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

Abstract

The present application discloses a task scheduling control method, device, electronic device and storage medium, the method comprising: receiving a task scheduling control request, the task scheduling control request carrying target resource information set for a target object; obtaining basic information of an object sample in an object sample set corresponding to the target object, business information completed by the object sample within a set historical period, auxiliary task execution information, and resource information obtained by executing the business information and the auxiliary task; performing business target scheduling on the target object according to the basic information, business information, resource information and target resource information of the object sample, and obtaining a business target scheduling result; performing auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information, and obtaining an auxiliary task target scheduling result; determining the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and outputting the task scheduling result.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a task scheduling control method, device, electronic device and storage medium. Background Art

[0002] Scientific and reasonable task scheduling is very important for the career development of corporate employees. The existing task scheduling is generally set manually. For example, an employee of an insurance company sets a target income of 100,000 yuan to be achieved this year, and needs to complete the following tasks: (1) Business tasks: one business insurance 1 and ten business insurance 2, among which business insurance 1 is annuity insurance and business insurance 2 is critical illness insurance; (2) Activity tasks: invite 20 customers to visit the community, explain critical illness related information to 100 customers, forward 500 business-related copywriting to WeChat Moments, etc. However, this task scheduling method is relatively subjective, especially for employees who lack experience and reasonable evaluation ability. This goal may be unrealistic and make them unable to complete the task. Unreasonable task scheduling will greatly affect the work efficiency of employees, and even discourage employees' enthusiasm and affect employee retention. In addition, the efficiency of task scheduling through manual setting is low. Summary of the invention

[0003] In order to solve the problem in the prior art that task scheduling performed in a manually set manner easily leads to unreasonable task scheduling and low planning efficiency, the embodiments of the present application provide a task scheduling control method, device, electronic device and storage medium.

[0004] In a first aspect, an embodiment of the present application provides a task scheduling control method, comprising:

[0005] receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object;

[0006] Obtaining basic information of object samples in an object sample set corresponding to the target object, business information completed by the object samples within a set historical period, auxiliary task execution information, and resource information obtained by executing the business information and the auxiliary tasks;

[0007] Performing business target scheduling on the target object according to the basic information of the object sample, the business information, the resource information and the target resource information to obtain a business target scheduling result;

[0008] Perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information to obtain the auxiliary task target scheduling result;

[0009] The business target scheduling result and the auxiliary task target scheduling result are determined as the task scheduling result corresponding to the target object, and the task scheduling result is output.

[0010] In a possible implementation, performing business target scheduling on the target object according to the basic information of the object sample, the business information, the resource information, and the target resource information to obtain a business target scheduling result specifically includes:

[0011] Classify the object samples according to a preset classification index to obtain each first object set after classification;

[0012] For each first object set, determining a first business target scheduling model coefficient combination corresponding to the first object set according to quantity information of each business corresponding to each first object sample in the first object set, resource information corresponding to each first object sample, and a business target scheduling model;

[0013] Aggregate the first business target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and determine the number of target classifications according to the Euclidean distance between the new class generated after each clustering and other classes;

[0014] Reclassifying the object samples in each of the first object sets according to the clustering rule of the target classification number and the first business objective scheduling model coefficient combination to obtain each of the classified second object sets, and re-determining the second business objective scheduling model coefficient combination corresponding to each of the second object sets based on the business objective scheduling model;

[0015] Determine a target second object set to which the target object belongs, and traverse all integer combinations of business quantities according to the target resource information, a combination of coefficients of a second business target scheduling model corresponding to the target second object set, and the business target scheduling model to obtain a target business combination;

[0016] A set of target business combinations is selected from each target business combination to obtain a business target scheduling result.

[0017] In a possible implementation, the business target scheduling model is the following linear regression equation:

[0018] Y=XB

[0019] For each first object set, determining a first business target scheduling model coefficient combination corresponding to the first object set according to quantity information of each business corresponding to each first object sample in the first object set, resource information corresponding to each first object sample, and a business target scheduling model, specifically includes:

[0020] For each first object set, the first service objective scheduling model coefficient combination corresponding to the first object set is calculated by the following formula:

[0021] B=(X T X) -1 XY

[0022] Where Y = {y 1 ,y 2 , …, y n}, Y is an n×1 dimensional matrix, y 1 ~y n represents resources corresponding to the 1st to nth first object samples in the first object set, where n represents the number of object samples in the first object set;

[0023] X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM}, X is a dimensional matrix, X ij ={x ij,1 , x ij,2 , ..., x ij,n}, X ij is an n×1 dimensional matrix, x ij,1 ~x ij,n represents the number of j-th sub-services in the i-th service corresponding to the first to n-th first object samples in the first object set, i=1-M, M represents the number of service types, S j Indicates the number of sub-businesses included in the i-th business category;

[0024] B represents the first service target scheduling model coefficient combination corresponding to the first object set, B = {β 11 , β 12 , …, β 1S1 , β 21 , β 22 , …, β 2S2 , …, β M1 , β M2 , …, β MSM}, B is a Dimensional matrix.

[0025] In a possible implementation, according to the target resource information, the second service target scheduling model coefficient combination corresponding to the target second object set, and the service target scheduling model, traversing all service quantity integer combinations to obtain a target service combination specifically includes:

[0026] The target business combination is obtained by traversing all integer combinations of business quantities through the following formula:

[0027]

[0028] Wherein, y represents the target resource of the target object;

[0029] B U represents the second business target scheduling model coefficient combination corresponding to the target second object set, B U ={β′ 11 , β′ 12 ,…,β′ 1S1 , β 21 , β′ 22 ,…,β′ 2S2 ,…,β′ M1 , β′ M2 ,…,β′ MSM};

[0030] X U Indicates the quantity parameter combination of each sub-service in each service corresponding to the target object, X U ={x 11 , x 12 , …, x 1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM}, x 11 ~x MsM Indicates the number parameters of various sub-services in various services corresponding to the target object;

[0031] B U *X U =β′ 11 × 11 +β′ 12 × 12 +…+β′ 1S1 × 1S1 +β′ 21 × 21 +β′ 22 × 22 +…+β′ 2S2 × 2S2 +…+β′M1 × M1 +β′ M2 × M2 +…+β′ MSM × MSM , x 11 ~x MSM It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of each corresponding sub-service.

[0032] In a possible implementation manner, the auxiliary task execution information includes execution frequency information of various types of auxiliary tasks;

[0033] The auxiliary task target scheduling is performed on the target object according to the business target scheduling result and the auxiliary task execution information to obtain the auxiliary task target scheduling result, specifically including:

[0034] For each second object set, determining a combination of auxiliary task target scheduling model coefficients corresponding to the second object set according to quantity information of each business corresponding to each second object sample in the second object set, execution times information of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model;

[0035] According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination.

[0036] In a possible implementation, according to the target business 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, traversing the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination specifically includes:

[0037] According to the number of the jth sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the jth sub-business in the i-th business corresponding to the target object;

[0038] The execution times of the same type of auxiliary tasks in the auxiliary task combinations corresponding to various sub-businesses in various businesses corresponding to the target object are added up respectively to obtain the target auxiliary task combination.

[0039] In a possible implementation, the auxiliary task target scheduling model is the following linear regression equation:

[0040] X ij =ZA ij

[0041] For each second object set, according to the quantity information of each business corresponding to each second object sample in the second object set, the number of execution times of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model, a coefficient combination of the auxiliary task target scheduling model corresponding to the second object set is determined, specifically including:

[0042] For each second object set, the auxiliary task target scheduling model coefficient combination corresponding to the second object set is calculated by the following formula:

[0043] A ij =(Z T Z) -1 ZX ij

[0044] Where Z = {Z 1 , Z 2 , …, Z k} is an r×k dimensional matrix, Z 1 ~Z k represents the number of times each type of auxiliary task is executed corresponding to the 1st to rth second object samples in the second object set, and k represents the number of types of auxiliary tasks;

[0045] X ij ={x ij,1 , x ij,2 , ..., x ij,r}, X ij is an r×1 dimensional matrix, x ij,1 ~x ij,r represents the number of j-th sub-services in the i-th service corresponding to each of the 1st to r-th second object samples in the second object set, i=1-M, M represents the number of service types;

[0046] A ij ={α ij,1 ,α ij,2 ,…,α ij,k} is a 1×k dimensional matrix, A ij Represents the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

[0047] In a possible implementation, according to the number of the jth sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the jth sub-business in the i-th business corresponding to the target object, specifically including:

[0048] The following formula is used to traverse the integer combination of the number of times all auxiliary tasks are executed to obtain the auxiliary task combination corresponding to the j-th sub-service in the i-th service corresponding to the target object:

[0049]

[0050] Among them, x ij Indicates the number of j-th type of sub-services in the i-th type of service corresponding to the target object;

[0051] A Uij A represents the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, Uij ={α′ ij,1 ,α′ ij,2 ,…,α′ ij,k};

[0052] Z U represents the execution times parameter combination of various auxiliary tasks corresponding to the j-th sub-service in the i-th service corresponding to the target object, Z U ={z ij,1 ,z ij,2 ,…,z ij,k}, z ij,1 ~z ij,k represents the execution times parameter of the auxiliary tasks of the 1st to kth types corresponding to the jth type of sub-service in the ith type of service corresponding to the target object, k represents the number of types of auxiliary tasks, z ij,1 ~z ij,k It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times the corresponding auxiliary tasks are executed.

[0053] In a possible implementation, the method further includes:

[0054] In the process of the target object executing the services and auxiliary tasks in the task scheduling result, obtaining an estimated number of each first service according to the number of executions of each type of unfinished auxiliary tasks and the auxiliary task target scheduling model;

[0055] Obtaining an estimated first remaining target resource according to the quantity of each first service and the service target scheduling model;

[0056] If the difference between the target resource and the first remaining target resource is greater than a preset threshold, the task of the target object is rescheduled.

[0057] In a second aspect, an embodiment of the present application provides a task scheduling control device, including:

[0058] A receiving unit, configured to receive a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object;

[0059] An acquisition unit, used to acquire basic information of object samples in an object sample set corresponding to the target object, business information completed by the object samples within a set historical period, auxiliary task execution information, and resource information obtained by executing the business information and the auxiliary tasks;

[0060] 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;

[0061] An auxiliary task scheduling unit, used to perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information, and obtain an auxiliary task target scheduling result;

[0062] The task scheduling unit is used to determine the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and output the task scheduling result.

[0063] In a possible implementation manner, the service scheduling unit is specifically configured to:

[0064] Classify the object samples according to a preset classification index to obtain each first object set after classification;

[0065] For each first object set, determining a first business target scheduling model coefficient combination corresponding to the first object set according to quantity information of each business corresponding to each first object sample in the first object set, resource information corresponding to each first object sample, and a business target scheduling model;

[0066] Aggregate the first business target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and determine the number of target classifications according to the Euclidean distance between the new class generated after each clustering and other classes;

[0067] Reclassifying the object samples in each of the first object sets according to the clustering rule of the target classification number and the first business objective scheduling model coefficient combination to obtain each of the classified second object sets, and re-determining the second business objective scheduling model coefficient combination corresponding to each of the second object sets based on the business objective scheduling model;

[0068] Determine a target second object set to which the target object belongs, and traverse all integer combinations of business quantities according to the target resource information, a combination of coefficients of a second business target scheduling model corresponding to the target second object set, and the business target scheduling model to obtain a target business combination;

[0069] A set of target business combinations is selected from each target business combination to obtain a business target scheduling result.

[0070] In a possible implementation, the business target scheduling model is the following linear regression equation:

[0071] Y=XB

[0072] The service scheduling unit is specifically used for:

[0073] For each first object set, the first service objective scheduling model coefficient combination corresponding to the first object set is calculated by the following formula:

[0074] B=(X T X) -1 XY

[0075] Where Y = {y 1 ,y 2 , …, y n}, Y is an n×1 dimensional matrix, y 1 ~y n represents resources corresponding to the 1st to nth first object samples in the first object set, where n represents the number of object samples in the first object set;

[0076] X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM}, X is a dimensional matrix, X ij ={x ij,1 , x ij,2 , ..., x ij,n}, X ij is an n×1 dimensional matrix, xij,1 ~x ij,n represents the number of j-th sub-services in the i-th service corresponding to the first to n-th first object samples in the first object set, i=1-M, M represents the number of service types, S j Indicates the number of sub-businesses included in the i-th business category;

[0077] B represents the first service target scheduling model coefficient combination corresponding to the first object set, B = {β 11 , β 12 , …, β 1S1 , β 21 , β 22 , …, β 2S2 , …, β M1 , β M2 , …, β MSM}, B is a Dimensional matrix.

[0078] In a possible implementation manner, the service scheduling unit is specifically configured to:

[0079] The target business combination is obtained by traversing all integer combinations of business quantities through the following formula:

[0080]

[0081] Wherein, y represents the target resource of the target object;

[0082] B U represents the second business target scheduling model coefficient combination corresponding to the target second object set, B U ={β′ 11 , β′ 12 ,…,β′ 1S1 , β 21 , β′ 22 ,…,β′ 2S2 ,…,β′ M1 , β′ M2 ,…,β′ MSM};

[0083] X U Indicates the quantity parameter combination of each sub-service in each service corresponding to the target object, X U ={x 11 , x 12 , …, x 1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM}, x11 ~x MsM Indicates the number parameters of various sub-services in various services corresponding to the target object;

[0084] B U *X U =β′ 11 × 11 +β′ 12 × 12 +…+β′ 1S1 × 1S1 +β′ 21 × 21 +β′ 22 × 22 +…+β′ 2S2 × 2S2 +…+β′ M1 × M1 +β′ M2 × M2 +…+β′ MSM × MSM , x 11 ~x MSM It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of each corresponding sub-service.

[0085] In a possible implementation manner, the auxiliary task execution information includes execution frequency information of various types of auxiliary tasks;

[0086] The auxiliary task scheduling unit is specifically used for:

[0087] For each second object set, determining a combination of auxiliary task target scheduling model coefficients corresponding to the second object set according to quantity information of each business corresponding to each second object sample in the second object set, execution times information of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model;

[0088] According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination.

[0089] In a possible implementation manner, the auxiliary task scheduling unit is specifically used to:

[0090] According to the number of the jth sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the jth sub-business in the i-th business corresponding to the target object;

[0091] The execution times of the same type of auxiliary tasks in the auxiliary task combination corresponding to each type of sub-business in each type of business corresponding to the target object are added up respectively to obtain the target auxiliary task combination.

[0092] In a possible implementation, the auxiliary task target scheduling model is the following linear regression equation:

[0093] X ij =ZA ij

[0094] The auxiliary task scheduling unit is specifically used for:

[0095] For each second object set, the auxiliary task target scheduling model coefficient combination corresponding to the second object set is calculated by the following formula:

[0096] A ij =(Z T Z) -1 ZX ij

[0097] Where Z = {Z 1 , Z 2 , …, Z k} is an r×k dimensional matrix, Z 1 ~Z k represents the number of times each type of auxiliary task is executed corresponding to the 1st to rth second object samples in the second object set, and k represents the number of types of auxiliary tasks;

[0098] X ij ={x ij,1 , x ij,2 , ..., x ij,r}, X ij is an r×1 dimensional matrix, x ij,1 ~x ij,r represents the number of j-th sub-services in the i-th service corresponding to each of the 1st to r-th second object samples in the second object set, i=1-M, M represents the number of service types;

[0099] A ij ={α ij,1 ,α ij,2 ,…,α ij,k} is a 1×k dimensional matrix, A ij Represents the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

[0100] In a possible implementation manner, the auxiliary task scheduling unit is specifically used to:

[0101] The following formula is used to traverse the integer combination of the number of times all auxiliary tasks are executed to obtain the auxiliary task combination corresponding to the j-th sub-service in the i-th service corresponding to the target object:

[0102]

[0103] Among them, x ij Indicates the number of j-th type of sub-services in the i-th type of service corresponding to the target object;

[0104] A Uij A represents the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, Uij ={α′ ij,1 ,α′ ij,2 ,…,α′ ij,k};

[0105] Z U represents the execution times parameter combination of various auxiliary tasks corresponding to the j-th sub-service in the i-th service corresponding to the target object, Z U ={z ij,1 ,z ij,2 ,…,z ij,k}, z ij,1 ~z ij,k represents the execution times parameter of the auxiliary tasks of the 1st to kth types corresponding to the jth type of sub-service in the ith type of service corresponding to the target object, k represents the number of types of auxiliary tasks, z ij,1 ~z ij,k It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times the corresponding auxiliary tasks are executed.

[0106] In a possible implementation manner, the device further includes:

[0107] A first obtaining unit is used to obtain the estimated number of each first business according to the number of executions of each type of unfinished auxiliary task and the auxiliary task target scheduling model during the process of the target object executing the business and auxiliary task in the task scheduling result;

[0108] A second obtaining unit, configured to obtain an estimated first remaining target resource according to the quantity of each first service and the service target scheduling model;

[0109] A processing unit is configured to reschedule the task for the target object if the difference between the target resource and the first remaining target resource is greater than a preset threshold.

[0110] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the task scheduling control method described in the present application when executing the program.

[0111] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the task scheduling control method described in the present application are implemented.

[0112] The beneficial effects of the embodiments of the present application are as follows:

[0113] The task scheduling control method, device, electronic device and storage medium provided by the embodiments of the present application include: a server receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set for a target object, obtaining basic information of an object sample in an object sample set corresponding to the target object, business information completed by the object sample within a set historical period, auxiliary task execution information, and resource information obtained by the object sample executing its corresponding business information and auxiliary tasks, performing business target scheduling on the target object based on the basic information, business information, resource information of the object sample and the target resource information set for the target object, obtaining a business target scheduling result, and performing auxiliary scheduling on the target object based on the business target scheduling result and the auxiliary task execution information. Task target scheduling, obtaining auxiliary task target scheduling results, and determining the business target scheduling results and the auxiliary task target scheduling results as the task scheduling results corresponding to the target object, and outputting the task scheduling results. Compared with the prior art, in the embodiment of the present application, based on the business information, auxiliary task execution information, and resource information obtained by executing business information and auxiliary tasks completed by the object samples in the object sample set within the set historical period, business target scheduling and auxiliary task target scheduling are automatically, accurately and comprehensively performed for the target object. Since the task execution and the resource situation obtained by executing the task of the object sample within the set historical period are referred to, the tasks scheduled for the target object are more reasonable, do not require manual settings, and improve the efficiency of task scheduling.

[0114] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0116] Figure 1 A schematic diagram of the implementation process of the task scheduling control method provided in the embodiment of the present application;

[0117] Figure 2 A schematic diagram of an implementation process for scheduling business objectives for a target object provided in an embodiment of the present application;

[0118] Figure 3 A clustering pedigree diagram of a first object set provided in an embodiment of the present application;

[0119] Figure 4 A schematic diagram of the implementation process of auxiliary task target scheduling for a target object provided in an embodiment of the present application;

[0120] Figure 5 A schematic diagram of an implementation flow of monitoring the task execution status of a target object provided in an embodiment of the present application;

[0121] Figure 6 A schematic diagram of the structure of a task scheduling control device provided in an embodiment of the present application;

[0122] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0123] In order to solve the problems in the background technology, the embodiments of the present application provide a task scheduling control method, device, electronic device and storage medium.

[0124] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0125] The task scheduling control method provided in the embodiment of the present application can be applied to a server or a terminal, wherein the server can be an independent physical server or a cloud server that provides basic cloud computing services such as a cloud server, a cloud database, and a cloud storage, and the terminal can be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., which is not limited in the embodiment of the present application. In this application, only the server is used as the execution subject for explanation.

[0126] like Figure 1As shown, it is a schematic diagram of the implementation process of the task scheduling control method provided in the embodiment of the present application. The task scheduling control method may specifically include the following steps:

[0127] S11. Receive a task scheduling control request, where the task scheduling control request carries target resource information set by a target object.

[0128] During specific implementation, the server receives a task scheduling control request, which carries target resource information set by a target object, wherein the target resource information is recorded as target income information.

[0129] S12. Obtain basic information of object samples in an object sample set corresponding to the target object, business information completed by the object samples within a set historical period, auxiliary task execution information, and execution business information and resource information obtained by the auxiliary tasks.

[0130] In specific implementation, the object samples in the object sample set are objects of the same type as the target object, and can perform the same business tasks and auxiliary tasks, wherein the auxiliary tasks refer to the business activity tasks that assist in completing the business tasks, and the auxiliary task execution information is the activity participation information. For example, in the insurance industry, the target object can be an insurance agent of an insurance company, and the object samples in the object sample set can be other insurance agents in the insurance company who perform the same type of business tasks and activity tasks as the insurance agent. The business includes multiple categories (i.e., types), and different categories of business each include multiple sub-businesses. For example, the business can be divided into: annuity insurance, serious illness insurance, accident insurance, etc. according to the category. Each type of business can be divided into different sub-businesses according to the premium level. For example, annuity insurance includes annuity insurance with a premium level of a1, annuity insurance with a premium level of a2, and annuity insurance with a premium level of a3. Then, annuity insurance with a premium level of a1, annuity insurance with a premium level of a2, and annuity insurance with a premium level of a3 are the three sub-businesses corresponding to the annuity insurance business. Taking annuity insurance as an example, activities to assist in completing annuity insurance tasks may include: inviting customers to visit the community, explaining annuity insurance related information to customers, forwarding annuity insurance related copy on social software such as WeChat Moments, etc.

[0131] The resource information obtained by executing business information and auxiliary tasks is: the income information obtained by executing business information and auxiliary tasks.

[0132] The basic information of the object sample may include, but is not limited to, the following information: age information, gender information, and other information of the object sample. The historical period can be set as needed. For example, if the target object is planning tasks for the next year, the historical period can be the previous year. If the target object is planning tasks for the next month, the historical period can be the previous month. The length of the historical period must correspond to the length of the task scheduling period corresponding to the target object. Taking one year as an example, if the target object is planning tasks for the next year, the server obtains the basic information of each object sample in the object sample set, the business information completed by the object sample in the previous year, the auxiliary task execution information (i.e., activity participation information), and the income information corresponding to the completion of the business information.

[0133] S13. Perform business target scheduling on the target object according to the basic information, business information, resource information and target resource information of the object sample to obtain a business target scheduling result.

[0134] In specific implementation, the server schedules the business target of the target object according to the basic information of the object sample, the quantity information of each business contained in the business information, the income information and the target income information set for the target object, and obtains the business target scheduling result.

[0135] Specifically, you can follow the following Figure 2 The process shown in the figure performs business target scheduling on the target object, including the following steps:

[0136] S131. Classify the object samples according to a preset classification index to obtain each first object set after classification.

[0137] During specific implementation, classification indicators can be set in advance. For example, the classification indicators may include: age, gender, etc. Age can be divided into several categories, for example, it can be divided into four categories: under 25 years old, 26-35 years old, 36-45 years old, and over 45 years old. The embodiment of the present application does not limit this.

[0138] Specifically, the server classifies the object samples in the object set according to a preset classification index to obtain each object set after classification, which is recorded as a first object set.

[0139] In the specific implementation process, for each group under each classification indicator, if the number of object samples in the group is less than the preset threshold, no further subdivision will be performed, otherwise, subdivision will continue according to the next classification indicator until all classification indicators are divided. Assume that there are 2 classification indicators: gender and age, and there are 200 object samples in the object sample set. According to gender, the object samples can be divided into two groups: male and female. If the number of female object samples is 20, which is less than the preset threshold (such as 30), the grouping can be performed directly according to age without considering gender. The 200 object samples are divided into 30 people under 25 years old, 40 people between 26 and 35 years old, 50 people between 36 and 45 years old, and 80 people over 45 years old, a total of 4 groups, i.e., 4 first object sets.

[0140] It should be noted that the classification indicators in the embodiments of the present application are not limited to the above two types. The object samples can also be classified in a more fine-grained manner in combination with other characteristics of the object samples (such as education level, years of work experience, etc.). The embodiments of the present application are not limited to this.

[0141] S132. For each first object set, determine the first business target scheduling model coefficient combination corresponding to the first object set according to the quantity information of each business corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample, and the business target scheduling model.

[0142] In specific implementation, the server determines, for each first object set, a combination of coefficients of the first business target scheduling model corresponding to the first object set based on the quantity information of each business corresponding to each first object sample in the first object set, the income information corresponding to each first object sample, and the business target scheduling model.

[0143] Specifically, a linear regression fitting can be performed on the relationship between the resources (i.e., income) of the object sample and the quantity of each business through a set business target scheduling model. The business target scheduling model can be set to the following linear regression equation:

[0144] Y=XB

[0145] The business target scheduling model coefficient combination B can be derived from the business target scheduling model, as shown below:

[0146] For each first object set, the first service objective scheduling model coefficient combination corresponding to the first object set is calculated by the following formula:

[0147] B=(X T X) -1 XY

[0148] Where Y = {y 1 ,y 2 , …, yn}, Y is an n×1 dimensional matrix, y 1 ~y n represents the resources (i.e., income) corresponding to the 1st to nth first object samples in the first object set, where n represents the number of object samples in the first object set;

[0149] X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM}, X is a dimensional matrix, X ij ={x ij,1 , x ij,2 , ..., x ij,n}, X ij is an n×1 dimensional matrix, x ij,1 ~x ij,n represents the number of j-th sub-services in the i-th service corresponding to the first to n-th first object samples in the first object set, i=1-M, M represents the number of service types, S j Indicates the number of sub-businesses included in the i-th business category;

[0150] B represents the first service target scheduling model coefficient combination corresponding to the first object set, B = {β 11 , β 12 , …, β 1S1 , β 21 , β 22 , …, β 2S2 , …, β M1 , β M2 , …, β MSM}, B is a Dimensional matrix.

[0151] In this way, the first business target scheduling model coefficient combination corresponding to each first object set can be calculated. Assuming there are P first object sets, the corresponding P first business target scheduling model coefficient combinations B can be calculated. 1 ~B P Taking the above example of dividing 200 object samples into 4 first object sets, the 4 first business target scheduling model coefficient combinations B can be calculated. 1 ~B 4 .

[0152] In the specific implementation process, for example, the business can be divided into three categories: annuity insurance, critical illness insurance, and accident insurance, then M = 3. Assuming that the annuity insurance business includes 4 types of sub-businesses with premium levels, the critical illness insurance business includes 5 types of sub-businesses with premium levels, and the accident insurance business includes 6 types of sub-businesses with premium levels, then S 1 =4, S 2 =5, S 3 =6.

[0153] S133, clustering the first business target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and determining the number of target classifications according to the Euclidean distance between the new class generated after each clustering and other classes.

[0154] In specific implementation, the server can aggregate the first business objective scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and use P first business objective scheduling model coefficient combinations as P cluster samples. The first business objective scheduling model coefficient in each cluster sample is used as the feature of the cluster sample, and the Euclidean distance between every two cluster samples is calculated, and the two categories with the smallest Euclidean distance are aggregated together to generate a new category. The Euclidean distance between the generated new category and the remaining P-1 cluster samples is then calculated, and the two categories with the smallest Euclidean distance are aggregated together, and so on. The cluster samples are aggregated into a corresponding number of categories according to the target number of categories, wherein the target number of categories can be determined in the following way: the number of categories corresponding to the maximum Euclidean distance between the new category generated after clustering and other categories is determined as the target number of categories. Still taking the example of dividing 200 object samples into 4 first object sets, the 4 first object sets are respectively recorded as A, B, C, and D. Group A contains object samples under 25 years old, group B contains object samples between 26 and 35 years old, group C contains object samples between 36 and 45 years old, and group D contains object samples over 45 years old. P=4. The first business target scheduling model coefficient combinations corresponding to the 4 first object sets A, B, C, and D are: B 1 ~B 4 The clustering pedigree diagram of the first business target scheduling model coefficient combination corresponding to the first object set A, B, C, D is as follows: Figure 3 As shown, from Figure 3 From bottom to top, assuming that in the first layer, B 1 and B 3 The Euclidean distance between them is the smallest, and B 1 and B 3 Aggregate into a new class G1, assuming B 1 and B 3 The Euclidean distance between G1 and B is 1. In the second layer, 2 The Euclidean distance between G1 and B is the smallest. 2Aggregate into a new class G2, G1 and B 2 The Euclidean distance between G2 and B is 3. 4 The Euclidean distance between them is 1. 1 and B 3 The Euclidean distance between G1 and B 2 The Euclidean distance between G2 and B 4 Among the Euclidean distances between them, the largest Euclidean distance is between G1 and B 2 The Euclidean distance between them is 3, then the first object sets are aggregated into G1 and B 2 After polymerization, no further polymerization will occur, G1 and B 2 After polymerization, it is divided into 2 categories, B 1 , B 2 , B 3 Divided into one category, B 4 Divided into one category, that is, the number of target categories is 2.

[0155] It should be noted that, in the embodiment of the present application, the Euclidean distance between every two cluster samples may also be the distance between every two cluster samples, and the embodiment of the present application is not limited to this.

[0156] S134. Reclassify the object samples in each first object set according to the clustering rule of the target classification number and the first business target scheduling model coefficient combination to obtain each second object set after classification, and re-determine the second business target scheduling model coefficient combination corresponding to each second object set based on the business target scheduling model.

[0157] In specific implementation, the server reclassifies the object samples in each first object set into a corresponding number of second object sets according to the number of target classifications according to the clustering rule of the first business target scheduling model coefficient combination in step S133.

[0158] Continuing with the previous example, the number of target categories is 2, and the clustering rule for the first business target scheduling model coefficient combination is: B 1 , B 2 , B 3 Divided into one category, B 4 Divided into one category, B 1 , B 2 , B 3 Corresponding to the first object set A, B, C, B 4 Corresponding to D, the object samples in A, B, and C are divided into one category, and the object samples in D are divided into one category, that is, the object samples under 25 years old, the object samples between 26 and 35 years old, and the object samples between 36 and 45 years old are divided into one group, and the object samples over 45 years old are divided into one group, and two object sample sets are regenerated, which are recorded as the second object sample sets.

[0159] Furthermore, the server re-determines the second business objective scheduling model coefficient combination corresponding to each second object set based on the business objective scheduling model. The calculation method of each second business objective scheduling model coefficient combination refers to the calculation method of the first business objective scheduling model coefficient combination, which will not be repeated here.

[0160] S135. Determine the target second object set to which the target object belongs, and traverse all integer combinations of business quantities according to the target resource information, the second business target scheduling model coefficient combination corresponding to the target second object set, and the business target scheduling model to obtain the target business combination.

[0161] In 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 age. Assuming that the target object is between 25 and 45 years old, the target second object set to which the target object belongs is the second object set composed of object samples between 25 and 45 years old. Assuming that the target object is older than 45 years old, the target second object set to which the target object belongs is the second object set composed of object samples older than 45 years old. Then, according to the preset target income information, the second business target scheduling model coefficient combination corresponding to the target second object set, and the business target scheduling model, all integer combinations of business quantities are traversed to obtain the target business combination.

[0162] Specifically, the target business combination can be obtained by traversing all integer combinations of business quantities through the following formula:

[0163]

[0164] Wherein, y represents the target resources (i.e., target income) of the target object;

[0165] B U represents the second business target scheduling model coefficient combination corresponding to the target second object set, B U ={β′ 11 , β′ 12 ,…,β′ 1S1 , β 21 , β′ 22 ,…,β′ 2S2 ,…,β′ M1 , β′ M2 ,…,β′ MSM};

[0166] X U Indicates the quantity parameter combination of each sub-service in each service corresponding to the target object, X U ={x 11 , x 12 , …, x1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM}, x 11 ~x MSM Indicates the number parameters of various sub-services in various services corresponding to the target object;

[0167] B U *X U =β′ 11 × 11 +β′ 12 × 12 +…+β′ 1s1 × 1s1 +β′ 21 × 21 +β′ 22 × 22 +…+β′ 2S2 × 2S2 +…+β′ M1 × M1 +β′ M2 × M2 +…+β′ MSM × MSM , x 11 ~x MsM It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of each corresponding sub-service.

[0168] In this way, all possible x 11 , x 12 , …, x 1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM combination.

[0169] Wherein, ω is a preset allowable error, for example, 5%, which is not limited in the present embodiment. 11 Greater than or equal to 0 and less than or equal to the upper limit of the number of Class 1 sub-services in Class 1 services, x 12 is greater than or equal to 0 and less than or equal to the upper limit of the number of Class 2 sub-services in Class 1 services, ..., and so on, x MSM The upper limit of the number of SM-th category sub-services in the M-th category service is greater than or equal to 0 and less than or equal to the upper limit of the number of SM-th category sub-services in the M-th category service.

[0170] x 11 ~x MsMThe upper limit of the number of sub-businesses in each corresponding business type can be determined in the following way:

[0171] For each sub-business in each type of business, the number of sub-businesses completed by each object sample in the target second object set is counted, and a set quantile is taken as the standard definition as the upper limit of the number of sub-businesses of this type in this type of business. Among them, the set quantile can be set by yourself, for example, it can be set to the 90% quantile, and the embodiment of the present application does not set this. 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, containing a total of 80 object samples, then the upper limit of the number of the first type of sub-business in the first type of business can be taken as: the 90% quantile of the number of the first type of sub-business in the first type of business completed by each of the 80 object samples.

[0172] S136: Select a group of target business combinations from each target business combination to obtain a business target scheduling result.

[0173] During specific implementation, one set of target business combinations may be selected from the target business combinations as the business target scheduling result.

[0174] In order to better fit the task execution habits of the target object, in a possible implementation, the target object can be screened according to its business task execution status within the same set historical period, and a target business combination with the closest proportion of each type of business can be selected. For example, within the set historical period, the ratio of the number of sub-businesses with a premium level of A1 to the number of A2 sub-businesses executed by the target object in Class A insurance business is 1:1. The obtained target business combination includes a target business combination with 3 A1 sub-businesses and 5 A2 sub-businesses, and also includes a target business combination with 5 A1 sub-businesses and 5 A2 sub-businesses. Then, the target business combination with 5 A1 sub-businesses and 5 A2 sub-businesses can be selected as the final business target scheduling result.

[0175] In the embodiment of the present application, since the object samples have many characteristics (age, gender, education, years of work experience, etc.), P is often relatively large, which will lead to too many classifications, and there are multiple business types. Each business type will establish a relationship with various auxiliary tasks. This growth is proportional, and more modeling samples are required. The entire calculation will be more complicated, and the relationship between the target income and the number of completed businesses of many object samples with different attributes is similar. Therefore, there is no need to establish more models. Therefore, after a classification based on the characteristics of the object samples, P first object sets are generated, that is, P class object samples are generated, and then clustered according to the regression coefficient corresponding to each class of object samples (that is, the business target scheduling model coefficient), and the P class object samples are aggregated into Q classes according to the regression coefficients corresponding to each class of object samples, that is, Q second object sets. In this way, while reducing the complexity of modeling and calculation, the rationality and accuracy of the business target scheduling and auxiliary task target scheduling of the target object based on the information of each class of object samples after clustering can still be guaranteed.

[0176] S14. Perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information to obtain the auxiliary task target scheduling result.

[0177] In specific implementation, the auxiliary task execution information includes the execution frequency information of various auxiliary tasks (that is, the activity participation information includes the participation frequency information of various activities). The server schedules the activity goals for the target object according to the business goal scheduling results and the participation frequency information of various activities contained in the activity participation information to obtain the activity goal scheduling results.

[0178] In specific implementation, Figure 4 The process shown performs auxiliary task target scheduling on the target object, which may specifically include the following steps:

[0179] S141. For each second object set, determine the auxiliary task target scheduling model coefficient combination corresponding to the second object set based on the quantity information of each business corresponding to each second object sample in the second object set, the number of execution times of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model.

[0180] During specific implementation, the server determines, for each second object set, a combination of coefficients of the auxiliary task target scheduling model corresponding to the second object set based on the quantity information of each business corresponding to each second object sample in the second object set, the number of participation information of each type of activity corresponding to each second object sample, and the auxiliary task target scheduling model.

[0181] Specifically, the auxiliary task target scheduling model can be set as the following linear regression equation: ij =ZA ijAccording to this equation, the coefficient combination A of the auxiliary task target scheduling model can be derived ij Specifically, for each second object set, the auxiliary task target scheduling model coefficient combination corresponding to the second object set is calculated by the following formula:

[0182] A ij =(Z T Z) -1 ZX ij

[0183] Where Z = {Z 1 , Z 2 , …, Z k} is an r×k dimensional matrix, Z 1 ~Z k represents the number of times each type of auxiliary task is executed (i.e., the number of times each type of activity is participated in) corresponding to the 1st to rth second object samples in the second object set, k represents the number of types of auxiliary tasks (i.e., the number of types of activities), and r represents the number of second object samples in the second sample set;

[0184] X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM}, X is a dimensional matrix, S j represents the number of sub-businesses included in the i-th business category, X ij ={x ij,1 , x ij,2 , ..., x ij,r}, X ij is an r×1 dimensional matrix, x ij,1 ~x ij,r represents the number of j-th sub-services in the i-th service corresponding to each of the 1st to r-th second object samples in the second object set, i=1-M, M represents the number of service types;

[0185] A ij ={α ij,1 ,α ij,2 ,…,α ij,k} is a 1×k dimensional matrix, A ij Represents the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

[0186] Assuming that the number of the second object sample set is Q, we can calculate A combination of auxiliary task target scheduling model coefficients.

[0187] S142. According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination.

[0188] During specific implementation, the server traverses the integer combinations of all auxiliary task execution times (i.e., activity participation times) based on the number of type j sub-business in type ith business corresponding to the target object contained in the target business 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, and obtains the auxiliary task combination (i.e., activity combination) corresponding to the j-th sub-business in type ith business corresponding to the target object.

[0189] Specifically, the following formula can be used to traverse the integer combination of the number of times all auxiliary tasks are executed to obtain the auxiliary task combination corresponding to the j-th sub-service in the i-th service corresponding to the target object:

[0190]

[0191] Among them, x ij Indicates the number of j-th type of sub-services in the i-th type of service corresponding to the target object;

[0192] A Uij represents the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, A Uij ={α′ ij,1 ,α′ ij,2 ,…,α′ ij,k};

[0193] Z U represents the parameter combination of the number of times each auxiliary task is executed corresponding to the j-th sub-business in the i-th business corresponding to the target object (i.e., the parameter combination of the number of times each activity is participated in), Z U ={z ij,1 ,z ij,2 ,…,z ij,k}, z ij,1 ~z ij,k represents the execution times parameter of the auxiliary tasks of the 1st to kth types (i.e., the activity participation times parameter) corresponding to the jth type of sub-business in the ith type of business corresponding to the target object, k represents the number of types of auxiliary tasks (i.e., activities), z ij,1 ~z ij,k It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times each corresponding type of auxiliary task is executed (i.e. the number of times each type of activity is participated in).

[0194] Among them, z ij,1 ~z ij,k The upper limit of the number of times you can participate in each type of activity can be determined in the following ways:

[0195] For the number of participations in each type of activity corresponding to each sub-business in each type of business, the number of participations in this type of activity by each object sample in the target second object set is counted, and a set quantile is taken as the standard definition as the upper limit of the number of participations in this type of activity corresponding to this type of sub-business in this type of business.

[0196] Furthermore, the execution times of the same type of auxiliary tasks (i.e., the number of activity participation times) in the auxiliary task combinations (i.e., activity combinations) corresponding to each type of sub-business in each type of business corresponding to the target object are added together to obtain the target auxiliary task combination (i.e., target activity combination).

[0197] S15: Determine the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and output the task scheduling result.

[0198] In specific implementation, the business target scheduling result and the activity target scheduling result are determined as the task scheduling control result corresponding to the target object, and the task scheduling result is output to enable the target object to execute the business in the target business combination and the activity in the target activity combination.

[0199] In a possible implementation manner, in the embodiment of the present application, during the process of the target object executing the business and auxiliary tasks (i.e., activities) in the task scheduling control result, the execution status of the target object can also be monitored and tracked, as shown in the following example: Figure 5 The process shown monitors the task execution status of the target object and may include the following steps:

[0200] S21. In the process of executing the services and auxiliary tasks in the task scheduling result of the target object, the estimated number of each first service is obtained according to the number of executions of various types of unfinished auxiliary tasks and the auxiliary task target scheduling model.

[0201] During specific implementation, the server can substitute the number of participations in various types of currently unfinished activities into the auxiliary task target scheduling model according to the preset time period during the process of the target object executing the business and activities in the task scheduling results, and estimate the number of various types of sub-businesses in the corresponding businesses (which can be recorded as the first business), wherein the preset time period can be set as needed. For example, if the time period is set to one year, the preset time period can be set to 1 month, and the embodiments of the present application do not limit this.

[0202] S22: Obtain estimated first remaining target resources according to the quantity of each first service and the service target scheduling model.

[0203] During specific implementation, the server substitutes the quantity of each sub-business in each first business into the business target scheduling model to calculate the estimated first remaining target income.

[0204] S23: If the difference between the target resource and the first remaining target resource is greater than a preset threshold, reschedule the task for the target object.

[0205] During specific implementation, the server calculates the difference between the pre-set target income and the estimated first remaining target income. If the difference is greater than the preset threshold, it is determined that the target object cannot complete the remaining tasks within the remaining time, and the task scheduling control method provided in steps S11 to S15 is used to reschedule the target object. The preset threshold can be set as needed, and the embodiment of the present application does not limit this.

[0206] In this way, based on the tracking of the target object's execution of the task, the possibility of achieving the target income can be evaluated in a timely manner, and the target object can be reminded in a timely manner to avoid setting unreasonable target income that cannot be achieved, so that the target object can set a reasonable target income to improve work efficiency.

[0207] For example, assuming that an insurance agent sets a target income of A yuan this year, the task scheduling result determined by the above-mentioned task scheduling control method of this application is: it is recommended that he complete 2 critical illness insurance policies with an average of 10,000 yuan and 5 annuity policies with an average of 100,000 yuan, and achieve them by visiting 20 customers and inviting customers to participate in 100 product presentation activities. The completion status of the insurance agent can be monitored regularly every month. If it is found that the insurance agent is unable to complete the target in May, a reasonable target income suggestion will be given to re-schedule the task of the insurance agent.

[0208] The task scheduling control method provided by the embodiment of the present application comprises the following steps: a server receives a task scheduling control request, wherein the task scheduling control request carries target resource information set for a target object, obtains basic information of an object sample in an object sample set corresponding to the target object, business information completed by the object sample within a set historical period, auxiliary task execution information, and resource information obtained by the object sample executing its corresponding business information and auxiliary tasks, performs business target scheduling on the target object based on the basic information, business information, resource information of the object sample, and target resource information set for the target object, obtains a business target scheduling result, and performs auxiliary task target scheduling on the target object based on the business target scheduling result and the auxiliary task execution information. , obtain the auxiliary task target scheduling result, and determine the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and output the task scheduling result. Compared with the prior art, in the embodiment of the present application, based on the business information, auxiliary task execution information, and resource information obtained by executing the business information and auxiliary tasks completed by the object samples in the object sample set within the set historical period, the business target scheduling and auxiliary task target scheduling are automatically, accurately and comprehensively performed for the target object. Since the task execution and the resource situation obtained by executing the task of the object sample within the set historical period are referred to, the tasks scheduled for the target object are more reasonable, do not require manual settings, and improve the efficiency of task scheduling.

[0209] Based on the same inventive concept, an embodiment of the present application also provides a task scheduling control device. Since the principle of solving the problem by the above-mentioned task scheduling control device is similar to that of the task scheduling control method, the implementation of the above-mentioned device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0210] like Figure 6 As shown, it is a structural diagram of a task scheduling control device provided in an embodiment of the present application, which may include:

[0211] The receiving unit 31 is used to receive a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object;

[0212] An acquisition unit 32 is used to acquire basic information of an object sample in an object sample set corresponding to the target object, business information completed by the object sample within a set historical period, auxiliary task execution information, and resource information obtained by executing the business information and the auxiliary task;

[0213] A service scheduling unit 33 is used 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;

[0214] An auxiliary task scheduling unit 34 is used to perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information to obtain an auxiliary task target scheduling result;

[0215] The task scheduling unit 35 is used to determine the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and output the task scheduling result.

[0216] In a possible implementation manner, the service scheduling unit 33 is specifically configured to:

[0217] Classify the object samples according to a preset classification index to obtain each first object set after classification;

[0218] For each first object set, determining a first business target scheduling model coefficient combination corresponding to the first object set according to quantity information of each business corresponding to each first object sample in the first object set, resource information corresponding to each first object sample, and a business target scheduling model;

[0219] Aggregate the first business target scheduling model coefficient combinations corresponding to each first object set according to a hierarchical clustering algorithm, and determine the number of target classifications according to the Euclidean distance between the new class generated after each clustering and other classes;

[0220] Reclassifying the object samples in each of the first object sets according to the clustering rule of the target classification number and the first business objective scheduling model coefficient combination to obtain each of the classified second object sets, and re-determining the second business objective scheduling model coefficient combination corresponding to each of the second object sets based on the business objective scheduling model;

[0221] Determine a target second object set to which the target object belongs, and traverse all integer combinations of business quantities according to the target resource information, a combination of coefficients of a second business target scheduling model corresponding to the target second object set, and the business target scheduling model to obtain a target business combination;

[0222] A set of target business combinations is selected from each target business combination to obtain a business target scheduling result.

[0223] In a possible implementation, the business target scheduling model is the following linear regression equation:

[0224] Y=XB

[0225] The service scheduling unit 33 is specifically used for:

[0226] For each first object set, the first service objective scheduling model coefficient combination corresponding to the first object set is calculated by the following formula:

[0227] B=(X T X) -1 XY

[0228] Where Y = {y 1 ,y 2 , …, y n}, Y is an n×1 dimensional matrix, y 1 ~y n represents resources corresponding to the 1st to nth first object samples in the first object set, where n represents the number of object samples in the first object set;

[0229] X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM}, X is a dimensional matrix, X ij ={x ij,1 , x ij,2 , ..., x ij,n}, X ij is an n×1 dimensional matrix, x ij,1 ~x ij,n represents the number of j-th sub-services in the i-th service corresponding to the first to n-th first object samples in the first object set, i=1-M, M represents the number of service types, S j Indicates the number of sub-businesses included in the i-th business category;

[0230] B represents the first service target scheduling model coefficient combination corresponding to the first object set, B = {β 11 , β 12 , …, β 1S1 , β 21 , β 22 , …, β 2S2 , …, β M1 , β M2 , …, β MSM}, B is a Dimensional matrix.

[0231] In a possible implementation manner, the service scheduling unit 33 is specifically configured to:

[0232] The target business combination is obtained by traversing all integer combinations of business quantities through the following formula:

[0233]

[0234] Wherein, y represents the target resource of the target object;

[0235] B U represents the second business target scheduling model coefficient combination corresponding to the target second object set, B U ={β′ 11 , β′ 12 ,…,β′ 1S1 , β 21 , β′ 22 ,…,β′ 2S2 ,…,β′ M1 , β′ M2 ,…,β′ MSM};

[0236] X U Indicates the quantity parameter combination of each sub-service in each service corresponding to the target object, X U ={x 11 , x 12 , …, x 1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM}, x 11 ~x MsM Indicates the number parameters of various sub-services in various services corresponding to the target object;

[0237] B U *X U =β′ 11 × 11 +β′ 12 × 12 +…+β′ 1S1 × 1S1 +β′ 21 × 21 +β′ 22 × 22 +…+β′ 2S2 × 2S2 +…+β′ M1 × M1 +β′ M2 × M2 +…+β′ MSM × MSM , x 11 ~xMSM It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of each corresponding sub-service.

[0238] In a possible implementation manner, the auxiliary task execution information includes execution frequency information of various types of auxiliary tasks;

[0239] The auxiliary task scheduling unit 34 is specifically used for:

[0240] For each second object set, determining a combination of auxiliary task target scheduling model coefficients corresponding to the second object set according to quantity information of each business corresponding to each second object sample in the second object set, execution times information of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model;

[0241] According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination.

[0242] In a possible implementation manner, the auxiliary task scheduling unit 34 is specifically configured to:

[0243] According to the number of the jth sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the jth sub-business in the i-th business corresponding to the target object;

[0244] The execution times of the same type of auxiliary tasks in the auxiliary task combinations corresponding to various sub-businesses in various businesses corresponding to the target object are added up respectively to obtain the target auxiliary task combination.

[0245] In a possible implementation, the auxiliary task target scheduling model is the following linear regression equation:

[0246] X ij =ZA ij

[0247] The auxiliary task scheduling unit 34 is specifically used for:

[0248] For each second object set, the auxiliary task target scheduling model coefficient combination corresponding to the second object set is calculated by the following formula:

[0249] A ij =(Z T Z)-1 ZX ij

[0250] Where Z = {Z 1 , Z 2 , …, Z k} is an r×k dimensional matrix, Z 1 ~Z k represents the number of times each type of auxiliary task is executed corresponding to the 1st to rth second object samples in the second object set, and k represents the number of types of auxiliary tasks;

[0251] X ij ={x ij,1 , x ij,2 , ..., x ij,r}, X ij is an r×1 dimensional matrix, x ij,1 ~x ij,r represents the number of j-th sub-services in the i-th service corresponding to each of the 1st to r-th second object samples in the second object set, i=1-M, M represents the number of service types;

[0252] A ij ={α ij,1 ,α ij,2 ,…,α ij,k} is a 1×k dimensional matrix, A ij Represents the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

[0253] In a possible implementation manner, the auxiliary task scheduling unit 34 is specifically configured to:

[0254] The following formula is used to traverse the integer combination of the number of times all auxiliary tasks are executed to obtain the auxiliary task combination corresponding to the j-th sub-service in the i-th service corresponding to the target object:

[0255]

[0256] Among them, x ij Indicates the number of j-th type of sub-services in the i-th type of service corresponding to the target object;

[0257] A Uij A represents the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, Uij ={α′ ij,1 ,α′ ij,2 ,…,α′ ij,k};

[0258] Z Urepresents the execution times parameter combination of various auxiliary tasks corresponding to the j-th sub-service in the i-th service corresponding to the target object, Z U ={z ij,1 ,z ij,2 ,…,z ij,k}, z ij,1 ~z ij,k represents the execution times parameter of the auxiliary tasks of the 1st to kth types corresponding to the jth type of sub-service in the ith type of service corresponding to the target object, k represents the number of types of auxiliary tasks, z ij,1 ~z ij,k It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times the corresponding auxiliary tasks are executed.

[0259] In a possible implementation manner, the device further includes:

[0260] A first obtaining unit is used to obtain the estimated number of each first business according to the number of executions of each type of unfinished auxiliary task and the auxiliary task target scheduling model during the process of the target object executing the business and auxiliary task in the task scheduling result;

[0261] A second obtaining unit, configured to obtain an estimated first remaining target resource according to the quantity of each first service and the service target scheduling model;

[0262] A processing unit is configured to reschedule the task for the target object if the difference between the target resource and the first remaining target resource is greater than a preset threshold.

[0263] Based on the same technical concept, the present application embodiment also provides an electronic device 400, referring to Figure 7 As shown, the electronic device 400 is used to implement the task scheduling control method described in the above method embodiment. The electronic device 400 of this embodiment may include: a memory 401, a processor 402, and a computer program stored in the memory and executable on the processor, such as a task scheduling control program. When the processor executes the computer program, the steps in the above task scheduling control method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented, for example, 31.

[0264] The specific connection medium between the memory 401 and the processor 402 is not limited in the embodiment of the present application. Figure 7 In the embodiment, the memory 401 and the processor 402 are connected via a bus 403. The bus 403 is Figure 7The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 403 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0265] The memory 401 may be a volatile memory, such as a random-access memory (RAM); the memory 401 may also be a non-volatile memory, such as a read-only memory, a 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 the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 401 may be a combination of the above memories.

[0266] Processor 402, used to implement Figure 1 A task scheduling control method shown includes:

[0267] The processor 402 is used to call the computer program stored in the memory 401 to execute the following Figure 1 Steps S11 to S15 shown in FIG.

[0268] An embodiment of the present application also provides a computer-readable storage medium that stores computer-executable instructions required to execute the above-mentioned processor, which includes a program required to execute the above-mentioned processor.

[0269] In some possible implementations, various aspects of the task scheduling control method provided in the present application may also be implemented in the form of a program product, which includes a program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the task scheduling control method according to various exemplary implementations of the present application described above in this specification. For example, the electronic device may execute the following steps: Figure 1 Steps S11 to S15 shown in FIG.

[0270] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0271] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0272] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0273] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0274] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0275] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these 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 also intended to include these modifications and variations.

Claims

1. A task scheduling control method, It is characterized in that include: receiving a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object; Obtaining basic information of object samples in an object sample set corresponding to the target object, business information completed by the object samples within a set historical period, auxiliary task execution information, and resource information obtained by executing the business information and the auxiliary tasks; According to the basic information of the object sample, the business information, the resource information and the target resource information, the target object is scheduled for business goals to obtain a business goal scheduling result, specifically including: classifying the object sample according to a preset classification index to obtain each first object set after classification; for each first object set, according to the quantity information of each business corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample and the business goal scheduling model, determining a first business goal scheduling model coefficient combination corresponding to the first object set; aggregating the first business goal scheduling model coefficient combination corresponding to each first object set according to a hierarchical clustering algorithm, and clustering the new class generated after each clustering with other The Euclidean distance between each class determines the number of target classifications; reclassifies the object samples in each first object set according to the clustering rule of the target classification number and the first business target scheduling model coefficient combination to obtain each second object set after classification, and re-determines the second business target scheduling model coefficient combination corresponding to each second object set based on the business target scheduling model; determines the target second object set to which the target object belongs, and traverses all integer combinations of business quantities according to the target resource information, the second business target scheduling model coefficient combination corresponding to the target second object set and the business target scheduling model to obtain a target business combination; selects a group of target business combinations from each target business combination to obtain a business target scheduling result; Perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information to obtain the auxiliary task target scheduling result; The business target scheduling result and the auxiliary task target scheduling result are determined as the task scheduling result corresponding to the target object, and the task scheduling result is output.

2. The method according to claim 1, It is characterized in that The business target scheduling model is the following linear regression equation: Y=XB For each first object set, determining a first business target scheduling model coefficient combination corresponding to the first object set according to quantity information of each business corresponding to each first object sample in the first object set, resource information corresponding to each first object sample, and a business target scheduling model, specifically includes: For each first object set, the first service objective scheduling model coefficient combination corresponding to the first object set is calculated by the following formula: B=(X T X) -1 XY Where Y = {y 1 ,y 2 , …, y n }, Y is an n×1 dimensional matrix, y 1 ~y n represents resources corresponding to the 1st to nth first object samples in the first object set, where n represents the number of object samples in the first object set; X={X 11 , X 12 …X 1S1 , X 21 , X 22 …X 2S2 , …, X M1 , X M2 …X MSM }, X is a dimensional matrix, X ij ={x ij,1 , x ij,2 , ..., x ij,n }, X ij is an n×1 dimensional matrix, x ij,1 ~x ij,n represents the number of j-th sub-services in the i-th service corresponding to the first to n-th first object samples in the first object set, i=1-M, M represents the number of service types, S j Indicates the number of sub-businesses included in the i-th business category; B represents the first service target scheduling model coefficient combination corresponding to the first object set, B = {β 11 , β 12 , …, β 1S1 , β 21 , β 22 , …, β 2S2 , …, β M1 , β M2 , …, β MSM }, B is a Dimensional matrix.

3. The method according to claim 1, It is characterized in that According to the target resource information, the second service target scheduling model coefficient combination corresponding to the target second object set and the service target scheduling model, traversing all service quantity integer combinations to obtain a target service combination specifically includes: The target business combination is obtained by traversing all integer combinations of business quantities through the following formula: Wherein, y represents the target resource of the target object; B U represents the second business target scheduling model coefficient combination corresponding to the target second object set, B U ={β 1 ′ 1 , β 1 ′ 2 , …, β 1 ′ S1 , β 21 , β 2 ′ 2 , …, β 2 ′ S2 , …, β ′ M1 , β ′ M2 ,…,β′ MSM }; X U Indicates the quantity parameter combination of each sub-service in each service corresponding to the target object, X U ={x 11 , x 12 , …, x 1S1 , x 21 , x 22 , …, x 2S2 , x M1 , x M2 , …, x MSM }, x 11 ~x MsM Indicates the number parameters of various sub-services in various services corresponding to the target object; B U *X U =β′ 11 ×x 11 +b 1 ′ 2 ×x 12 +…+b′ 1S1 ×x 1S1 +b′ 21 ×x 21 +b′ 22 ×x 22 +…+ β′ 2S2 × 2S2 +…+β′ M1 × M1 +β′ M2 × M2 +…+β′ MSM × MSM , x 11 ~x MSM It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of each corresponding sub-service.

4. The method according to claim 1 or 3, It is characterized in that The auxiliary task execution information includes execution times of various auxiliary tasks; The auxiliary task target scheduling is performed on the target object according to the business target scheduling result and the auxiliary task execution information to obtain the auxiliary task target scheduling result, specifically including: For each second object set, determining a combination of auxiliary task target scheduling model coefficients corresponding to the second object set according to quantity information of each business corresponding to each second object sample in the second object set, execution times information of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model; According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination.

5. The method according to claim 4, It is characterized in that According to the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the target auxiliary task combination, specifically including: According to the number of the jth sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the jth sub-business in the i-th business corresponding to the target object; The execution times of the same type of auxiliary tasks in the auxiliary task combination corresponding to each type of sub-business in each type of business corresponding to the target object are added up respectively to obtain the target auxiliary task combination.

6. The method according to claim 4, It is characterized in that The auxiliary task target scheduling model is the following linear regression equation: X ij =ZA ij For each second object set, according to the quantity information of each business corresponding to each second object sample in the second object set, the number of execution times of each type of auxiliary task corresponding to each second object sample, and the auxiliary task target scheduling model, a coefficient combination of the auxiliary task target scheduling model corresponding to the second object set is determined, specifically including: For each second object set, the auxiliary task target scheduling model coefficient combination corresponding to the second object set is calculated by the following formula: A ij =(Z T Z) -1 ZX ij Where Z = {Z 1 , Z 2 , …, Z k } is an r×k dimensional matrix, Z 1 ~Z k represents the number of times each type of auxiliary task is executed corresponding to the 1st to rth second object samples in the second object set, and k represents the number of types of auxiliary tasks; X ij ={x ij,1 , x ij,2 , ..., x ij,r }, X ij is an r×1 dimensional matrix, x ij,1 ~x ij,r represents the number of j-th sub-services in the i-th service corresponding to each of the 1st to r-th second object samples in the second object set, i=1-M, M represents the number of service types; A ij ={α ij,1 ,α ij,2 ,…,α ij,k } is a 1×k dimensional matrix, A ij Represents the auxiliary task target scheduling model coefficient combination corresponding to the second object set.

7. The method according to claim 5, It is characterized in that According to the number of the j-th sub-business in the i-th business corresponding to the target object contained in the target business 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, traverse the integer combination of all auxiliary task execution times to obtain the auxiliary task combination corresponding to the j-th sub-business in the i-th business corresponding to the target object, specifically including: The following formula is used to traverse the integer combination of the number of times all auxiliary tasks are executed to obtain the auxiliary task combination corresponding to the j-th sub-service in the i-th service corresponding to the target object: Among them, x ij Indicates the number of the jth type of sub-services in the ith type of service corresponding to the target object; A Uij A represents the auxiliary task target scheduling model coefficient combination corresponding to the target second object set to which the target object belongs, Uij ={α′ ij,1 ,α′ ij,2 ,…,α′ ij,k }; Z U represents the execution times parameter combination of various auxiliary tasks corresponding to the j-th sub-service in the i-th service corresponding to the target object, Z U ={z ij,1 ,z ij,2 ,…,z ij,k }, z ij,1 ~z ij,k represents the execution times parameter of the auxiliary tasks of the 1st to kth types corresponding to the jth type of sub-service in the ith type of service corresponding to the target object, k represents the number of types of auxiliary tasks, z ij,1 ~z ij,k It is an integer greater than or equal to zero and less than or equal to the upper limit of the number of times the corresponding auxiliary tasks are executed.

8. The method according to claim 4, It is characterized in that Also includes: In the process of the target object executing the business and auxiliary tasks in the task scheduling result, obtaining an estimated number of each first business according to the number of executions of each type of unfinished auxiliary tasks and the auxiliary task target scheduling model; Obtaining an estimated first remaining target resource according to the quantity of each first service and the service target scheduling model; If the difference between the target resource and the first remaining target resource is greater than a preset threshold, the task of the target object is rescheduled.

9. A task scheduling control device, It is characterized in that include: A receiving unit, configured to receive a task scheduling control request, wherein the task scheduling control request carries target resource information set by a target object; An acquisition unit, configured to acquire basic information of an object sample in an object sample set corresponding to the target object, business information completed by the object sample within a set historical period, auxiliary task execution information, and resource information obtained by executing the business 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 business scheduling unit is specifically used to: classify the object samples according to the preset classification index to obtain the classified first object sets; for each first object set, determine the first business target scheduling model coefficient combination corresponding to the first object set according to the quantity information of each business corresponding to each first object sample in the first object set, the resource information corresponding to each first object sample and the business target scheduling model; aggregate the first business target scheduling model coefficient combination corresponding to each first object set according to the hierarchical clustering algorithm, and determine the target classification number according to the Euclidean distance between the new class generated after each clustering and other classes; reclassify the object samples in each first object set according to the clustering rule of the target classification number and the first business target scheduling model coefficient combination to obtain the classified second object sets, and re-determine the second business target scheduling model coefficient combination corresponding to each second object set based on the business target scheduling model; determine the target second object set to which the target object belongs, and traverse all integer combinations of business quantities according to the target resource information, the second business target scheduling model coefficient combination corresponding to the target second object set and the business target scheduling model to obtain the target business combination; select a group of target business combinations from each target business combination to obtain the business target scheduling result; An auxiliary task scheduling unit, used to perform auxiliary task target scheduling on the target object according to the business target scheduling result and the auxiliary task execution information, and obtain an auxiliary task target scheduling result; The task scheduling unit is used to determine the business target scheduling result and the auxiliary task target scheduling result as the task scheduling result corresponding to the target object, and output the task scheduling result.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the task scheduling control method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the steps of the task scheduling control method according to any one of claims 1 to 8 are implemented.

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