CN117669919A - Multi-task distribution method, system, equipment and medium - Google Patents

Multi-task distribution method, system, equipment and medium Download PDF

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CN117669919A
CN117669919A CN202311484151.4A CN202311484151A CN117669919A CN 117669919 A CN117669919 A CN 117669919A CN 202311484151 A CN202311484151 A CN 202311484151A CN 117669919 A CN117669919 A CN 117669919A
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comprehensive
task
capacity
matrix
capability
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王骊
翁慧颖
方华
孙小江
胡晓哲
鄢雯璨
傅欣
周少伟
王立果
李晟玥
陈瑜
高瞻
王刘俊
张莹
金日强
李岩
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Materials Branch of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The application relates to the technical field of power warehouse task allocation, in particular to a multi-task allocation method, a system, equipment and a medium, and the technical scheme is as follows: acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated; acquiring index weights, and carrying out weighted summation on the historical capability index data sets according to the index weights to obtain a comprehensive capability evaluation set; converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array; and solving an allocation matrix according to the role requirement vector and the comprehensive capacity matrix, so that the capacity of workers is more comprehensively evaluated, and the efficiency and quality of task completion are improved.

Description

Multi-task distribution method, system, equipment and medium
Technical Field
The present disclosure relates to the field of task allocation in power warehouses, and in particular, to a method, a system, an apparatus, and a medium for multi-task allocation.
Background
The power warehouse is an important component of the power industry and carries key tasks such as storage, maintenance and distribution of power equipment, and in order to ensure the reliability and efficiency of power supply, the task distribution in the warehouse is of great importance. However, the tasks of power warehouses often cover a variety of different nature and complexity of work, such as equipment maintenance, inventory management, logistics coordination, and the like. These tasks require workers of different skill and ability levels to perform, and thus an intelligent task allocation method is needed to match the worker's ability and task needs to improve work quality and efficiency.
Conventional empirical scheduling methods tend to be inefficient and of poor quality, and therefore require the use of existing optimization methods in order to achieve an efficient task assignment scheme. In recent years, crowdsourcing platforms are rapidly developed, and a plurality of enterprises issue work tasks to the crowdsourcing platforms, collect solutions which are as excellent as possible, and improve the task completion efficiency through distributed collaboration. Aiming at the lack of measurement on the uncertainty of the capability of the workers in the crowdsourcing task, a software crowdsourcing task distribution method supporting the fuzzy measurement of the capability of the workers and role coordination is provided, the multi-attribute capability matching degree of the workers is evaluated according to the number of fuzzy intervals, and the comprehensive competence of the workers is calculated by using a fuzzy analytic hierarchy process. Task allocation in a power warehouse is a conventional multitasking problem, i.e., each employee can be qualified for the tasks, but the quality of the assessment based on different employees to accomplish the tasks varies. Algorithms that use crowdsourcing as an application scenario are not suitable for conventional multitasking algorithms. In addition, the existing research is not provided with comprehensive analysis on the capability of workers, the influence of matching degree of crowdsourcing tasks and the capability of workers on the distribution result is not comprehensively considered, and the problems are to be solved.
Disclosure of Invention
In order to more comprehensively evaluate the capability of workers and improve the efficiency and quality of task completion, the application provides a multi-task distribution method, a multi-task distribution system, multi-task distribution equipment and a multi-task distribution medium, which adopt the following technical scheme:
in a first aspect, the present application provides a method for multitasking, including:
acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
acquiring index weights, and carrying out weighted summation on the historical capability index data sets according to the index weights to obtain a comprehensive capability evaluation set;
converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
and solving an allocation matrix according to the character requirement vector and the comprehensive capacity matrix.
Preferably, the indexes of the comprehensive ability evaluation set include working skills, working quality, working efficiency and working attitude.
Preferably, the role requirement vector represents the minimum number of tasks that a worker needs to perform.
Preferably, the comprehensive capacity matrix is the matching capacity of each worker for each task.
Preferably, the step of converting the comprehensive ability evaluation set into a comprehensive ability cloud model to obtain a comprehensive ability matrix comprises the following specific steps:
introducing a cloud model theory, cm= { Ex, en, he },
wherein QS is a comprehensive capability evaluation set; ex is the average of the set QS; sigma is the standard deviation of Ex; s is S 2 Is the sample variance of Ex; n is the number of samples of the set QS, and the Euclidean distance is used for calculating the similarity, and the calculation formula is as follows:
converting the comprehensive capability evaluation set of the workers into a comprehensive capability cloud model, wherein p is a comprehensive capability value and the cloud model is { p, 0}, under the condition that the workers only finish one task in the history record; the cloud model for comprehensive ability evaluation of m workers is described as:
wherein,the comprehensive capacity cloud model of the worker for the task;
definition cm - And cm + The expression is as follows, which is the worst and best state of worker ability:
the competence of the worker for the task can be obtained by the method that:
and obtaining the comprehensive energy moment array Q.
Preferably, the method further comprises:
and analyzing according to the comprehensive capacity matrix and the distribution matrix to obtain the performance of the workgroup for indicating the completion quality of all tasks.
Preferably, the historical capability index data set represents a number of historical capability index values for which one worker completes a task to be assigned.
In a second aspect, the present application provides a multi-tasking system comprising:
a first acquisition module: the method comprises the steps of acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
and a second acquisition module: the comprehensive capacity evaluation method comprises the steps of obtaining index weights, and carrying out weighted summation on a historical capacity index data set according to the index weights to obtain a comprehensive capacity evaluation set;
capability evaluation module: the comprehensive capacity evaluation set is used for converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
the task allocation module: for solving an allocation matrix based on the character requirement vector and the comprehensive capacity matrix.
In a third aspect, the present application provides a multitasking device comprising a memory storing a computer program and a processor arranged to run the computer program to perform a multitasking method as described above.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform a method of multitasking as described above when run.
To sum up, compared with the prior art, the beneficial effects brought by the technical scheme provided by the application at least include:
according to the method, a historical capability index data set of similar tasks of workers is selected according to the tasks to be distributed, the role requirement vector of the tasks to be distributed is determined, after the historical capability index data set of the workers is obtained, the comprehensive capability evaluation set is obtained through weighting and summing according to the selected index weights, the historical comprehensive capability evaluation value of the workers is converted into a comprehensive capability cloud model, a comprehensive capability moment array is obtained after further processing, the capability of the workers is evaluated more comprehensively and accurately, the tasks are flexibly and accurately distributed according to the distribution matrix obtained according to the capability of the workers, and the efficiency and quality of task completion are improved.
Drawings
Fig. 1 is a schematic flow chart of a method for assigning multiple tasks according to an embodiment of the present application.
FIG. 2 is a worker a according to an embodiment of the present application 4 Capability assessment index information.
FIG. 3 is a worker a according to an embodiment of the present application 4 Historical comprehensive capability set.
FIG. 4 is a worker a according to an embodiment of the present application 4 And a comprehensive capacity cloud model for each subtask.
Fig. 5 is a table of employee task quality assessment according to an embodiment of the present application.
Fig. 6 is a schematic block diagram of a multi-task distribution system according to an embodiment of the present application.
Reference numerals illustrate:
1. a first acquisition module; 2. a second acquisition module; 3. a capability evaluation module; 4. and a task allocation module.
Detailed Description
The following further details the application in connection with fig. 1-6, and the terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting.
Referring to fig. 1, a method for assigning multiple tasks according to the present application specifically includes:
step S1: acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
step S2: acquiring index weights, and carrying out weighted summation on the historical capability index data sets according to the index weights to obtain a comprehensive capability evaluation set;
step S3: converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
step S4: and solving an allocation matrix according to the character requirement vector and the comprehensive capacity matrix.
Specifically, the embodiment of the application combines cloud model theory with an E-CARGO algorithm to solve the problem of power warehouse task allocation. According to the task to be allocated, a historical capability index data set of the same kind of task of a worker is selected, the role requirement vector of the allocation task is determined, after the historical capability index data set of the worker is obtained, the comprehensive capability evaluation set is obtained through weighting and summing according to the selected index weights, the capability of the worker can be comprehensively and accurately evaluated through cloud model theory, and various uncertain factors such as skill level, experience and adaptability are considered. The historical comprehensive capacity evaluation value of the workers is converted into a comprehensive capacity cloud model, the comprehensive capacity moment array is obtained after further processing, the capacity of the workers is evaluated more comprehensively and accurately, the task allocation becomes more self-adaptive and intelligent by the application of the E-CARGO algorithm, the task allocation is flexibly adjusted according to the properties of the task and the capacity of the workers, the allocation matrix is obtained according to the task allocation, the task is flexibly and accurately allocated, and the efficiency and the quality of task completion are improved.
In conventional multitasking, each worker is able to perform the tasks of the power warehouse, but in practice the quality of the assessment of the completion of these tasks varies from worker to worker. The task to be allocated is a task set of Ω= { Ω 1 ,Ω n ,...,Ω n }，Ω n For the nth task, each task is completed by one worker. Historical evaluation information of workers is obtained from management staff, and Λ= { Λ is caused 1 ,Λ 2 ,...,Λ m }，Λ m Representing an mth worker, each worker is capable of performing a plurality of tasks. Order the Representing worker Λ m Completion of task omega n The historical capability index data set represents a plurality of historical capability index values for which one worker completes a task to be assigned. The scoring of workers to complete the same task is different, and the abilities of different workers are different, so that the quality of the same task is different.
The calculation of the comprehensive energy moment array Q of the worker is mainly based on historical evaluation of the worker, and the comprehensive energy matrix is the matching capacity of each worker for each task and is obtained based on a cloud theoretical model. The history evaluation of workers mainly comprises four parts, namely working skills, working quality, working efficiency and working attitude. The four indexes are in the range of [0,1], and the ratio is 2:5:1:2 respectively. The ratio is derived from the warehouse manager. In this way we can obtain a reasonable set of comprehensive competency evaluations QS for a worker for a particular task.
As one embodiment, the method for converting the comprehensive ability evaluation set into the comprehensive ability cloud model comprises the following specific steps of:
and introducing a cloud model theory, wherein cm= { Ex, en and He }, ex is expected, en is entropy, he is super-entropy, the expected value is the most representative value of the comprehensive capacity, the entropy represents the granularity range of the comprehensive capacity, and the super-entropy represents the uncertainty of the granularity of the comprehensive capacity. The three numerical features of the cloud model may be calculated by the following formula:
wherein QS is a comprehensive capability evaluation set; ex is the average of the set QS; sigma is the standard deviation of Ex; s is S 2 Is the sample variance of Ex; n is the number of samples of the set QS, skills required by different tasks are different, and it is quite important to identify the differences between different workers for different tasks by calculating the similarity between cloud models. In the embodiment of the application, the similarity is calculated by adopting the Euclidean distance, and the calculation formula is as follows:
converting the comprehensive capability evaluation set of the workers into a comprehensive capability cloud model, wherein p is a unique comprehensive capability value, and the cloud model is { p, 0}, under the condition that the workers only finish one task in the history record; the cloud model for comprehensive ability evaluation of m workers is described as:
wherein,the comprehensive capacity cloud model of the worker for the task; workers have fluctuations that may have different comprehensive capacity values when completing a task. The higher the Ex value, the stronger the comprehensive ability of the worker, the lower the En and He values, the more stable the comprehensive ability of the worker, thereby defining cm - And cm + The expression is as follows, which is the worst and best state of worker ability:
from this, the competence of the worker ai for the task rj can be calculated using the following formula:
and obtaining the comprehensive energy moment array Q. Wherein Q [ i, j]The larger the value of (a) is, the description of worker a i For task r j The stronger the integration capability of (c).
After the evaluation matrix Q is obtained, a solution of the allocation matrix is required. Since the maximum value in Q [ i, j ] is 1, in order to convert the problem of solving the maximum value of the allocation problem into the problem of solving the minimum value, a matrix C is defined in which C [ i, j ] =1 to Q [ i, j ]. The problem of assignment of the Q matrix at this time is converted into assignment of the C matrix.
Solution adopted in the embodiment of the applicationThe procedure is a conversion method of generalized assignment problem. Herein definition of alpha i For the least number of tasks performed by person i, α' i The number of tasks that are most performed for person i; definition beta j To perform task j the minimum number of people, β' j The maximum number of people to perform task j; d is the total assigned number of people. The specific conversion process is as follows:
copy rows i of the C matrix alpha' i -1 row, then j of the replicated matrix is replicated β' j -1, obtaining a new matrix:
the matrix is sharedGo (go)/(go)>Columns C ij (i=1, …, m; j=1, …, n) is an α' i Line, beta' j Subarrays of columns, each element being C ij 。
Is provided with y=max (t, v, d), p=u-y, q=s-y, p->Adding p rows and q columns to obtain an expansion matrix:
wherein G is kj Is 1 column, beta' j Matrix of columns, k=1, 2, …, p; j=1, 2,..n.
Here, the Wherein the method comprises the steps of
Here F il Is an alpha' i Matrix of rows, 1 column, i=1, 2, …, m; l=1, 2, q, wherein->
Here, theWherein M is a sufficiently large number, < >>Is a sufficiently large number greater than M. The solution to matrix B is then that of matrix Q. The solution of matrix B here uses the hungarian algorithm.
After the allocation matrix is solved according to the role requirement vector and the comprehensive capacity matrix, the problem of allocation of the E-CARGO model to the task of the worker can be abstracted as sigma: =<E,C,O,R,A,G>. Wherein: e represents a problem environment involving a plurality of workers and a plurality of tasks; c is class set of abstract concepts in E; o is a specific object related to CAn object set; r is a task set to be allocated, represented as role; a is a candidate worker set, represented as agent; g is the work group, i.e. team of workers established by the task allocation algorithm. In the embodiment of the application, the tasks are mapped into roles, and the task set Ω= { Ω 1 ,Ω 2 ,...,Ω n Corresponding role set r= { R } 1 ,r 2 ,...,r n -a }; mapping workers as agents, a set of workers Λ= { Λ 1 ,Λ 2 ,...,Λ m Corresponding proxy set a= { a } 1 ,a 2 ,...,a m Task completion by workers as agents acting as an index set of worker's capabilitiesCorresponding proxy qualification set isIn addition, a simplified definition of the concepts associated with the present problem in E-CARGO is required.
Task demand vector L for workers. L (L) j Representing worker Λ j The minimum number of tasks that need to be completed. If L j Not less than 1, representing Λ j Multiple tasks need to be completed.
The worker comprehensive ability evaluation matrix Q. Q is an m×n matrix, Q [ i, j]∈[0,1](i is more than or equal to 0 and less than or equal to m; j is more than or equal to 0 and less than or equal to n). It represents worker a i For task r to be allocated j Is adequate. The comprehensive capacity of workers can be measured according to factors such as working skills, working quality, working efficiency, working attitude and the like.
The roles assign matrix X. X is an m n matrix, where X [ i, j ] ∈ {0,1} (0.ltoreq.i.ltoreq.m; 0.ltoreq.j.ltoreq.n), if X [ i, j ] =1, indicates that agent i is assigned to role j, i.e., worker i is assigned to role j, the agent at this time is referred to as an assigned agent, and if X [ i, j ] =0, indicates that agent i is not assigned to role j.
As one embodiment, the method further comprises:
and analyzing according to the comprehensive capacity matrix and the distribution matrix to obtain the performance of the workgroup for indicating the completion quality of all tasks.
Specifically, the workgroup performance ρ. All workers assigned tasks are formed into a work group G. ρ represents the sum of the competence values of all workers in G. The larger ρ is, the higher the quality of completion of all tasks. For a set of tasks, it is desirable to maximize the ability to work with workers, ensuring that ρ is highest for all tasks. A certain task is not necessarily assigned to the most competent person.
Based on the above definition, the objective function of the multitasking problem in the project:
s.t X[i,j]∈{0,1}；0≤i<m,0≤j<n；
as one implementation, there is currently a set of tasks { r } 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 Worker set { a }, worker set 1 ,a 2 ,a 3 ,a 4 }. I.e. there are 6 tasks, requiring 4 workers to complete. Each employee is now able to perform these tasks, but the quality of the assessment based on the completion of these tasks by the different employees varies. The character requirement vector is l= [1, 2]Worker a 1 Completing a task, worker a 2 Completing a task, worker a 3 And a 4 Each accomplishes two tasks. The index set is k= { k 1 ,k 2 ,k 3 ,k 4 And respectively representing the working skill, the working quality, the working efficiency and the working attitude. Below by worker a 4 The historical capability index information of the similar tasks is used as an example to calculate the evaluation value of the similar tasks on each task. Worker a 4 The capability evaluation index information is referred to fig. 2.
According to the set index weights 2:5:1:2, the worker a can be obtained 4 Historical integrated capability set of (c) and worker a 4 Is provided for each subtask. Worker a 4 Historical synthesis capability set referring to fig. 3.
Worker a 4 The comprehensive ability cloud model for each subtask is described with reference to fig. 4.
After computing the comprehensive capacity cloud model of all workers for each subtask, cm is obtained - ＝{0.37,0.1,0.03}，cm + = {0.857,0,0}. From this, a worker set { a } 1 ,a 2 ,a 3 ,a 4 For task set { r } 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 The comprehensive capacity assessment matrix Q is specifically shown in figure 5, and figure 5 is an employee task quality assessment table.
From the Q matrix described above, from C [ i, j ] =1 to Q [ i, j ], a C matrix can be obtained:
here l= [1, 2]Equivalent to each task being completed by only one person, a 1 And a 2 Each completing a task, a 3 And a 4 Each accomplishes two tasks. Corresponding vector α= [1, 2]，α′＝[1,1,2,2]，β＝[1,1,1,1,1,1]，β′＝[1,1,1,1,1,1]Corresponding to this Headcount of task d=4, y=max (t, v, d) =6, p=u-y=0, q=s-y=0. From the above calculation, matrix +.>The same matrix as matrix B. Copy rows i of the C matrix alpha' i -1 rows, i.e. the third and fourth rows of the C matrix are each duplicated one row; due to beta' j -1 = 0, the column direction of the c matrix is unchanged. Finally, we get +.>Matrix:
solving the matrix by using a Hungary algorithm:
the final assigned matrix X can be obtained from the above solution:
from the above distribution matrix X, it can be derived that worker a 1 Assigned task r 1 Worker a 2 Assigned task r 6 Worker a 3 Assigned task r 3 And r 4 Worker a 4 Assigned task r 2 And r 5 . The maximum ρ is 5.404.
Referring to fig. 5, a multi-tasking distribution system is provided for an embodiment of the present application, the system comprising:
the first acquisition module 1: the method comprises the steps of acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
the second acquisition module 2: the comprehensive capacity evaluation method comprises the steps of obtaining index weights, and carrying out weighted summation on a historical capacity index data set according to the index weights to obtain a comprehensive capacity evaluation set;
capability evaluation module 3: the comprehensive capacity evaluation set is used for converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
task allocation module 4: for solving an allocation matrix based on the character requirement vector and the comprehensive capacity matrix.
The present embodiments provide a multitasking device comprising a memory storing a computer program and a processor arranged to run the computer program to perform a multitasking method as described above.
Embodiments of the present application provide a computer readable storage medium having a computer program stored therein, wherein the computer program is configured to perform a method of multitasking as described above when run.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the apparatus and the product described above may refer to the corresponding procedures in the foregoing method embodiments, which are not described herein again.
In the several embodiments provided herein, it should be understood that the disclosed methods, systems, apparatus, and program products may be embodied in other ways.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A method of multitasking, comprising:
acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
acquiring index weights, and carrying out weighted summation on the historical capability index data sets according to the index weights to obtain a comprehensive capability evaluation set;
converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
and solving an allocation matrix according to the character requirement vector and the comprehensive capacity matrix.
2. The method of claim 1, wherein the indicators of the aggregate competence assessment set include work skills, work quality, work efficiency, and work attitude.
3. The multitasking method of claim 1, wherein said character requirement vector represents a minimum number of tasks a worker needs to accomplish.
4. The method of claim 1, wherein the aggregate capacity matrix is a match capacity of each worker for each task.
5. The method for assigning multiple tasks according to claim 4, wherein the step of converting the comprehensive ability evaluation set into a comprehensive ability cloud model to obtain a comprehensive ability matrix comprises the steps of:
introducing a cloud model theory, cm= { Ex, en, he },
wherein QS is a comprehensive capability evaluation set; ex is the average of the set QS; sigma is the standard deviation of Ex; s is S 2 Is the sample variance of Ex; n is the number of samples of the set QS, and the Euclidean distance is used for calculating the similarity, and the calculation formula is as follows:
converting the comprehensive capability evaluation set of the workers into a comprehensive capability cloud model, wherein p is a comprehensive capability value and the cloud model is { p, 0}, under the condition that the workers only finish one task in the history record; the cloud model for comprehensive ability evaluation of m workers is described as:
wherein,the comprehensive capacity cloud model of the worker for the task;
definition cm - And cm + The expression is as follows, which is the worst and best state of worker ability:
the competence of the worker for the task can be obtained by the method that:
and obtaining the comprehensive energy moment array Q.
6. The method of multitasking assignment of claim 1, further comprising:
and analyzing according to the comprehensive capacity matrix and the distribution matrix to obtain the performance of the workgroup for indicating the completion quality of all tasks.
7. The method of multitasking in accordance with claim 1, characterized in that said historical capability index data set represents a number of historical capability index values for one of the workers to complete a task to be assigned.
8. A multi-tasking distribution system comprising:
a first acquisition module: the method comprises the steps of acquiring a task to be allocated, selecting a historical capability index data set of similar tasks of workers according to the task to be allocated, and determining a role requirement vector of the task to be allocated;
and a second acquisition module: the comprehensive capacity evaluation method comprises the steps of obtaining index weights, and carrying out weighted summation on a historical capacity index data set according to the index weights to obtain a comprehensive capacity evaluation set;
capability evaluation module: the comprehensive capacity evaluation set is used for converting the comprehensive capacity evaluation set into a comprehensive capacity cloud model to obtain a comprehensive capacity moment array;
the task allocation module: for solving an allocation matrix based on the character requirement vector and the comprehensive capacity matrix.
9. A multitasking apparatus comprising a memory storing a computer program and a processor arranged to run the computer program to perform a multitasking method as claimed in any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program, wherein the computer program is arranged to execute the multitasking method of any of claims 1-7 when run.
CN202311484151.4A 2023-11-08 2023-11-08 Multi-task distribution method, system, equipment and medium Pending CN117669919A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118153921A (en) * 2024-05-10 2024-06-07 浙江海亮科技有限公司 Intelligent allocation method and system for lesson preparation tasks

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118153921A (en) * 2024-05-10 2024-06-07 浙江海亮科技有限公司 Intelligent allocation method and system for lesson preparation tasks

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