CN108647321A - A kind of intelligence multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method - Google Patents
A kind of intelligence multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method Download PDFInfo
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- CN108647321A CN108647321A CN201810445762.0A CN201810445762A CN108647321A CN 108647321 A CN108647321 A CN 108647321A CN 201810445762 A CN201810445762 A CN 201810445762A CN 108647321 A CN108647321 A CN 108647321A
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- 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
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- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
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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
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- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a kind of intelligent multi-source heterogeneous manufacture big data integrated models in workshop and semantic computation method, using the first procedure as root vertex, using time course stream as the model trunk of the evolution direction of model；Using each manufacturing procedure as crotch node, association and the process related resource, derivative form crotch part, and the real time data in manufacturing process establishes model as the leaf nodes of the multi-source heterogeneous manufacture big data integrated model in intelligent workshop；Workshop is obtained in the comprehensive process progress msg of product, the information on load of equipment and in product circulation and logistics trolley transport trace information by calculation formula, passes through many-sided description, association, fusion, excavation, semantic computation and Knowledge Evolvement for realizing multi-source heterogeneous manufacture big data such as dimension, granularity, the scale of construction of associated manufaturing data.
Description
Technical field
The invention belongs to intelligence manufacture and manufacturing systems engineering field, more particularly to a kind of intelligent multi-source heterogeneous manufacture in workshop
Big data integrated model and semantic computation method.
Background technology
Intelligence manufacture provides thinking for the manufacturing transition and upgrade in the world.Under intelligence manufacture pattern, Sensor Network is depended on
The application of the emerging information technology such as network, Internet of Things, information physical system, the manufacturing process in intelligent workshop become transparence, in real time
Change, flexibility, intelligence.And the Realtime manufacturing data generated in manufacturing process are acquired, model, calculate, store, are serviced
Etc. being the main line for realizing above-mentioned target.Since Realtime manufacturing data have, isomery, multi-source, amount are big, sparse, relevance is strong, value
The features such as density is low needs to carry out unified Modeling to the multi-source heterogeneous manufacture big data in intelligent workshop, it is different to form a kind of multi-source
Big data integrated model and semantic computation method are made in structure.
Current existing Data Modeling Method includes E-R models, dimensional model, DataVault models, Anchor models
Deng the rarer correlation model method for carrying out schematization modeling to the multi-source heterogeneous big data of intelligent workshop manufacturing process.There is scholar
The workshop manufacturing process data cube model (Data-Cuboid) based on radio RF recognition technology is had studied, is to manufacture
A kind of integrated exploration of workshop data modeling.However, this method is not comprehensive there is manufaturing data is associated with, it can not react and manufacture
Detail state information in journey, i.e. data dimension and data granularity is more single, causes the quality of data and data value relatively low.
Invention content
For modeling problem present in the utilization of intelligent workshop manufacturing process big data, it is a primary object of the present invention to structures
A kind of intelligent multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method are built, to realize that multi-source heterogeneous manufacture counts greatly
According to description, association, fusion, excavation, semantic wound at Knowledge Evolvement.In order to achieve the above object, technical scheme of the present invention
For：
Establish the multi-source heterogeneous manufacture big data integrated model in intelligent workshop；
Step 1：Using in intelligent workshop in first of manufacturing procedure of product part as root vertex, when with manufacturing procedure
Evolution direction of the sequence stream as model builds the trunk portion of the multi-source heterogeneous manufacture big data integrated model in intelligent workshop；
Step 2：Using each manufacturing procedure as crotch node, association with the relevant process equipment of the process, cutter, fixture,
The branches nodes such as measurer, operator, logistics trolley and auxiliary manufacturing recourses, the multi-source heterogeneous manufacture in the derivative intelligent workshop of formation are big
The crotch part of data integration model；
Step 3：The reality with multi-source, isomery respectively generated in different branch node association manufacturing processes
When data, as intelligent workshop it is multi-source heterogeneous manufacture big data integrated model leaf nodes.
Branch node is process equipment, cutter, fixture, measurer, operator, logistics trolley and auxiliary in the step 2
Manufacturing recourses.
Different manufacturing procedures is distributed on the trunk of the multi-source heterogeneous manufacture big data integrated model in the intelligent workshop
MOi, each manufacturing procedure MOiCorresponding to a crotch node DB in integrated modeli, the crotch at same time node
It is concurrency relation between node, is sequential relationship between the crotch node at different time node.Can formalized description be：
Wherein：R is a N × N-dimensional relational matrix, R=[Ri]N×N, indicate the logical relation between different crotch nodes,
Including concurrency relation and sequential relationship, respectively by symbolAnd symbolIt indicates, i.e.,：
Meanwhile the formation of the manufacturing procedure stream of manufacture big data integrated model DT and part maps one by one, i.e.,：
The branch node of the multi-source heterogeneous manufacture big data integrated model in the intelligent workshop, is simultaneously between branch node
Row relationship, is described as follows：
Wherein：DRi,jIndicate j-th of branch node on i-th of crotch node, symbol " V " indicate different branch nodes it
Between concurrency relation.
The leaf nodes of the multi-source heterogeneous manufacture big data integrated model in the intelligent workshopIt is subordinated to branch node
DRi,j, classification includes but not limited to the process data, the qualitative data in product part, related manufacturing recourses of manufacturing procedure
Running parameter data.It is concurrency relation between different leaf nodes, each leaf nodes include a static build-in attribute data
Collect and what one changed over time dynamically associates data set.Static build-in attribute data set and dynamically associate what data set was included
Data category includes but not limited to that the structural datas such as numeric data, character data, interval censored data and semantic data, sound regard
Frequency is according to equal unstructured datas.Leaf nodesCan formalized description be：
Wherein：Indicate k-th of leaf nodes on j-th of branch node, DSetstaIndicate static build-in attribute number
According to collection, DSetdynExpression dynamically associates data set.
Based on a kind of above-mentioned multi-source heterogeneous manufacture in intelligent workshop of the intelligent multi-source heterogeneous manufacture big data integrated model in workshop
Big data semantic computation method, which is characterized in that include the following steps：
Step 1：By that in the unique identification code index of product integrated model DT, can calculate and give birth to according to following formula
At product workshop manufacturing process comprehensive process progress semantic information：
Wherein：Prgs indicates that the comprehensive process progress msg in product, id indicate that the unique identification in product encodes, dynamic
Progress data in related information indicate the process processing progress in product；
Step 2：By the unique identification code index of process equipment integrated model DT, can be calculated according to following formula
Generate the load semantic information of process equipment：
Wherein：Wkld indicates that the live load of process equipment, ID indicate the unique identification coding of process equipment, dynamically associate
T data in information indicate to execute the total time of processing on the process equipment in the i-th procedure of product；
Step 3：By the unique identification code index of logistics trolley integrated model DT, can be calculated according to following formula
It generates and transports track semantic information in product circulation and logistics trolley：
Wherein：Tjty indicates that, in product circulation and logistics trolley transport track, ID' indicates that the unique identification of logistics trolley is compiled
Code, dynamically associate position data in information indicate product in different time points where position.
The beneficial effects of the invention are as follows：
The present invention establishes a kind of intelligent multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method, by answering
The modeling of the intelligent workshop manufaturing data of multi-layer is realized with the organizational form set in natural world and is integrated.From associated system
From the point of view of making dimension, granularity, the scale of construction of data etc., the integrated model that the present invention is established can preferably adapt to current intelligent vehicle
Between in manufaturing data the characteristics of, certain support can be provided for the excavation of manufaturing data, semantic computation, Knowledge Evolvement.
Description of the drawings
Fig. 1 is a kind of multi-source heterogeneous manufacture large data sets in intelligent workshop into illustraton of model；
Fig. 2 is a kind of multi-source heterogeneous manufacture large data sets in intelligent workshop into modeling procedure figure.
Specific implementation mode
It elaborates with reference to the accompanying drawings and examples to the present invention.Attached drawing described herein is one of the application
Point, for the present invention is further expalined, but do not constitute limitation of the invention.
1, a kind of multi-source heterogeneous manufacture big data integrated model in intelligent workshop of the present invention is as shown in Figure 1, the model
Specific modeling pattern be：
As shown in Fig. 2, (1) establishes trunk and the crotch part of manufacture big data integrated model：To be made in intelligent workshop
First of manufacturing procedure of product part is root vertex, using manufacturing procedure sequential flow as the evolution direction of model, structure manufacture
The trunk of big data integrated model.Using each manufacturing procedure as crotch node, the pass between trunk and crotch is established as follows
Connection relationship, and determine the logical relation between each crotch：
Wherein：R is a N × N-dimensional relational matrix, R=[Ri]N×N, indicate the logical relation between different crotch nodes,
Including concurrency relation and sequential relationship, respectively by symbolAnd symbolIt indicates, i.e.,：
(2) the branch part of manufacture big data integrated model is established：With with the relevant process equipment of manufacturing procedure, cutter,
Fixture, measurer, operator, logistics trolley and auxiliary manufacturing recourses are node, and derivative formed manufactures big data integrated model
Branch part is concurrency relation between branch node.As needed, branch node can further decompose into more fine-grained son
Branch node.It clicks formula and establishes incidence relation between crotch and branch：
Wherein：DRi,jIndicate j-th of branch node on i-th of crotch node, symbolIndicate different branch nodes
Between concurrency relation.
(3) the leaf part of manufacture big data integrated model is established：Around each branch node, association is relevant with the node
The classification of multi-source heterogeneous manufaturing data, the manufaturing data includes static inherent data and dynamically associates data, the manufaturing data
Attribute includes but not limited to the structural datas such as numeric data, character data, interval censored data and semantic data, audio and video number
According to equal unstructured datas.The incidence relation between branch and leaf, and formalized description leaf nodes are established as follows
Including static state thus have and data and dynamically associate data：
Wherein：Indicate k-th of leaf nodes on j-th of branch node, DSetstaIndicate static build-in attribute number
According to collection, DSetdynExpression dynamically associates data set.
2, the multi-source heterogeneous manufacture large data sets Cheng Mo by being established in the unique identification code index present invention of product
Type DT calculates the comprehensive process progress semantic information generated in product in workshop manufacturing process according to following formula：
Wherein：Prgs indicates that the comprehensive process progress msg in product, id indicate that the unique identification in product encodes, dynamic
Progress data in related information indicate the process processing progress in product.
3, the multi-source heterogeneous manufacture large data sets established by the unique identification code index of the process equipment present invention at
Model DT calculates the load semantic information for generating process equipment according to following formula：
Wherein：Wkld indicates that the live load of process equipment, ID indicate the unique identification coding of process equipment, dynamically associate
T data in information indicate to execute the total time of processing on the process equipment in the i-th procedure of product.
4, the multi-source heterogeneous manufacture large data sets established by the unique identification code index of the logistics trolley present invention at
Model DT is calculated to generate according to following formula and is transported track semantic information in product circulation and logistics trolley：
Wherein：Tjty indicates that, in product circulation and logistics trolley transport track, ID' indicates that the unique identification of logistics trolley is compiled
Code, dynamically associate position data in information indicate product in different time points where position.
Claims (6)
1. a kind of multi-source heterogeneous manufacture big data integrated model in intelligence workshop, modeling procedure are：
Step 1：Using in intelligent workshop in first of manufacturing procedure of product part as root vertex, with manufacturing procedure sequential flow
As the evolution direction of model, the trunk portion of the multi-source heterogeneous manufacture big data integrated model in intelligent workshop is built；
Step 2：Using each manufacturing procedure as crotch node, association and the relevant process equipment of the process, cutter, fixture, amount
The branches nodes such as tool, operator, logistics trolley and auxiliary manufacturing recourses, it is derivative to form the big number of the multi-source heterogeneous manufacture in intelligent workshop
According to the crotch part of integrated model；
Step 3：The real-time number with multi-source, isomery respectively generated in different branch node association manufacturing processes
According to the leaf nodes as the multi-source heterogeneous manufacture big data integrated model in intelligent workshop.
2. a kind of multi-source heterogeneous manufacture big data integrated model in intelligent workshop according to claim 1, which is characterized in that institute
It is process equipment, cutter, fixture, measurer, operator, logistics trolley and auxiliary manufacturing recourses to state branch node in step 2.
3. a kind of multi-source heterogeneous manufacture big data integrated model in intelligent workshop according to claim 1, which is characterized in that institute
Different manufacturing procedure MO is distributed on the trunk for the multi-source heterogeneous manufacture big data integrated model in intelligent workshop statedi, Mei Gejia
Work process MOiCorresponding to a crotch node DB in integrated modeli, it is between the crotch node at same time node
Concurrency relation is between the crotch node at different time node as sequential relationship.Can formalized description be：
Wherein：R is a N × N-dimensional relational matrix, R=[Ri]N×N, indicate the logical relation between different crotch nodes, including
Concurrency relation and sequential relationship are indicated by symbol " V " and symbol " ┫ ", i.e., respectively：Ri,j∈{V,┫}。
Meanwhile the formation of the manufacturing procedure stream of manufacture big data integrated model DT and part maps one by one, i.e.,：
4. a kind of multi-source heterogeneous manufacture big data integrated model in intelligent workshop according to claim 1, which is characterized in that institute
The branch node of the multi-source heterogeneous manufacture big data integrated model in the intelligent workshop stated is concurrency relation between branch node, presses
Following formula is described：
Wherein：DRi,jIndicate that j-th of branch node on i-th of crotch node, symbol " V " indicate between different branch nodes
Concurrency relation.
5. a kind of multi-source heterogeneous manufacture big data integrated model in intelligent workshop according to claim 1, which is characterized in that institute
The leaf nodes for the multi-source heterogeneous manufacture big data integrated model in intelligent workshop statedIt is subordinated to branch node DRi,j, class
Not Bao Kuodanbuxianyu manufacturing procedure process data, in the qualitative data of product part, the running parameter of related manufacturing recourses
Data.It is concurrency relation between different leaf nodes, each leaf nodes include a static build-in attribute data set and one
What is changed over time dynamically associates data set.Static build-in attribute data set and dynamically associate the data category that data set is included
The including but not limited to structural datas such as numeric data, character data, interval censored data and semantic data, audio, video data etc.
Unstructured data.Leaf nodesCan formalized description be：
Wherein：Indicate k-th of leaf nodes on j-th of branch node, DSetstaIndicate static build-in attribute data set,
DSetdynExpression dynamically associates data set.
6. based on a kind of intelligent workshop multi-source of the intelligent multi-source heterogeneous manufacture big data integrated model in workshop described in claim 1
Isomery manufactures big data semantic computation method, which is characterized in that includes the following steps：
Step 1：By that in the unique identification code index of product integrated model DT, can calculate and generate according to following formula
Comprehensive process progress semantic information of the product in workshop manufacturing process：
Wherein：Prgs indicates that the comprehensive process progress msg in product, id indicate that the unique identification in product encodes, and dynamically associates
Progress data in information indicate the process processing progress in product；
Step 2：By the unique identification code index of process equipment integrated model DT, it can calculate and generate according to following formula
The load semantic information of process equipment：
Wherein：Wkld indicates that the live load of process equipment, ID indicate the unique identification coding of process equipment, dynamically associate information
In T data indicate to execute total time of processing on the process equipment in the i-th procedure of product；
Step 3：By the unique identification code index of logistics trolley integrated model DT, it can calculate and generate according to following formula
Track semantic information is transported in product circulation and logistics trolley：
Wherein：Tjty indicates that, in product circulation and logistics trolley transport track, ID' indicates the unique identification coding of logistics trolley,
Dynamically associate position data in information indicate product in different time points where position.
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