CN113159548A - Service matching method for network collaborative manufacturing - Google Patents

Service matching method for network collaborative manufacturing Download PDF

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CN113159548A
CN113159548A CN202110394829.4A CN202110394829A CN113159548A CN 113159548 A CN113159548 A CN 113159548A CN 202110394829 A CN202110394829 A CN 202110394829A CN 113159548 A CN113159548 A CN 113159548A
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qos
service
services
candidate service
matching
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王辉
贾宁
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Tongji University
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Tongji University
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention provides a service matching method for network collaborative manufacturing, and belongs to the field of network collaborative manufacturing. In the first part, technical resources are regarded as a cloud service and described by a six-element group, and in the second part, a service matching algorithm considering QoS and correlation simultaneously is provided. Based on the result of function matching, the algorithm screens out a QoS candidate service set with higher QoS. On the basis of the QoS candidate service set, services which are screened out in the previous step due to the fact that the QoS is not high enough but have high enough relevance are additionally reserved through the relevance of the services, and the relevant services can further improve the QoS of the services in the subsequent combination process. The algorithm can generate a more comprehensive candidate service set, and is beneficial to subsequent service combination.

Description

Service matching method for network collaborative manufacturing
Technical Field
The invention relates to the field of network collaborative manufacturing, in particular to a technical resource intelligent matching method based on QoS and correlation.
Background
A networked collaborative manufacturing system is an open, multi-platform, interoperable manufacturing system interconnected by a computer network from a variety of heterogeneous distributed manufacturing resources that can provide stable, reliable, high quality, on-demand manufacturing resources and manufacturing capabilities throughout the life of the manufacturing. It is based on advanced manufacturing models and information technologies such as internet, internet of things, virtualization, service oriented technologies, cloud computing, etc.
The network collaborative manufacturing technology resource can be regarded as a service, and the matching and combination of the services are key steps for providing high-quality services for users. The service matching can realize efficient, intelligent and accurate service search and is mainly divided into keyword-based matching and semantic ontology-based matching. The service combination can enable a user to obtain more comprehensive services, and methods used for service combination modeling mainly comprise a service ontology description method, a description logic method, a dynamic description logic method, an ontology workflow method and the like.
Service matching and combining are two closely related links, and a complete and high-quality candidate service set is the basis of service combination. In the previous studies, the correlation between services was usually considered only in the service composition phase, but not in the service matching phase. For services which have not high quality, but can improve the overall quality when combined with other services, a candidate service set may be screened out in a matching stage, so that a better service combination result cannot be obtained. Therefore, it is also necessary to consider the association relationship in the service matching phase.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent technical resource matching method based on QoS and correlation.
Technical scheme
A service matching method for network collaborative manufacturing is characterized in that in a first part, technical resources are regarded as a cloud service and are described by a six-element group, and in a second part, a service matching algorithm considering QoS and correlation at the same time is provided. Based on the result of function matching, the algorithm screens out a QoS candidate service set with higher QoS. On the basis of the QoS candidate service set, services which are screened out in the previous step due to the fact that the QoS is not high enough but have high enough relevance are additionally reserved through the relevance of the services, and the relevant services can further improve the QoS of the services in the subsequent combination process. The algorithm can generate a more comprehensive candidate service set, and is beneficial to subsequent service combination.
Based on the candidate service set generated by functional matching, the candidate service set with higher quality is screened out based on non-functional matching, namely through QoS of the candidate service, relevance among services and the like.
The invention has the characteristics that: in the first part, a service hexahydric group model is used, and in the second part, a service matching algorithm considering QoS and correlation simultaneously is provided. The algorithm standardizes the QoS of the services in the candidate service set based on the result of function matching, realizes the quality ranking of the services by the standard Euclidean distance with the ideal services, and obtains the QoS candidate service set with higher QoS. On the basis of the QoS candidate service set, services which are screened out in the previous step due to the fact that the QoS is not high enough but have high enough relevance are additionally reserved through the relevance of the services, and the relevant services can further improve the QoS of the services in the subsequent combination process. Therefore, the algorithm can generate a more comprehensive candidate service set, and is beneficial to subsequent service combination.
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FIG. 1 is a general flow chart of the service matching combination of the present invention
Description model of cloud services in the first part of FIG. 2
QoS and correlation based service matching algorithm flow in the second part of FIG. 3
Detailed Description
The invention takes the technical resources as a cloud service. As shown in fig. 1, the service matching combination is divided into two steps of matching and combining, wherein, the service matching has two steps,
the first step is function matching, namely screening and constructing a candidate service set meeting requirements through basic information, type information, state information, function information and the like of the service;
and the second step is non-functional matching, and based on the candidate service set generated in the first step, the candidate service set with higher quality is screened out through the QoS of the candidate service, the relevance among the services and the like.
The algorithm involved in the invention is non-functional matching.
In the first part of the function matching of the present invention, the cloud service S is represented as a six-tuple (as shown in fig. 2):
S＝<Tbasic,Tcategory,Tstatus,Tfunction,TQoS,Tcorrelate>
in the formula (I), the compound is shown in the specification,
Tbasicbasic information of the cloud service, such as name, profile, is included to briefly describe the cloud service.
TcategoryThe category to which the cloud service belongs is represented, and mainly includes manufacturing resources, manufacturing capability, and manufacturing knowledge. Wherein, the manufacturing resources comprise hardware resources or equipment in manufacturing activities, such as machine tools, materials and the like; the manufacturing capability is a software resource related to the manufacturing process, and can complete tasks such as product development, design, analysis, simulation, test, manufacturing process management and the like, such as computer aided design and enterprise resource planning; manufacturing knowledge refers primarily to various technical resources, such as design manuals, experience, models, cases, standards, protocols, courses, and so forth.
TstatusAnd the state information represents the cloud service and reflects the current working state of the manufacturing resources, including idle, not full, overload and the like.
TfunctionThe function information representing the cloud service reflects the manufacturing capability of the resource, including input, output, execution condition, effect, and the like of the service.
TQoSThe quality of the cloud service is represented, the evaluation information of the cloud service is reflected, and the evaluation information mainly comprises execution time, cost, reliability, time without failure, credibility and the like.
TcorrelateThe method is used for describing the service with quality correlation with S, and comprises the service name, the service provider and the influence degree of the service name, the service provider and the service provider on the QoS after the service name and the service provider are matched.
The description model considers the basic information, the type information, the state information and the function information of the cloud service for function matching, and also considers the quality correlation between QoS and the cloud service for non-function matching. The description model reflects the basic characteristics of the cloud service and lays a foundation for intelligent matching and combination of the cloud service.
Based on the model of fig. 2 described above, the cloud service is described by six-tuple, and then the following service matching algorithm is performed on the cloud service described by six-tuple (see fig. 3).
In the second part of non-functional matching of the present invention, a service matching algorithm based on QoS and correlation is proposed (as shown in fig. 3), on one hand, services with high QoS can be found and services with low QoS can be excluded, and on the other hand, some services with correlation although the QoS is not high enough are additionally reserved, so that the QoS can be improved when combining, and finally, a comprehensive candidate service set is formed.
Step one, constructing an original decision matrix. Assuming that n candidate services are provided, each candidate service has m QoS indexes, and constructing an original decision matrix X (X)ij)n×mWherein x isijRepresents the jth QoS index of the ith candidate service.
Step two, normalizing the QoS index. The QoS metrics can be classified into three categories, revenue type (larger is better), cost type (smaller is better) and median type, wherein the median type metric has a "median", and the closer to the median, the better such metrics are. In order to make the QoS value in the original decision matrix have the same direction as the profit-type index, it is necessary to take the opposite number for the cost-type index, the reciprocal of the absolute value of the median difference for the median-type index, and select a maximum value to truncate the too large value. The QoS index is then normalized using the following equation:
Figure BDA0003018141170000031
wherein, x'ijIs the jth QoS index of the ith directionally adjusted candidate service, mu 'is the mean of the jth directionally adjusted QoS indexes, and epsilon' is the standard deviation of the jth directionally adjusted QoS indexes. After the normalization process, the original decision matrix X can be converted into a normalized decision matrix Y ═ Y (Y)ij)n×mWherein y isijA jth standard QoS value representing an ith candidate service.
And step three, calculating ideal service. Defining an ideal value for each QoS index as
Figure BDA0003018141170000032
Then it is idealService y*＝(v1 v2 … vm)。
Step four, calculating the standard Euclidean distance between each candidate service and the ideal service according to the following formula:
Figure BDA0003018141170000033
wherein ω isjRepresents the weight of the jth QoS.
And step five, sorting the candidate services from small to large according to the standard Euclidean distance between the candidate services and the ideal services, and selecting a plurality of services with the minimum distance from the ideal services to form a QoS candidate service set.
And step six, generating a relevant candidate service set. Let the QoS weight vector ω be (ω)1 ω2 … ωm) Setting tcorA threshold for the degree of correlation, which may be determined by the user, for services whose degree of correlation exceeds the threshold, to be considered strongly correlated services. First, let the relevant candidate service set equal to the QoS candidate service set. For each service in the QoS candidate service set, setting the relevance vector as q ═ q (q)1 q2 … qm)'. Let the correlation c be ω q, if c ≧ tcorThen the relevant service is added into the relevant candidate service set.
The above algorithm forms two sets of candidate service sets, a QoS candidate service set and a related candidate service set. The QoS candidate service set reserves a service with higher QoS, and the relevant candidate service set additionally reserves a service with strong correlation based on the former, and although the QoS values of these additionally reserved services are not high, there is an additional QoS improvement when they are combined with the relevant service. Therefore, the algorithm provides a comprehensive set of candidate services for the composition process.

Claims (3)

1. A service matching method for network collaborative manufacturing is characterized in that technical resources are used as a cloud service, six-element groups are used for describing, and matching is carried out on the services described by the six-element groups;
service matching has two steps:
the first step is function matching, namely screening and constructing a candidate service set meeting requirements through basic information, type information, state information, function information and the like of the service;
and the second step is non-functional matching, and based on the candidate service set generated in the first step, the candidate service set with higher quality is screened out through the QoS of the candidate service, the relevance among the services and the like.
2. The method of claim 1, wherein the first part is used for ranking and screening high quality services based on QoS, and the second part is used for retaining some high-relevance services screened out by the first part based on the relevance between services to form a new candidate service set, i.e. a final matching result.
3. The method as claimed in claim 1, wherein the second part is a QoS and correlation based intelligent matching method for technical resources, the input of which is a candidate service set generated by functional matching, and the output of which is a candidate service set obtained by screening based on QoS and correlation, comprising the steps of:
step one, constructing an initial decision matrix X according to the QoS of a candidate service set;
unifying the direction of the QoS value and normalizing to generate a standard decision matrix Y;
step three, calculating ideal service Y through a standard decision matrix Y*；
Step four, calculating the standard Euclidean distance d between each candidate service and the ideal servicei；
Step five, according to the standard Euclidean distance d between the candidate service and the ideal serviceiSelecting a plurality of first services as a QoS candidate service set according to the sequence from small to large;
step six, initializing the relevant candidate service set to be in the same state as the QoS candidate service set, and then for each relevant service of each service in the QoS candidate service set, if the correlation degree is not less than the correlation degree threshold value tcorThe relevant service is added to the relevant candidate service set.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101719933A (en) * 2009-11-25 2010-06-02 北京航空航天大学 Combination method of manufacturing grid resource services orienting whole life cycle and supporting semantemes
CN104519112A (en) * 2014-04-09 2015-04-15 丹阳市天恒信息科技有限公司 Intelligent selecting framework for staged cloud manufacturing services
CN106447219A (en) * 2016-10-14 2017-02-22 北方民族大学 Cloud service provider assessment method for auto-control valve enterprise under cloud manufacturing mode
CN111683141A (en) * 2020-06-06 2020-09-18 中国科学院电子学研究所苏州研究院 User demand-oriented dynamic QoS service selection method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101719933A (en) * 2009-11-25 2010-06-02 北京航空航天大学 Combination method of manufacturing grid resource services orienting whole life cycle and supporting semantemes
CN104519112A (en) * 2014-04-09 2015-04-15 丹阳市天恒信息科技有限公司 Intelligent selecting framework for staged cloud manufacturing services
CN106447219A (en) * 2016-10-14 2017-02-22 北方民族大学 Cloud service provider assessment method for auto-control valve enterprise under cloud manufacturing mode
CN111683141A (en) * 2020-06-06 2020-09-18 中国科学院电子学研究所苏州研究院 User demand-oriented dynamic QoS service selection method and system

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