CN108919760A - A kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number - Google Patents

A kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number Download PDF

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CN108919760A
CN108919760A CN201810731107.1A CN201810731107A CN108919760A CN 108919760 A CN108919760 A CN 108919760A CN 201810731107 A CN201810731107 A CN 201810731107A CN 108919760 A CN108919760 A CN 108919760A
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lathe
intelligent workshop
workshop
entity
workpiece
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CN108919760B (en
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惠记庄
丁凯
雷景媛
史合
张富强
王刚锋
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Changan University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32015—Optimize, process management, optimize production line
    • 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
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • General Factory Administration (AREA)

Abstract

The invention discloses a kind of intelligent workshop autonomy production process dynamic linkage control methods twin based on number, including：(1) number twin model of the intelligent workshop entity in information space is constructed, and establishes the actual situation mapping relations between entity and model；(2) bidirectional data transfers channel is established, realizes actual situation linkage；(3) real-time status data for acquiring manufacturing recourses is uploaded to the twin model of number and updates；(4) real-time simulation is carried out to intelligent workshop operating status, and calls algorithm prediction lathe task layout sequence optimal solution and executes the lathe numbering of next process；(5) optimal solution is parsed, and by reaching each manufacturing recourses entity between intelligent vehicle under bidirectional data transfers channel in the form of production ordering；(6) (3)~(5) step is recycled, until workpiece all process steps complete the process.By synchronous interaction, the dynamic linkage control of intelligent workshop autonomy production process is realized.

Description

A kind of intelligent workshop autonomy production process dynamic linkage control twin based on number Method
Technical field
The present invention relates to intelligence manufacture field, in particular to a kind of intelligent workshop autonomy production process twin based on number Dynamic linkage control method.
Background technique
Intelligence manufacture has become hot spot studied both at home and abroad at present, German Industrial 4.0, American industry internet, China's system It makes 2025 equal strategical plannings and intelligence manufacture has all been classified as important theme.The implementation of intelligence manufacture depends not only upon advanced manufacture skill The development of art, while also relying on the application of emerging information technology.Under intelligence manufacture environment, enterprise manufacturing shop should have compared with Good production flexibility, autonomy and production process manages ability, to meet customized customer demand.
Emerging information technology (such as Internet of Things, number are twin) provides technology base for the autonomized production run in manufacturing shop Plinth.For example, manufacture technology of Internet of things built people from workshop (operator), machine (machining tool, travelling bogie etc.), object (product, Auxiliary tool etc.) it is ubiquitous interconnect, realize each physical entity it is recognizable, it is traceable, interaction can be communicated；The twin skill of number Art constructs the closed loop logic in workshop " context aware-simulation calculation-Coordination Decision-production execute ", by information space data/ Real-time interoperability between model and physical space entity realizes manufacture process transparence, high efficiency and autonomized.So And more lacking the case that above-mentioned emerging information technology is carried out to integrated application at present, General Implementing effect is not up to intelligence Workshop carries out the requirement of efficient dynamic production process control.
Summary of the invention
It is an object of the invention to aiming at the problem that practicing above, provide a kind of intelligent workshop twin based on number Autonomous production process dynamic linkage control method, it is twin by the intelligent workshop entity in physical space and the number in information space The dynamic linkage control of intelligent workshop autonomy production process is realized in real-time synchronization linkage between raw model.
To achieve the above object, the technical scheme is that：
A kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number, includes the following steps：
(1) number twin model of the intelligent workshop entity in information space is constructed, and is established between entity and model Actual situation mapping relations；
(2) the bidirectional data transfers channel of actual situation linkage is established, intelligent workshop running state data is real-time by the channel It is synchronized to the twin model of number, and calculated result or decision instruction that the twin model of number calls all kinds of algorithms to solve are logical by this Entity between intelligent vehicle is reached under road, realizes actual situation linkage；
(3) real-time status data of manufacturing recourses when workpiece circulates in intelligent workshop is acquired by workshop Internet of Things, The twin model of number is uploaded to by bidirectional data transfers channel and is updated；
(4) real-time simulation is carried out to intelligent workshop operating status in the twin model of number, calculates actual production and ran Schedule variance in journey, and the prediction of lathe task Arrangement algorithm is called to determine that the machining tool for executing workpiece next process is compiled Number, generate lathe task layout sequence optimal solution；
(5) semantic parsing is carried out to lathe task layout sequence optimal solution, and passes through two-way number in the form of production ordering According to manufacturing recourses entity is reached under transmission channel, manufacturing recourses entity acts accordingly according to given instruction execution；
(6) (3)~(5) step is recycled, until workpiece all process steps complete the process.
As a further improvement of the present invention, the bidirectional data transfers channel in step (2), is assisted using AutomationML It discusses as the Data Transport Protocol between intelligent workshop entity and the twin model of number, the real time data of upload and the production assigned Instruction meets AutomationML agreement.
As a further improvement of the present invention, the schedule variance in the actual production operational process in step (4), value are pressed It is calculated according to following formula：
In formula：
Δ T indicates the schedule variance value in workpiece actual production process；
PjAnd P .Stj.Ft work piece production is respectively indicated in the works at the beginning of jth procedure and the end time；
PjAnd P .astj.aft the reality of the jth procedure by the acquisition of workshop internet of things equipment RFID reader is respectively indicated Time started and physical end time；
M indicates the completed manufacturing procedure sum of workpiece under current time t.M value is determined by following judgment criterion：if(t ≤Pm+1.St∧t≥(Pm.Dt+Pm)) .St then M=m；
NiIndicate the manufacturing procedure sum that workpiece actually accomplishes under current time t.N value is determined by following judgment criterion： if(t≤Pn+1.ast∧t≥Pn) .aft then N=n.
As a further improvement of the present invention, the lathe task Arrangement algorithm in step (4), using hidden Markov model Method is modeled, and implementation step is as follows：
Step 1：Work pieces process historical data is learnt to learning algorithm using preceding, obtains and is compiled for lathe task The hidden Markov model parameter of row, including：State transition probability matrix, manufacturing procedure between manufacturing procedure and between lathe The probability matrix of mapping association；
Step 2：According to the hidden Markov model established, by the manufacturing procedure that workpiece has been completed under current time t Sequence, currently the real-time running state of all machining tools in intelligent workshop is solved as mode input；
Step 3：Optimal lathe task layout sequence is exported using viterbi algorithm recursion, which is probability of happening Maximum sequence further determines that the lathe numbering where the next process processing of workpiece under current time t.
Compared with prior art, the invention has the advantages that：
The present invention establishes the actual situation mapping relations between intelligent workshop entity and the twin model of number, passes through bi-directional data Transmission channel realizes real-time upload, the twin model emulation data of number or the production ordering of intelligent workshop production process data In real time assign, the lathe assignment instructions in production ordering by lathe task Arrangement algorithm resolve.By synchronizing, Realize the dynamic linkage control of intelligent workshop autonomy production process.
Detailed description of the invention
Fig. 1 is the execution flow chart based on the twin intelligent workshop autonomy production process dynamic linkage control method of number；
Fig. 2 is the schematic diagram of lathe task Arrangement algorithm model；
Fig. 3 is that the twin model of number is complete actual situation mapping graph to physical plant.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings.Attached drawing described herein is one of the application Point, for the present invention is further expalined, but and do not constitute a limitation of the invention.
A kind of execution stream of the intelligent workshop autonomy production process dynamic linkage control method twin based on number of the present invention Journey figure is as shown in Figure 1, include the following steps：
(1) number twin model of the intelligent workshop entity in information space is constructed, and is established between entity and model Actual situation mapping relations, i.e.,：Wherein, PJS indicates that the intelligent workshop entity in physical space, VJS indicate information The twin model of number in space,Indicate PJS and the actual situation mapping relations of VJS between the two；
(2) the bidirectional data transfers channel of actual situation linkage is established, and uses AutomationML Data Transport Protocol, intelligence Workshop operation status data (including work status data, conditions of machine tool/task data, AGV logistics trolley operational data, operation Person's task data etc.) by the channel real-time synchronization to the twin model of number, and the twin model of number calls all kinds of algorithms to solve Calculated result or decision instruction out is linked by reaching entity between intelligent vehicle, realization actual situation under the channel；
(3) by Internet of Things acquire workpiece circulate in intelligent workshop when real time position, reach/leave the position when Between and the manufacturing recourses such as machining tool, travelling bogie operating state data, above- mentioned information pass through bidirectional data transfers channel It is uploaded to the twin model of number；
(4) real-time simulation is carried out to intelligent workshop operating status in the twin model of number, calculates actual production and ran Schedule variance in journey, and the prediction of lathe task Arrangement algorithm is called to determine that the machining tool for executing workpiece next process is compiled Number, generate lathe task layout sequence optimal solution.The algorithm is real-time with work pieces process process route, workpiece real time position, lathe The data such as operating status are as input.Wherein：
Schedule variance value in actual production operational process described in (4-1) is calculated according to following formula：
In formula：
Δ T indicates the schedule variance value in workpiece actual production process；
PjAnd P .Stj.Ft work piece production is respectively indicated in the works at the beginning of jth procedure and the end time；
PjAnd P .astj.aft the reality of the jth procedure by the acquisition of workshop internet of things equipment RFID reader is respectively indicated Time started and physical end time；
M indicates the completed manufacturing procedure sum of workpiece under current time t.M value is determined by following judgment criterion：if(t ≤Pm+1.St∧t≥(Pm.Dt+Pm)) .St then M=m；
NiIndicate the manufacturing procedure sum that workpiece actually accomplishes under current time t.N value is determined by following judgment criterion： if(t≤Pn+1.ast∧t≥Pn) .aft then N=n.
Lathe task Arrangement algorithm, is modeled using hidden markov model approach described in (4-2), and principle is as schemed Shown in 2, specific implementation step is as follows：
Step 1：Work pieces process historical data is learnt to learning algorithm using preceding, obtains and is compiled for lathe task The hidden Markov model parameter of row, including：State transition probability matrix, manufacturing procedure between manufacturing procedure and between lathe The probability matrix of mapping association；
Step 2：According to the hidden Markov model established, by the manufacturing procedure that workpiece has been completed under current time t The real-time running state (idle or occupancy) of sequence, the currently all machining tools in intelligent workshop is solved as mode input；
Step 3：Optimal lathe task layout sequence is exported using viterbi algorithm recursion, which is probability of happening Maximum sequence further determines that the lathe numbering where the next process processing of workpiece under current time t；
(5) parsing of AutomationML semanteme is carried out to lathe task layout sequence optimal solution, and with the shape of production ordering Formula, which passes through, reaches the manufacturing recourses entity such as workpiece, machining tool, travelling bogie, manufacturing recourses entity under bidirectional data transfers channel It is acted accordingly according to given instruction execution；
(6) (3)~(5) step is recycled, until all manufacturing procedures of workpiece are completed.
Through the above steps, it realizes to the real time monitoring of intelligent workshop autonomy production process, emulation, task layout, into one Step, which is realized, controls the dynamic linkage of intelligent workshop autonomy production process.
As shown in figure 3, there are 3 numerical control machining center (machines in the workshop by taking the intelligence manufacture workshop of certain aviation components as an example Bed 1, lathe 2, lathe 3), 2 mechanical arms, 1 AGV travelling bogie, workpiece 1, workpiece 2 execute production in the intelligence workshop and appoint Business.First according to the physical layout in the workshop, the corresponding twin model of number is established, the twin model of the number is to physical plant Complete actual situation mapping；
The Real-time Production Process data in intelligent workshop are acquired by internet of things equipment such as configured RFID, sensors, And the twin model of number is uploaded to by bidirectional data transfers channel, AutomationML agreement.Work is known according to real time data Part 1 has progressed to third procedure, and workpiece 2 has progressed to five processes；
The twin model of number call manufacturing schedule deviation computing module calculate actual production process and planned production process it Between schedule variance, wherein：The Projected completion time of the third procedure of workpiece 1 is 5min, and according to real time data drift gage Calculation reflects that the process actual finish time is 6min 20s, delays 1min 20s than plan, the twin model of number is inclined by this Difference feeds back to corresponding lathe, assigns the instruction of quickening progress；
Meanwhile the twin model of number calls lathe task Arrangement algorithm module, and using manufacturing schedule deviation as input Condition, the optimal lathe that simulation calculation goes out to execute next process is lathe 3, and processing can be by workpiece 1 the 4th on the lathe The completion date of process compresses 1min 20s~2min, to realize that 1 overall processing progress of workpiece and plan processing progress are kept Unanimously, avoid dragging the generation of phase phenomenon.After calculating, the twin model of number is in real time by " the 4th procedure of workpiece 1 is by machine The production orderings such as 3 processing of bed ", " AGV travelling bogie transports workpiece 1 to lathe 3 and clamping from lathe 1 " are sent to 3 He of lathe AGV travelling bogie.Further, lathe 3 and AGV travelling bogie execute corresponding production ordering.
The above examples are only used to illustrate the technical scheme of the present invention rather than its limitations, although referring to above-described embodiment pair The present invention is described in detail, and those of ordinary skill in the art can still carry out specific embodiments of the present invention Modification perhaps equivalent replacement and these exist without departing from any modification of spirit and scope of the invention or equivalent replacement Apply within pending claims of the invention.

Claims (4)

1. a kind of intelligent workshop autonomy production process dynamic linkage control method twin based on number, which is characterized in that including Following steps：
(1) number twin model of the intelligent workshop entity in information space is constructed, and establishes the actual situation between entity and model Mapping relations；
(2) the bidirectional data transfers channel of actual situation linkage is established, intelligent workshop running state data passes through the channel real-time synchronization To the twin model of number, and calculated result or decision instruction that the twin model of number calls all kinds of algorithms to solve pass through under the channel Intelligent workshop entity is reached, realizes actual situation linkage；
(3) real-time status data that manufacturing recourses when workpiece circulates in intelligent workshop are acquired by workshop Internet of Things, passes through Bidirectional data transfers channel is uploaded to the twin model of number and updates；
(4) real-time simulation is carried out to intelligent workshop operating status in the twin model of number, calculated in actual production operational process Schedule variance, and the prediction of lathe task Arrangement algorithm is called to determine the machining tool number for executing workpiece next process, it is raw At lathe task layout sequence optimal solution；
(5) semantic parsing is carried out to lathe task layout sequence optimal solution, and is passed in the form of production ordering by bi-directional data Manufacturing recourses entity is reached under defeated channel, manufacturing recourses entity acts accordingly according to given instruction execution；
(6) (3)~(5) step is recycled, until workpiece all process steps complete the process.
2. the intelligent workshop autonomy production process dynamic linkage control method twin based on number according to claim 1, It is characterized in that, the bidirectional data transfers channel in step (2), using AutomationML agreement as intelligent workshop entity with Data Transport Protocol between the twin model of number, the real time data of upload meet with the production ordering assigned AutomationML agreement.
3. the intelligent workshop autonomy production process dynamic linkage control method twin based on number according to claim 1, It is characterized in that, the schedule variance in actual production operational process in step (4), value are calculated according to following formula：
In formula：
Δ T indicates the schedule variance value in workpiece actual production process；
PjAnd P .Stj.Ft work piece production is respectively indicated in the works at the beginning of jth procedure and the end time；
PjAnd P .astj.aft respectively indicate by workshop internet of things equipment RFID reader acquisition jth procedure it is practical Time and physical end time；
M indicates the completed manufacturing procedure sum of workpiece under current time t；M value is determined by following judgment criterion：if(t≤ Pm+1.St∧t≥(Pm.Dt+Pm)) .St then M=m；
NiIndicate the manufacturing procedure sum that workpiece actually accomplishes under current time t；N value is determined by following judgment criterion：if(t≤ Pn+1.ast∧t≥Pn) .aft then N=n.
4. the intelligent workshop autonomy production process dynamic linkage control method twin based on number according to claim 1, It is characterized in that, the lathe task Arrangement algorithm in step (4), is modeled using hidden markov model approach, implement Steps are as follows：
Step 1：Work pieces process historical data is learnt to learning algorithm using preceding, is obtained for lathe task layout Hidden Markov model parameter, including：It state transition probability matrix, manufacturing procedure between manufacturing procedure and is mapped between lathe Associated probability matrix；
Step 2：According to the hidden Markov model established, the manufacturing procedure sequence that workpiece under current time t has been completed Column, currently the real-time running state of all machining tools in intelligent workshop is solved as mode input；
Step 3：Optimal lathe task layout sequence is exported using viterbi algorithm recursion, which is probability of happening maximum Sequence, further determine that workpiece under current time t next process processing where lathe numbering.
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