CN108093083B - Cloud manufacturing task scheduling method and device and terminal - Google Patents
Cloud manufacturing task scheduling method and device and terminal Download PDFInfo
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Abstract
The invention provides a cloud manufacturing task scheduling method, a cloud manufacturing task scheduling device and a terminal, and relates to the technical field of cloud manufacturing, wherein the method comprises the following steps: receiving a task to be distributed submitted by a user; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements; determining a non-functional requirement matrix corresponding to the non-functional requirement and determining a functional requirement matrix corresponding to the functional requirement; determining a task scheduling scheme corresponding to a task to be distributed by utilizing a genetic algorithm according to the non-functional demand matrix, the functional demand matrix and pre-stored service provider information; the corresponding relation between the tasks to be distributed and the service providers is recorded in the task scheduling scheme. The invention can effectively and reliably match with a proper service provider according to the individual requirements of the user, and better improves the user experience.
Description
Technical Field
The invention relates to the technical field of cloud manufacturing, in particular to a cloud manufacturing task scheduling method, a cloud manufacturing task scheduling device and a terminal.
Background
Cloud manufacturing is a new manufacturing mode which organizes online manufacturing resources (manufacturing cloud) according to user requirements by utilizing a network and a cloud manufacturing service platform and provides various on-demand manufacturing services for users, and can effectively solve the problems of manufacturing resource deficiency, coexistence of manufacturing resource idling phenomenon, insufficient manufacturing capability and excessive manufacturing capability in the current manufacturing industry. By establishing a public service platform (cloud manufacturing service platform) for sharing manufacturing resources, the cloud computing thought and the information technology can be used for realizing the optimal configuration and high sharing of the manufacturing resources.
The cloud manufacturing platform mainly aims at a manufacturing service demander and a manufacturing service provider, and how to match the requirement task of the manufacturing service demander with a proper manufacturing service provider, that is, how to schedule a proper manufacturing service provider to provide a corresponding service for the service demander so as to improve the user satisfaction degree is an important research topic at present. However, the inventor finds that, in the research process, the current cloud manufacturing platform performs task scheduling based on a uniform production mode, it is difficult to match a suitable manufacturing service provider for the personalized requirements of a service demander, and the user experience is poor.
Disclosure of Invention
In view of this, the present invention provides a cloud manufacturing task scheduling method, a cloud manufacturing task scheduling device, and a terminal, which can effectively and reliably match a suitable service provider according to a personalized requirement of a user, and better improve user experience.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a cloud manufacturing task scheduling method, including: receiving a task to be distributed submitted by a user; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements; determining a non-functional requirement matrix corresponding to the non-functional requirement, and determining a functional requirement matrix corresponding to the functional requirement; wherein the non-functional requirement matrix conforms to a consistency check condition; determining a task scheduling scheme corresponding to the task to be distributed by utilizing a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information; wherein, the corresponding relation between the tasks to be distributed and the service providers is recorded in the task scheduling scheme.
Further, the non-functional requirements include the importance of each non-functional requirement factor; the non-functional demand factors include time factors, cost factors and quality factors; the step of determining the non-functional requirement matrix corresponding to the non-functional requirement includes: establishing an initial non-functional requirement matrix according to the importance degree of each non-functional requirement factor submitted by the user; the initial non-functional requirement matrix records the importance degree ratio of the non-functional requirement factors between every two factors; judging whether the initial non-functional requirement matrix meets consistency test conditions or not; if not, adjusting the importance degree ratio between every two non-functional demand factors recorded in the initial non-functional demand matrix, and judging whether the non-functional demand matrix obtained after each adjustment meets the consistency check condition; if the adjustment times are lower than the preset adjustment times, obtaining a non-functional demand matrix meeting the consistency check condition, and determining the non-functional demand matrix meeting the consistency check condition as a non-functional demand matrix corresponding to the non-functional demand; and if the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times does not meet the consistency check condition, determining the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times as the non-functional requirement matrix corresponding to the non-functional requirement.
Further, the step of determining a functional requirement matrix corresponding to the functional requirement includes: generating a subtask directed graph according to the functional requirements submitted by the user; the subtask directed graph comprises a subtask sequence, a subtask type and a subtask connection relation; the functional requirements comprise information of each subtask of the tasks to be distributed; generating a functional requirement matrix based on the subtask directed graph; and the functional requirement matrix embodies the connection relation between every two subtasks and the subtask type sequence.
Further, the step of determining the task scheduling scheme corresponding to the task to be allocated by using a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information includes: solving the maximum eigenvalue of the non-functional demand matrix, and determining a non-functional demand vector corresponding to the non-functional demand matrix based on the maximum eigenvalue; the non-functional requirement vector comprises the weight of each non-functional requirement factor; establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each non-functional demand factor, the upper limit value and the lower limit value of each non-functional demand factor, pre-stored service provider information and the functional demand matrix; the service scheduling solution reflects the corresponding relation between the tasks to be distributed and the service providers; and solving an optimal service scheduling solution of the task scheduling function through a genetic algorithm, and taking the optimal service scheduling solution as a task scheduling scheme corresponding to the task to be distributed.
Further, the step of determining the non-functional requirement vector corresponding to the non-functional requirement matrix based on the maximum eigenvalue comprises solving the eigenvector β corresponding to the maximum eigenvalue, and transposing the eigenvector to obtain βT(ii) a Determining a non-functional requirement vector corresponding to the non-functional requirement matrix
Further, the step of establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each non-functional requirement factor, the upper limit value and the lower limit value of each non-functional requirement factor, pre-stored service provider information and the functional requirement matrix includes; establishing a task scheduling function f (X) corresponding to a service scheduling solution according to the following formula:
wherein X is the service scheduling solution, TmaxThe task completion time upper limit value corresponding to X; t isminThe lower limit value of the corresponding task completion time; cmaxThe upper limit value of the task completion cost corresponding to the X; cminThe lower limit value of the task completion cost corresponding to the X; qmaxThe task completion quality upper limit value corresponding to X; qminA lower limit value of task completion cost corresponding to X ηtWeight of time factor, ηcWeight for cost factor, ηqT (X) is the task completion time corresponding to X; c (X) is the task completion cost corresponding to X; q (X) is the task completion quality corresponding to X.
Further, in the genetic algorithm, setting the ith gene number of the chromosome to be equal to the sequence number of the service provider selected by the subtask corresponding to the subtask sequence number i in the functional requirement.
In a second aspect, an embodiment of the present invention further provides a cloud manufacturing task scheduling device, including: the task receiving module is used for receiving tasks to be distributed submitted by users; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements; the matrix determining module is used for determining a non-functional requirement matrix corresponding to the non-functional requirement and determining a functional requirement matrix corresponding to the functional requirement; wherein the non-functional requirement matrix conforms to a consistency check condition; the scheme determining module is used for determining a task scheduling scheme corresponding to the task to be distributed by utilizing a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information; wherein, the corresponding relation between the tasks to be distributed and the service providers is recorded in the task scheduling scheme.
In a third aspect, an embodiment of the present invention provides a terminal, where the terminal includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in any one of the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, embodiments of the present invention provide a computer storage medium for storing computer software instructions for an apparatus according to the second aspect.
The embodiment of the invention provides a cloud manufacturing task scheduling method, a cloud manufacturing task scheduling device and a cloud manufacturing task scheduling terminal, wherein a non-functional requirement matrix corresponding to a non-functional requirement of a user and a functional requirement matrix corresponding to a functional requirement of the user are determined, personalized functional requirements and non-functional requirements proposed by the user can be converted into mathematical expression forms for unified processing, a task scheduling scheme can be further determined by utilizing a genetic algorithm based on the non-functional requirement matrix, the functional requirement matrix and service provider information, the genetic algorithm can reliably and effectively search for an optimal solution, and a proper service provider capable of well meeting the user requirement can be found.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating a cloud manufacturing task scheduling method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a directed graph of subtasks according to an embodiment of the present invention;
FIG. 3 is a flow chart of another cloud manufacturing task scheduling method provided by the embodiment of the invention;
fig. 4 is a block diagram illustrating a cloud manufacturing task scheduling apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In view of the fact that task scheduling is performed on the basis of a uniform production mode on a cloud manufacturing platform in the prior art, it is difficult to match a suitable manufacturing service provider for personalized requirements of a service demander, and user experience is poor, in order to improve the problem, the cloud manufacturing task scheduling method, the cloud manufacturing task scheduling device and the cloud manufacturing task scheduling terminal provided by the embodiments of the present invention are applicable to the cloud manufacturing platform, and the embodiments of the present invention are described in detail below.
The first embodiment is as follows:
referring to a flowchart of a cloud manufacturing task scheduling method shown in fig. 1, the method may be executed by a terminal, where the terminal may be an intelligent terminal, a server, or the like; the method specifically comprises the following steps:
step S102, receiving a task to be distributed submitted by a user; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements. The user is also the service demander; in practical application, a task to be distributed can be decomposed into one or more subtasks, and the cloud manufacturing task scheduling method can schedule corresponding matched service providers for the subtasks.
Functional requirements are requirements that must be met for this task (hard requirements), and for ease of understanding, the following are exemplified: assuming that the task to be assigned is to make a product a, the functional requirement is a hard condition that the product a must have, such as that the processing technology of the product a needs to strictly follow three preset flow steps, or that the product a must be provided with grooves, holes and the like at specified positions. In the embodiment, the functional requirements are personalized, and different users can provide different personalized tasks according to personal requirements and preferences, such as the user can provide personalized functional requirements of product type, product shape, product color, additional functional modules and the like according to personal preferences.
The non-functional requirements are degree requirements on the basis of meeting functional requirements, and include factor requirements such as task completion time, task completion cost, task completion quality and the like, when a task is completed, the non-functional requirements need to be considered, and all factors of the non-functional requirements need to be balanced. The non-functional requirements vary from user to user, with varying requirements for the importance of non-functional requirements such as time to complete a task, cost, quality, etc.
Step S104, determining a non-functional requirement matrix corresponding to the non-functional requirement and determining a functional requirement matrix corresponding to the functional requirement.
Step S106, determining a task scheduling scheme corresponding to the task to be distributed by utilizing a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information; the corresponding relation between the tasks to be distributed and the service providers is recorded in the task scheduling scheme. And the corresponding relation between the tasks to be distributed and the service providers recorded in the task scheduling scheme, namely the optimal matching relation, is also preferably obtained. The service provider information may include service information of each service provider (such as a manufacturer, a processor, etc.), such as a service type, a task completion time, a task completion cost, a service provider location, and the like. In practical applications, each service provider may correspond to an identity number to facilitate storage and searching. The cloud manufacturing platform can be provided with a service providing database, and the corresponding relation between the serial number of each service provider and the information such as the service type, the time for completing each task, the cost, the position of the service provider and the like is correspondingly established. For example, a task to be allocated by a user can be divided into 3 subtasks, the cloud service platform determines that each subtask corresponds to 100 service providers respectively through searching, and finally, the unique service provider (preferred service provider) which is most matched with each subtask can be determined through a genetic algorithm, so that a preferred task scheduling scheme is obtained. It should be noted that the above is merely illustrative and should not be considered as limiting.
The embodiment of the invention provides a cloud manufacturing task scheduling method, which can convert individual functional requirements and non-functional requirements proposed by a user into mathematical expression forms for unified processing by determining a non-functional requirement matrix corresponding to the non-functional requirements of the user and a functional requirement matrix corresponding to the functional requirements of the user, and further can determine a task scheduling scheme by utilizing a genetic algorithm based on the non-functional requirement matrix, the functional requirement matrix and service provider information, the genetic algorithm can reliably and effectively search for an optimal solution, namely a suitable service provider which can well meet the requirements of the user can be found, and the task scheduling mode can effectively and reliably match the suitable service provider according to the individual requirements of the user, thereby improving the user experience.
In one embodiment, the non-functional requirements include a degree of importance of each non-functional requirement factor; non-functional demand factors include time factors, cost factors, and quality factors; the step of determining the non-functional requirement matrix corresponding to the non-functional requirement in step S104 may be performed as follows:
(1) establishing an initial non-functional requirement matrix according to the importance degree of each non-functional requirement factor submitted by a user; wherein, the initial non-functional requirement matrix records the importance degree ratio between every two non-functional requirement factors. The user can set the importance degree of the time factor, the cost factor and the quality factor according to the requirement of the user, the construction of the initial non-functional requirement matrix in the embodiment is also an expression form of the analytic hierarchy process, and a matrix of 3 x 3 order can be constructed by taking three factors including the time factor, the cost factor and the quality factor as examples, wherein a in the matrixijRepresenting the importance ratio of the factor i to the factor j, wherein if the importance ratio is 1, it represents that the two factors have the same importance compared with each other; if the importance degree ratio is 3, the former is slightly more important than the latter; if the importance ratio is 5, it means that the former is significantly more important than the latter; if the importance degree ratio is 7, it means that the former is more important than the latter; if the importance degree ratio is 9, it means that the former is extremely important than the latter; of course, ratios of 2, 4, 6, and 8 represent intermediate values of the above-described adjacent judgment. And conversely 1/3, 1/5, 1/7, 1/9, etc. In addition, if the ratio of the importance degree of the factor i to the factor j is aijI.e. filling the ith row and jth column position of the matrix, then factor j and factor iThe ratio of the degree of importance of is aji＝1/aijFilling in the ith column position of the jth row of the matrix. The importance ratio between non-functional demand factors, in one embodiment, may include: time/time, time/cost, time/quality, cost/cost, cost/time, cost/quality, quality/time, quality/cost.
(2) And judging whether the initial non-functional requirement matrix meets the consistency check condition. Because there is no fixed reference object when the user determines the importance degree of each factor, an error is likely to occur during comparison, that is, the importance degree of each factor initially set by the user may not be reasonable, so that the consistency check needs to be performed on the initial non-functional requirement matrix, and the specific implementation manner of the consistency check can be implemented by referring to the related technology, which is not described herein again. And if the consistency ratio corresponding to the initial non-functional requirement matrix is less than 0.1 after the verification, the initial non-functional requirement matrix is considered to meet the consistency verification condition, otherwise, the initial non-functional requirement matrix is not met.
(3) If not, adjusting the importance degree ratio between every two non-functional demand factors recorded in the initial non-functional demand matrix, and judging whether the non-functional demand matrix obtained after each adjustment meets the consistency check condition. The adjustment process, i.e., the consistency correction process, may, in one embodiment, instruct the user to readjust the importance of each factor and re-determine the factor importance ratio between each two. Only when the current non-functional requirement matrix is determined not to meet the consistency test condition, the next adjustment is needed, namely, the consistency correction is carried out on the current non-functional requirement matrix which does not meet the consistency test condition; if it is determined that the current non-functional requirement matrix has satisfied the consistency check condition, no further adjustment is made.
(4) And if the adjustment times are lower than the preset adjustment times, obtaining a non-functional demand matrix meeting the consistency check condition, and determining the non-functional demand matrix meeting the consistency check condition as a non-functional demand matrix corresponding to the non-functional demand. Of course, the maximum adjustment times can be preset, and the adjustment times should not exceed the maximum adjustment times, so that the adjustment times are limited, and the matrix determination efficiency is improved. Assuming that the preset adjusting times are 10 times, if the non-functional requirement matrix meeting the consistency checking condition is obtained when the adjusting times are less than 10 times, the non-functional requirement matrix meeting the consistency checking condition is obtained.
(5) And if the obtained non-functional demand matrix does not meet the consistency check condition when the adjustment times reach the preset adjustment times, determining the obtained non-functional demand matrix when the adjustment times reach the preset adjustment times as the non-functional demand matrix corresponding to the non-functional demand. That is, if the non-functional requirement matrix obtained when the adjustment times reach the preset upper limit value fails to pass the consistency check, the significance of consuming time and energy for adjustment is also small, so that the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times can be directly determined as the non-functional requirement matrix corresponding to the non-functional requirement, and subsequent calculation is performed based on the non-functional requirement matrix.
Assuming that the preset adjustment times (i.e., the preset maximum adjustment times) are H times, the matrix C is a non-functional requirement matrix after the user has performed the H consistent adjustments:
the maximum eigenvalue λ of the matrix C can be calculatedmax4.4291, the consistency index CI is 0.7145, and the consistency ratio CR is 0.7145/0.9 is 0.7939>0.1, which means that the matrix C fails the consistency check and does not satisfy the consistency check condition. However, the matrix C is obtained after H times of consistency adjustment, and reaches the upper limit value of the adjustment times, so that the matrix C is directly determined as the non-functional requirement matrix corresponding to the non-functional requirement without performing consistency adjustment again.
In an embodiment, the step of determining the functional requirement matrix corresponding to the functional requirement in step S104 may be performed as follows:
(1) generating a subtask directed graph according to the functional requirements submitted by the user; the subtask directed graph comprises a subtask sequence, a subtask type and a subtask connection relation; the functional requirements comprise information of each subtask of the tasks to be distributed. For ease of understanding, reference may be made to a schematic diagram of a subtask directed graph shown in FIG. 2. The task to be allocated of the user can be divided into 5 subtasks, the number corresponding to the subtask sequence is only the identification number of each subtask, the number corresponding to the subtask type represents the type number, each task type corresponds to a number, and for example, the subtask with the subtask type 1 is an assembly node, and the subtask with the subtask type 4 is a packaging node. In fig. 2, numerals in parentheses below each subtask node respectively represent the input number and the output number; for example, the corresponding (2,1) under the module with subtask sequence 4 represents two subtask inputs and 1 subtask output. The arrows in fig. 2 indicate the connection of the subtasks. Because the functional requirements comprise the information of each subtask of the task to be distributed, a subtask directed graph can be generated based on the information of each subtask of the task to be distributed, and the type, the connection relation and the like of each subtask can be clearly and simply displayed in the form of the subtask directed graph.
(2) Generating a functional requirement matrix based on the subtask directed graph; the functional requirement matrix represents the connection relationship between every two subtasks and the subtask type sequence.
For ease of understanding, taking the subtask directed graph shown in fig. 2 as an example, a specific process of generating the functional requirement matrix is illustrated here:
first, according to the subtask directed graph, the subtask type sequence corresponding to the subtask node whose subtask sequence is {1,2,3,4,5} is determined to be b ═ {3,2,5,1,4 }.
Because the number of the subtask nodes is 5, a matrix A of 5 × 5 is constructed, if the element of the ith row and the jth column of the A matrix is 1, the subtask node with the sequence i is indicated to have a connection relation with the subtask node with the sequence j, and if the element of the ith row and the jth column of the A matrix is 0, the subtask node with the sequence i is indicated to have no connection relation with the subtask node with the sequence j, and based on this, the matrix A is determined as follows:
further, a functional requirements matrix B ═ Ab may be generated based on matrix aT]：
By the method, the functional requirements submitted by the user can be converted into the functional requirement matrix in the mathematical form, so that the subsequent calculation processing is facilitated.
In one embodiment, the step S106 may be performed as follows:
(1) in one embodiment, the method comprises the steps of solving a maximum eigenvalue of a non-functional demand matrix, determining a non-functional demand vector corresponding to the non-functional demand matrix based on the maximum eigenvalue, wherein the non-functional demand vector comprises weights of non-functional demand factors, solving an eigenvector β corresponding to the maximum eigenvalue, and transposing the eigenvector to obtain βT(ii) a Finally, determining the non-functional demand vector corresponding to the non-functional demand matrix
The non-functional demand vector may also be referred to as a weight vector that embodies user preferences.
For ease of understanding, the non-functional requirement matrix is the matrix C described above, and is schematically illustrated as follows:
based on the foregoing calculation, λ can be knownmax4.4291, since each eigenvalue corresponds to an eigenvector, the solution determines λmaxThe corresponding characteristic vector is β ═ 0.4081,0.6341,0.6568]T；
The vector β is transposed and each element is then divided by the sum of the three elements β, i.e., substituted
Obtained η ═ ηt,ηc,ηq]＝[0.2402,0.3732,0.3866]Wherein, ηtWeight of time factor, ηcWeight for cost factor, ηqIs the weight of the quality factor. It is understood that in the present embodiment, the factors of the matrix C are sequentially ordered by time, cost, and quality, and the position of each factor in the matrix may be changed in practical applications.
(2) Establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each non-functional demand factor, the upper limit value and the lower limit value of each non-functional demand factor, pre-stored service provider information and a functional demand matrix; the service scheduling solution reflects the corresponding relation between the tasks to be distributed and the service providers.
In one embodiment, the task scheduling function f (x) corresponding to the service scheduling solution is established according to the following formula:
wherein X is the service scheduling solution, TmaxThe task completion time upper limit value corresponding to X; t isminThe lower limit value of the corresponding task completion time; cmaxThe upper limit value of the task completion cost corresponding to the X; cminThe lower limit value of the task completion cost corresponding to the X; qmaxThe task completion quality upper limit value corresponding to X; qminA lower limit value of task completion cost corresponding to X ηtWeight of time factor, ηcWeight for cost factor, ηqT (X) is the task completion time corresponding to X; c (X) is the task completion cost corresponding to X; q (X) is the task completion quality corresponding to X.
For ease of understanding, the reference η ═ ηt,ηc,ηq]＝[0.2402,0.3732,0.3866]For example, the task scheduling function f (x) corresponding to the service scheduling solution can be expressed as:
(3) and solving the optimal service scheduling solution of the task scheduling function through a genetic algorithm, and taking the optimal service scheduling solution as a task scheduling scheme corresponding to the task to be distributed. In the genetic algorithm, the ith gene number of the chromosome is set to be equal to the sequence number of the service provider selected by the subtask corresponding to the subtask with the functional requirement.
On the basis of the foregoing, reference may be made to a flowchart of another cloud manufacturing task scheduling method shown in fig. 3, where the flowchart simply illustrates the following steps:
step S302, setting an upper limit H of the required adjustment times, wherein the upper limit of the required adjustment times is also the preset adjustment times.
In step S304, the initialization required adjustment number k is 1. That is, the required adjustment number is initialized to k equal to 1.
And step S306, generating a subtask directed graph according to the functional requirements submitted by the user.
And step S308, determining a functional requirement matrix according to the subtask directed graph.
Step S310, consistency check is carried out on the non-functional requirement matrix corresponding to the non-functional requirement submitted by the user.
In step S312, it is determined whether the consistency check passes. If yes, go to step S320, and if no, go to step S314.
In step S314, it is determined whether k > -H is true, and if yes, step S320 is executed, and if no, step S316 is executed.
Step S316, performing consistency correction on the current non-functional requirement matrix.
In step S318, k is set to k + 1. Step S312 continues after step S318.
And step S320, obtaining a non-functional demand vector according to the determined non-functional demand matrix. Wherein, the non-functional requirement vector is also the user preference weight vector.
And step S322, solving the optimal task scheduling scheme by adopting a genetic algorithm.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the cloud manufacturing task scheduling method described in fig. 3 may refer to the corresponding process in the foregoing embodiment, and is not described herein again.
Example two:
corresponding to the cloud manufacturing task scheduling method provided in the foregoing embodiment, the present embodiment provides a cloud manufacturing task scheduling device, and referring to a structural block diagram of the cloud manufacturing task scheduling device shown in fig. 4, the cloud manufacturing task scheduling device includes the following modules:
a task receiving module 402, configured to receive a task to be allocated submitted by a user; the tasks to be distributed comprise personalized functional requirements and personalized non-functional requirements.
A matrix determining module 404, configured to determine a non-functional requirement matrix corresponding to the non-functional requirement, and determine a functional requirement matrix corresponding to the functional requirement; wherein the non-functional requirement matrix conforms to the consistency check condition.
A scheme determining module 406, configured to determine, according to the non-functional requirement matrix, the functional requirement matrix, and pre-stored service provider information, a task scheduling scheme corresponding to the task to be allocated by using a genetic algorithm; the corresponding relation between the tasks to be distributed and the service providers is recorded in the task scheduling scheme.
According to the cloud manufacturing task scheduling device provided by the embodiment of the invention, the non-functional requirement matrix corresponding to the non-functional requirement of the user and the functional requirement matrix corresponding to the functional requirement of the user are determined, the personalized functional requirement and the non-functional requirement proposed by the user can be converted into mathematical expression forms for unified processing, further, based on the non-functional requirement matrix, the functional requirement matrix and the service provider information, a task scheduling scheme can be determined by utilizing a genetic algorithm, the genetic algorithm can reliably and effectively search for an optimal solution, namely, a proper service provider which can well meet the user requirement can be found, the task scheduling mode can effectively and reliably match the proper service provider according to the personalized requirement of the user, and the user experience degree is improved.
In one embodiment, the non-functional requirements include a degree of importance of each non-functional requirement factor; non-functional demand factors include time factors, cost factors, and quality factors.
The matrix determination module 404 is configured to: establishing an initial non-functional requirement matrix according to the importance degree of each non-functional requirement factor submitted by a user; the initial non-functional requirement matrix records the importance degree ratio of the non-functional requirement factors between every two factors; judging whether the initial non-functional requirement matrix meets consistency test conditions or not; if not, adjusting the importance degree ratio between every two non-functional demand factors recorded in the initial non-functional demand matrix, and judging whether the non-functional demand matrix obtained after each adjustment meets the consistency check condition; if the adjustment times are lower than the preset adjustment times, obtaining a non-functional demand matrix meeting the consistency check condition, and determining the non-functional demand matrix meeting the consistency check condition as a non-functional demand matrix corresponding to the non-functional demand; and if the obtained non-functional demand matrix does not meet the consistency check condition when the adjustment times reach the preset adjustment times, determining the obtained non-functional demand matrix when the adjustment times reach the preset adjustment times as the non-functional demand matrix corresponding to the non-functional demand.
The matrix determination module 404 is further configured to: generating a subtask directed graph according to the functional requirements submitted by the user; the subtask directed graph comprises a subtask sequence, a subtask type and a subtask connection relation; the functional requirements comprise information of each subtask of the tasks to be distributed; generating a functional requirement matrix based on the subtask directed graph; the functional requirement matrix represents the connection relationship between every two subtasks and the subtask type sequence.
The solution determining module 406 includes a vector solving unit, a function determining unit, and a solution solving unit, which are described as follows:
the vector solving unit is used for solving the maximum eigenvalue of the non-functional demand matrix and determining the non-functional demand vector corresponding to the non-functional demand matrix based on the maximum eigenvalue; non-functional demand directionThe vector solving unit is further used for solving the eigenvector β corresponding to the maximum eigenvalue and transposing the eigenvector to obtain βT(ii) a Determining a non-functional requirement vector corresponding to a non-functional requirement matrix
The function determining unit is used for establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each non-functional demand factor, the upper limit value and the lower limit value of each non-functional demand factor, pre-stored service provider information and a functional demand matrix; the service scheduling solution reflects the corresponding relation between the tasks to be distributed and the service providers. The function determination unit is further configured to: establishing a task scheduling function f (X) corresponding to a service scheduling solution according to the following formula:
wherein X is the service scheduling solution, TmaxThe task completion time upper limit value corresponding to X; t isminThe lower limit value of the corresponding task completion time; cmaxThe upper limit value of the task completion cost corresponding to the X; cminThe lower limit value of the task completion cost corresponding to the X; qmaxThe task completion quality upper limit value corresponding to X; qminA lower limit value of task completion cost corresponding to X ηtWeight of time factor, ηcWeight for cost factor, ηqT (X) is the task completion time corresponding to X; c (X) is the task completion cost corresponding to X; q (X) is the task completion quality corresponding to X.
And the scheme solving unit is used for solving the optimal service scheduling solution of the task scheduling function through a genetic algorithm, and taking the optimal service scheduling solution as a task scheduling scheme corresponding to the task to be distributed. In the genetic algorithm, the ith gene number of the chromosome is set to be equal to the sequence number of the service provider selected by the subtask corresponding to the subtask with the functional requirement.
The device provided by the embodiment has the same implementation principle and technical effect as the foregoing embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiment for the portion of the embodiment of the device that is not mentioned.
Example three:
corresponding to the foregoing embodiments, the present embodiment provides a terminal including a memory for storing a program that supports a processor to execute the cloud manufacturing task scheduling method provided in the first embodiment, and a processor configured to execute the program stored in the memory.
Further, this embodiment also provides a computer storage medium for storing computer software instructions for the cloud manufacturing task scheduling device provided in the second embodiment.
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention, including: the processor 50, the memory 51, the bus 52 and the communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is arranged to execute executable modules, such as computer programs, stored in the memory 51.
The memory 51 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 52 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
The memory 51 is used for storing a program, the processor 50 executes the program 501 after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 50, or implemented by the processor 50.
The processor 50 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 50. The Processor 50 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the method in combination with the hardware thereof.
The cloud manufacturing task scheduling method, the cloud manufacturing task scheduling device and the computer program product of the terminal provided by the embodiments of the present invention include a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A cloud manufacturing task scheduling method is characterized by comprising the following steps:
receiving a task to be distributed submitted by a user; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements;
determining a non-functional requirement matrix corresponding to the non-functional requirement, and determining a functional requirement matrix corresponding to the functional requirement;
determining a task scheduling scheme corresponding to the task to be distributed by utilizing a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information; recording a corresponding relation between a task to be distributed and a service provider in the task scheduling scheme;
the non-functional requirements include the importance of each non-functional requirement factor; the non-functional demand factors include time factors, cost factors, and quality factors; the step of determining the non-functional requirement matrix corresponding to the non-functional requirement includes: establishing an initial non-functional requirement matrix according to the importance degree of each non-functional requirement factor submitted by the user; the initial non-functional requirement matrix records the importance degree ratio of the non-functional requirement factors between every two factors; judging whether the initial non-functional requirement matrix meets consistency test conditions or not; if not, adjusting the importance degree ratio between every two non-functional demand factors recorded in the initial non-functional demand matrix, and judging whether the non-functional demand matrix obtained after each adjustment meets the consistency check condition; if the adjustment times are lower than the preset adjustment times, obtaining a non-functional demand matrix meeting the consistency check condition, and determining the non-functional demand matrix meeting the consistency check condition as a non-functional demand matrix corresponding to the non-functional demand; and if the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times does not meet the consistency check condition, determining the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times as the non-functional requirement matrix corresponding to the non-functional requirement.
2. The method of claim 1, wherein the step of determining a functional requirement matrix corresponding to the functional requirement comprises:
generating a subtask directed graph according to the functional requirements submitted by the user; the subtask directed graph comprises a subtask sequence, a subtask type and a subtask connection relation; the functional requirements comprise information of each subtask of the tasks to be distributed;
generating a functional requirement matrix based on the subtask directed graph; and the functional requirement matrix embodies the connection relation between every two subtasks and the subtask type sequence.
3. The method according to claim 1, wherein the step of determining the task scheduling scheme corresponding to the task to be allocated by using a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information comprises:
solving the maximum eigenvalue of the non-functional demand matrix, and determining a non-functional demand vector corresponding to the non-functional demand matrix based on the maximum eigenvalue; the non-functional requirement vector comprises the weight of each non-functional requirement factor;
establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each non-functional demand factor, the upper limit value and the lower limit value of each non-functional demand factor, pre-stored service provider information and the functional demand matrix; the service scheduling solution reflects the corresponding relation between the tasks to be distributed and the service providers;
and solving an optimal service scheduling solution of the task scheduling function through a genetic algorithm, and taking the optimal service scheduling solution as a task scheduling scheme corresponding to the task to be distributed.
4. The method of claim 3, wherein the step of determining the non-functional requirement vector corresponding to the non-functional requirement matrix based on the maximum eigenvalue comprises:
solving the eigenvector β corresponding to the maximum eigenvalue;
transposing the feature vector to obtain βT；
Determining a non-functional requirement vector corresponding to the non-functional requirement matrix
5. The method according to claim 3, wherein the step of establishing a task scheduling function corresponding to a service scheduling solution according to the weight of each of the non-functional requirement factors, the upper limit value and the lower limit value of each of the non-functional requirement factors, the pre-stored service provider information and the functional requirement matrix comprises;
establishing a task scheduling function f (X) corresponding to a service scheduling solution according to the following formula:
wherein X is the service scheduling solution, TmaxThe task completion time upper limit value corresponding to X; t isminThe lower limit value of the corresponding task completion time; cmaxThe upper limit value of the task completion cost corresponding to the X; cminThe lower limit value of the task completion cost corresponding to the X; qmaxThe task completion quality upper limit value corresponding to X; qminA lower limit value of task completion cost corresponding to X ηtWeight of time factor, ηcWeight for cost factor, ηqT (X) is the task completion time corresponding to X; c (X) is the task completion cost corresponding to X; q (X) is the task completion quality corresponding to X.
6. The method of claim 1, wherein in the genetic algorithm, the ith gene number of a chromosome is set to be equal to the sequence number of the service provider selected by the subtask whose sequence number in the functional requirement is i.
7. A cloud manufacturing task scheduling apparatus, comprising:
the task receiving module is used for receiving tasks to be distributed submitted by users; the task to be distributed comprises personalized functional requirements and personalized non-functional requirements;
the matrix determining module is used for determining a non-functional requirement matrix corresponding to the non-functional requirement and determining a functional requirement matrix corresponding to the functional requirement;
the scheme determining module is used for determining a task scheduling scheme corresponding to the task to be distributed by utilizing a genetic algorithm according to the non-functional requirement matrix, the functional requirement matrix and pre-stored service provider information; recording a corresponding relation between a task to be distributed and a service provider in the task scheduling scheme;
the non-functional requirements include the importance of each non-functional requirement factor; the non-functional demand factors include time factors, cost factors, and quality factors; the matrix determination module is further to: establishing an initial non-functional requirement matrix according to the importance degree of each non-functional requirement factor submitted by the user; the initial non-functional requirement matrix records the importance degree ratio of the non-functional requirement factors between every two factors; judging whether the initial non-functional requirement matrix meets consistency test conditions or not; if not, adjusting the importance degree ratio between every two non-functional demand factors recorded in the initial non-functional demand matrix, and judging whether the non-functional demand matrix obtained after each adjustment meets the consistency check condition; if the adjustment times are lower than the preset adjustment times, obtaining a non-functional demand matrix meeting the consistency check condition, and determining the non-functional demand matrix meeting the consistency check condition as a non-functional demand matrix corresponding to the non-functional demand; and if the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times does not meet the consistency check condition, determining the non-functional requirement matrix obtained when the adjustment times reach the preset adjustment times as the non-functional requirement matrix corresponding to the non-functional requirement.
8. A terminal, characterized in that the terminal comprises a memory for storing a program enabling a processor to perform the method of any of claims 1 to 6 and a processor configured for executing the program stored in the memory.
9. A computer storage medium storing program code comprising instructions operable to perform the method of any one of claims 1 to 6.
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