CN109636168A - A kind of cloud platform scheduling resource intelligent matching process based on Ontology - Google Patents
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Abstract
The invention discloses a kind of, and the cloud platform based on Ontology dispatches resource intelligent matching process, by the semantic vocabulary for collecting manufacturing recourses, establish semantic dictionary, and with semantic dictionary be matching search basis, the semantic search of the Rapid matching of resource under cloud platform is realized by similarity and comprehensive matching degree, realize that this method needs to study emphatically the foundation of Ontology dictionary, the calculating of semantic similarity, three aspect of calculating of comprehensive matching degree, they are basis, core and the support for realizing cloud platform scheduling of resource.Pass through this matching process, a comprehensive and quality services resource route can be provided for user, and find suitable affiliate, this method overcome traditional semantic searching method can only single searching resource mechanism, the search matching for releasing the technological process line, by the single matching way for being matched to production line, provides optimum production process process for demand enterprise, in terms of solving the problems, such as process flow scheduled production, production efficiency is improved.
Description
Technical field
The present invention relates to cloud manufacturing service resource management fields, specifically the cloud platform scheduling to be a kind of based on Ontology
Resource intelligent matching process.
Background technique
With the development of the technologies such as big data, Internet of Things, cloud computing, information physical emerging system and its in manufacturing infiltration
Thoroughly, knowledge based, service-oriented, quick efficient, environmentally protective network-enabled intelligent manufacture new model --- cloud manufacture have been bred.
Dispersion, idle manufacturing recourses are virtualized taxonomic revision with advanced technology by manufacturing industry, are distributed to cloud platform, are user
The manufacture Life cycle service for using as needed, obtaining at any time is provided, final realize more efficient uses greatly slack resources, realize
Manufacture resource sharing and enterprise collaboration.Resource service matching process is to realize the search of cloud manufacturing platform resource intelligent, matched pass
Key technology.After manufacturing recourses requestor proposes demand information, cloud platform can be quickly quasi- according to resource service matching process
It really searched in a large amount of cloud manufacturing recourses, match the resource for meeting requirement description.The performance of resource service matching process
Superiority and inferiority directly affects the performance of cloud platform.So this just needs a kind of cloud platform intelligent scheduling resource based on Ontology
Method of completing the square supports the search and matching of cloud manufacturing service.
Summary of the invention
The present invention proposes one kind and is based on to solve the search of manufacturing platform resource intelligent, matched key technical problem
The cloud platform intelligent scheduling reso urce matching method of Ontology.
The technical solution adopted by the present invention is that: a kind of cloud platform scheduling resource intelligent matching process based on Ontology,
The matching process the following steps are included:
The resources of production semantic vocabulary that step 1. collects arrangement resource provider establishes virtual class according to semantic vocabulary
Model forms semantic dictionary, storing data cloud platform；
Step 2. demander issues demand resource information, and determines service goal (price, delivery date, scale, the matter of demand
Amount, it is other) each section weight, weight summation be 1；
Step 3. searches for semantic node dictionary, computing semantic similarity；
Step 4. provides matching object according to similarity size matched data cloud semanteme dictionary.
Step 5. calculates the distance between matching object weight, semantic route similarity, calculates service comprehensive matching degree；
Step 6. compares comprehensive matching degree, and sort production line, determines output object.
The step 1 includes following sub-step:
Step 1.1 collects the manufacturing service word for arranging and having semantic similar, semantic related, semantic epitaxial relationship in manufacturing industry
It converges；
Manufacturing service vocabulary is carried out modeling of class, shape according to industry type, software and hardware type, service type by step 1.2
At the Ontology of manufacturing recourses；
Step 1.3 is in the disaggregated model of Ontology, set attribute between the mutual Ontology of framework, building numerical control dress
Standby resource ontology tree；Form semantic dictionary, storing data cloud platform.
The step 2 includes following sub-step:
Step 2.1 demander inputs required Service Source information, quantity in data cloud platform resource demand information module, and
Determine each section weight of the service goal (price, delivery date, scale, quality, other) of demand, weight summation is 1；
The step 3 includes following sub-step:
Step divides plate to the resource information that step 3.1 inputs demander according to the production process；
It is corresponding to find it for the semantic dictionary of the step 3.2 reticle block search data cloud platform of step according to the production process
Semantic node vocabulary；
Step 3.3 calculates the similarity between each segmentation plate semantic vocabulary and corresponding semantic node；
Wherein, Simk(Oi,Oj) be kth walk production technology needed for numerical control equipment resource concept similarity, Coik(Oi,
Oj) be kth walk production technology needed for numerical control equipment resource semantic registration, Disk(Oi,Oj) it is that kth walks needed for production technology
The semantic distance of numerical control equipment resource.I is the semantic vocabulary for dividing plate, and j is corresponding semantic node.
Coik(Oi,Oj) be between ontology tree concept include identical parent number.I.e. in numerical control equipment resource ontology tree
Since the number for passing up to common concept node in root node path experienced two concept nodes.Semantic registration shows
Same degree between concept, Concept Similarity and semantic registration are positively correlated, with the increase of semantic registration, between concept
Similarity increase.
Two concept node Oi,OjBetween semantic registration calculation formula it is as follows:
Coik(Oi,Oj)=| Path (Oi)∩Path(Oj)|
Wherein, Path (Oi) indicate from OiStart to pass up to root node upperseat concept node set experienced； Path
(Oj) indicate from OjStart to pass up to root node upperseat concept node set experienced.
Disk(Oi,Oj) --- refer in numerical control equipment resource ontology tree, connects all accesses of any two concept node
The number on the side that middle shortest path is included.There is certain association between semantic distance and similarity.Any two concept
Similarity value is generally all in [0,1] within the scope of this.Two concepts are same concept, then semantic distance is 0, at this time concept
Between similarity be 1；Communication path is not present between two concepts, then semantic distance is infinity, similar between concept at this time
Degree is 0；There are communication path between two concepts, then arbitrary number of the semantic distance between (0,1), semantic distance calculation formula
It is as follows:
Wherein, Weight (eij) --- read between node include on shortest path side weight.I is the semanteme for dividing plate
Vocabulary, j are corresponding semantic node.
Calculation formula are as follows:
Wherein, Weight (Parent (Oi)) it is concept node OiDirect father node Parent (Oi) and OiBetween side
Weight；N is the depth of concept node, the i.e. number of plies locating for it；Out(Oi,Oj) be concept node width, i.e. its direct child
Node.
The step 4 includes following sub-step,
The node semantics word that step 4.1 meets matching relationship is mapped to cloud database
Step 4.2 carries out keyword search using node semantics word as search key.
Even if step 5 matching degree is by as follows:
Search of the step 5.1 according to keyword, the workshop of numerical control equipment resource needed for locking each process；
Step 5.2 calculates the distance weighting of adjacent process, semantic route similarity；
Wherein dAB-mnFor (A is before B) in A to B adjacent process, A technique m factory to B technique n
Physical distance between factory location, dAB-mFor A technique in A to B adjacent process m factory is arrived respectively between all factories of B technique
Physical distance.
Step 5.3 calculates comprehensive matching degree H.
Wherein w1,w2,w3,w4,w5Respectively indicate the requirement delivery date of party in request
(T), Service Source price (P), the quality (Q) of supplier, the scale (S) of supplier, weight shared by other (C) exist with demander
Service weight set by demander is related in step 2.
The step 6 includes following sub-step:
The calculating matching degree of more each production process of step 6.1；
Optimal Production route is arranged according to matching degree and is exported by step 6.2, supply and demand side selection.
The beneficial effects of the present invention are: a kind of cloud platform based on Ontology dispatches resource intelligent matching process, pass through
Collect the semantic vocabulary of manufacturing recourses, establish semantic dictionary, and take semantic dictionary as the basis of matching search, by similarity with
Comprehensive matching degree realizes the semantic search of the Rapid matching of resource under cloud platform, Yao Shixian this method need research emphatically this
The foundation of body semanteme dictionary, the calculating of semantic similarity, three aspect of calculating of comprehensive matching degree, they are to realize cloud platform resource
Basis, core and the support of scheduling.By this matching process, one can be provided for user comprehensively and quality services provide
Source route, and suitable affiliate is found, this method overcomes traditional semantic searching method can only single searching resource
Mechanism, release the technological process line search matching, by the single matching way for being matched to production line, for demand, enterprise is provided most
The excellent technological process of production improves production efficiency in terms of solving the problems, such as process flow scheduled production.
Detailed description of the invention
Fig. 1 is supply and demand intelligent Matching illustraton of model of the invention；
Fig. 2 is flow chart and process effect picture of the invention, wherein (a) is flow chart of the invention, is (b) present invention
Process effect picture；
Fig. 3 is the schematic diagram of numerical control equipment resource of the invention.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited
In this.
Such as Fig. 1, the model describe this realities based on Ontology data cloud platform intelligent scheduling reso urce matching method
Existing mode and data flow.
The first step, resource provider provide oneself idle manufacturing recourses, construct the cloud database of manufacturing recourses, this is this
Intelligent Matching method can complete matched key point；
Second step, resource requirement side issue the resource requirement of oneself, and resource requirement is divided according to processing step, and determine
Corresponding service weight；
Each section process requirements manufacturing resource information after segmentation is carried out the search of semantic dictionary, determines each step by third step
Rapid relevant semantic vocabulary, then retrieves cloud database according to the height of similitude, to realize that resource semantic vocabulary searches for function
Energy；
4th step after determining manufacturing recourses needed for each section, calculates production technology road according to comprehensive matching degree algorithm
Line returns to the selection of resource requirement side.
As Fig. 2, (a) describe this bulk flow based on Ontology data cloud platform intelligent scheduling reso urce matching method
Journey and step, (b) be with detailed process effect corresponding to overall flow figure, be briefly described below:
Step 1. compiles the semantic vocabulary of manufacturing service resource, establishes Ontology dictionary；It include such as in step 1
Lower step:
(1) compile in manufacturing service resource have semantic similar, semantic extension, semanteme include, the word of semantic relation
It converges.
(2) modeling of class is carried out according to the type of manufacturing recourses to these vocabulary and forms preliminary Ontology.
(3) in the preliminary Ontology of formation, between the vertical and horizontal of each neighboring semantic ontology be it is mutually isolated,
So needing to add certain semantic relation between adjacent vertical and horizontal to indicate its mutual relationship, formed final
Ontology dictionary.
Such as Fig. 3, here it is the Ontology dictionaries formed by 3 steps in front, only only considered money here
The classification of type in source, as can be seen from the figure parent of the cloud manufacturing recourses as top layer, it by semantic extension at resources of human talents,
6 subclass resources such as information resources, design resource；Resource is designed in subclass resource contains CAD resource, engineering qualified teachers again
Four source, knowledge resource, CAE resource resource categories, design resource and this four resource categories have semantic inclusion relation, with this
Analogize and resource is subjected to semantic extension relationship, semantic inclusion relation extension, language is completed by semantic extension relationship, semantic inclusion relation
Adopted longitudinal extension；AutoCAD, UG, Pro/E, Catia belong to CAD resource, have semantic similarity relation；It is three-dimensional reverse, reverse
In semantically equivalent, they have semantic equivalence relationship for engineering, reversal technique；In manufacturing recourses, the talent, is set information
Meter, equipment, material, Service Source complement each other, they have semantic correlativity, are closed by semantic similarity relation, semantic equivalence
System, semantic correlativity complete semantic be laterally extended.
Step 2. resource requirement side issues demand information, demand information is divided by processing step, and determine respective service
Weight；Include the following steps: in step 2
(1) resource requirement side is in manufacturing service platform requirement information management module, Service Source demand needed for inputting, defeated
Demand can be divided according to processing step when entering, for example demander's demand box parts (will produce box parts, part
The requirement such as corresponding rapidoprint, positioning accuracy, roughness is needed, and to produce such a workpiece, then needs lathe, boring
Several numerical control equipment resources such as bed, milling machine cooperate), so, when party in request is in demand information management module input demand
When, the quantity of other numerical control equipments such as quantity, the quantity of milling machine of the quantity, boring machine that need lathe and their institutes can be inputted
Corresponding positioning accuracy, roughness, number of motion axes, such as Fig. 2In(b) shown in the first step.
Step 3. inquires Ontology dictionary, computing semantic similarity by segmentation step；Include the following steps: in step 3
(1) the demand numerical control equipment resource first to the first step of segmentation step carries out inquiry Ontology dictionary, if energy
Its corresponding semantic node word is found in ontology dictionary, then carries out the calculating of semantic similarity, if can not find corresponding language
Adopted node word, this method just end automatically, and carry out the demand numerical control equipment resource query of next step.
(2) semantic similarity between demand vocabulary and node semantics word is calculated, calculating formula of similarity is as follows:
Wherein, Simk(Oi,Oj) be kth walk production technology needed for numerical control equipment resource concept similarity, Coik(Oi,
Oj) be kth walk production technology needed for numerical control equipment resource semantic registration, Disk(Oi,Oj) it is that kth walks needed for production technology
The semantic distance of numerical control equipment resource.
①Coik(Oi,Oj) be between ontology tree concept include identical parent number.I.e. in numerical control equipment resource ontology tree
In since the number for passing up to common concept node in root node path experienced two concept nodes.Semantic registration table
Same degree between bright concept, Concept Similarity and semantic registration are positively correlated, with the increase of semantic registration, concept
Between similarity increase.
Two concept node Oi,OjBetween semantic registration calculation formula it is as follows:
Coik(Oi,Oj)=| Path (Oi)∩Path(Oj)|
Wherein, Path (Oi) indicate from OiStart to pass up to root node upperseat concept node set experienced； Path
(Oj) indicate from OjStart to pass up to root node upperseat concept node set experienced.
②Disk(Oi,Oj) --- refer in numerical control equipment resource ontology tree, all of connection any two concept node are led to
The number on the side that shortest path is included in road.There is certain association between semantic distance and similarity.Any two concept
Similarity value generally all in [0,1] within the scope of this.Under normal circumstances, two concepts are same concept, then it is semantic away from
From being 0, similarity is 1 between concept at this time；Communication path is not present between two concepts, then semantic distance is infinity, at this time
Similarity between concept is 0；There are communication path between two concepts, then arbitrary number of the semantic distance between (0,1),
Semantic distance calculation formula is as follows:
Wherein, Weight (eij) --- read between node include on shortest path side weight.
Calculation formula are as follows:
Wherein, Weight (Parent (Oi)) it is concept node OiDirect father node Parent (Oi) and OiBetween side
Weight；For the depth of concept node, the i.e. number of plies locating for it；Out(Oi,Oj) be concept node width, i.e. its direct child
Node.
The threshold value of semantic similarity is 0.5, that is to say, that semantic similarity Simk(Oi,Oj) node semantics less than 0.5
Word can not carry out Semantic mapping, can not carry out cloud database search, in this case, can skip this search, into
The inquiry of the next processing step of row.This threshold value is set by the analysis of related semantic knowledge and linguistics principle.And such as
Fruit semantic similarity Simk(Oi,Oj) value be greater than 0.5, then can carry out Semantic mapping, and then carry out cloud database search, look for
To the Service Source of this plate.Its final search result can search out a group of the different workshops of different geographical
It closes, as shown in Fig. 2 (b) second step.
Step 4. calculates the distance between adjacent procedure division step weight, semantic route similarity, passes through intelligent Matching
Degree calculates the comprehensive matching degree between Service Source, and to analysis sort result；Analytical calculation process is as follows:
Since different numerical control resources processings has certain processing sequence, the vehicle of the different regions as shown in Fig. 2 (b) third step
There are different physical distances for bed workshop and the milling machine of different regions and boring machine workshop, so from entire production work
Skill considers that, it is necessary to calculate the distance between adjacent procedure division step weight, distance weighting calculates as follows:
Wherein diPhysical distance between two places.
There is the distance between adjacent weight, the similarity between semanteme matches.Calculate its semantic route similarity.
Semantic route similarity calculation is as follows:
Finally calculate comprehensive matching degree:
The algorithm uses multi-objective decision mode, and by delivery date (T), Service Source price (P), the quality (Q) of supplier is supplied
The scale (S) of side, the parameters such as other (C) are included in judgement content, and calculation formula is as follows
Wherein w1,w2,w3,w4,w5Respectively indicate the requirement delivery date (T) of party in request, Service Source price (P), supplier's
Quality (Q), the scale (S) of supplier, weight shared by other (C), with service weight set by demander in step 2 demander
It is related.
Step 5. analysis sequence calculates resulting comprehensive matching degree, and output determines process route by demander.It exports result
As shown in the 4th step of Fig. 2 (b).
Claims (7)
1. a kind of cloud platform based on Ontology dispatches resource intelligent matching process, it is characterised in that: the matching process includes
Following steps:
The resources of production semantic vocabulary that step 1. collects arrangement resource provider establishes virtual class model according to semantic vocabulary,
Form semantic dictionary, storing data cloud platform；
Step 2. demander issues demand resource information, and determines each section weight of the service goal of demand, and weight summation is 1；
Step 3. searches for semantic node dictionary, computing semantic similarity；
Step 4. provides matching object according to similarity size matched data cloud semanteme dictionary；
Step 5. calculates the distance between matching object weight, semantic route similarity, calculates service comprehensive matching degree；
Step 6. compares comprehensive matching degree, and sort production line, determines output object.
2. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
Be: the step 1 includes following sub-step:
Step 1.1 collects the manufacturing service vocabulary for arranging and having semantic similar, semantic related, semantic epitaxial relationship in manufacturing industry；
Manufacturing service vocabulary is carried out modeling of class according to industry type, software and hardware type, service type by step 1.2, forms system
Make the Ontology of resource；
Step 1.3 is in the disaggregated model of Ontology, set attribute between the mutual Ontology of framework, building numerical control equipment money
Source ontology tree；Form semantic dictionary, storing data cloud platform.
3. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
Be: the step 2 includes following sub-step:
Step 2.1 demander determines in data cloud platform resource demand information module, Service Source information, quantity needed for inputting
Each section weight of the service goal of demand, weight summation are 1.
4. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
Be: the step 3 includes following sub-step:
Step divides plate to the resource information that step 3.1 inputs demander according to the production process；
The semantic dictionary of the step 3.2 reticle block search data cloud platform of step according to the production process, finds its corresponding language
Adopted node vocabulary；
Step 3.3 calculates the similarity between each segmentation plate semantic vocabulary and corresponding semantic node；
Wherein, Simk(Oi,Oj) be kth walk production technology needed for numerical control equipment resource concept similarity, Coik(Oi,Oj) it is the
The semantic registration of numerical control equipment resource needed for k walks production technology, Disk(Oi,Oj) it is numerical control equipment needed for kth walks production technology
The semantic distance of resource；I is the semantic vocabulary for dividing plate, and j is corresponding semantic node；
Coik(Oi,Oj) be between ontology tree concept include identical parent number；I.e. from two in numerical control equipment resource ontology tree
A concept node starts to pass up to the number of common concept node in root node path experienced；Semantic registration shows concept
Between same degree, Concept Similarity and semantic registration be positively correlated, the phase with the increase of semantic registration, between concept
Increase like degree；
Two concept node Oi,OjBetween semantic registration calculation formula it is as follows:
Coik(Oi,Oj)=| Path (Oi)∩Path(Oj)|
Wherein, Path (Oi) indicate from OiStart to pass up to root node upperseat concept node set experienced；Path(Oj) indicate
From OjStart to pass up to root node upperseat concept node set experienced；
Disk(Oi,Oj) --- refer in numerical control equipment resource ontology tree, connects in all accesses of any two concept node most
The number on the side that short path is included；There is certain association between semantic distance and similarity；Any two concept it is similar
Value is spent generally all in [0,1] within the scope of this；Two concepts are same concept, then semantic distance is 0, at this time between concept
Similarity is 1；Communication path is not present between two concepts, then semantic distance is infinity, and the similarity between concept is at this time
0；There are communication path between two concepts, then arbitrary number of the semantic distance between (0,1), semantic distance calculation formula is such as
Under:
Wherein, Weight (eij) --- read between node include on shortest path side weight；I is the semantic word for dividing plate
It converges, j is corresponding semantic node；
Calculation formula are as follows:
Wherein, Weight (Parent (Oi)) it is concept node OiDirect father node Parent (Oi) and OiBetween side weight；n
For the depth of concept node, the i.e. number of plies locating for it；Out(Oi,Oj) be concept node width.
5. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
Be: the step 4 includes following sub-step,
The node semantics word that step 4.1 meets matching relationship is mapped to cloud database
Step 4.2 carries out keyword search using node semantics word as search key.
6. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
It is: even if step 5 matching degree is by as follows:
Search of the step 5.1 according to keyword, the workshop of numerical control equipment resource needed for locking each process；
Step 5.2 calculates the distance weighting of adjacent process, semantic route similarity；
Wherein dAB-mnFor (A is before B) in A to B adjacent process, A technique m factory to B technique n factory place
Physical distance between ground, dAB-mFor A technique in A to B adjacent process m factory arrive respectively the physics between all factories of B technique away from
From；
Step 5.3 calculates comprehensive matching degree H；
Wherein w1,w2,w3,w4,w5The requirement delivery date T of party in request, Service Source price P are respectively indicated, the quality Q of supplier supplies
Weight shared by the scale S, other C of side is related to service weight set by demander.
7. a kind of cloud platform based on Ontology according to claim 1 dispatches resource intelligent matching process, feature
Be: the step 6 includes following sub-step:
The calculating matching degree of more each production process of step 6.1；
Optimal Production route is arranged according to matching degree and is exported by step 6.2, supply and demand side selection.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110059985A (en) * | 2019-05-07 | 2019-07-26 | 无锡派克新材料科技股份有限公司 | A kind of forging process for fuel technique Method of Fuzzy Matching |
| CN110334919A (en) * | 2019-06-20 | 2019-10-15 | 西北工业大学 | A kind of production line reso urce matching method and device |
| CN112085458A (en) * | 2020-07-20 | 2020-12-15 | 天津博诺智创机器人技术有限公司 | Cloud manufacturing platform resource service matching method |
| CN112364889A (en) * | 2020-10-20 | 2021-02-12 | 重庆大学 | Manufacturing resource intelligent matching system based on cloud platform |
| CN114565429A (en) * | 2022-02-22 | 2022-05-31 | 哈尔滨工程大学 | Intelligent supplier matching method based on semantic graph model |
| CN116629585A (en) * | 2023-07-24 | 2023-08-22 | 南昌大学 | Process management system and method using ontology |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984714A (en) * | 2014-05-07 | 2014-08-13 | 湖北工业大学 | Ontology semantics-based supply and demand matching method for cloud manufacturing service |
-
2018
- 2018-12-05 CN CN201811480192.5A patent/CN109636168A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984714A (en) * | 2014-05-07 | 2014-08-13 | 湖北工业大学 | Ontology semantics-based supply and demand matching method for cloud manufacturing service |
Non-Patent Citations (10)
| Title |
|---|
| 刘慧敏等: "一种基于本体语义的云制造服务供需智能匹配方法", 《湖北工业大学学报》 * |
| 易树平等: "面向智能车间的工艺规划辅助决策方法", 《浙江大学学报(工学版)》 * |
| 景旭文等: "面向e-制造的动态工艺规划支持系统研究", 《现代制造工程》 * |
| 汤华茂等: "云制造资源虚拟化描述模型及集成化智能服务模式研究", 《中国机械工程》 * |
| 王晓妍等: "云制造环境下数控装备资源服务匹配方法研究", 《制造技术与机床》 * |
| 王海波等: "面向企业间业务协作的异构系统集成方法", 《轻工机械》 * |
| 盛步云等: "云制造服务平台供需智能匹配的研究与实现", 《计算机集成制造系统》 * |
| 罗俊丽: "制造资源本体的语义相似度计算方法研究", 《现代计算机(专业版)》 * |
| 郑立斌等: "网络化制造资源建模与搜索的研究", 《机械设计与制造》 * |
| 陈明强等: "基于语义的机加工服务智能匹配", 《机械工程与自动化》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110059985A (en) * | 2019-05-07 | 2019-07-26 | 无锡派克新材料科技股份有限公司 | A kind of forging process for fuel technique Method of Fuzzy Matching |
| CN110334919A (en) * | 2019-06-20 | 2019-10-15 | 西北工业大学 | A kind of production line reso urce matching method and device |
| CN110334919B (en) * | 2019-06-20 | 2022-05-27 | 西北工业大学 | Production line resource matching method and device |
| CN112085458A (en) * | 2020-07-20 | 2020-12-15 | 天津博诺智创机器人技术有限公司 | Cloud manufacturing platform resource service matching method |
| CN112364889A (en) * | 2020-10-20 | 2021-02-12 | 重庆大学 | Manufacturing resource intelligent matching system based on cloud platform |
| CN114565429A (en) * | 2022-02-22 | 2022-05-31 | 哈尔滨工程大学 | Intelligent supplier matching method based on semantic graph model |
| CN116629585A (en) * | 2023-07-24 | 2023-08-22 | 南昌大学 | Process management system and method using ontology |
| CN116629585B (en) * | 2023-07-24 | 2023-09-19 | 南昌大学 | Process management system and method using ontology |
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