to the incentive information is, the larger the contribution coefficient of the group member is. The response degree can be determined according to at least one of the following information: the number of messages sent by the group member in the group, the number of responses made by the group member in the group, the login activity of the group member, the number of times that the group member guides other group members to complete the task to be executed, etc.

[0126] After the distribution coefficient of the group member is determined, the product of the distribution coefficient of the group member and the target quantity of rights is determined as the quantity of rights allocated to the group member, and then the corresponding task rights are distributed to the distribution group members based on the quantity of rights of each group member.

[0127] In the above example, based on the level coefficient and contribution coefficient of the group members and the target amount of benefits allocated to the group, the task benefits are distributed to the group members, so that the more important the role and the greater the contribution of the group members, the greater the amount of benefits allocated to the group members, which helps to improve the possibility of completing the task to be executed by the group members, so as to improve the overall completion rate of the group. In addition, since the task benefits are reasonably allocated among group members in different roles, it helps to continuously attract more new objects to participate in the task to be executed, and maintain a high proportion of old objects to continuously participate in the task to be executed.

[0128] One or more embodiments of the present application provide a data processing method, which divides a first object set participating in a task to be executed into a plurality of groups, and for each group, determines second execution information of the task to be executed by the group based on first execution information of group members in the group for the task to be executed, and distributes task benefits of the task to be executed to the group members of the group based on the second execution information of the group. Since the first execution information of any group member in the group for the task to be executed directly affects the second execution information of the group, and then affects the task benefits distributed to all group members, the above data processing method enables group members in the same group to improve the overall task completion effect of the group by timely completing the task to be executed, thereby improving the completion rate of the task to be executed. In addition, since the above method is a "non-negative pressure" scheme, it does not bring negative effects to any object executing the task, thereby not excessively limiting the objects participating in the task to be executed, which is beneficial to improve the number of objects participating in the task to be executed, thereby improving the business processing efficiency.

[0129] The data processing method provided by the embodiments of the present application can be applied to various business scenarios involving resource transfer, such as but not limited to consumer credit distribution scenarios, file download scenarios, online transaction scenarios of e-commerce platforms, etc. The data processing method provided by the embodiments of the present application will be described below by taking the consumer credit distribution scenario and the file download scenario as examples.

[0130] In the credit resource distribution scenario, the object is a credit user, and the user interacts with the server through the client to apply for credit resources from the server. In this case, the task to be executed by the user is to repay the credit resources on time.

[0131] The data processing method provided by the embodiments of the present application, as shown in Figure 3 The user applies for credit through the client, and after the credit is approved, the client applies to the server to extract the credit resources. After the server verifies the application of the client, the credit resources are distributed to the user's account. If the user applies to participate in the group competition activity, the server adds the user to the first object set.

[0132] Then, the server divides the first object set into multiple groups, and for each group, determines a role of a group member of the group, pushes an incentive message to the group member based on the role of the group member, so as to encourage the group member to return the credit resource. The server also determines overall credit resource return information of the multiple groups based on the credit resource return information of the group members, and issues a certain number of points to the group members of the group as task benefits based on the overall credit resource return information of the group.

[0133] Through the above data processing method, the group members in the same group can improve the overall task completion effect of the group by timely completing the to-be-executed task, and timely return the credit resource, thereby improving the credit resource return rate. In addition, since the above method belongs to a "non-negative pressure" scheme, that is, by enhancing the positive willingness of the user to return the credit resource, improving the overall credit resource return quality, and reducing the unqualified rate, the user will not be negatively affected, and the user applying for the credit resource will not be excessively restricted, which is beneficial to improve the number of users applying for the loan to the server, thereby improving the utilization rate of the credit resource.

[0134] In the file download scenario, each client applying for the network resource is an object, and the to-be-executed task participated by the client is to return the network resource on time.

[0135] The data processing method provided by the embodiment of the application, the server pushes an invitation message to the client after allocating the bandwidth to the client. After receiving the confirmation instruction of the client to the requirement message, the client is added to the first object set.

[0136] The server divides the first object set into multiple groups, and for each group, determines a role of a group member of the group, pushes an incentive message to the group member based on the role of the group member, so as to encourage the group member to return the credit resource. The server also determines overall credit resource return information of the multiple groups based on the credit resource return information of the group members, and issues a certain number of points to the group members of the group as task benefits based on the overall credit resource return information of the group.

[0137] Through the above data processing method, the group members in the same group can improve the overall task completion effect of the group by timely completing the to-be-executed task, and timely return the credit resource, thereby improving the credit resource return rate. In addition, since the above method belongs to a "non-negative pressure" scheme, that is, by enhancing the positive willingness of the user to return the credit resource, improving the overall credit resource return quality, and reducing the unqualified rate, the user will not be negatively affected, and the user applying for the credit resource will not be excessively restricted, which is beneficial to improve the number of users applying for the loan to the server, thereby improving the utilization rate of the credit resource.

[0138] The data processing method provided by the embodiments of the present application is similar to the data processing process in the above-mentioned consumption credit issuing scenario and file downloading scenario in the process of data processing in other business scenarios, and will not be repeated here.

[0139] Based on the same inventive concept, the embodiments of the present application also provide a data processing apparatus. Please refer to Figure 4 The structural schematic diagram of a data processing apparatus 400 provided by one embodiment of the present application, the apparatus 400 comprises a grouping unit 410, a determining unit 420 and an issuing unit 430.

[0140] The grouping unit 410 is configured to divide a first object set participating in a to-be-executed task into a plurality of groups.

[0141] The determining unit 420 is configured to, for each group, determine second execution information of the group for the to-be-executed task based on first execution information of group members in the group for the to-be-executed task.

[0142] The issuing unit 430 is configured to issue a task right of the to-be-executed task to the group members of the group based on the second execution information of the group.

[0143] In another embodiment, the grouping unit is configured to:

[0144] predict an expected task completion probability of each object in the first object set based on an object feature of the object;

[0145] divide the first object set into a plurality of groups based on the expected task completion probability of the object in the first object set.

[0146] In another embodiment, when the grouping unit divides the first object set into a plurality of groups based on the expected task completion probability of the object in the first object set, the grouping unit performs the following steps:

[0147] select a specified number of objects from the first object set to obtain a second account object set, and the distribution characteristics of the expected task completion probability of the objects in the second object set are similar to the distribution characteristics of the expected task completion probability of the objects in the first object set;

[0148] cluster the first object set based on the expected task completion probability of the object in the first object set, and obtain a plurality of groups by taking the objects in the second object set as initial clustering centers.

[0149] In another embodiment, when the grouping unit selects a specified number of objects from the first object set to obtain a second object set, the grouping unit performs the following steps:

[0150] divide the first object set into a plurality of subsets based on a probability of completing a task expected to be performed by an object in the first object set;

[0151] determine a sampling ratio based on a ratio between the specified number and a number of objects in the first object set;

[0152] sample an object from each subset based on the sampling ratio;

[0153] determine a second object set based on the objects sampled from the plurality of subsets.

[0154] In another embodiment, the determining unit is further configured to determine a role of a group member in the group;

[0155] The data processing apparatus further includes:

[0156] a pushing unit configured to push incentive information of the task to be performed to the group member based on the role of the group member.

[0157] In another embodiment, when determining the role of the group member, the determining unit is configured to perform the following steps:

[0158] determine a role score of the group member based on a probability of completing a task expected to be performed by the group member and historical assistance task information;

[0159] determine the role of the group member based on the role score of the group member.

[0160] In another embodiment, the historical assistance task information includes a historical completion rate when the group member performs a historical assistance task in a first role and a historical contribution coefficient of a historical group to which the group member belongs when the group member performs a historical assistance task in a second role;

[0161] In another embodiment, when determining the role score of the group member based on the probability of completing a task expected to be performed by the group member and the historical assistance task information, the determining unit is configured to perform the following steps:

[0162] perform a first exponential operation based on the probability of completing a task expected to be performed by the group member to obtain a first value;

[0163] perform a second exponential operation based on the historical completion rate to obtain a second value;

[0164] determine the role score of the group member based on the first value, the second value, and the historical contribution coefficient.

[0165] In another embodiment, the pushing unit is configured to:

[0166] displaying a conversation window to the group members, the conversation window being used for instant messaging among the group members of the same group;

[0167] displaying, in the conversation window, incentive information of the to-be-executed task based on the role of the group member.

[0168] In another embodiment, the determination unit is further configured to determine, in response to a consultation message sent by the group member in the conversation window, a reply message of the consultation message based on a conversation robot;

[0169] The pushing unit is further configured to display the reply message in the conversation window.

[0170] In another embodiment, the distribution unit is configured to:

[0171] determine a target number of rights to be distributed to the group based on second execution information of the group;

[0172] distribute, to the group members of the group, task rights of the to-be-executed task based on the target number of rights.

[0173] In another embodiment, when determining the target number of rights to be distributed to the group based on second execution information of the group, the distribution unit performs the following steps:

[0174] determine a first task unqualified rate of the first object set based on a task overdue duration of an object in the first object set and a number of objects in the first object set;

[0175] determine a second task unqualified rate of the group based on a task overdue duration of a group member of the group and a number of group members included in the group;

[0176] determine an allocation coefficient of the group based on the second execution information of the group, the first task unqualified rate, and the second task unqualified rate;

[0177] determine the target number of rights to be distributed to the group based on the allocation coefficient of the group and an available number of rights.

[0178] In another embodiment, when determining the allocation coefficient of the group based on the second task execution information of the group, the first task unqualified rate, and the second task unqualified rate, the distribution unit performs the following steps:

[0179] perform a third exponential operation based on the second task execution information of the group to obtain a third score;

[0180] determining a difference between the first task unqualified rate and the second task unqualified rate, and performing a fourth exponential operation based on the difference to obtain a fourth score value;

[0181] determining a distribution coefficient of the group based on a ratio between the third score value and the fourth score value.

[0182] In another embodiment, the issuing unit, when issuing the task interest of the to-be-executed task to the group member of the group based on the target interest quantity, performs the following steps:

[0183] In the case that the group member receives the incentive information of the to-be-executed task, determining a distribution coefficient of the group member based on response information of the group member to the incentive information;

[0184] issuing the task interest of the to-be-executed task to the group member based on the distribution coefficient of the group member and the target interest quantity.

[0185] In another embodiment, the incentive information corresponds to a role of the group member in the group;

[0186] When the issuing unit determines the distribution coefficient of the group member based on the response information of the group member to the incentive information, the issuing unit performs the following steps:

[0187] determining a level coefficient of the group member based on the role of the group member;

[0188] determining a contribution coefficient of the group member based on the response information;

[0189] determining the distribution coefficient of the group member based on a product of the level coefficient and the contribution coefficient.

[0190] In another embodiment, the determining unit is further configured to:

[0191] determining a third task unqualified rate of the third object set based on a task overdue duration of objects in the third object set and a number of objects in the third object set, wherein each object in the third object set has the to-be-executed task, and the first object set includes objects in the third object set that meet a preset screening condition;

[0192] determining the available interest quantity based on a difference between the third task unqualified rate and the first task unqualified rate.

[0193] In another embodiment, the data processing apparatus further includes:

[0194] an invitation unit configured to push an invitation message to an object having a to-be-executed task;

[0195] an adding unit, configured to add an object having a task to be executed into the first object set in response to a confirmation instruction of the invitation message.

[0196] Obviously, the data processing apparatus provided by the embodiments of the present application can be used as Figure 2 the execution subject of the data processing method shown in the embodiments of the present application, for example Figure 2 In the data processing method shown in the embodiments of the present application, step S202 can be executed by the grouping unit 410 in the data processing apparatus shown in the embodiments of the present application, step S204 can be executed by the task assigning unit 420 in the data processing apparatus shown in the embodiments of the present application, and step S206 can be executed by the determining unit 430 in the data processing apparatus shown in the embodiments of the present application. Figure 4 In the data processing method shown in the embodiments of the present application, step S202 can be executed by the grouping unit 410 in the data processing apparatus shown in the embodiments of the present application, step S204 can be executed by the task assigning unit 420 in the data processing apparatus shown in the embodiments of the present application, and step S206 can be executed by the determining unit 430 in the data processing apparatus shown in the embodiments of the present application. Figure 4 In the data processing method shown in the embodiments of the present application, step S202 can be executed by the grouping unit 410 in the data processing apparatus shown in the embodiments of the present application, step S204 can be executed by the task assigning unit 420 in the data processing apparatus shown in the embodiments of the present application, and step S206 can be executed by the determining unit 430 in the data processing apparatus shown in the embodiments of the present application. Figure 4 In the data processing method shown in the embodiments of the present application, step S202 can be executed by the grouping unit 410 in the data processing apparatus shown in the embodiments of the present application, step S204 can be executed by the task assigning unit 420 in the data processing apparatus shown in the embodiments of the present application, and step S206 can be executed by the determining unit 430 in the data processing apparatus shown in the embodiments of the present application.

[0197] According to another embodiment of the present application, Figure 4 In the data processing apparatus shown in the embodiments of the present application, each unit can be combined into one or several other units respectively or all, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In the embodiments of the present application, the data processing apparatus can also include other units, which can also be implemented by other units in actual applications, and can be implemented by multiple units in cooperation.

[0198] According to another embodiment of the present application, the data processing apparatus shown in the embodiments of the present application can be constructed by running a computer program (including program code) capable of executing each step involved in the corresponding method shown in the embodiments of the present application on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), a Read-Only Memory (ROM), etc. Figure 2 In the data processing apparatus shown in the embodiments of the present application, each unit can be combined into one or several other units respectively or all, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In the embodiments of the present application, the data processing apparatus can also include other units, which can also be implemented by other units in actual applications, and can be implemented by multiple units in cooperation. Figure 4

[0199] ​The above describes particular embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or necessary.

[0200] Figure 5 Figure 1 is a structural schematic diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 5 At the hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The memory can include a memory, such as a high-speed random-access memory (RAM), and can further include a non-volatile memory, such as at least one disk memory. Of course, the electronic device can further include other hardware required by a business.

[0201] The processor, the network interface, and the memory can be connected to each other through the internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, and a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the figure to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0202] The memory is used to store a program. Specifically, the program can include program code, which includes computer operation instructions. The memory can include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0203] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs, and forms a data processing apparatus at the logical level. The processor executes the program stored in the memory, and is specifically used to perform the following operations:

[0204] divide the first object set participating in the task to be executed into multiple groups;

[0205] For each group, based on the first execution information of the group members for the task to be executed, the second execution information of the group for the task to be executed is determined;

[0206] Furthermore, based on the second execution information of the group, the task rights of the task to be executed are issued to the group members of the group.

[0207] The above is as stated in this application. Figure 2 The methods executed by the data processing apparatus disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0208] The electronic device can also perform Figure 2 The method, and implement the data processing device in Figure 2 , Figure 3 The functions of the embodiments shown are not described in detail here.

[0209] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0210] The embodiment of the present application further provides a computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, and when the instructions are executed by a portable electronic device comprising a plurality of applications, the portable electronic device is enabled to perform the method of the embodiment of the present application, and specifically to perform the following operations: Figure 2 The method of the embodiment of the present application, and specifically to perform the following operations:

[0211] divide the first object set participating in the to-be-executed task into a plurality of groups;

[0212] for each group, determine second execution information of the group for the to-be-executed task based on first execution information of group members in the group for the to-be-executed task;

[0213] and, based on the second execution information of the group, distribute task rights of the to-be-executed task to the group members of the group.

[0214] The embodiment of the present application further provides a computer program product, which comprises a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all steps of the data processing method provided by the embodiment of the present application.

[0215] In summary, the above only describes the preferred embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0216] The system, device, module or unit illustrated in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain function. A typical implementation device is a computer. Specifically, the computer may, for example, be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0217] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0218] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed, or inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0219] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

Claims

1. A data processing method, characterized in that, include: Divide the first set of objects participating in the task to be executed into multiple groups; For each group, based on the first execution information of the group members for the task to be executed, the second execution information of the group for the task to be executed is determined; as well as, Based on the second execution information of the group, the task rights of the task to be executed are issued to the group members.

2. The method according to claim 1, characterized in that, The first set of objects participating in the task to be executed is divided into multiple groups, including: For each object in the first object set, predict the expected task completion probability of the object based on the object characteristics; Based on the expected task completion probability of objects in the first object set, the first object set is divided into multiple groups.

3. The method according to claim 2, characterized in that, The first object set is divided into multiple groups based on the expected task completion probability of objects in the first object set, including: A specified number of objects are selected from the first object set to obtain a second account object set. The distribution characteristics of the expected task completion probability of the objects in the second object set are similar to the distribution characteristics of the expected task completion probability of the objects in the first object set. Using the objects in the second object set as initial cluster centers, the first object set is clustered based on the expected task completion probability of the objects in the first object set to obtain multiple groups.

4. The method according to claim 1, characterized in that, Before determining the second task execution information of the group for the task to be executed based on the first execution information of group members within the group for the task to be executed, the method further includes: Determine the roles of the group members within the group; Based on the roles of the group members, incentive information for the tasks to be performed is pushed to the group members.

5. The method according to claim 4, characterized in that, Determining the roles of group members includes: Based on the expected task completion probability and historical auxiliary task information of the group members, the role score of the group members is determined; The roles of the group members are determined based on their role scores.

6. The method according to claim 5, characterized in that, The historical auxiliary task information includes: the historical completion rate of the group members when performing historical auxiliary tasks of the first role, and the historical contribution coefficient of the group members to their respective historical groups when performing historical auxiliary tasks of the second role; The process of determining the role score of each group member based on their expected task completion probability and historical auxiliary task information includes: A first value is obtained by performing a first exponential calculation based on the expected task completion probability of the group members; A second exponential calculation is performed based on the historical completion rate to obtain a second value; Based on the first value, the second value, and the historical contribution coefficient, the role score of the group member is determined.

7. The method according to claim 1, characterized in that, The step of issuing task rights for the task to be executed to group members based on the second execution information of the group includes: Based on the second execution information of the group, determine the target number of benefits to be issued to the group; Based on the target number of rights, the task rights for the tasks to be performed are distributed to the group members of the group.

8. The method according to claim 7, characterized in that, The determination of the target number of benefits to be issued to the group based on the second execution information of the group includes: Based on the task overdue duration of objects in the first object set and the number of objects in the first object set, determine the first task non-compliance rate of the first object set; The second task failure rate of the group is determined based on the task overdue duration of the group members and the number of group members contained in the group; Based on the second execution information of the group, the first task failure rate, and the second task failure rate, the allocation coefficient of the group is determined; Based on the allocation coefficient and available equity quantity of the group, the target equity quantity to be issued to the group is determined.

9. The method according to claim 8, characterized in that, The determination of the allocation coefficient for the group based on the second task execution information, the first task failure rate, and the second task failure rate includes: A third index calculation is performed based on the second task execution information of the group to obtain a third score; Determine the difference between the first task failure rate and the second task failure rate, and perform a fourth index calculation based on the difference to obtain a fourth score; The allocation coefficient of the group is determined based on the ratio between the third score and the fourth score.

10. The method according to claim 7, characterized in that, The step of distributing the task rights for the pending task to the group members based on the target number of rights includes: When a group member receives the incentive information for the task to be executed, the allocation coefficient of the group member is determined based on the group member's response to the incentive information; Based on the allocation coefficient of the group members and the target equity quantity, the task equity of the task to be executed is issued to the group members.

11. The method according to claim 10, characterized in that, The incentive information corresponds to the role of the group member in the group; The determination of the allocation coefficient of the group members based on their response information to the incentive information includes: Based on the roles of the group members, determine the level coefficient of the group members; Based on the response information, the contribution coefficient of the group members is determined; The allocation coefficients for the group members are determined based on the product of the level coefficient and the contribution coefficient.

12. The method according to claim 8, characterized in that, Before determining the target number of benefits to be issued to the group based on the second execution information of the group, the method further includes: Based on the task overdue time of objects in the third object set and the number of objects in the third object set, the third task non-compliance rate of the third object set is determined; all objects in the third object set have the task to be executed, and the first object set includes objects in the third object set that meet the preset screening conditions; The number of available rights is determined based on the difference between the failure rate of the third task and the failure rate of the first task.

13. A data processing apparatus, characterized in that, include: Grouping unit, used to divide the first set of objects participating in the task to be executed into multiple groups; A determining unit is configured to, for each group, determine the second execution information of the group for the task to be executed based on the first execution information of the group members within the group for the task to be executed; The issuing unit is used to issue the task rights of the task to be executed to the group members based on the second execution information of the group.

14. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the data processing method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the data processing method as described in any one of claims 1 to 12.

16. A computer program product, characterized in that, The computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps in the data processing method as claimed in any one of claims 1 to 12.

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