CN114037349A

CN114037349A - Task scheduling control method and device, electronic equipment and storage medium

Info

Publication number: CN114037349A
Application number: CN202111429593.XA
Authority: CN
Inventors: 李傲梅; 孟繁姝; 刘杰; 芦捷; 王一鸣; 尹淏
Original assignee: Taikang Life Insurance Co ltd; Taikang Insurance Group Co Ltd
Current assignee: Taikang Life Insurance Co ltd; Taikang Insurance Group Co Ltd
Priority date: 2021-11-29
Filing date: 2021-11-29
Publication date: 2022-02-11

Abstract

The application discloses a task scheduling control method, a task scheduling control device, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object; acquiring basic information of an object sample in an object sample set corresponding to a target object, business information of the object sample completed in a set historical period, auxiliary task execution information and resource information obtained by executing the business information and the auxiliary task; performing service target scheduling on the target object according to the basic information, the service information, the resource information and the target resource information of the object sample to obtain a service target scheduling result; performing auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result; and determining the service target scheduling result and the auxiliary task target scheduling result as task scheduling results corresponding to the target object, and outputting the task scheduling results.

Description

Task scheduling control method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a task scheduling control method and apparatus, an electronic device, and a storage medium.

Background

Scientific and reasonable task scheduling is very important for professional development of enterprise employees, and the conventional task scheduling is generally set manually and autonomously. For example, an insurance company employee sets the income as 10 ten thousand dollars as the target to be completed in this year, and needs to complete the following tasks: (1) and (4) service class tasks: one business insurance 1 and 2 ten business insurance, wherein the business insurance 1 is an annual fund risk, and the business insurance 2 is a heavy disease risk; (2) activity class tasks: inviting 20 clients to visit the community, explaining the relevant information of serious illness for 100 clients, forwarding 500 business-related files of friend circles and the like. However, the task scheduling method is subjective, especially for employees with inexperienced and reasonable evaluation ability, the objective may be that the employees cannot complete the task due to non-conformity, and the unreasonable task scheduling may greatly affect the work efficiency of the employees, even eliminate the enthusiasm of the employees, affect the retention of the employees, and the efficiency of task scheduling by a manual setting method is low.

Disclosure of Invention

In order to solve the problems that task scheduling is unreasonable and planning efficiency is low easily caused by manual task scheduling in the prior art, embodiments of the present application provide a task scheduling control method and apparatus, an electronic device, and a storage medium.

In a first aspect, an embodiment of the present application provides a task scheduling control method, including:

receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object;

acquiring basic information of an object sample in an object sample set corresponding to the target object, business information of the object sample completed in a set historical period, auxiliary task execution information and resource information obtained by executing the business information and the auxiliary task;

performing service target scheduling on the target object according to the basic information of the object sample, the service information, the resource information and the target resource information to obtain a service target scheduling result;

performing auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result;

and determining the service target scheduling result and the auxiliary task target scheduling result as a task scheduling result corresponding to the target object, and outputting the task scheduling result.

In a possible implementation manner, performing service target scheduling on the target object according to the basic information of the object sample, the service information, the resource information, and the target resource information, to obtain a service target scheduling result, specifically includes:

classifying the object samples according to preset classification indexes to obtain each classified first object set;

for each first object set, determining a first service target scheduling model coefficient combination corresponding to the first object set according to the quantity information of each service corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample and a service target scheduling model;

aggregating the first service target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and determining a target classification number according to Euclidean distances between a new class generated after each clustering and other classes;

re-classifying the object samples in each first object set according to the clustering rule of the target classification number and the first service object scheduling model coefficient combination to obtain each classified second object set, and re-determining the second service object scheduling model coefficient combination corresponding to each second object set based on the service object scheduling model;

determining a target second object set to which the target object belongs, and traversing integer combinations of all service quantities according to the target resource information, a second service target scheduling model coefficient combination corresponding to the target second object set and the service target scheduling model to obtain a target service combination;

and selecting a group of target service combinations from all the target service combinations to obtain a service target scheduling result.

In one possible implementation, the service objective scheduling model is the following linear regression equation:

Y＝XB

for each first object set, determining a first service target scheduling model coefficient combination corresponding to the first object set according to the quantity information of each service corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample, and a service target scheduling model, specifically including:

for each first object set, calculating a first service target scheduling model coefficient combination corresponding to the first object set by the following formula:

B＝(X^TX)^-1XY

wherein Y is { Y ═ Y₁，y₂，…，y_nY is an n x 1 dimensional matrix, Y₁～y_nRepresenting resources corresponding to 1 st to n first object samples in the first object set, wherein n represents the number of the object samples in the first object set;

X＝{X₁₁，X₁₂…X_1S1，X₂₁，X₂₂…X_2S2，…，X_M1，X_M2…X_MSMis one

Dimension matrix, X_ij＝{x_ij,1，x_ij,2，……，x_ij,n}，X_ijIs an n x 1 dimensional matrix, x_ij,1～x_ij,nIndicating the number of jth sub-services in ith services corresponding to 1 st to nth first object samples in the first object set, wherein i is 1 to M, M indicates the number of types of services, and S_jRepresenting the number of the types of sub-services contained in the ith type of service;

b represents a first traffic target scheduling model coefficient combination corresponding to the first object set, B ═ β₁₁，β₁₂，…，β_1S1，β₂₁，β₂₂，…，β_2S2，…，β_M1，β_M2，…，β_MSMIs a

A dimension matrix.

In a possible implementation manner, traversing integer combinations of all service numbers according to the target resource information, a second service target scheduling model coefficient combination corresponding to the target second object set, and the service target scheduling model, to obtain a target service combination, specifically including:

and traversing the integer combination of all the service quantities through the following formula to obtain the target service combination:

wherein y represents a target resource of the target object;

B_Urepresenting a second service object scheduling model coefficient combination corresponding to the target second object set, B_U＝{β′₁₁，β′₁₂，…，β′_1S1，β₂₁，β′₂₂，…，β′_2S2，…，β′_M1，β′_M2，…，β′_MSM}；

X_URepresenting various sub-classes in various services corresponding to the target objectNumber parameter combinations of services, X_U＝{x₁₁，x₁₂，…，x_1S1，x₂₁，x₂₂，…，x_2S2，x_M1，x_M2，…，x_MSM}，x₁₁～x_MsMThe quantity parameter represents various sub-services in various services corresponding to the target object;

B_U*X_U＝β′₁₁×x₁₁+β′₁₂×x₁₂+…+β′_1S1×x_1S1+β′₂₁×x₂₁+β′₂₂×x₂₂+…+β′_2S2×x_2S2+…+β′_M1×x_M1+β′_M2×x_M2+…+β′_MSM×x_MSM，x₁₁～x_MSMthe number of the sub services is an integer which is greater than or equal to zero and less than or equal to the upper limit of the number of the corresponding sub services.

In a possible implementation manner, the auxiliary task execution information includes various types of auxiliary task execution times information;

performing auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result, which specifically comprises:

for each second object set, determining an auxiliary task target scheduling model coefficient combination corresponding to the second object set according to the quantity information of each service corresponding to each second object sample in the second object set, the execution times information of each auxiliary task corresponding to each second object sample and an auxiliary task target scheduling model;

and traversing integer combinations of execution times of all auxiliary tasks according to the target service combination, the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs and the auxiliary task target scheduling model to obtain a target auxiliary task combination.

In a possible implementation manner, traversing integer combinations of execution times of all auxiliary tasks according to the target service combination, an auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, and the auxiliary task target scheduling model, to obtain a target auxiliary task combination, specifically including:

traversing integer combinations of execution times of all auxiliary tasks according to the number of jth sub-services in ith services corresponding to the target object, auxiliary task target scheduling model coefficient combinations corresponding to a target second object set to which the target object belongs and the auxiliary task target scheduling model, wherein the jth sub-services are contained in the target service combinations, and the auxiliary task combinations corresponding to the jth sub-services in ith services corresponding to the target object are obtained;

and adding the execution times of the auxiliary tasks of the same type in the auxiliary task combination corresponding to each type of sub-service in each type of service corresponding to the target object respectively to obtain a target auxiliary task combination.

In one possible implementation, the auxiliary task target scheduling model is the following linear regression equation:

X_ij＝ZA_ij

for each second object set, determining an auxiliary task object scheduling model coefficient combination corresponding to the second object set according to the quantity information of each service corresponding to each second object sample in the second object set, the execution frequency information of each auxiliary task corresponding to each second object sample, and an auxiliary task object scheduling model, specifically including:

for each second object set, calculating the auxiliary task target scheduling model coefficient combination corresponding to the second object set by the following formula:

A_ij＝(Z^TZ)^-1ZX_ij

wherein Z ═ { Z ═ Z₁，Z₂，…，Z_kIs a matrix of dimensions r x k, Z₁～Z_kRepresenting the execution times of various auxiliary tasks corresponding to 1 st to r second object samples in the second object set, wherein k represents the number of the types of the auxiliary tasks;

X_ij＝{x_ij,1，x_ij,2，……，x_ij,r}，X_ijis an r x 1 dimensional matrix, x_ij,1～x_ij,rThe number of jth sub-services in ith services corresponding to 1 st to r th second object samples in the second object set is represented, i is 1 to M, and M represents the number of the types of the services;

A_ij＝{α_ij,1,α_ij,2,…,α_ij,kis a1 xk dimensional matrix, A_ijAnd representing the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

In a possible implementation manner, traversing an integer combination of execution times of all auxiliary tasks according to the number of jth sub-services in ith services corresponding to the target object included in the target service combination, an auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, and the auxiliary task target scheduling model, to obtain an auxiliary task combination corresponding to jth sub-services in ith services corresponding to the target object, specifically includes:

traversing the integer combinations of the execution times of all the auxiliary tasks through the following formula to obtain the auxiliary task combination corresponding to the jth sub-service in the ith service corresponding to the target object:

wherein x is_ijRepresenting the number of jth class sub-services in ith class services corresponding to the target object;

A_Uijthe auxiliary task object scheduling model coefficient combination corresponding to the target second object set representing the attribution of the target object A_Uij＝{α′_ij,1,α′_ij,2,…,α′_ij,k}；

Z_URepresenting the parameter combination of the execution times of various auxiliary tasks corresponding to the jth sub-service in the ith service corresponding to the target object, Z_U＝{z_ij,1,z_ij,2,…,z_ij,k}，z_ij,1～z_ij,kRepresenting the parameters of the execution times of 1-k auxiliary tasks corresponding to the jth sub-service in the ith service corresponding to the target object, k representing the number of the types of the auxiliary tasks, and z representing the number of the types of the auxiliary tasks_ij,1～z_ij,kThe number of the auxiliary tasks is an integer which is greater than or equal to zero and less than or equal to the upper limit of the execution times of each corresponding auxiliary task.

In one possible embodiment, the method further includes:

in the process that the target object executes the services and the auxiliary tasks in the task scheduling result, obtaining the estimated quantity of each first service according to the execution times of various uncompleted auxiliary tasks and the auxiliary task target scheduling model;

obtaining estimated first residual target resources according to the quantity of each first service and the service target scheduling model;

and if the difference value between the target resource and the first residual target resource is greater than a preset threshold value, performing task scheduling on the target object again.

In a second aspect, an embodiment of the present application provides a task scheduling control apparatus, including:

the system comprises a receiving unit, a processing unit and a processing unit, wherein the receiving unit is used for receiving a task scheduling control request which carries target resource information set by a target object;

an obtaining unit, configured to obtain basic information of an object sample in an object sample set corresponding to the target object, service information that the object sample completes within a set history period, auxiliary task execution information, and resource information obtained by executing the service information and the auxiliary task;

a service scheduling unit, configured to perform service target scheduling on the target object according to the basic information of the object sample, the service information, the resource information, and the target resource information, and obtain a service target scheduling result;

the auxiliary task scheduling unit is used for performing auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result;

and the task scheduling unit is used for determining the service target scheduling result and the auxiliary task target scheduling result as task scheduling results corresponding to the target object and outputting the task scheduling results.

In a possible implementation manner, the service scheduling unit is specifically configured to:

Y＝XB

the service scheduling unit is specifically configured to:

B＝(X^TX)^-1XY

A dimension matrix.

wherein y represents a target resource of the target object;

X_UA number parameter combination X representing various sub-services in various services corresponding to the target object_U＝{x₁₁，x₁₂，…，x_1S1，x₂₁，x₂₂，…，x_2S2，x_M1，x_M2，…，x_MSM}，x₁₁～x_MsMThe quantity parameter represents various sub-services in various services corresponding to the target object;

the auxiliary task scheduling unit is specifically configured to:

In a possible implementation manner, the auxiliary task scheduling unit is specifically configured to:

X_ij＝ZA_ij

the auxiliary task scheduling unit is specifically configured to:

A_ij＝(Z^TZ)^-1ZX_ij

wherein Z ═ { Z ═ Z₁，Z₂，…，Z_kIs a matrix of dimension r × k，Z₁～Z_kRepresenting the execution times of various auxiliary tasks corresponding to 1 st to r second object samples in the second object set, wherein k represents the number of the types of the auxiliary tasks;

Z_URepresenting the parameter combination of the execution times of various auxiliary tasks corresponding to the jth sub-service in the ith service corresponding to the target object, Z_U＝{z_ij,1,z_ij,2,…,z_ij,k}，z_ij,1～z_ij,kRepresenting 1 st to k th auxiliary corresponding to j th sub-service in i th service corresponding to the target objectA parameter of the number of times of executing the task, k representing the number of types of auxiliary tasks, z_ij,1～z_ij,kThe number of the auxiliary tasks is an integer which is greater than or equal to zero and less than or equal to the upper limit of the execution times of each corresponding auxiliary task.

In a possible implementation, the apparatus further includes:

a first obtaining unit, configured to obtain, according to the number of times of execution of various uncompleted auxiliary tasks and the auxiliary task target scheduling model, an estimated number of each first service in a process of executing the service and the auxiliary task in the task scheduling result by the target object;

a second obtaining unit, configured to obtain a pre-estimated first remaining target resource according to the number of each first service and the service target scheduling model;

and the processing unit is used for re-scheduling the task of the target object if the difference value between the target resource and the first residual target resource is greater than a preset threshold value.

In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the task scheduling control method described in the present application when executing the program.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the task scheduling control method described in the present application.

The beneficial effects of the embodiment of the application are as follows:

in the task scheduling control method, the device, the electronic device, and the storage medium provided in the embodiments of the present application, the server receives a task scheduling control request, where the task scheduling control request carries target resource information set by a target object, obtains basic information of a target sample in a target sample set corresponding to the target object, service information and auxiliary task execution information completed by the target sample in a set history period, and service information and resource information obtained by the auxiliary task corresponding to the execution of the target sample by the target sample, performs service target scheduling on the target object according to the basic information, the service information and the resource information of the target sample and the target resource information set by the target object, obtains a service target scheduling result, performs auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information, and obtains an auxiliary task target scheduling result, compared with the prior art, in the embodiment of the application, based on the service information, the auxiliary task execution information and the resource information obtained by executing the service information and the auxiliary task, which are completed by the object sample in the object sample set within the set historical time period, the service target scheduling and the auxiliary task target scheduling are automatically, accurately and comprehensively performed on the target object.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic flowchart illustrating an implementation process of a task scheduling control method according to an embodiment of the present application;

fig. 2 is a schematic diagram of an implementation flow of service target scheduling for a target object according to an embodiment of the present application;

fig. 3 is a clustering lineage diagram of a first object set provided in an embodiment of the present application;

fig. 4 is a schematic flow chart of implementation of performing auxiliary task target scheduling on a target object according to an embodiment of the present application;

fig. 5 is a schematic implementation flow diagram for monitoring task execution conditions of a target object according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a task scheduling control apparatus according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to solve the problems in the background art, embodiments of the present application provide a task scheduling control method and apparatus, an electronic device, and a storage medium.

The preferred embodiments of the present application will be described below with reference to the accompanying drawings of the specification, it should be understood that the preferred embodiments described herein are merely for illustrating and explaining the present application, and are not intended to limit the present application, and that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

The task scheduling control method provided by the embodiment of the application can be applied to a server and can also be applied to a terminal, wherein the server can be an independent physical server or a cloud server providing basic cloud computing services such as a cloud server, a cloud database, cloud storage and the like, and the terminal can be but is not limited to: the smart phone, the tablet computer, the notebook computer, the desktop computer, and the like are not limited in this application. In the present application, only the server is described as an execution subject.

As shown in fig. 1, which is a schematic view of an implementation flow of a task scheduling control method provided in an embodiment of the present application, the task scheduling control method may specifically include the following steps:

and S11, receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object.

In specific implementation, the server receives a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object, and the target resource information is recorded as target income information.

S12, acquiring basic information of an object sample in an object sample set corresponding to the target object, setting service information, auxiliary task execution information and resource information obtained by executing the service information and the auxiliary task, wherein the service information, the auxiliary task execution information and the resource information are completed by the object sample in a historical period.

In specific implementation, the object samples in the object sample set are the same type of objects as the target object, and can execute the same business task and auxiliary task, wherein the auxiliary task is an operation activity task for assisting in completing the business task, and the auxiliary task execution information is activity participation information. For example, in the insurance industry, the target object may be a certain insurance agent of a certain insurance company, the object sample in the object sample set may be other insurance agents of the insurance company that operate the same type of business task and activity task as the insurance agent, the business includes multiple categories (i.e. categories), the businesses of different categories each include multiple types of sub-businesses, for example, the businesses may be divided into: the various services can be divided into different sub-services according to different premium levels, for example, the annuity includes an annuity with a premium level of a1, an annuity with a premium level of a2 and an annuity with a premium level of a3, and the annuity with a premium level of a1, the annuity with a premium level of a2 and the annuity with a premium level of a3 are three sub-services corresponding to the annuity. Taking the business category as the annuity insurance as an example, the activity tasks for assisting in completing the annuity insurance task may include, for example: inviting the client to visit the community, explaining the annuity related information for the client, forwarding the annuity related file in a friend circle and other social software, and the like.

The resource information obtained by executing the service information and the auxiliary task is: and executing the business information and income information obtained by the auxiliary task.

The underlying information of the object sample may include, but is not limited to, the following information: age information, sex information, and the like of the target sample. The historical time period may be set by itself as needed, for example, if the task of the target object in the next year is planned, the historical time period may be the previous year, and if the task of the target object in the next month is planned, the historical time period may be the previous month, and the duration of the historical time period needs to correspond to the duration of the task scheduling time period corresponding to the target object. Taking a year as an example, if the task of the target object in the next year is planned, the server obtains basic information of each object sample in the object sample set, service information completed by the object sample in the previous year, auxiliary task execution information (i.e., activity participation information), and income information correspondingly obtained by completing the service information.

S13, performing service target scheduling on the target object according to the basic information, the service information, the resource information and the target resource information of the object sample to obtain a service target scheduling result.

In specific implementation, the server performs service target scheduling on the target object according to the basic information of the object sample, the quantity information and income information of each service contained in the service information and target income information set by the target object, and obtains a service target scheduling result.

Specifically, the business target scheduling may be performed on the target object according to the process shown in fig. 2, including the following steps:

s131, classifying the object samples according to preset classification indexes to obtain each classified first object set.

In specific implementation, the classification index may be preset, for example, the classification index may include: the age and sex may be classified into four categories, for example, under 25 years old, 26-35 years old, 36-45 years old, and over 45 years old, which are not limited in the examples of the present application.

Specifically, the server classifies the object samples in the object set according to preset classification indexes, and obtains each classified object set, which is recorded as a first object set.

In the specific implementation process, for each group under each classification index, if the number of object samples in the group is smaller than a preset threshold value, the group is not subdivided, otherwise, the group is continuously subdivided according to the next classification index until all the classification indexes are completely subdivided. Assume that there are 2 categorical indices: gender and age, there are 200 subjects in the set of subjects, and the subjects can be divided into two groups according to gender: male and female, if the number of the target samples with the gender of female is 20, and is less than the preset threshold (such as 30), the grouping can be directly performed according to the age without considering the gender, and the 200 target samples are divided into 4 groups of 30 persons under 25 years old, 30 persons 26-35 years old and 40 persons 36-45 years old and 50 persons over 45 years old, and 80 persons over 45 years old, namely 4 first target sets.

It should be noted that the classification indexes in the embodiment of the present application are not limited to the two types described above, and the object samples may be classified with a finer granularity by combining with other features (such as academic calendar, working life, and the like) of the object samples, which is not limited in the embodiment of the present application.

S132, aiming at each first object set, determining a first service target scheduling model coefficient combination corresponding to the first object set according to the quantity information of each service corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample and a service target scheduling model.

In specific implementation, for each first object set, the server determines, according to the quantity information of each service corresponding to each first object sample in the first object set, the revenue information corresponding to each first object sample, and the service target scheduling model, a first service target scheduling model coefficient combination corresponding to the first object set.

Specifically, a linear regression fitting may be performed on the relationship between the resource (i.e., revenue) of the target sample and the number of each service through a set service target scheduling model, and the service target scheduling model may be set as the following linear regression equation:

Y＝XB

the service target scheduling model coefficient combination B can be derived through the service target scheduling model, as shown in detail below:

B＝(X^TX)^-1XY

wherein Y is { Y ═ Y₁，y₂，…，y_nY is an n x 1 dimensional matrix, Y₁～y_nRepresenting resources (i.e. revenue) corresponding to 1 st to nth first object samples in the first object set, wherein n represents the number of object samples in the first object set;

A dimension matrix.

In this way, the first traffic objective scheduling model coefficient combination corresponding to each first object set can be calculated, and if there are P first object sets, the corresponding P first traffic objective scheduling model coefficient combinations B can be calculated₁～B_P. Taking the above example of dividing 200 object samples into 4 first object sets, 4 first service object scheduling model coefficient combinations B can be calculated₁～B₄。

On toolIn the implementation process, for example, the services may be divided into: three types of annual insurance, serious insurance and accident insurance, M is 3, and if the annual insurance service comprises a sub-service with a premium level of 4 types, the serious insurance service comprises a sub-service with a premium level of 5 types, and the accident insurance service comprises a sub-service with a premium level of 6 types, S is₁＝4，S₂＝5，S₃＝6。

S133, aggregating the first service target scheduling model coefficient combinations corresponding to the first object sets according to a hierarchical clustering algorithm, and determining the target classification number according to Euclidean distances between a new class generated after each clustering and other classes.

In specific implementation, the server may aggregate the first service target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, take the P first service target scheduling model coefficient combinations as P clustering samples, take the first service target scheduling model coefficient in each clustering sample as the feature of the clustering sample, calculate the euclidean distance between every two clustering samples, aggregate the two clusters with the smallest euclidean distance together to generate a new cluster, calculate the euclidean distances between the generated new cluster and the remaining P-1 clustering samples, aggregate the two clusters with the smallest euclidean distance together, and so on, aggregate the clustering samples into a corresponding number of clusters according to the target classification number, where the target classification number may be determined by: and determining the classification number corresponding to the maximum Euclidean distance between the new class generated after clustering and other classes as a target classification number. Still taking an example of dividing 200 object samples into 4 first object sets, the 4 first object sets are respectively identified as A, B, C, D, an object sample under 25 years old is included in group a, an object sample under 26-35 years old is included in group B, an object sample under 36-45 years old is included in group C, an object sample over 45 years old is included in group D, P is 4, and the first business target scheduling model coefficient combinations corresponding to the 4 first object sets A, B, C, D are respectively: b is₁～B₄The clustering pedigree of the first traffic objective scheduling model coefficient combination corresponding to the first object set A, B, C, D is shown in fig. 3, from bottom to top in fig. 3In the first layer, B₁And B₃Has the minimum Euclidean distance between them, and B₁And B₃Aggregate into a new class G1, assuming B₁And B₃Has a Euclidean distance of 1, and in the second layer, G1 and B₂The Euclidean distance between G1 and B is the minimum₂Converge into a new class G2, G1 and B₂Between the Euclidean distance of 3, G2 and B₄The euclidean distance between them is 1. Due to the fact that in B₁And B₃Between the Euclidean distance G1 and B₂Between the Euclidean distance G2 and B₄Among the Euclidean distances therebetween, the maximum Euclidean distance is G1 and B₂The euclidean distance between, i.e.: 3, aggregating the first object sets into G1 and B₂After polymerization, i.e.no further polymerization, G1 and B₂After polymerization, are classified into class 2, B₁、B₂、B₃Are divided into one class, B₄The classification is a class, namely the number of target classes is 2.

It should be noted that, in the embodiment of the present application, the euclidean distance between every two cluster samples may also be a distance between every two cluster samples, which is not limited in the embodiment of the present application.

S134, re-classifying the object samples in each first object set according to the clustering rule of the combination of the target classification number and the first business target scheduling model coefficient to obtain each classified second object set, and re-determining the second business target scheduling model coefficient combination corresponding to each second object set based on the business target scheduling model.

In specific implementation, the server re-divides the object samples in each first object set into the corresponding number of second object sets according to the target classification number according to the clustering rule of the first service target scheduling model coefficient combination in step S133.

Continuing the above example, the number of target classes is 2, and the clustering rule of the first service target scheduling model coefficient combination is: b is₁、B₂、B₃Are divided into one class, B₄Are divided into one class, B₁、B₂、B₃Respectively corresponding to the first set of objects A, B, C, B₄Corresponding to D, thenAccordingly, the object samples in A, B, C are classified into one group, and the object samples in D are classified into one group, that is, the object samples under the age of 25, the object samples under the age of 26 to 35, and the object samples under the age of 36 to 45 are classified into one group, and the object samples over the age of 45 are classified into one group, so that two object sample sets are newly generated and are marked as a second object sample set.

Furthermore, the server re-determines the second service target scheduling model coefficient combination corresponding to each second object set based on the service target scheduling model, and the calculation mode of each second service target scheduling model coefficient combination refers to the calculation mode of the first service target scheduling model coefficient combination, which is not described herein again.

S135, determining a target second object set to which the target object belongs, and traversing integer combinations of all service quantities according to the target resource information, a second service target scheduling model coefficient combination corresponding to the target second object set and a service target scheduling model to obtain a target service combination.

In a specific implementation, the second object set to which the target object belongs is determined according to the classification index, for example, in the above example, the classification index is an age, and assuming that the age of the target object is before 25 to 45 years old, the target second object set to which the target object belongs is the second object set composed of object samples of 25 to 45 years old, and assuming that the age of the target object is greater than 45 years old, the target second object set to which the target object belongs is the second object set composed of object samples of 45 years old or older. And traversing integer combinations of all the service quantities according to preset target income information, a second service target scheduling model coefficient combination corresponding to the target second object set and a service target scheduling model to obtain a target service combination.

Specifically, the target service combination may be obtained by traversing the integer combination of all the service quantities through the following formula:

wherein y represents a target resource (i.e., target revenue) of the target object;

Thus, all possible x are solved₁₁，x₁₂，…，x_1S1，x₂₁，x₂₂，…，x_2S2，x_M1，x_M2，…，x_MSMCombinations of (a) and (b).

Where ω is a preset allowable error, which may be, for example, 5%, and x is not limited in the embodiments of the present application₁₁The upper limit of the number of the 1 st type sub-services in the 1 st type services is more than or equal to 0 and less than or equal to x₁₂Upper limit of the number of class 2 sub-services in class 1 service greater than or equal to 0, …, and so on, x_MSM0 or more and 0 or lessAnd the upper limit of the number of SM class sub-services in the M class services.

x₁₁～x_MsMThe upper limit of the number of each sub-service in each corresponding service can be determined by the following method:

and counting the number of the sub-services finished by each object sample in the target second object set aiming at each sub-service in each class of service, and defining a set quantile as a standard as the upper limit of the number of the sub-services in the class of service. The setting quantile can be set by itself, for example, the setting quantile can be set to be 90% quantile, which is not set in the embodiment of the present application. Continuing with the above example, assuming that the target second object set is a second object set composed of object samples over 45 years old, and contains 80 object samples in total, the upper limit of the number of the first type sub-services in the first type service may be: these 80 object samples each complete a 90% quantile of the number of first type sub-services in the first type of service.

S136, selecting a group of target service combinations from the target service combinations to obtain a service target scheduling result.

In specific implementation, a group of target service combinations can be selected from each target service combination as a service target scheduling result.

In order to better fit the task execution habit of the target object, in one possible implementation, the target object may be screened according to the business task execution conditions of the target object in the same set historical period, and the target business combination with the closest business proportion is selected, for example, in the set history period, the ratio of the number of sub-services of the class A insurance service with the premium level A1 to the number of sub-services of the class A2 is 1:1, the obtained target service combination comprises a target service combination with 3A 1 sub-services and 5A 2 sub-services, and also comprises a target service combination with 5A 1 sub-services and 5A 2 sub-services, a target service combination including 5 a1 sub-services and 5 a2 sub-services may be selected as a final service target scheduling result.

In the embodiment of the application, because the characteristics (age, sex, academic calendar, working life, and the like) of the object samples are more, P is often larger, which results in excessive classification, and there are a plurality of business types, each business type establishes a relationship with various auxiliary tasks, the growth is proportional, more modeling samples are also required, the whole calculation is more complicated, and the relationships between the target income and the number of completed businesses of a plurality of object samples with different attributes are similar, so that more models do not need to be established, therefore, P first object sets are generated after one classification according to the characteristics of the object samples, that is, P object samples are generated, clustering is performed according to the regression coefficients (that is, business target scheduling model coefficients) corresponding to each class of object samples, and the P class of object samples are aggregated into Q class according to the regression coefficients corresponding to various classes of object samples, the Q second object sets are adopted, so that the modeling and calculation complexity is reduced, and meanwhile, the reasonability and the accuracy of the service target scheduling and the auxiliary task target scheduling of the target object according to the information of the various clustered object samples can be ensured.

And S14, performing auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result.

In specific implementation, the auxiliary task execution information comprises various auxiliary task execution frequency information (namely, the activity participation information comprises various activity participation frequency information), and the server performs activity target scheduling on the target object according to the service target scheduling result and the various activity participation frequency information contained in the activity participation information to obtain an activity target scheduling result.

In specific implementation, performing auxiliary task target scheduling on a target object according to the flow shown in fig. 4 may specifically include the following steps:

and S141, aiming at each second object set, determining an auxiliary task target scheduling model coefficient combination corresponding to the second object set according to the quantity information of each service corresponding to each second object sample in the second object set, the execution times information of each auxiliary task corresponding to each second object sample and an auxiliary task target scheduling model.

In specific implementation, for each second object set, the server determines, according to the quantity information of each service corresponding to each second object sample in the second object set, the activity participation frequency information of each type corresponding to each second object sample, and the auxiliary task target scheduling model, an auxiliary task target scheduling model coefficient combination corresponding to the second object set.

Specifically, the auxiliary task target scheduling model may be set as the following linear regression equation: x_ij＝ZA_ijAnd the coefficient combination A of the auxiliary task target scheduling model can be deduced according to the equation_ijSpecifically, for each second object set, calculating the auxiliary task target scheduling model coefficient combination corresponding to the second object set by the following formula:

A_ij＝(Z^TZ)^-1ZX_ij

wherein Z ═ { Z ═ Z₁，Z₂，…，Z_kIs a matrix of dimensions r x k, Z₁～Z_kRepresenting the execution times of various types of auxiliary tasks (namely, the activity participation times of various types) corresponding to 1 st to r th second object samples in the second object set, k representing the type quantity of the auxiliary tasks (namely, the type quantity of the activities), and r representing the quantity of the second object samples in the second sample set;

Dimension matrix, S_jIndicates the number of sub-services, X, included in the i-th service class_ij＝{x_ij,1，x_ij,2，……，x_ij,r}，X_ijIs an r x 1 dimensional matrix, x_ij,1～x_ij,rThe number of jth sub-services in ith services corresponding to 1 st to r th second object samples in the second object set is represented, i is 1 to M, and M represents the number of the types of the services;

Assuming that the number of the second object sample sets is Q, it can be calculated

And combining the coefficients of the auxiliary task target scheduling model.

And S142, traversing integer combinations of execution times of all auxiliary tasks according to the auxiliary task target scheduling model coefficient combinations and the auxiliary task target scheduling models corresponding to the target service combinations, the target second object sets to which the target objects belong, and obtaining target auxiliary task combinations.

In specific implementation, the server traverses integer combinations of execution times (namely, activity participation times) of all auxiliary tasks according to the number of jth sub-services in ith services corresponding to the target objects contained in the target service combinations, the auxiliary task target scheduling model coefficient combinations corresponding to target second object sets to which the target objects belong and the auxiliary task target scheduling models, and obtains auxiliary task combinations (namely, activity combinations) corresponding to jth sub-services in ith services corresponding to the target objects.

Specifically, the integer combination of the execution times of all the auxiliary tasks may be traversed by the following formula to obtain the auxiliary task combination corresponding to the jth sub-service in the ith service corresponding to the target object:

Z_URepresenting various auxiliary task execution times parameter combinations (namely various activity participation time parameter combinations) corresponding to the jth sub-service in the ith service corresponding to the target object, and Z_U＝{z_ij,1,z_ij,2,…,z_ij,k}，z_ij,1～z_ij,kRepresenting the number of times of executing the 1 st to k th auxiliary tasks (namely, the parameters of the activity participation times) corresponding to the jth sub-service in the ith service corresponding to the target object, k representing the number of the types of the auxiliary tasks (namely, the activities), and z_ij,1～z_ij,kThe number of times of execution of each type of auxiliary task (i.e. the number of times of participation of each type of activity) is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times of execution of each corresponding type of auxiliary task (i.e. the number of times of participation of each type of activity).

Wherein z is_ij,1～z_ij,kThe upper limit of the participation times of each corresponding type of activities can be determined by the following method:

and counting the participation times of each object sample in the target second object set to the type of activity according to the participation times of each type of activity corresponding to each sub-service in each type of service, and defining a set quantile serving as a standard as an upper limit of the participation times of the type of activity corresponding to the type of sub-service in the type of service.

Further, the execution times (i.e. activity participation times) of the auxiliary tasks of the same kind in the auxiliary task combinations (i.e. activity combinations) corresponding to the sub-services of each kind in the various kinds of services corresponding to the target object are added respectively to obtain the target auxiliary task combination (i.e. target activity combination).

And S15, determining the service target scheduling result and the auxiliary task target scheduling result as task scheduling results corresponding to the target object, and outputting the task scheduling results.

In specific implementation, the service target scheduling result and the activity target scheduling result are determined as task scheduling control results corresponding to the target object, and the task scheduling results are output, so that the target object executes the services in the target service combination and the activities in the target activity combination.

In a possible implementation manner, in this embodiment of the application, in the process of executing the service and the auxiliary task (i.e., the activity) in the task scheduling control result by the target object, the execution condition of the target object may also be monitored and tracked, and the task execution condition of the target object is monitored according to the flow shown in fig. 5, which may include the following steps:

and S21, obtaining the estimated quantity of each first service according to the execution times of various unfinished auxiliary tasks and the auxiliary task target scheduling model in the process of executing the services and the auxiliary tasks in the task scheduling result by the target object.

In specific implementation, the server may substitute, in the process of executing the service and the activity in the task scheduling result by the target object, the number of participation times of each type of currently unfinished activity into the auxiliary task target scheduling model according to a preset time period, and estimate the number of each type of sub-service in each corresponding service (which may be denoted as a first service), where the preset time period may be set according to needs, for example, the set time period is set to one year, and the preset time period may be set to 1 month.

And S22, obtaining the estimated first residual target resource according to the quantity of each first service and the service target scheduling model.

During specific implementation, the server substitutes the number of various sub-services in each first service into the service target scheduling model, and calculates to obtain the estimated first remaining target income.

And S23, if the difference value between the target resource and the first residual target resource is larger than the preset threshold value, re-scheduling the task for the target object.

In specific implementation, the server calculates a difference between a preset target income and a first estimated remaining target income, determines that the target object cannot complete the remaining tasks within the remaining time if the difference is greater than a preset threshold, and reschedules the task on the target object according to the task scheduling control method provided in steps S11 to S15, where the preset threshold may be set by itself as needed, and the embodiment of the present application does not limit this.

Therefore, the possibility of achieving the target income can be evaluated in time according to the tracking condition of the target object on the task execution condition, the target object can be reminded in time, the situation that the target object cannot be achieved due to unreasonable target income setting is avoided, and the target object is enabled to set reasonable target income so as to improve the working efficiency.

As an example, assuming that a target income of an insurance agent in the current year is set to be a ten thousand yuan, a task scheduling result determined by the task scheduling control method according to the present application is: and 2 heavy insurance policies of 1 ten thousand yuan and 5 annual insurance policies of 10 ten thousand yuan are recommended to be completed, and the completion condition of the insurance agent can be regularly monitored every month by visiting 20 clients and inviting the clients to participate in 100 parturient activities, and if 5 months are found, the insurance agent can not complete the target, and reasonable target income recommendation is prompted to reschedule the task of the insurance agent.

In the task scheduling control method provided by the embodiment of the application, a server receives a task scheduling control request, the task scheduling control request carries target resource information set by a target object, basic information of the target sample in a target sample set corresponding to the target object, service information and auxiliary task execution information completed by the target sample in a set historical period, service information and auxiliary task execution information corresponding to the execution of the target sample and resource information obtained by an auxiliary task are obtained, service target scheduling is performed on the target object according to the basic information, the service information and the resource information of the target sample and the target resource information set by the target object, a service target scheduling result is obtained, auxiliary task target scheduling is performed on the target object according to the service target scheduling result and the auxiliary task execution information, an auxiliary task target scheduling result is obtained, and the service target scheduling result and the auxiliary task target scheduling result are determined as task scheduling corresponding to the target object Compared with the prior art, in the embodiment of the application, the service target scheduling and the auxiliary task target scheduling are automatically, accurately and comprehensively performed on the target object based on the service information, the auxiliary task execution information and the resource information obtained by executing the service information and the auxiliary task, which are completed by the target sample in the set historical period, and the resource condition obtained by executing the task and executing the task of the target sample in the set historical period is referred, so that the task scheduled for the target object is more reasonable, manual setting is not needed, and the task scheduling efficiency is improved.

Based on the same inventive concept, embodiments of the present application further provide a task scheduling control device, and because the principle of the task scheduling control device for solving the problem is similar to the task scheduling control method, the implementation of the device can refer to the implementation of the method, and repeated details are not described again.

As shown in fig. 6, which is a schematic structural diagram of a task scheduling control apparatus provided in the embodiment of the present application, the task scheduling control apparatus may include:

a receiving unit 31, configured to receive a task scheduling control request, where the task scheduling control request carries target resource information set by a target object;

an obtaining unit 32, configured to obtain basic information of an object sample in an object sample set corresponding to the target object, service information that the object sample completes in a set history period, auxiliary task execution information, and resource information obtained by executing the service information and the auxiliary task;

a service scheduling unit 33, configured to perform service target scheduling on the target object according to the basic information of the object sample, the service information, the resource information, and the target resource information, so as to obtain a service target scheduling result;

an auxiliary task scheduling unit 34, configured to perform auxiliary task target scheduling on the target object according to the service target scheduling result and the auxiliary task execution information, so as to obtain an auxiliary task target scheduling result;

and the task scheduling unit 35 is configured to determine the service target scheduling result and the auxiliary task target scheduling result as task scheduling results corresponding to the target object, and output the task scheduling results.

In a possible implementation manner, the service scheduling unit 33 is specifically configured to:

Y＝XB

the service scheduling unit 33 is specifically configured to:

B＝(X^TX)^-1XY

A dimension matrix.

wherein y represents a target resource of the target object;

B_Urepresents the object ofSecond service object scheduling model coefficient combination corresponding to the two object sets, B_U＝{β′₁₁，β′₁₂，…，β′_1S1，β₂₁，β′₂₂，…，β′_2S2，…，β′_M1，β′_M2，…，β′_MSM}；

the auxiliary task scheduling unit 34 is specifically configured to:

In a possible implementation manner, the auxiliary task scheduling unit 34 is specifically configured to:

X_ij＝ZA_ij

the auxiliary task scheduling unit 34 is specifically configured to:

A_ij＝(Z^TZ)^-1ZX_ij

X_ij＝{x_ij,1，x_ij,2，……，x_ij,r}，X_ijis an r x 1 dimensional matrix, x_ij,1～x_ij,rRepresenting the ith type service corresponding to the 1 st to the r th second object samples in the second object setThe number of the jth sub-service, i is 1-M, and M represents the number of the types of the services;

In a possible implementation, the apparatus further includes:

Based on the same technical concept, an embodiment of the present application further provides an electronic device 400, and referring to fig. 7, the electronic device 400 is configured to implement the task scheduling control method described in the foregoing method embodiment, where the electronic device 400 of this embodiment may include: a memory 401, a processor 402 and a computer program, such as a task scheduling control program, stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in the above-described respective embodiments of the task scheduling control method, such as step S11 shown in fig. 1. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above-described device embodiments, for example, 31.

The embodiment of the present application does not limit the specific connection medium between the memory 401 and the processor 402. In the embodiment of the present application, the memory 401 and the processor 402 are connected by the bus 403 in fig. 7, the bus 403 is represented by a thick line in fig. 7, and the connection manner between other components is merely illustrative and is not limited thereto. The bus 403 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.

The memory 401 may be a volatile memory (volatile memory), such as a random-access memory (RAM); the memory 401 may also be a non-volatile memory (non-volatile memory) such as, but not limited to, a read-only memory (rom), a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD), or the memory 401 may be any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 401 may be a combination of the above memories.

The processor 402 is configured to implement a task scheduling control method shown in fig. 1, and includes:

the processor 402 is used for calling the computer program stored in the memory 401 to execute the steps S11-S15 shown in fig. 1.

The embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions required to be executed by the processor, and includes a program required to be executed by the processor.

In some possible embodiments, various aspects of the task scheduling control method provided by the present application may also be implemented in the form of a program product including program code for causing an electronic device to perform the steps in the task scheduling control method according to various exemplary embodiments of the present application described above in this specification when the program product runs on the electronic device, for example, the electronic device may perform steps S11 to S15 shown in fig. 1.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A task scheduling control method is characterized by comprising the following steps:

2. The method of claim 1, wherein performing service object scheduling on the target object according to the basic information of the object sample, the service information, the resource information, and the target resource information to obtain a service object scheduling result specifically comprises:

3. The method of claim 2, wherein the traffic objective scheduling model is the following linear regression equation:

Y＝XB

B＝(X^TX)^-1XY

A dimension matrix.

4. The method of claim 2, wherein traversing integer combinations of all service quantities according to the target resource information, a second service target scheduling model coefficient combination corresponding to the target second object set, and the service target scheduling model to obtain a target service combination, specifically comprises:

wherein y represents a target resource of the target object;

B_Uto representA second service object scheduling model coefficient combination corresponding to the object second object set, B_U＝{β′₁₁，β′₁₂，…，β′_1S1，β₂₁，β′₂₂，…，β′_2S2，…，β′_M1，β′_M2，…，β′_MSM}；

5. The method according to claim 2 or 4, wherein the auxiliary task execution information includes various types of auxiliary task execution times information;

6. The method of claim 5, wherein traversing an integer combination of execution times of all auxiliary tasks according to the target service combination, an auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, and the auxiliary task target scheduling model to obtain a target auxiliary task combination specifically includes:

7. The method of claim 5, wherein the auxiliary task target scheduling model is a linear regression equation:

X_ij＝ZA_ij

A_ij＝(Z^TZ)^-1ZX_ij

8. The method according to claim 6, wherein traversing an integer combination of execution times of all auxiliary tasks according to a number of jth sub-services in ith services corresponding to the target object included in the target service combination, an auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, and the auxiliary task target scheduling model, to obtain an auxiliary task combination corresponding to jth sub-services in ith services corresponding to the target object, specifically includes:

9. The method of claim 5, further comprising:

10. A task scheduling control apparatus, comprising:

11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the task scheduling control method according to any one of claims 1 to 9 when executing the program.

12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for controlling the scheduling of tasks according to any one of claims 1 to 9.