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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task
matrix
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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

一种多任务分配方法、系统、设备及介质A multi-task allocation method, system, equipment and medium

技术领域Technical field

本申请涉及电力仓库任务分配的技术领域,尤其是涉及一种多任务分配方法、系统、设备及介质。This application relates to the technical field of power warehouse task allocation, and in particular to a multi-task allocation method, system, equipment and medium.

背景技术Background technique

电力仓库是电力行业的重要组成部分,承载着电力设备的存储、维护和分配等关键任务,为了保障电力供应的可靠性和效率,仓库内的任务分配至关重要。然而,电力仓库的任务通常涵盖了各种不同性质和复杂性的工作,例如设备维修、库存管理、物流协调等等。这些任务需要不同技能和能力水平的工人来执行,因此需要一种智能的任务分配方法来匹配工人的能力和任务的需求,以提高工作质量和效率。Power warehouses are an important part of the power industry, carrying key tasks such as storage, maintenance and distribution of power equipment. In order to ensure the reliability and efficiency of power supply, task distribution within the warehouse is crucial. However, power warehouse tasks typically cover a variety of jobs of varying nature and complexity, such as equipment maintenance, inventory management, logistics coordination, and more. These tasks require workers with different skills and ability levels to perform, so an intelligent task allocation method is needed to match workers' abilities and task requirements to improve work quality and efficiency.

传统的经验式排班方式往往效率低下,且质量不佳,因此为了获得高效的任务指派方案,需要借助已有的优化方法。近年来,众包平台发展迅速,众多企业将其工作任务发布到众包平台,征集尽可能优秀的解决方案,并通过分布式协作提高任务的完成效率。针对众包任务中缺乏对工人能力不确定性的度量,提出一种支持工人能力模糊度量和角色协同的软件众包任务分配方法,该方法以模糊区间数评估工人的多属性能力匹配度,使用模糊层次分析法计算工人的综合胜任能力。在电力仓库中的任务分配,为常规多任务分配问题,即每位员工均能胜任这些任务,但基于不同员工完成这些任务的评估质量各有差异。以众包为应用场景的算法并不适用于常规多任务分配算法。此外,现有研究尚缺乏对工人能力的全面分析,未综合考虑众包任务与工人能力的匹配程度对分配结果的影响,上述问题有待解决。The traditional empirical scheduling method is often inefficient and of poor quality. Therefore, in order to obtain an efficient task assignment solution, it is necessary to rely on existing optimization methods. In recent years, crowdsourcing platforms have developed rapidly. Many companies publish their work tasks to crowdsourcing platforms, solicit the best possible solutions, and improve the efficiency of task completion through distributed collaboration. Aiming at the lack of measurement of workers' ability uncertainty in crowdsourcing tasks, a software crowdsourcing task allocation method is proposed that supports fuzzy measurement of workers' abilities and role collaboration. This method uses fuzzy interval numbers to evaluate workers' multi-attribute ability matching degree, using The fuzzy analytic hierarchy process is used to calculate the comprehensive competency of workers. Task allocation in power warehouses is a general multi-task allocation problem, that is, each employee is qualified to perform these tasks, but the quality of evaluation based on the completion of these tasks by different employees varies. Algorithms based on crowdsourcing are not suitable for conventional multi-task allocation algorithms. In addition, existing research still lacks a comprehensive analysis of workers' abilities and does not comprehensively consider the impact of the matching degree of crowdsourcing tasks and workers' abilities on the allocation results. The above problems need to be solved.

发明内容Contents of the invention

为了更全面地评估工人的能力,提升任务完成的效率和质量,本申请提供一种多任务分配方法、系统、设备及介质,采用如下技术方案:In order to more comprehensively evaluate workers' abilities and improve the efficiency and quality of task completion, this application provides a multi-task allocation method, system, equipment and media, using the following technical solutions:

第一方面,本申请提供一种多任务分配方法,包括:In the first aspect, this application provides a multi-task allocation method, including:

获取待分配任务,根据待分配任务选取工人同类任务的历史能力指标数据集,确定待分配任务的角色要求向量;Obtain the tasks to be assigned, select the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determine the role requirement vector of the tasks to be assigned;

获取指标权重,根据指标权重对历史能力指标数据集加权求和得到综合能力评价集合;Obtain the indicator weight, and weight and sum the historical capability indicator data sets according to the indicator weight to obtain the comprehensive capability evaluation set;

将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵;Convert the comprehensive capability evaluation set into a comprehensive capability cloud model to obtain a comprehensive capability matrix;

根据角色要求向量和综合能力矩阵解得分配矩阵。The distribution matrix is obtained based on the role requirement vector and the comprehensive ability matrix solution.

优选的,所述综合能力评价集合的指标包括工作技能、工作质量、工作效率和工作态度。Preferably, the indicators of the comprehensive ability evaluation set include work skills, work quality, work efficiency and work attitude.

优选的,所述角色要求向量表示工人需要完成的最少任务数量。Preferably, the role requirement vector represents the minimum number of tasks that the worker needs to complete.

优选的,所述综合能力矩阵为各个工人对每个任务的匹配能力。Preferably, the comprehensive capability matrix is the matching capability of each worker for each task.

优选的,所述将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵的具体步骤为:Preferably, the specific steps for converting the comprehensive capability evaluation set into a comprehensive capability cloud model to obtain the comprehensive capability matrix are:

引入云模型理论,cm={Ex,En,He},Introducing cloud model theory, cm={Ex,En,He},

其中,QS为综合能力评价集合;Ex是集合QS的平均值;σ是Ex的标准差;S2是Ex的样本方差;N是集合QS的样本个数,采用欧式距离来计算相似性,计算公式如下:Among them, QS is the comprehensive ability evaluation set; Ex is the average value of the set QS; σ is the standard deviation of Ex; S 2 is the sample variance of Ex; N is the number of samples in the set QS. Euclidean distance is used to calculate the similarity. The formula is as follows:

将工人的综合能力评价集合转化成综合能力云模型,在历史记录中工人只完成过一次任务的情况下,p为综合能力值,云模型为{p,0,0};对m个工人的综合能力评价的云模型描述为:Convert the worker's comprehensive ability evaluation set into a comprehensive ability cloud model. When the worker has only completed the task once in the historical record, p is the comprehensive ability value, and the cloud model is {p,0,0}; for m workers The cloud model of comprehensive capability evaluation is described as:

其中,是工人对于任务的综合能力云模型;in, It is a cloud model of workers’ comprehensive capabilities for tasks;

定义cm-和cm+为工人能力的最差和最好状态,其表达式如下:Define cm - and cm + as the worst and best states of worker ability, and their expressions are as follows:

由此可以得到工人对于任务的胜任能力为:From this, we can get the worker’s competency for the task as:

得到综合能力矩阵Q。Obtain the comprehensive capability matrix Q.

优选的,还包括:Preferably, it also includes:

根据综合能力矩阵和分配矩阵分析得到表示所有任务完成质量高低的工作组性能。Based on the analysis of the comprehensive capability matrix and the allocation matrix, the work group performance indicating the quality of completion of all tasks is obtained.

优选的,所述历史能力指标数据集表示其中一个工人完成待分配任务的若干个历史能力指标值。Preferably, the historical capability indicator data set represents several historical capability indicator values in which a worker completed the task to be assigned.

第二方面,本申请提供一种多任务分配系统,包括:In the second aspect, this application provides a multi-task allocation system, including:

第一获取模块:用于获取待分配任务,根据待分配任务选取工人同类任务的历史能力指标数据集,确定待分配任务的角色要求向量;The first acquisition module: used to obtain the tasks to be assigned, select the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determine the role requirement vector of the tasks to be assigned;

第二获取模块:用于获取指标权重,根据指标权重对历史能力指标数据集加权求和得到综合能力评价集合;The second acquisition module: used to obtain indicator weights, and weighted summation of historical capability indicator data sets based on the indicator weights to obtain a comprehensive capability evaluation set;

能力评价模块:用于将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵;Capability evaluation module: used to convert the comprehensive capability evaluation set into a comprehensive capability cloud model to obtain a comprehensive capability matrix;

任务分配模块:用于根据角色要求向量和综合能力矩阵解得分配矩阵。Task allocation module: used to obtain the allocation matrix based on the role requirement vector and the comprehensive capability matrix solution.

第三方面,本申请提供一种多任务分配设备,包括存储器和处理器,所述存储器存储计算机程序,所述处理器被设置为运行所述计算机程序以执行如前所述的多任务分配方法。In a third aspect, the present application provides a multi-task allocation device, including a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to perform the multi-task allocation method as described above. .

第四方面,本申请提供一种计算机可读存储介质,所述计算机可读存储介质中存储有计算机程序,其中,所述计算机程序被设置为运行时执行如前所述的多任务分配方法。In a fourth aspect, the present application provides a computer-readable storage medium in which a computer program is stored, wherein the computer program is configured to execute the multi-task allocation method as described above when running.

综上所述,与现有技术相比,本申请提供的技术方案带来的有益效果至少包括:To sum up, compared with the existing technology, the beneficial effects brought by the technical solution provided by this application at least include:

本申请根据待分配任务选取工人同类任务的历史能力指标数据集,确定分配任务的角色要求向量在得到工人的历史能力指标数据集后,根据选定后的各指标权重加权求和得到综合能力评价集合,将工人历史综合能力评价值转化为综合能力云模型,在进一步处理后得到综合能力矩阵,更全面、准确地评估工人的能力,并以此为依据得到分配矩阵,进行灵活准确地分配任务,提升任务完成的效率和质量。This application selects the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determines the role requirement vector of the assigned tasks. After obtaining the historical capability indicator data set of workers, the comprehensive capability evaluation is obtained based on the weighted sum of the weights of each selected indicator. Collection, the workers' historical comprehensive ability evaluation values are converted into a comprehensive ability cloud model, and after further processing, a comprehensive ability matrix is obtained to more comprehensively and accurately evaluate the workers' abilities, and based on this, an allocation matrix is obtained to allocate tasks flexibly and accurately. , improve the efficiency and quality of task completion.

附图说明Description of drawings

图1是本申请实施例所述一种多任务分配方法的流程示意图。Figure 1 is a schematic flowchart of a multi-task allocation method according to an embodiment of the present application.

图2是本申请实施例所述工人a4能力评价指标信息。Figure 2 is the ability evaluation index information of worker a 4 described in the embodiment of this application.

图3是本申请实施例所述工人a4历史综合能力集合。Figure 3 is a historical comprehensive ability set of worker a 4 according to the embodiment of this application.

图4是本申请实施例所述工人a4对各子任务的综合能力云模型。Figure 4 is a comprehensive capability cloud model of worker a 4 for each sub-task in the embodiment of this application.

图5是本申请实施例所述员工任务质量评价表。Figure 5 is an employee task quality evaluation form according to the embodiment of this application.

图6是本申请实施例所述一种多任务分配系统的模块示意图。Figure 6 is a schematic module diagram of a multi-task allocation system according to the embodiment of the present application.

附图标记说明:Explanation of reference symbols:

1、第一获取模块;2、第二获取模块;3、能力评价模块;4、任务分配模块。1. The first acquisition module; 2. The second acquisition module; 3. Capability evaluation module; 4. Task allocation module.

具体实施方式Detailed ways

以下结合图1-图6对本申请作进一步详细说明,在本申请实施例中使用的术语是仅仅出于描述特定实施例的目的,而非旨在限制。The present application will be further described in detail below with reference to FIGS. 1-6 . The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to be limiting.

参照图1,本申请所涉及的一种多任务分配方法,具体包括:Referring to Figure 1, a multi-task allocation method involved in this application specifically includes:

步骤S1:获取待分配任务,根据待分配任务选取工人同类任务的历史能力指标数据集,确定待分配任务的角色要求向量;Step S1: Obtain the tasks to be assigned, select the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determine the role requirement vector of the tasks to be assigned;

步骤S2:获取指标权重,根据指标权重对历史能力指标数据集加权求和得到综合能力评价集合;Step S2: Obtain the indicator weights, and weight and sum the historical capability indicator data sets according to the indicator weights to obtain the comprehensive capability evaluation set;

步骤S3:将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵;Step S3: Convert the comprehensive capability evaluation set into a comprehensive capability cloud model to obtain a comprehensive capability matrix;

步骤S4:根据角色要求向量和综合能力矩阵解得分配矩阵。Step S4: Obtain the distribution matrix based on the role requirement vector and comprehensive ability matrix solution.

具体地,本申请实施例将云模型理论与E-CARGO算法相结合以解决电力仓库任务分配的问题。根据待分配任务选取工人同类任务的历史能力指标数据集,确定分配任务的角色要求向量在得到工人的历史能力指标数据集后,根据选定后的各指标权重加权求和得到综合能力评价集合,通过云模型理论,能够全面、准确地评估工人的能力,考虑到各种不确定因素,例如技能水平、经验和适应性。将工人历史综合能力评价值转化为综合能力云模型,在进一步处理后得到综合能力矩阵,更全面、准确地评估工人的能力,E-CARGO算法的应用将使任务分配变得更加自适应和智能,根据任务的性质和工人的能力进行灵活调整,以此为依据得到分配矩阵,进行灵活准确地分配任务,提升任务完成的效率和质量。Specifically, the embodiment of the present application combines cloud model theory with the E-CARGO algorithm to solve the problem of power warehouse task allocation. Select the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determine the role requirement vector of the assigned tasks. After obtaining the historical capability indicator data set of workers, the comprehensive capability evaluation set is obtained based on the weighted sum of the weights of each selected indicator. Through cloud model theory, workers' abilities can be comprehensively and accurately assessed, taking into account various uncertain factors such as skill level, experience, and adaptability. The worker's historical comprehensive ability evaluation value is converted into a comprehensive ability cloud model. After further processing, a comprehensive ability matrix is obtained to evaluate the worker's ability more comprehensively and accurately. The application of the E-CARGO algorithm will make task allocation more adaptive and intelligent. , flexibly adjust according to the nature of the task and the ability of the workers, and use this as a basis to obtain the allocation matrix, allocate tasks flexibly and accurately, and improve the efficiency and quality of task completion.

在常规多任务分配中,每位工人均能胜任电力仓库的任务,但是实际上不同工人完成这些任务的评估质量各有差异。待分配任务是任务集为Ω={Ω1n,...,Ωn},Ωn为第n个任务,每一个任务由一个工人完成。从管理人员那里获得工人的历史评价信息,令Λ={Λ12,...,Λm},Λm代表第m个工人,每个工人能完成多个任务。令 表示工人Λm完成任务Ωn的第k个历史能力指标值,历史能力指标数据集表示其中一个工人完成待分配任务的若干个历史能力指标值。工人完成同一项任务的评分存在差异,并且不同工人的能力不尽相同,完成同一项任务的质量也存在着差异。In conventional multi-tasking, each worker is qualified for the tasks in the power warehouse, but in fact the evaluation quality of different workers completing these tasks varies. The tasks to be assigned are task sets Ω={Ω 1n ,...,Ω n }, where Ω n is the nth task, and each task is completed by a worker. Obtain the historical evaluation information of workers from managers, let Λ = {Λ 1 , Λ 2 ,..., Λ m }, Λ m represents the m-th worker, and each worker can complete multiple tasks. make Represents the k-th historical capability index value of worker Λ m completing task Ω n . The historical capability indicator data set represents several historical capability indicator values of one of the workers completing the task to be assigned. There are differences in the ratings of workers completing the same task, and the abilities of different workers are different, and the quality of completing the same task is also different.

工人的综合能力矩阵Q的计算主要是根据工人的历史评价,综合能力矩阵为各个工人对每个任务的匹配能力,然后基于云理论模型来获得。工人的历史评价主要包含四个部分,分别是工作技能、工作质量、工作效率、工作态度。四个指标的范围为[0,1],分别占比2:5:1:2。该比例的来源于仓库管理方。通过这种方式,我们可以获得工人针对某一特定任务的合理的综合能力评价集合QS。The calculation of the worker's comprehensive ability matrix Q is mainly based on the worker's historical evaluation. The comprehensive ability matrix is the matching ability of each worker for each task, and is then obtained based on the cloud theory model. The historical evaluation of workers mainly includes four parts, namely work skills, work quality, work efficiency, and work attitude. The range of the four indicators is [0,1], accounting for 2:5:1:2 respectively. This ratio comes from the warehouse management side. In this way, we can obtain a reasonable comprehensive ability evaluation set QS of workers for a specific task.

作为其中一种实施方式,将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵的具体步骤为:As one of the implementation methods, the comprehensive capability evaluation set is converted into a comprehensive capability cloud model, and the specific steps to obtain the comprehensive capability matrix are:

引入云模型理论,cm={Ex,En,He},其中Ex为期望,En为熵,He为超熵,期望是综合能力最具有代表的值,熵表示综合能力的粒度范围,超熵代表的是综合能力粒度的不确定性。云模型的三个数值特征可以由如下的公式计算:Introducing the cloud model theory, cm = {Ex, En, He}, where Ex is expectation, En is entropy, and He is super entropy. Expectation is the most representative value of comprehensive ability, entropy represents the granularity range of comprehensive ability, and super entropy represents It is the uncertainty of the granularity of comprehensive capabilities. The three numerical characteristics of the cloud model can be calculated by the following formula:

其中,QS为综合能力评价集合;Ex是集合QS的平均值;σ是Ex的标准差;S2是Ex的样本方差;N是集合QS的样本个数,不同的任务需要的技能不一样,通过计算云模型之间相似性来识别不同工人之间对不同任务的差异性是相当重要的。本申请实施例采用欧式距离来计算相似性,计算公式如下:Among them, QS is the comprehensive ability evaluation set; Ex is the average value of the set QS; σ is the standard deviation of Ex; S 2 is the sample variance of Ex; N is the number of samples of the set QS. Different tasks require different skills. It is important to identify the differences between different workers on different tasks by computing similarities between cloud models. The embodiment of this application uses Euclidean distance to calculate similarity, and the calculation formula is as follows:

将工人的综合能力评价集合转化成综合能力云模型,在历史记录中工人只完成过一次任务的情况下,p为唯一的综合能力值,云模型为{p,0,0};对m个工人的综合能力评价的云模型描述为:Convert the worker's comprehensive ability evaluation set into a comprehensive ability cloud model. When the worker has only completed the task once in the historical record, p is the only comprehensive ability value, and the cloud model is {p,0,0}; for m The cloud model of workers’ comprehensive ability evaluation is described as:

其中,是工人对于任务的综合能力云模型;工人的存在波动性,在完成任务时会有不同的综合能力值。Ex值越高,工人的综合能力越强,En和He值越低,工人的综合能力越稳定,由此,定义cm-和cm+为工人能力的最差和最好状态,其表达式如下:in, It is a cloud model of workers' comprehensive ability for tasks; workers have fluctuations and will have different comprehensive ability values when completing tasks. The higher the Ex value, the stronger the worker's comprehensive ability, the lower the En and He values, the more stable the worker's comprehensive ability. Therefore, cm - and cm + are defined as the worst and best states of the worker's ability, and their expressions are as follows :

由此可以得到工人ai对于任务rj的胜任能力可以用如下的公式计算:From this we can get that worker ai’s competency for task rj can be calculated using the following formula:

得到综合能力矩阵Q。其中Q[i,j]的值越大,说明工人ai对于任务rj的综合能力越强。Obtain the comprehensive capability matrix Q. The larger the value of Q[i,j], the stronger the comprehensive ability of worker a i for task r j .

在得到评估矩阵Q,需要分配矩阵的求解。由于Q[i,j]中的最大值为1,为了把求分配问题最大值问题转化为求最小值问题,在此,在定义矩阵C,其中的C[i,j]=1-Q[i,j]。此时对Q矩阵的分配问题就转变为求C矩阵的分配。After obtaining the evaluation matrix Q, the distribution matrix needs to be solved. Since the maximum value in Q[i,j] is 1, in order to convert the maximum value of the allocation problem into the minimum value problem, here, the matrix C is defined, where C[i,j]=1-Q[ i, j]. At this time, the allocation problem of the Q matrix is transformed into the allocation of the C matrix.

本申请实施例采用的求解过程为广义指派问题的转换方法。这里定义αi为人员i的最少执行的任务数,α′i为人员i的最多执行的任务数;定义βj为执行任务j的最少人数,β′j为执行任务j的最多人数;d为总的被指派的人数。具体的转换过程如下:The solution process used in the embodiments of this application is a conversion method for generalized assignment problems. Here, α i is defined as the minimum number of tasks performed by person i, α′ i is the maximum number of tasks performed by person i; β j is defined as the minimum number of people performing task j, and β′ j is the maximum number of people performing task j; d is the total number of assigned persons. The specific conversion process is as follows:

将C矩阵的各行i复制α′i-1行,然后将复制后的矩阵的j复制β′j-1,得到新矩阵:Copy each row i of the C matrix by α′ i -1 rows, and then copy j of the copied matrix by β′ j -1 to obtain a new matrix:

该矩阵共有行,/>列,Cij(i=1,…,m;j=1,…,n)是一个α′i行,β′j列的子阵,各元素为CijThis matrix has a total of OK,/> Column, C ij (i=1,…,m; j=1,…,n) is a subarray with α′ i rows and β′ j columns, and each element is C ij .

y=max(t,v,d),p=u-y,q=s-y,对/>增加p行,q列,得到一个扩展矩阵:set up y=max(t,v,d), p=uy, q=sy, right/> Add p rows and q columns to get an extended matrix:

其中Gkj为一个1列,β′j列的矩阵,k=1,2,…,p;j=1,2,..,n。Among them, G kj is a matrix with 1 column and β′ j column, k=1,2,...,p; j=1,2,...,n.

此处 其中 here in

此处Fil为一个α′i行,1列的矩阵,i=1,2,…,m;l=1,2,..,q, 其中/> Here F il is a matrix with α′ i rows and 1 column, i=1,2,…,m; l=1,2,…,q, Among them/>

此处的其中M为一个足够大的数,/>为一个比M大的足够大的数。则对矩阵B的求解就是矩阵Q的求解。这里的矩阵B的求解采用匈牙利算法。Here's Where M is a large enough number,/> is a sufficiently large number larger than M. Then the solution to matrix B is the solution to matrix Q. The matrix B here is solved using the Hungarian algorithm.

根据角色要求向量和综合能力矩阵解得分配矩阵后,E-CARGO模型对工人的任务的分配问题可以抽象为:∑∷=<E,C,O,R,A,G>。其中:E表示一个涉及多个工人和多个任务的问题环境environment;C是E中抽象概念的类class集合;O是与C相关的具体对象object集合;R是待分配的任务集合,代表为role;A是候选的工人集合,代表为agent;G是工作组group,即由任务分配算法建立的工人团队。本申请实施例中,将任务映射为角色,任务集合Ω={Ω12,...,Ωn}对应的角色集合R={r1,r2,...,rn};将工人映射为代理,工人集合Λ={Λ12,...,Λm}对应代理集合A={a1,a2,...,am},将工人完成任务为代理扮演角色,工人能力的指标集合对应的代理资格集合为此外,还需对E-CARGO中与本问题相关的概念做一下简化定义。After obtaining the allocation matrix based on the role requirement vector and the comprehensive capability matrix solution, the E-CARGO model's task allocation problem for workers can be abstracted as: ∑∷=<E,C,O,R,A,G>. Among them: E represents a problem environment involving multiple workers and multiple tasks; C is a class collection of abstract concepts in E; O is a specific object collection related to C; R is a collection of tasks to be assigned, represented by role; A is a candidate worker set, represented by agent; G is a work group group, that is, a worker team established by a task allocation algorithm. In the embodiment of this application, tasks are mapped to roles, and the task set Ω={Ω 12 ,...,Ω n } corresponds to the role set R={r 1 ,r 2 ,...,r n } ; Mapping workers as agents, the worker set Λ = {Λ 1 , Λ 2 ,..., Λ m } corresponds to the agent set A = {a 1 , a 2 ,..., a m }, and the worker's task completion is Agents play roles, a collection of indicators of worker capabilities The corresponding agent qualification set is In addition, it is necessary to make a simplified definition of the concepts related to this issue in E-CARGO.

工人的任务需求向量L。Lj表示工人Λj需要完成的最少任务数量。如果Lj≥1,表示Λj需要完成多项任务。The worker’s task demand vector L. L j represents the minimum number of tasks that worker Λ j needs to complete. If L j ≥ 1, it means that Λ j needs to complete multiple tasks.

工人综合能力评估矩阵Q。Q是一个m×n的矩阵,Q[i,j]∈[0,1](0≤i≤m;0≤j≤n)。它表示工人ai对于待分配任务rj的胜任程度。工人的综合能力可以根据工作技能、工作质量、工作效率、工作态度等因素衡量。Worker comprehensive ability evaluation matrix Q. Q is an m×n matrix, Q[i,j]∈[0,1](0≤i≤m; 0≤j≤n). It represents the degree of competence of worker a i for the task r j to be assigned. The comprehensive ability of workers can be measured based on factors such as work skills, work quality, work efficiency, and work attitude.

角色分配矩阵X。X是一个m×n的矩阵,其中X[i,j]∈{0,1}(0≤i≤m;0≤j≤n),如果X[i,j]=1,表示代理i被分配给了角色j,也就是工人i被分配给了角色j,此时的代理被称为已分配的代理,如果X[i,j]=0,则表示代理i没有被分配给角色j。Role assignment matrix X. X is an m×n matrix, where X[i,j]∈{0,1} (0≤i≤m; 0≤j≤n). If Assigned to role j, that is, worker i is assigned to role j. The agent at this time is called an assigned agent. If X[i,j]=0, it means that agent i is not assigned to role j.

作为其中一种实施方式,还包括:As one of the implementation methods, it also includes:

根据综合能力矩阵和分配矩阵分析得到表示所有任务完成质量高低的工作组性能。Based on the analysis of the comprehensive capability matrix and the allocation matrix, the work group performance indicating the quality of completion of all tasks is obtained.

具体地,工作组性能ρ。所有被分配了任务的工人行成一个工作组G。ρ表示G中所有工人的能力值总和。ρ越大,表示所有任务的完成质量越高。对于一组任务,需要最大化发挥工人的能力,确保所有任务的ρ最高。某一任务并不一定分配给胜任程度最高的人。Specifically, work group performance ρ. All workers assigned tasks form a work group G. ρ represents the sum of ability values of all workers in G. The larger ρ is, the higher the completion quality of all tasks is. For a set of tasks, it is necessary to maximize the worker's ability and ensure that the ρ of all tasks is the highest. A given task is not necessarily assigned to the most competent person.

基于以上的定义,该项目中多任务分配问题的目标函数:Based on the above definition, the objective function of the multi-task allocation problem in this project is:

s.t X[i,j]∈{0,1};0≤i<m,0≤j<n;s.t X[i,j]∈{0,1}; 0≤i<m,0≤j<n;

作为其中一种实施方式,当前有任务集合{r1,r2,r3,r4,r5,r6},工人集合{a1,a2,a3,a4}。即有6项任务,需要4个工人去完成。现在每个员工均能胜任这些任务,但是基于不同员工完成这些任务的评估质量各有差异。角色要求向量为L=[1,1,2,2],工人a1完成一项任务,工人a2完成一项任务,工人a3和a4各完成两项任务。指标集合为k={k1,k2,k3,k4},分别代表工作技能、工作质量、工作效率、工作态度。下面以工人a4完成同类任务的历史能力指标信息为例计算其在各任务上的评价值。工人a4能力评价指标信息参照图2。As one of the implementation methods, there are currently a task set {r 1 , r 2 , r 3 , r 4 , r 5 , r 6 } and a worker set {a 1 , a 2 , a 3 , a 4 }. That is, there are 6 tasks that require 4 workers to complete. Today every employee is competent in these tasks, but the quality of assessments based on how well each employee completes these tasks varies. The role requirement vector is L=[1,1,2,2]. Worker a 1 completes one task, worker a 2 completes one task, and workers a 3 and a 4 each complete two tasks. The indicator set is k={k 1 , k 2 , k 3 , k 4 }, which respectively represent work skills, work quality, work efficiency, and work attitude. The following uses the historical ability index information of worker a 4 in completing similar tasks as an example to calculate its evaluation value on each task. Refer to Figure 2 for worker a4 ’s ability evaluation index information.

根据设定的各指标权重2:5:1:2,可以得到工人a4的历史综合能力集合和工人a4的对各子任务的综合能力云模型。工人a4历史综合能力集合参照图3。According to the set weight of each indicator 2:5:1:2, the historical comprehensive ability set of worker a 4 and the comprehensive ability cloud model of worker a 4 for each sub-task can be obtained. Refer to Figure 3 for the historical comprehensive ability set of worker a 4 .

工人a4对各子任务的综合能力云模型为参照图4。The comprehensive capability cloud model of worker a 4 for each sub-task is shown in Figure 4.

在计算全部工人的对各子任务的综合能力云模型后,得到cm-={0.37,0.1,0.03},cm+={0.857,0,0}。由此可以得到工人集合{a1,a2,a3,a4}对任务集合{r1,r2,r3,r4,r5,r6}的综合能力的评估矩阵Q,具体对应如图5所示,图5为员工任务质量评价表。After calculating the comprehensive capability cloud model of all workers for each sub-task, cm - ={0.37,0.1,0.03}, cm + ={0.857,0,0} are obtained. From this, we can get the evaluation matrix Q of the comprehensive ability of the worker set {a 1 , a 2 , a 3 , a 4 } to the task set {r 1 , r 2 , r 3 , r 4 , r 5 , r 6 }, specifically The correspondence is shown in Figure 5, which is the employee task quality evaluation form.

由上述的Q矩阵,根据C[i,j]=1-Q[i,j],由此可以得到C矩阵:From the above Q matrix, according to C[i,j]=1-Q[i,j], the C matrix can be obtained:

这里L=[1,1,2,2],相当于每个任务有且仅有一人完成,a1与a2各完成一个任务,a3与a4各完成两件任务。对应向量α=[1,1,2,2],α′=[1,1,2,2],β=[1,1,1,1,1,1],β′=[1,1,1,1,1,1],与此对应的 任务的总人数d=4,y=max(t,v,d)=6,p=u-y=0,q=s-y=0。从上面的计算可以得出,矩阵/>与矩阵B为同一矩阵。将C矩阵的各行i复制α′i-1行也就是,把C矩阵的第三行与第四行各复制一行;由于β′j-1=0,C矩阵的列方向不做变化。最后,我们得到如下的/>矩阵:Here L = [1, 1, 2, 2], which is equivalent to one and only one person completing each task. A 1 and a 2 each complete one task, and a 3 and a 4 each complete two tasks. Corresponding vector α=[1,1,2,2], α′=[1,1,2,2], β=[1,1,1,1,1,1], β′=[1,1 ,1,1,1,1], corresponding to this The total number of people on the task is d=4, y=max(t,v,d)=6, p=uy=0, q=sy=0. From the above calculation, it can be concluded that the matrix/> It is the same matrix as matrix B. Copying each row i of the C matrix to the α′ i -1 row means copying one row each of the third and fourth rows of the C matrix; since β′ j -1=0, the column direction of the C matrix does not change. Finally, we get the following /> matrix:

对该矩阵利用匈牙利算法进行求解:This matrix is solved using the Hungarian algorithm:

根据上面的求解可以得到最终的分配的矩阵X:According to the above solution, the final assigned matrix X can be obtained:

从上面的分配矩阵X可以得出,工人a1分配的任务r1,工人a2分配的任务r6,工人a3分配的任务r3与r4,工人a4分配的任务r2与r5。最大ρ为5.404。 From the above allocation matrix 5 . The maximum ρ is 5.404.

参照图5,为本申请实施例提供一种多任务分配系统,该系统包括:Referring to Figure 5, a multi-task allocation system is provided for this embodiment of the present application. The system includes:

第一获取模块1:用于获取待分配任务,根据待分配任务选取工人同类任务的历史能力指标数据集,确定待分配任务的角色要求向量;The first acquisition module 1: used to obtain the tasks to be assigned, select the historical capability indicator data set of similar tasks of workers based on the tasks to be assigned, and determine the role requirement vector of the tasks to be assigned;

第二获取模块2:用于获取指标权重,根据指标权重对历史能力指标数据集加权求和得到综合能力评价集合;Second acquisition module 2: used to obtain indicator weights, and weighted summation of historical capability indicator data sets based on the indicator weights to obtain a comprehensive capability evaluation set;

能力评价模块3:用于将综合能力评价集合转化为综合能力云模型,得到综合能力矩阵;Capability evaluation module 3: used to convert the comprehensive capability evaluation set into a comprehensive capability cloud model to obtain a comprehensive capability matrix;

任务分配模块4:用于根据角色要求向量和综合能力矩阵解得分配矩阵。Task allocation module 4: used to obtain the allocation matrix based on the role requirement vector and comprehensive capability matrix solution.

本申请实施例提供一种多任务分配设备,包括存储器和处理器,所述存储器存储计算机程序,所述处理器被设置为运行所述计算机程序以执行如前所述的多任务分配方法。An embodiment of the present application provides a multi-task allocation device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the multi-task allocation method as described above.

本申请实施例提供一种计算机可读存储介质,所述计算机可读存储介质中存储有计算机程序,其中,所述计算机程序被设置为运行时执行如前所述的多任务分配方法。Embodiments of the present application provide a computer-readable storage medium in which a computer program is stored, wherein the computer program is configured to execute the multi-task allocation method as described above when running.

所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,上述描述的装置和产品的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the devices and products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

在本申请所提供的几个实施例中,应该理解到,所披露的方法、系统、装置和程序产品,可以通过其它的方式实现。In the several embodiments provided in this application, it should be understood that the disclosed methods, systems, devices and program products can be implemented in other ways.

另外,在本申请各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

以上所述,以上实施例仅用以说明本申请的技术方案,而非对其限制;尽管参照前述实施例对本申请进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。As mentioned above, the above embodiments are only used to illustrate the technical solution of the present application, but not to limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still make the foregoing technical solutions. The technical solutions described in each embodiment may be modified, or some of the technical features may be equivalently replaced; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions in each embodiment of the present application.

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 (2)

* 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
CN119151267A (en) * 2024-11-20 2024-12-17 上海深蔚科技有限公司 Crowd-sourced task distribution method for complex task multi-person collaboration

Cited By (2)

* 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
CN119151267A (en) * 2024-11-20 2024-12-17 上海深蔚科技有限公司 Crowd-sourced task distribution method for complex task multi-person collaboration

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