CN113159628A - Task allocation method and device - Google Patents
Task allocation method and device Download PDFInfo
- Publication number
- CN113159628A CN113159628A CN202110522436.7A CN202110522436A CN113159628A CN 113159628 A CN113159628 A CN 113159628A CN 202110522436 A CN202110522436 A CN 202110522436A CN 113159628 A CN113159628 A CN 113159628A
- Authority
- CN
- China
- Prior art keywords
- task execution
- task
- distributed
- execution object
- tasks
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Technology Law (AREA)
- Data Mining & Analysis (AREA)
- Multi Processors (AREA)
Abstract
The invention discloses a task allocation method and a task allocation device, and relates to the technical field of big data. One embodiment of the method comprises: acquiring information of tasks to be distributed and task execution object information, wherein the information of the tasks to be distributed comprises the types of the tasks to be distributed, and the task execution object information comprises the attributes of the task execution objects; according to the type of the task to be distributed and the attribute of the task execution object, constructing an incidence relation between the task to be distributed and the task execution object, wherein the incidence relation indicates whether the task to be distributed is matched with the task execution object or not; determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm; and when the target task execution object is determined, allocating the tasks to be allocated to the target task execution object. According to the embodiment, automatic allocation of tasks is realized, the efficiency and the accuracy of task allocation are improved, reasonable allocation of the tasks is guaranteed, and therefore the internal human resources of the bank are fully utilized.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a task allocation method and a task allocation device.
Background
For banks, not all business personnel directly face customers, so the demands put forth by customers, that is, tasks received by the banks need to be distributed inside the banks, and the traditional distribution mode is to distribute the tasks to corresponding workers by manpower in a loop-by-loop manner.
However, with the increase of the bank task amount, the traditional method of distributing tasks manually is time-consuming and labor-consuming, and cannot ensure the reasonable distribution of tasks, which not only easily causes distribution errors, but also causes the internal human resources of the bank to be underutilized.
Disclosure of Invention
In view of this, embodiments of the present invention provide a task allocation method and apparatus, which can automatically construct an association relationship between a task to be allocated and a task execution object after acquiring task information to be allocated and task execution object information, determine a target task execution object from the task execution object by using a minimum cost max flow algorithm, and allocate the task to be allocated to the target task execution object, thereby implementing automatic allocation of the task, not only improving efficiency and accuracy of task allocation, but also ensuring reasonable allocation of the task, and further implementing full utilization of human resources inside a bank.
To achieve the above object, according to an aspect of an embodiment of the present invention, a task allocation method is provided.
The task allocation method of the embodiment of the invention comprises the following steps:
acquiring information of tasks to be distributed and task execution object information, wherein the information of the tasks to be distributed comprises the types of the tasks to be distributed, and the task execution object information comprises the attributes of the task execution objects;
according to the type of the task to be distributed and the attribute of the task execution object, constructing an incidence relation between the task to be distributed and the task execution object, wherein the incidence relation indicates whether the task to be distributed is matched with the task execution object or not;
determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm;
and when the target task execution object is determined, allocating the tasks to be allocated to the target task execution object.
Alternatively,
according to the types of the tasks to be distributed and the attributes of the task execution objects, constructing an association relation between the tasks to be distributed and the task execution objects, wherein the association relation comprises the following steps:
for each task execution object, executing:
determining whether the type of the task to be distributed corresponds to the attribute of the task execution object, and if so, determining that the task to be distributed is matched with the task execution object; otherwise, the task to be distributed is determined not to be matched with the task execution object.
Alternatively,
the method further comprises the following steps:
and generating a bipartite graph between the tasks to be distributed and the task execution objects according to the incidence relation, wherein the bipartite graph comprises the matching degree of the tasks to be distributed and the task execution objects, and the matching degree is obtained based on the incidence relation.
Alternatively,
calculating the matching degree according to any one or more of the following conditions: the task execution object is a score when the task execution object is a priority processing object of a target task to be allocated, a quality score when the task execution object processes a historical task of which the type is the same as that of the target task to be allocated, an efficiency score when the task execution object processes the historical task, whether the task execution object belongs to an initiating mechanism of the target task to be allocated, and/or a task execution proportion when the task execution object processes the historical task; the type of the target task to be distributed corresponds to the attribute of the task execution object.
Alternatively,
determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm, wherein the method comprises the following steps:
and determining a target task execution object in the bipartite graph according to the matching degree by using a minimum cost maximum flow algorithm.
Alternatively,
and when the association relation indicates that the task to be distributed is not matched with the task execution object, the matching degree is zero.
Alternatively,
when the target task execution object is determined, allocating the tasks to be allocated to the target task execution object, including:
determining the number to be allocated of tasks to be allocated corresponding to each target task execution object aiming at a plurality of target task execution objects with the same attribute;
and distributing the tasks to be distributed matched with the target task execution objects to the plurality of target task execution objects according to the quantity to be distributed.
Alternatively,
according to the number to be distributed, distributing the tasks to be distributed matched with the target task execution objects to a plurality of target task execution objects, wherein the method comprises the following steps:
calculating a first variance of a plurality of quantities to be distributed;
and when the first variance is larger than a first preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset first balance strategy.
Alternatively,
the task execution object information further includes: the historical task amount of the task execution object completed in a preset historical time period;
according to the number to be distributed, distributing the tasks to be distributed matched with the target task execution objects to a plurality of target task execution objects, wherein the method comprises the following steps:
summing the quantity to be distributed corresponding to each target task execution object and the historical task quantity of the target task execution objects respectively to obtain a plurality of total task quantities, wherein the plurality of total task quantities correspond to the plurality of target task execution objects one to one;
calculating a second variance of the plurality of total task quantities;
and when the second variance is larger than a second preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset second balance strategy.
Alternatively,
the task execution object information further includes: a task capacity of the task execution object;
for each target task execution object, executing:
calculating the difference value between the quantity to be distributed and the task capacity;
and when the difference value is larger than a third preset threshold value, selecting the tasks to be distributed with the same number as the difference value, and distributing the tasks to be distributed to other task execution objects with the same attributes as the target task execution object.
Alternatively,
the task information to be distributed also comprises: the priority of the task to be allocated;
and determining a target task execution object according to the association relation and the priority by using a minimum cost maximum flow algorithm.
Alternatively,
the task execution object information further includes: the task execution efficiency of the task execution object;
and determining a target task execution object by using a minimum cost maximum flow algorithm according to the incidence relation, the priority and the task execution efficiency.
Alternatively,
when the target task execution object is not determined, the method further comprises the following steps:
marking tasks to be distributed which do not determine target task execution objects in the tasks to be distributed as tasks to be distributed with distribution failure;
increasing the failure times of the tasks to be distributed which are distributed in failure, and determining whether the failure times are less than a time threshold value;
and when the failure times are determined to be smaller than the time threshold value, the priority of the tasks to be distributed which are distributed in failure is improved, and the target task execution object is determined again according to the improved priority.
Alternatively,
and when the failure times are determined to be not less than the time threshold, sending prompt information about the distribution failure of the tasks to be distributed.
Alternatively,
after the task to be distributed is distributed to the target task execution object, the method further comprises the following steps:
determining whether tasks to be distributed which are successfully distributed but are not processed in time-out exist in the tasks to be distributed;
if so, the priority of the tasks to be distributed which are successfully distributed but not processed after time-out is improved, and the target task execution object is re-determined according to the improved priority.
Alternatively,
the minimum cost maximum flow algorithm is any one of the following: SPFA algorithm, Zkw algorithm, Dijkstra algorithm, or Dinic algorithm.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a task assigning apparatus.
The task allocation device comprises an information acquisition module, an association relation construction module, an object determination module and an allocation module; wherein:
the information acquisition module is used for acquiring information of tasks to be distributed and task execution object information, wherein the information of the tasks to be distributed comprises the types of the tasks to be distributed, and the task execution object information comprises the attributes of the task execution objects;
the association relationship construction module is used for constructing an association relationship between the tasks to be distributed and the task execution objects according to the types of the tasks to be distributed and the attributes of the task execution objects, wherein the association relationship indicates whether the tasks to be distributed are matched with the task execution objects or not;
the object determining module is used for determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm;
and the distribution module is used for distributing the tasks to be distributed to the target task execution objects when the target task execution objects are determined.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a task assigning electronic device.
An electronic device for task allocation according to an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by one or more processors, the one or more processors realize the task allocation method of the embodiment of the invention.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable storage medium.
A computer-readable storage medium of an embodiment of the present invention stores thereon a computer program that, when executed by a processor, implements a task allocation method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: after the information of the tasks to be distributed and the information of the task execution objects are obtained, the incidence relation between the tasks to be distributed and the task execution objects is automatically established, then the minimum cost maximum flow algorithm is utilized, the target task execution objects are determined from the task execution objects, and the tasks to be distributed are distributed to the target task execution objects, so that the automatic distribution of the tasks is realized, the efficiency and the accuracy of the task distribution are improved, the reasonable distribution of the tasks is ensured, and the full utilization of the human resources in the bank is realized.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a task assignment method according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a bipartite graph;
FIG. 3 is a schematic diagram of the main steps of a method of assigning tasks to be assigned that match target task execution objects to a plurality of target task execution objects;
FIG. 4 is a schematic diagram of the main steps of another method for assigning tasks to be assigned that match target task execution objects to a plurality of target task execution objects;
FIG. 5 is a schematic diagram of the main steps of a method of assigning a task to be assigned to a target task execution object;
FIG. 6 is a schematic diagram of the main steps of a task assignment method when the target task execution object is not determined;
FIG. 7 is a schematic diagram of the main modules of a task assignment device according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of the major modules of a task distribution system according to an embodiment of the present invention;
FIG. 9 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 10 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments of the present invention and the technical features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram of main steps of a task allocation method according to an embodiment of the present invention.
As shown in fig. 1, a task allocation method according to an embodiment of the present invention mainly includes the following steps:
step S101: acquiring information of tasks to be distributed and task execution object information, wherein the information of the tasks to be distributed comprises the types of the tasks to be distributed, and the information of the task execution objects comprises the attributes of the task execution objects.
In the embodiment of the invention, the information of the task to be distributed can be obtained from the task pool to be distributed, and then the information of the task execution object can be obtained from the task execution object resource pool; the task information to be distributed includes the types of the tasks to be distributed, such as the input tasks, the audit tasks and the like, and the task execution object information includes the attributes of the task execution object, such as departments, posts, working states and the like of the task execution object.
In the embodiment of the present invention, at intervals, the task information to be allocated may be obtained from the task pool to be allocated, and then the task execution object in the idle state at the current time may be obtained from the task execution object resource pool. And when the information of the task to be allocated is not acquired from the task pool to be allocated, it indicates that no new task to be allocated exists, and at this time, the task execution object may not be acquired from the task execution object resource pool, so as to save the computing resources.
In the embodiment of the invention, the information of the tasks to be distributed can also comprise the priority of the tasks to be distributed, whether the tasks to be distributed specify the task execution object and the like besides the types of the tasks to be distributed; the task execution object information may include, in addition to the attribute of the task execution object, a historical task amount of the task execution object completed within a preset historical period, a task capacity of the task execution object, a task execution efficiency of the task execution object, and the like.
Step S102: and constructing an incidence relation between the tasks to be distributed and the task execution objects according to the types of the tasks to be distributed and the attributes of the task execution objects, wherein the incidence relation indicates whether the tasks to be distributed are matched with the task execution objects or not.
In the embodiment of the present invention, a process of constructing an association relationship between a task to be allocated and a task execution object according to a type of the task to be allocated and an attribute of the task execution object includes: for each task execution object, executing: determining whether the type of the task to be distributed corresponds to the attribute of the task execution object, and if so, determining that the task to be distributed is matched with the task execution object; otherwise, the task to be distributed is determined not to be matched with the task execution object. That is, after determining the attribute of the obtained task execution object, for each task execution object, the obtained information of the task to be allocated may be traversed: for the task to be distributed with the category corresponding to the attribute of the task execution object, determining that the task to be distributed is matched with the task execution object; and for the task to be distributed with the category not corresponding to the attribute of the task execution object, determining that the task to be distributed is not matched with the task execution object.
In the embodiment of the present invention, after the association relationship is constructed, a bipartite graph between the task to be distributed and the task execution object may be generated according to the association relationship, where the bipartite graph includes a matching degree between the task to be distributed and the task execution object, and the matching degree is obtained based on the association relationship. And when the association relation indicates that the task to be distributed is not matched with the task execution object, the matching degree is zero.
As shown in fig. 2, fig. 2 is a bipartite graph, where T1, T2, T3, T4, …, Ti, …, Tm represent tasks to be allocated, m is the total number of tasks to be allocated, U1, U2, U3, U4, …, Uj, …, Un represent task execution objects, and n is the total number of task execution objects. For the matched task to be allocated and task execution object, an arrow connecting line may be used between the task to be allocated and the task execution object, and each line has a matching degree Q (i, j) between the task to be allocated and the task execution object. For the task to be allocated and the task execution object which are not matched, no edge connection is performed, and the matching degree is 0, as shown in the task to be allocated T1 and the task execution object U3 in fig. 2, since T1 and U3 are not matched, no arrow connection line is used between the two, and the matching degree is 0. However, the task T4 to be allocated in fig. 2 has no association with all the current task execution objects, and belongs to the task to be allocated without determining the target task execution object, and the processing mode is explained as follows; if the task execution object U4 has no association with all the tasks to be allocated, the tasks to be allocated are not allocated to it.
In the embodiment of the present invention, the matching degree may be calculated according to any one or more of the following conditions: the task execution object is a score when the task execution object is a priority processing object of a target task to be allocated, a quality score when the task execution object processes a historical task of which the type is the same as that of the target task to be allocated, an efficiency score when the task execution object processes the historical task, whether the task execution object belongs to an initiating mechanism of the target task to be allocated, and/or a task execution proportion when the task execution object processes the historical task; the type of the target task to be distributed corresponds to the attribute of the task execution object.
Specifically, for the matched task to be allocated and task execution object, the matching degree on each line in the bipartite graph can be calculated according to the following formula:
Q(i，j)＝αW0+βW1+γW2+δW3+εW4
wherein, W0 represents a score when the task execution object is a priority processing object of the target task to be allocated; w1 represents a quality score when the task execution target processes a history task of the same kind as the target task to be assigned; w2 represents an efficiency score when the task execution target processes a historical task; w3 represents whether the task execution object belongs to the target task to be distributed initiating mechanism, if yes, W3 takes 100, if no, W3 takes 0; w4 represents the task execution weight when the task execution target processes the historical task; alpha, beta, gamma, delta and epsilon are all coefficients, and the coefficients can be fixed values and can also be automatically adjusted or manually set according to needs. For example, when the kind of the task to be distributed is the entry task, higher efficiency is required, and the coefficient γ may be set higher than other coefficients at this time; and when the type of the task to be distributed is the auditing task, higher quality is required, and the coefficient beta can be set to be higher than other coefficients so as to meet the requirements of different types of tasks to be distributed.
When one or more conditions of W0, W1, W2, W3, or W4 are not required for a certain kind of task to be assigned when calculating the degree of matching, the coefficient corresponding to the unnecessary conditions may also be set to 0; when another kind of task to be allocated requires conditions other than W0, W1, W2, W3 and W4, the corresponding conditions and coefficients thereof may also be automatically adjusted or manually set as required, and this scheme is not particularly limited.
The task execution weight when the task execution object processes the historical task may be obtained according to the size of the role played by the task execution object in processing the historical task, may also be obtained according to the workload completed when the task execution object processes the historical task, and may also be obtained according to whether the task execution object processes the historical task is a main worker, that is, if the task execution weight is the main worker, the task execution weight is 100, otherwise, the task execution weight is 0, and may also be obtained according to other manners, which is not specifically limited in this scheme.
In the embodiment of the invention, after the bipartite graph between the task to be distributed and the task execution object is generated, the problem of distributing the task can be converted into solving
The maximization problem of (a); wherein:
q (i, j) represents the matching degree of the task Ti to be distributed and the task execution object Uj in the bipartite graph, i belongs to [1,2 … m ], j belongs to [1,2 … n ];
xijwhether the task Ti to be distributed is distributed to the task execution object Uj or not is shown, and the specific relation is as follows:
step S103: and determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm.
The minimum cost maximum flow algorithm can solve the path combination with the highest bipartite graph matching degree sum on the basis of ensuring the maximum flow, namely the optimal task-task execution object matching combination to be distributed. Therefore, in a preferred embodiment of the present invention, a least-cost maximum flow algorithm is used to solve the bipartite graph, namely: and determining a target task execution object in the bipartite graph according to the matching degree by using a minimum cost maximum flow algorithm.
In the embodiment of the present invention, the least cost maximum flow algorithm is any one of the following: SPFA algorithm, Zkw algorithm, Dijkstra algorithm, or Dinic algorithm.
In this embodiment of the present invention, the task information to be allocated may further include: the priority of the task to be allocated; at this time, the target task execution object can be determined according to the incidence relation and the priority by using a minimum cost maximum flow algorithm.
In this embodiment of the present invention, the task execution object information may further include: the task execution efficiency of the task execution object; at this time, a minimum cost maximum flow algorithm can be utilized to determine a target task execution object according to the incidence relation, the priority and the task execution efficiency.
Step S104: and when the target task execution object is determined, allocating the tasks to be allocated to the target task execution object.
In the embodiment of the invention, when the target task execution object is determined, the matching degree of a plurality of target task execution objects and some tasks to be allocated is consistent, so that the tasks are intensively allocated to a single executive. Therefore, the scheme further performs the equalization processing on the matching result according to the rule. That is, on the premise of the same weight, tasks are transferred from executives with a large amount of assigned tasks to executives with a small amount of assigned tasks. The assigned priority is not transferred.
In the embodiment of the invention, when the target task execution object is determined, the number to be allocated of the tasks to be allocated corresponding to each target task execution object can be determined for a plurality of target task execution objects with the same attribute; and distributing the tasks to be distributed matched with the target task execution objects to a plurality of target task execution objects according to the quantity to be distributed.
In the embodiment of the invention, when the tasks to be distributed, which are matched with the target task execution objects, are distributed to the target task execution objects according to the quantity to be distributed, the quantity to be distributed of the target task execution objects obtained by solving through the minimum cost maximum flow algorithm can be adjusted in a balanced manner, so that the tasks to be distributed, which are relatively balanced, are distributed to the target task execution objects, and the phenomenon that the target task execution objects with outstanding capability in all aspects are distributed with overweight tasks is avoided. Specifically, as shown in fig. 3, fig. 3 is a method for allocating a task to be allocated, which matches a target task execution object, to a plurality of target task execution objects, and the method mainly includes the following steps:
step S301: calculating first variances of a plurality of quantities to be distributed corresponding to a plurality of target task execution objects;
step S302: and when the first variance is larger than a first preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset first balance strategy.
In the embodiment of the present invention, when the task to be allocated, which is matched with the target task execution object, is allocated to the target task execution objects according to the quantity to be allocated, the quantity to be allocated of the target task execution objects may also be adjusted in a balanced manner according to the solution of the minimum cost max flow algorithm and the historical task quantity of the target task execution objects completed in the preset historical time period, so that the quantity of the task to be allocated, which is allocated to the target task execution objects in a certain period of time, is more balanced, and the target task execution objects with outstanding capabilities in various aspects are prevented from being allocated with the overweight tasks in a certain period of time. Specifically, when the task execution object information further includes a historical task amount of the task execution object completed within a preset historical period, another method for allocating the task to be allocated, which is matched with the target task execution object, to the plurality of target task execution objects is shown in fig. 4, and the method mainly includes the following steps:
step S401: summing the quantity to be distributed corresponding to each target task execution object and the historical task quantity of the target task execution objects respectively to obtain a plurality of total task quantities, wherein the plurality of total task quantities correspond to the plurality of target task execution objects one to one;
step S402: calculating a second variance of the plurality of total task quantities;
step S403: and when the second variance is larger than a second preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset second balance strategy.
In the embodiment of the present invention, the first equalization policy and the second equalization policy may be the same or different. When a task execution object is designated for a certain task to be allocated, the designated task execution object may be used as a target task execution object of the task to be allocated, and the task to be allocated is not transferred to other task execution objects in the process of balance adjustment.
In the embodiment of the present invention, the task execution object information may further include task capacity of the task execution object, where the task capacity is the maximum number of tasks to be allocated that can be executed by the task execution object; at this time, the number of tasks to be allocated to the task execution object is also limited by the task capacity, for example, if the task capacity of a certain task execution object is 10, this indicates that it can be allocated with 10 tasks to be allocated at most.
Specifically, when the task execution object information further includes the task capacity of the task execution object, a method for allocating the task to be allocated to the target task execution object is shown in fig. 5, that is, for each target task execution object, the following steps are performed:
step S501: calculating the difference value between the quantity to be distributed and the task capacity;
step S502: and when the difference value is larger than a third preset threshold value, selecting the tasks to be distributed with the same number as the difference value, and distributing the tasks to be distributed to other task execution objects with the same attributes as the target task execution object.
In the embodiment of the present invention, for a task to be allocated for which a target task execution object is not determined, as shown in a task to be allocated T4 in fig. 2, a task allocation method at this time is shown in fig. 6, where the method mainly includes the following steps:
step S601: marking tasks to be distributed which do not determine target task execution objects in the tasks to be distributed as tasks to be distributed with distribution failure;
step S602: increasing the failure times of the tasks to be distributed which are distributed in failure, and determining whether the failure times are less than a time threshold value;
step S603: when the failure times are determined to be smaller than the time threshold, the priority of the tasks to be distributed which are distributed in failure is improved, and the target task execution object is determined again according to the improved priority;
step S604: and when the failure times are determined to be not less than the time threshold, sending prompt information about the distribution failure of the tasks to be distributed.
In the embodiment of the present invention, after allocating the task to be allocated to the target task execution object, the method may further include: determining whether tasks to be distributed which are successfully distributed but are not processed in time-out exist in the tasks to be distributed; if so, the priority of the tasks to be distributed which are successfully distributed but not processed after time-out is improved, and the target task execution object is re-determined according to the improved priority.
In the embodiment of the invention, the task to be distributed which is successfully distributed but is not processed overtime can be marked as the task to be distributed which fails to be distributed, the failure times are increased, when the failure times are less than the threshold value of the times, the priority of the task to be distributed which fails to be distributed is improved, and the target task execution object is determined again according to the improved priority; and when the failure times are not less than the time threshold value, sending prompt information about the distribution failure of the tasks to be distributed.
In a preferred embodiment of the present invention, after the prompt information about the assignment failure of the task to be assigned is sent, the task to be assigned with the assignment failure multiple times can be assigned manually.
According to the task allocation method provided by the embodiment of the invention, after the information of the task to be allocated and the information of the task execution object are obtained, the incidence relation between the task to be allocated and the task execution object can be automatically constructed, then the minimum cost maximum flow algorithm is utilized to determine the target task execution object from the task execution object, and the task to be allocated is allocated to the target task execution object, so that the automatic allocation of the task is realized, the efficiency and the accuracy of the task allocation are improved, the reasonable allocation of the task is ensured, and the full utilization of the human resources in the bank is further realized.
Fig. 7 is a schematic diagram of main blocks of a task assigning apparatus according to an embodiment of the present invention.
As shown in fig. 7, a task assigning apparatus 700 according to an embodiment of the present invention includes: an information acquisition module 701, an association relationship construction module 702, an object determination module 703 and an allocation module 704; wherein:
the information acquisition module 701 is configured to acquire task information to be allocated and task execution object information, where the task information to be allocated includes a type of a task to be allocated, and the task execution object information includes an attribute of a task execution object;
an association relationship establishing module 702, configured to establish an association relationship between the task to be allocated and the task execution object according to the type of the task to be allocated and the attribute of the task execution object, where the association relationship indicates whether the task to be allocated and the task execution object are matched;
an object determining module 703, configured to determine, according to the association relationship, a target task execution object by using a minimum cost maximum flow algorithm;
and the allocating module 704 is configured to allocate the task to be allocated to the target task execution object when the target task execution object is determined.
In this embodiment of the present invention, the association relationship building module 702 is further configured to: for each task execution object, executing: determining whether the type of the task to be distributed corresponds to the attribute of the task execution object, and if so, determining that the task to be distributed is matched with the task execution object; otherwise, the task to be distributed is determined not to be matched with the task execution object.
In this embodiment of the present invention, the association relationship building module 702 is further configured to: and generating a bipartite graph between the tasks to be distributed and the task execution objects according to the incidence relation, wherein the bipartite graph comprises the matching degree of the tasks to be distributed and the task execution objects, and the matching degree is obtained based on the incidence relation.
In this embodiment of the present invention, the association relationship building module 702 is further configured to: calculating the matching degree according to any one or more of the following conditions: the task execution object is a score when the task execution object is a priority processing object of a target task to be allocated, a quality score when the task execution object processes a historical task of which the type is the same as that of the target task to be allocated, an efficiency score when the task execution object processes the historical task, whether the task execution object belongs to an initiating mechanism of the target task to be allocated, and/or a task execution proportion when the task execution object processes the historical task; the type of the target task to be distributed corresponds to the attribute of the task execution object.
In this embodiment of the present invention, the object determining module 703 is further configured to: and determining a target task execution object in the bipartite graph according to the matching degree by using a minimum cost maximum flow algorithm.
In the embodiment of the invention, when the association relation indicates that the task to be allocated is not matched with the task execution object, the matching degree is zero.
In this embodiment of the present invention, the allocating module 704 is further configured to: determining the number to be allocated of tasks to be allocated corresponding to each target task execution object aiming at a plurality of target task execution objects with the same attribute; and distributing the tasks to be distributed matched with the target task execution objects to the plurality of target task execution objects according to the quantity to be distributed.
In this embodiment of the present invention, the allocating module 704 is further configured to: calculating a first variance of a plurality of quantities to be distributed; and when the first variance is larger than a first preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset first balance strategy.
In this embodiment of the present invention, the task execution object information further includes: the historical task amount of the task execution object completed in a preset historical time period; an assigning module 704, further configured to: summing the quantity to be distributed corresponding to each target task execution object and the historical task quantity of the target task execution objects respectively to obtain a plurality of total task quantities, wherein the plurality of total task quantities correspond to the plurality of target task execution objects one to one; calculating a second variance of the plurality of total task quantities; and when the second variance is larger than a second preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset second balance strategy.
In this embodiment of the present invention, the task execution object information further includes: a task capacity of the task execution object; an assigning module 704, further configured to: for each target task execution object, executing: calculating the difference value between the quantity to be distributed and the task capacity; and when the difference value is larger than a third preset threshold value, selecting the tasks to be distributed with the same number as the difference value, and distributing the tasks to be distributed to other task execution objects with the same attributes as the target task execution object.
In the embodiment of the present invention, the task information to be allocated further includes: the priority of the task to be allocated; an object determination module 703, further configured to: and determining a target task execution object according to the association relation and the priority by using a minimum cost maximum flow algorithm.
In this embodiment of the present invention, the task execution object information further includes: the task execution efficiency of the task execution object; an object determination module 703, further configured to: and determining a target task execution object by using a minimum cost maximum flow algorithm according to the incidence relation, the priority and the task execution efficiency.
In this embodiment of the present invention, when the target task execution object is not determined, the allocating module 704 is further configured to: marking tasks to be distributed which do not determine target task execution objects in the tasks to be distributed as tasks to be distributed with distribution failure; increasing the failure times of the tasks to be distributed which are distributed in failure, and determining whether the failure times are less than a time threshold value; and when the failure times are determined to be smaller than the time threshold value, the priority of the tasks to be distributed which are distributed in failure is improved, and the target task execution object is determined again according to the improved priority.
In this embodiment of the present invention, the allocating module 704 is further configured to: and when the failure times are determined to be not less than the time threshold, sending prompt information about the distribution failure of the tasks to be distributed.
In this embodiment of the present invention, after allocating the task to be allocated to the target task execution object, the allocating module 704 is further configured to: determining whether tasks to be distributed which are successfully distributed but are not processed in time-out exist in the tasks to be distributed; if so, the priority of the tasks to be distributed which are successfully distributed but not processed after time-out is improved, and the target task execution object is re-determined according to the improved priority.
In the embodiment of the present invention, the least cost maximum flow algorithm is any one of the following: SPFA algorithm, Zkw algorithm, Dijkstra algorithm, or Dinic algorithm.
According to the task allocation device disclosed by the embodiment of the invention, after the information of the task to be allocated and the information of the task execution object are obtained, the incidence relation between the task to be allocated and the task execution object can be automatically constructed, then the minimum cost maximum flow algorithm is utilized, the target task execution object is determined from the task execution object, and the task to be allocated is allocated to the target task execution object, so that the automatic allocation of the task is realized, the efficiency and the accuracy of the task allocation are improved, the reasonable allocation of the task is ensured, and the full utilization of the human resources in the bank is further realized.
Besides the task allocation device allocates the tasks to be allocated, in a preferred embodiment of the present invention, the tasks to be allocated may be allocated by the intelligent management and control platform and the task policy scheduling platform together. Specifically, as shown in fig. 8, fig. 8 is a task allocation system, where the task allocation system 800 includes an intelligent management and control platform 801 and a task policy scheduling platform 802; wherein:
the intelligent control platform 801 is used for acquiring task information to be distributed according to priority; acquiring task execution object information; sending a task allocation request to the task policy scheduling platform 802, where the task allocation request includes information of a task to be allocated and information of a task execution object; receiving an allocation result returned by the task policy scheduling platform 802; checking the distribution result to determine whether a task to be distributed fails to be distributed exists; allocating the successfully allocated tasks to be allocated to corresponding target task execution objects; executing a preset redistribution strategy on the tasks to be distributed which are distributed in a failure mode;
the task policy scheduling platform 802 is configured to receive and analyze a task allocation request sent by the intelligent management and control platform 801 to obtain information of a task to be allocated and information of a task execution object; constructing a bipartite graph according to task information to be distributed and task execution object information; obtaining a distribution result by using the minimum cost maximum flow according to the bipartite graph; the allocation results are merged and then sent to the intelligent management and control platform 801.
The preset reallocation policy may include:
increasing the failure times of the tasks to be distributed which are distributed in failure, and determining whether the failure times are less than a time threshold value;
when the failure times are determined to be smaller than the time threshold, the priority of the tasks to be distributed which are distributed in failure is improved, and the target task execution object is determined again according to the improved priority;
and when the failure times are determined to be not less than the time threshold, sending prompt information about the distribution failure of the task to be distributed.
Fig. 9 shows an exemplary system architecture 900 to which a task allocation method or a task allocation apparatus according to an embodiment of the present invention can be applied.
As shown in fig. 9, the system architecture 900 may include terminal devices 901, 902, 903, a network 904, and an electronic device 905. Network 904 is the medium by which communication links are provided between terminal devices 901, 902, 903 and electronic device 905. Network 904 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 901, 902, 903 to interact with the electronic device 905 over the network 904 to receive or send messages or the like. The terminal devices 901, 902, 903 may have various client applications installed thereon, such as internet banking, a bank management system, and the like.
The terminal devices 901, 902, 903 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The electronic device 905 may be a server that provides various services, such as a back-office management server that provides support for a bank management system browsed by a user using the terminal devices 901, 902, 903. The background management server may analyze and perform other processing on the received data such as the task allocation request, and feed back a processing result (for example, task allocation information) to the terminal device.
It should be noted that, a task allocation method provided by the embodiment of the present invention is generally executed by the electronic device 905, and accordingly, a task allocation apparatus is generally disposed in the electronic device 905.
It should be understood that the number of terminal devices, networks, and electronic devices in fig. 9 is merely illustrative. There may be any number of terminal devices, networks, and electronic devices, as desired for implementation.
Referring now to FIG. 10, a block diagram of a computer system 1000 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 10, the computer system 1000 includes a Central Processing Unit (CPU)1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data necessary for the operation of the system 1000 are also stored. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.
The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The driver 1010 is also connected to the I/O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1010 as necessary, so that a computer program read out therefrom is mounted into the storage section 1008 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication part 1009 and/or installed from the removable medium 1011. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 1001.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an information acquisition module, an association relationship construction module, an object determination module, and an assignment module. The names of these modules do not limit the modules themselves in some cases, and for example, the information acquisition module may also be described as a "module for acquiring information of tasks to be allocated and information of task execution objects".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring information of tasks to be distributed and task execution object information, wherein the information of the tasks to be distributed comprises the types of the tasks to be distributed, and the task execution object information comprises the attributes of the task execution objects; according to the type of the task to be distributed and the attribute of the task execution object, constructing an incidence relation between the task to be distributed and the task execution object, wherein the incidence relation indicates whether the task to be distributed is matched with the task execution object or not; determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm; and when the target task execution object is determined, allocating the tasks to be allocated to the target task execution object.
According to the technical scheme of the embodiment of the invention, after the information of the task to be distributed and the information of the task execution object are obtained, the incidence relation between the task to be distributed and the task execution object can be automatically constructed, then the minimum cost maximum flow algorithm is utilized to determine the target task execution object from the task execution object, and the task to be distributed is distributed to the target task execution object, so that the automatic distribution of the task is realized, the efficiency and the accuracy of the task distribution are improved, the reasonable distribution of the task is ensured, and the full utilization of the human resources in the bank is further realized.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (19)
1. A task allocation method, comprising:
acquiring task information to be distributed and task execution object information, wherein the task information to be distributed comprises the type of a task to be distributed, and the task execution object information comprises the attribute of a task execution object;
establishing an incidence relation between the tasks to be distributed and the task execution objects according to the types of the tasks to be distributed and the attributes of the task execution objects, wherein the incidence relation indicates whether the tasks to be distributed are matched with the task execution objects or not;
determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm;
and when the target task execution object is determined, distributing the task to be distributed to the target task execution object.
2. The method according to claim 1, wherein the constructing an association relationship between the task to be distributed and the task execution object according to the category of the task to be distributed and the attribute of the task execution object comprises:
for each task execution object, performing:
determining whether the type of the task to be distributed corresponds to the attribute of the task execution object, if so, determining that the task to be distributed is matched with the task execution object; otherwise, determining that the task to be distributed is not matched with the task execution object.
3. The method of claim 1, further comprising:
and generating a bipartite graph between the task to be distributed and the task execution object according to the incidence relation, wherein the bipartite graph comprises the matching degree of the task to be distributed and the task execution object, and the matching degree is obtained based on the incidence relation.
4. The method of claim 3,
calculating the matching degree according to any one or more of the following conditions: the task execution object is a score when the task execution object is a priority processing object of a target task to be allocated, a quality score when the task execution object processes a historical task of which the type is the same as that of the target task to be allocated, an efficiency score when the task execution object processes the historical task, whether the task execution object belongs to an initiating mechanism of the target task to be allocated, and/or a task execution proportion when the task execution object processes the historical task; and the type of the target task to be distributed corresponds to the attribute of the task execution object.
5. The method of claim 3, wherein determining the target task execution object according to the correlation using a least cost maximum flow algorithm comprises:
and determining the target task execution object in the bipartite graph according to the matching degree by using a minimum cost maximum flow algorithm.
6. The method of claim 3,
and when the incidence relation indicates that the task to be distributed is not matched with the task execution object, the matching degree is zero.
7. The method according to claim 1, wherein when the target task execution object is determined, the allocating the task to be allocated to the target task execution object comprises:
determining the number to be allocated of the tasks to be allocated corresponding to each target task execution object aiming at a plurality of target task execution objects with the same attribute;
and distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects according to the quantity to be distributed.
8. The method according to claim 7, wherein the allocating the task to be allocated, which matches the target task execution object, to the target task execution objects according to the number to be allocated comprises:
calculating a first variance of a plurality of the quantities to be distributed;
and when the first variance is larger than a first preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset first balance strategy.
9. The method of claim 7, wherein the task execution object information further comprises: the historical task amount completed by the task execution object;
the allocating the tasks to be allocated, which are matched with the target task execution objects, to the target task execution objects according to the number to be allocated comprises:
summing the quantity to be distributed corresponding to each target task execution object and the historical task quantity of the target task execution object respectively to obtain a plurality of total task quantities, wherein the total task quantities correspond to the target task execution objects one to one;
calculating a second variance of the plurality of total task quantities;
and when the second variance is larger than a second preset threshold value, distributing the tasks to be distributed matched with the target task execution objects to the target task execution objects by using a preset second balance strategy.
10. The method of claim 7, wherein the task execution object information further comprises: a task capacity of the task execution object;
for each target task execution object, executing:
calculating the difference value between the quantity to be distributed and the task capacity;
and when the difference value is larger than a third preset threshold value, selecting the tasks to be distributed with the same number as the difference value, and distributing the tasks to be distributed to other task execution objects with the same attributes as the target task execution object.
11. The method according to claim 1, wherein the task information to be allocated further comprises: the priority of the task to be distributed;
and determining the target task execution object according to the incidence relation and the priority by using a minimum cost maximum flow algorithm.
12. The method of claim 11, wherein the task execution object information further comprises: the task execution efficiency of the task execution object;
and determining the target task execution object according to the incidence relation, the priority and the task execution efficiency by using a minimum cost maximum flow algorithm.
13. The method of claim 11, when the target task execution object is not determined, further comprising:
marking tasks to be distributed, which are not determined as the target task execution objects, of the tasks to be distributed as tasks to be distributed with distribution failure;
increasing the failure times of the tasks to be distributed which are distributed in failure, and determining whether the failure times are smaller than a time threshold value;
and when the failure times are determined to be smaller than the time threshold, the priority of the tasks to be distributed which are distributed in failure is improved, and the target task execution object is determined again according to the improved priority.
14. The method of claim 13,
and sending prompt information about the distribution failure of the task to be distributed when the failure times are determined to be not less than the time threshold.
15. The method of claim 11, further comprising, after said assigning the task to be assigned to the target task execution object:
determining whether tasks to be distributed which are distributed successfully but are not processed in time-out exist in the tasks to be distributed;
if so, the priority of the tasks to be distributed which are successfully distributed but not processed after time-out is improved, and the target task execution object is re-determined according to the improved priority.
16. The method according to any one of claims 1 to 15,
the minimum cost maximum flow algorithm is any one of the following algorithms: SPFA algorithm, Zkw algorithm, Dijkstra algorithm, or Dinic algorithm.
17. A task allocation device is characterized by comprising an information acquisition module, an association relation construction module, an object determination module and an allocation module; wherein:
the information acquisition module is used for acquiring task information to be distributed and task execution object information, wherein the task information to be distributed comprises the type of a task to be distributed, and the task execution object information comprises the attribute of a task execution object;
the incidence relation construction module is used for constructing the incidence relation between the tasks to be distributed and the task execution objects according to the types of the tasks to be distributed and the attributes of the task execution objects, wherein the incidence relation indicates whether the tasks to be distributed are matched with the task execution objects or not;
the object determining module is used for determining a target task execution object according to the incidence relation by using a minimum cost maximum flow algorithm;
and the distribution module is used for distributing the tasks to be distributed to the target task execution objects when the target task execution objects are determined.
18. A task allocation electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-16.
19. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-16.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110522436.7A CN113159628A (en) | 2021-05-13 | 2021-05-13 | Task allocation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110522436.7A CN113159628A (en) | 2021-05-13 | 2021-05-13 | Task allocation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113159628A true CN113159628A (en) | 2021-07-23 |
Family
ID=76874851
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110522436.7A Pending CN113159628A (en) | 2021-05-13 | 2021-05-13 | Task allocation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113159628A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657759A (en) * | 2021-08-17 | 2021-11-16 | 北京百度网讯科技有限公司 | Task processing method, device, equipment and storage medium |
| CN113869596A (en) * | 2021-10-12 | 2021-12-31 | 北京房江湖科技有限公司 | Task prediction processing method, device, product and medium |
| CN115907441A (en) * | 2022-11-10 | 2023-04-04 | 北京乾图科技有限公司 | Business process simulation method and system |
| CN116723225A (en) * | 2023-06-16 | 2023-09-08 | 广州银汉科技有限公司 | Automatic allocation method and system for game tasks |
-
2021
- 2021-05-13 CN CN202110522436.7A patent/CN113159628A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657759A (en) * | 2021-08-17 | 2021-11-16 | 北京百度网讯科技有限公司 | Task processing method, device, equipment and storage medium |
| CN113657759B (en) * | 2021-08-17 | 2023-10-31 | 北京百度网讯科技有限公司 | Task processing method, device, equipment and storage medium |
| CN113869596A (en) * | 2021-10-12 | 2021-12-31 | 北京房江湖科技有限公司 | Task prediction processing method, device, product and medium |
| CN115907441A (en) * | 2022-11-10 | 2023-04-04 | 北京乾图科技有限公司 | Business process simulation method and system |
| CN115907441B (en) * | 2022-11-10 | 2024-06-11 | 北京乾图科技有限公司 | Business process simulation method and system |
| CN116723225A (en) * | 2023-06-16 | 2023-09-08 | 广州银汉科技有限公司 | Automatic allocation method and system for game tasks |
| CN116723225B (en) * | 2023-06-16 | 2024-05-17 | 广州银汉科技有限公司 | Automatic allocation method and system for game tasks |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113159628A (en) | Task allocation method and device | |
| Lin et al. | A pretreatment workflow scheduling approach for big data applications in multicloud environments | |
| US20190095249A1 (en) | System, method, and medium for facilitating auction-based resource sharing for message queues in an on-demand services environment | |
| JP5767480B2 (en) | Information processing apparatus, information processing system, arrangement configuration determining method, program, and recording medium | |
| US8209695B1 (en) | Reserving resources in a resource-on-demand system for user desktop utility demand | |
| US9253048B2 (en) | Releasing computing infrastructure components in a networked computing environment | |
| WO2019056695A1 (en) | Task scheduling method and apparatus, terminal device, and computer readable storage medium | |
| WO2020148342A1 (en) | A method and a system for managing the computing resources of a cloud computing platform | |
| CN111861225B (en) | Task allocation method and device, electronic equipment and storage medium | |
| CN112016794B (en) | Resource quota management method and device and electronic equipment | |
| US20220156124A1 (en) | Systems and methods for managing resources in a virtual desktop infrastructure | |
| CN109152061A (en) | Channel agility method, apparatus, server and storage medium | |
| CN112132674A (en) | Transaction processing method and device | |
| WO2022262476A1 (en) | Dynamic renewable runtime resource management | |
| US20130238383A1 (en) | Auto-adjusting worker configuration for grid-based multi-stage, multi-worker computations | |
| US8548881B1 (en) | Credit optimization to minimize latency | |
| CN112241319A (en) | Method, electronic device and computer program product for balancing load | |
| CN110618855A (en) | Task allocation method and device, electronic equipment and storage medium | |
| US11366694B1 (en) | Estimating attributes of running workloads on platforms in a system of multiple platforms as a service | |
| US11693697B2 (en) | Optimizing placements of workloads on multiple platforms as a service based on costs and service levels | |
| WO2021068585A1 (en) | Resource processing method and apparatus, electronic device, and medium | |
| US11276017B2 (en) | Method and system for estimating efforts for software managed services production support engagements | |
| CN115344359A (en) | Computing power resource allocation method, device, computer readable storage medium and equipment | |
| Hosseini et al. | Modeling and analysis of a cooperative service network | |
| US11704156B2 (en) | Determining optimal placements of workloads on multiple platforms as a service in response to a triggering event |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |