CN107168263A - A kind of knitting MES Production-Plan and scheduling methods excavated based on big data - Google Patents
A kind of knitting MES Production-Plan and scheduling methods excavated based on big data Download PDFInfo
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- 238000009940 knitting Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000004519 manufacturing process Methods 0.000 claims abstract description 41
- 230000008569 process Effects 0.000 claims abstract description 28
- 238000013499 data model Methods 0.000 claims abstract description 14
- 238000013439 planning Methods 0.000 claims abstract description 14
- 238000009941 weaving Methods 0.000 claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 238000007405 data analysis Methods 0.000 claims abstract description 9
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- 238000004891 communication Methods 0.000 claims description 14
- 238000005516 engineering process Methods 0.000 claims description 14
- 241001269238 Data Species 0.000 claims description 10
- 238000012423 maintenance Methods 0.000 claims description 10
- 238000007418 data mining Methods 0.000 claims description 9
- 239000004744 fabric Substances 0.000 claims description 9
- 238000012546 transfer Methods 0.000 claims description 9
- 238000009412 basement excavation Methods 0.000 claims description 8
- 238000007726 management method Methods 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 claims description 6
- 238000013138 pruning Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000011958 production data acquisition Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 2
- 239000004753 textile Substances 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 3
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- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
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- 238000005070 sampling Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
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- G—PHYSICS
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- 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
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- G—PHYSICS
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- 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
- G05B2219/32252—Scheduling production, machining, job shop
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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
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- 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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Abstract
The invention discloses a kind of knitting MES Production-Plan and scheduling methods excavated based on big data, belong to Textile Engineering application field.The inventive method comprises the following steps, S1, sets up multidimensional knitting creation data model；S2, based on Hadoop distributed platforms build big data analysis platform；S3, with Apriori association rules mining algorithms the constraint of planned dispatching is excavated under MapReduce frameworks；S4, based on contract demand analysis order planning priority；S5, comprehensive scheduling priority and planned dispatching constraint, obtain order planning Gantt chart；S6, in real time monitoring ERP and ZigBee data, when finding the anomalous events such as order variation delivery date, weaving process renewal, loom catastrophic failure, the dynamic adjustment production schedule.
Description
Technical field
The present invention relates to a kind of knitting MES Production-Plan and scheduling methods excavated based on big data, belong to Textile Engineering
Application field.
Background technology
In the whole production process of knitting enterprise, production planning management occupies more consequence with traffic control,
It is related to the significant datas such as product quality, Yield Grade, delivery cycle and utilization rate of equipment and installations.At present, most of knitting enterprise
Arranged production according further to the experience of workshop director or Job-Shop person, because order volume is relatively more, kind is turned frequently, institute
With arranged purely by experience it is single occur unavoidably order dispatch not in time, very flexible, divisions of responsibility it is unclear.Most of production
The arrangement of plan is transmitted step by step from higher level department to department of subordinate by paper document, and review process is cumbersome and paper document
Storability and trackability it is not strong.Therefore, realize knitting IT application in enterprises to be knitted enterprise survival and development to close weight
Will.
Be knitted MES as the shop layer between enterprise's upper strata planning management and bottom Shop floor control production management technology with
Real time information system, can be solved the above problems by reasonable, efficient planning and scheduling Workshop Production task.
Automate with knitting equipment, the fast development of networking, whole weaving process is daily at an unprecedented rate
Magnanimity Product Process, machine energy consumption and production process data are produced, in addition also includes sensor sensing data, network and passes
The unstructured datas such as transmission of data, defect image data, therefore knitting production process data possesses big data " 4V " substantially
The characteristics of (Volume, Velocity, Varity, Value), be a typical big data.In this context, it is knitted enterprise
Entered from " data age " " big data epoch ".
The content of the invention
Enterprise is knitted present invention aims at help and realizes that production process states cycle real-time tracing entirely, is dug using big data
The plan of quickly arranging production of office's technology and dynamic dispatching.
The present invention provides a kind of knitwear production method excavated based on big data, comprises the following steps,
S1, multidimensional knitting creation data model is set up, including collection and sets up communication protocol at multidimensional knitting data；
S2, based on Hadoop distributed platforms build include ERP data, ZigBee data and MES system historical data
Big data analysis platform；
S3, with Apriori association rules mining algorithms the constraint of planned dispatching is entered under MapReduce frameworks
Row is excavated, obtain the correspondence resource constraint factor of order, process constraint factor, yield and quality constraint and planned time constraint because
Element；
S4, based on contract demand analysis order planning priority；
S5, comprehensive scheduling priority and planned dispatching constraint, obtain order planning Gantt chart；
S6, real-time monitoring ERP and ZigBee data, find order variation delivery date, weaving process renewal, loom burst event
During the anomalous events such as barrier, the dynamic adjustment production schedule.
Specifically, multidimensional knitting data is gathered in the step S1 includes the data of following 6 dimensions of collection：
1) workshop environmental data is gathered：It is healthy including influence knitting equipment performance, grey quality, plant personnel
Ambient parameter.For example：Workshop temperature, humidity, flyings filoplume situation etc..
2) knitting equipment running state parameter is gathered：Temperature, oil pressure, acceleration run including knitting equipment etc..Pass through
Sensor completes collection.
3) knitting equipment operating condition data are gathered：Including the knitting equipment speed of mainshaft, operational mode, machine stop times.
4) maintenance log of knitting equipment is gathered：Failure cause including knitting equipment, maintenance duration, maintenance are arranged
Apply, change record.
5) block lathe work operation performance data is gathered：Including spinner when class's yield, knitting technology, fault type and quantity note
Record, energy consumption data.
6) weaving process class data are gathered：On actual beginning process time and expected concluding time and machine including order
The current manufacturing schedule of product.
Specifically, multidimensional knitting data is gathered in the step S1, it is complete by the sensor including with lower sensor
Into：Temperature when 1. Temperature Humidity Sensor, collection workshop moisture temperature and machine operation, 2. oil pressure sensor, gathers knitting machines
Oil pressure data, 3. velocity sensor, gathers the speed of knitting machines, high, easy for installation using optoelectronic induction principle, precision；④
The identity information of RFID frequency read/writes, collection spinner and mechanician；5. dimensional code scanner, the production of acquisition and recording fabric
Amount, quality information；6. intelligent electric meter, gathers the energy consumption data of knitting machines.
Specifically, collection multidimensional knitting data is additionally included in workshop and installs at least one for producing in the step S1
The industry of data storage and transmission calculates IPC, and one is installed on every knitting machines is used for the terminal work of production data acquisition
Control machine (hereinafter referred to as terminating machine), and WEB service end system is installed in central control room.
Specifically, setting up communication protocol in the step S1 includes：
1) RS485 serial communications are passed through between sensor and terminal industrial computer；
2) data transfer uses the ZigBee channel radios based on IEEE 802.15.4 standards between terminal industrial computer and IPC
Letter technology；
3) data transfer between IPC and WEB service end system follows associated internet communication protocol.
Specifically, the data source of the big data analysis platform based on Hadoop is ERP data, BOM in the step S2
Data, knitting workshop terminating machine ZigBee real-time data collections and MES system historical data etc..
Specifically, when being excavated in the step S3 to constraint, method during specific excavation is as follows：
1) minimum support min_support and min confidence min_confidence is set, it is flat in big data analysis
Candidate's K item collections are produced in platform；Wherein k is positive integer；
2) beta pruning is carried out to candidate k item collections, obtains Frequent Set, specific beta pruning way is each record of traversal Candidate Set
Ti, calculate TiSupport support (Ti), if support (Ti)<Min_support then deletes this record；T is traveled through simultaneouslyi
In each nonvoid subset, if exist non-Frequent Set also delete this record；
3) Frequent Set of acquisition is carried out, from connecting, forming the Candidate Set of next round；
4) repeat step 2) and step 3) minimum support is met until not new item collection；
5) according to the final Frequent Set of formation, the confidence level between Frequent Set items is calculated, is filtered out less than min_
Confidence item collection, so as to generate Strong association rule, obtains constraint data；
6) the constraint data for obtaining excavation are written to Hbase or edis or NoSQL databases, and pass through Web exhibitions
Show.
Specifically, the resource constraint factor of order is produced by ERP order managements data and multidimensional knitting in the step S3
Knitting equipment operating condition data, knitting equipment maintenance log in data model, weaving process class data mining are obtained；
The loom knitting technology data mining that process constraint factor is gathered in real time by MES process datas and ZigBee is obtained；Yield and quality is about
The block lathe work operation performance data that Shu Yinsu is knitted in creation data model by MES historical datas and multidimensional, which is excavated, to be obtained；Plan
The weaving process class data mining that time-constrain factor is knitted in creation data model by MES planning datas and multidimensional is obtained.
Specifically, contract requirements are analyzed in the step S4 and determine scheduling priority, concrete analysis content produces for fabric
Technological requirement, fabric production cycle (including draw a design and put into serial production), order total amount, client's important level, delay completion fine
With collisional transfer requirement etc..
Specifically, in the step S5, obtained constraint will be excavated as precondition, contract requirements are used as target
Function, then relevant parameter is inputted, the production schedule is finally exported, and show with gunter diagram form at Web ends.
Specifically, in the step S6, monitoring ERP and ZigBee data, it is found that order variation or Workshop Production are inclined in real time
The production schedule is adjusted from dynamic in time is planned to.
The emerging technologies such as present invention comprehensive utilization big data, cloud computing and Internet of Things, design and realize the knitting many product of MES
Plant the automatic scheduling of small lot order, abnormal information promptly and accurately to catch, produce implementation status monitoring and active perception in real time, improve
The level of IT application of traditional knitting industry, instead of traditional manual patrol and write by hand form, in big data to automate
On the basis of excavate the constraint of the resource constraint factor of order, process constraint factor, yield and quality constraint and planned time,
The intelligence plan of arranging production and dynamic dispatching are realized, the time of information transmission in production process is greatly reduced, improved simultaneously
The production efficiency in knitting workshop, par devices comprehensive utilization ratio improves 8%, it is ensured that the knitting production process of Agility,
Greatly improve enterprise competitiveness and fine-grained management degree.
Brief description of the drawings
Fig. 1 is the knitting MES Production-Plan and scheduling methods based on big data excavation in one embodiment of the present invention
Flow chart.
Fig. 2 be in one embodiment of the present invention under MapReduce frameworks with Apriori association rule algorithms to data
The flow chart for being excavated and being analyzed.
Fig. 3 is the platform structure block diagram of big data excavation in one embodiment of the present invention.
Embodiment
The knitting MES Production-Plan and scheduling methods excavated based on big data shown in Fig. 1, its specific steps are included
S1, set up multidimensional knitting creation data model (including multidimensional knitting data acquisition technique and communication protocol)；S2, based on Hadoop
Distributed platform, which is built, includes the big data analysis platform of ERP data, ZigBee data and MES system historical data；S3,
The constraint of planned dispatching is excavated with Apriori association rules mining algorithms under MapReduce frameworks, obtained pair
Answer resource constraint factor, process constraint factor, yield and quality constraint and the planned time constraint of order；S4, according to conjunction
With demand analysis order planning priority；S5, comprehensive scheduling priority and planned dispatching constraint, obtain order planning sweet
Spy's figure；S6, in real time monitoring ERP and ZigBee data, find order variation delivery date, weaving process renewal, loom catastrophic failure
Etc. anomalous event when, dynamic adjustment the production schedule.
Specifically,
STEP1：Multidimensional knitting creation data model (including multidimensional knitting data acquisition technique and communication protocol) is set up, it is main
To include herein below：
One is installed on every knitting machines is used for the terminal industrial computer (hereinafter referred to as terminating machine) of production data acquisition,
And various sensors are installed in workshop and at least one fills the industrial computer IPC for storing and transmitting for creation data,
Remote cloud server can also be installed in central control room or using user terminal browser, WEB server and data server
The WEB service end system of three-tier architecture；The sensor includes：1. Temperature Humidity Sensor, gathers workshop moisture temperature and machine
Temperature during operation, 2. oil pressure sensor, gathers knitting machines oil pressure data, 3. velocity sensor, gathers the speed of knitting machines,
It is high, easy for installation using optoelectronic induction principle, precision；4. RFID frequency read/writes, gather the identity letter of spinner and mechanician
Breath；5. dimensional code scanner, the Yield and quality information of acquisition and recording fabric；6. intelligent electric meter, gathers the energy consumption number of knitting machines
According to.
By RS485 serial communications between sensor and terminal industrial computer, the data transfer between terminal industrial computer and IPC
Using the ZigBee wireless communication technologys based on IEEE 802.15.4 standards, the data transfer between IPC and remote cloud server
Follow associated internet communication protocol；
1) workshop environmental data is gathered：It is main to include that knitting equipment performance, grey quality, plant personnel body are influenceed
The ambient parameter of body health, for example：Workshop temperature, humidity, flyings filoplume situation etc..Environmental data is conducive to analyzing more scientificly
Grey quality and knitting equipment running status rule affected by environment；
2) knitting equipment running state parameter is gathered：The parameter of health status including reflection knitting equipment operation, for example：
Temperature, oil pressure, acceleration etc., such data sampling frequency are high, are completed by different sensors；
3) knitting equipment operating condition data are gathered：Mainly include the knitting equipment speed of mainshaft, operational mode, shutdown time
Number, as the reference conditions for carrying out overhaul of the equipments and the analysis foundation of the equipment level of production；
4) maintenance log of knitting equipment is gathered：The main failure cause including knitting equipment, maintenance duration, maintenance
Measure, replacing record, these data help to set up high-quality equipment running status forecast model.
5) block lathe work operation performance data is gathered：Mainly include spinner and work as class's yield, knitting technology, fault species sum
Amount record, energy consumption data, such data are used to analyze employee's average product and skilled operation degree, prediction product final mass effect
Really.
6) weaving process class data are gathered：Refer mainly to actual beginning process time and expected concluding time and the machine of order
The upper current manufacturing schedule of product.
STEP2：Being built based on Hadoop distributed platforms includes ERP data, ZigBee data and MES system historical data
Big data analysis platform, its structure is as shown in Figure 3；
STEP3：The excavation of constraint is carried out on the basis of STEP1 and STEP2, is comprised the following steps that：
1) as shown in figure 3, ZigBee is collected into workshop real-time production data, ERP system data, in MES system data
HDFS is reached, data are managed by HDFS, is stored into NoSQL or edis or Hbase databases.
2) data mining is carried out using MapReduce, the resource constraint factor of order is by ERP order managements data and multidimensional
It is knitted knitting equipment operating condition data, knitting equipment maintenance log, weaving process class data in creation data model
Excavation is obtained；The loom knitting technology data mining that process constraint factor is gathered in real time by MES process datas and ZigBee is obtained；
The block lathe work operation performance data that yield and quality constraint is knitted in creation data model by MES historical datas and multidimensional is excavated
Arrive；Planned time constraint is knitted the weaving process class data mining in creation data model by MES planning datas and multidimensional
Obtain.Specifically, as shown in Figure 2：
1. minimum support min_support and min confidence min_confidence is set, it is flat in big data analysis
Candidate's K item collections are produced in platform；Wherein k is positive integer；
2. beta pruning is carried out to candidate k item collections, obtains Frequent Set, specific beta pruning way is each record of traversal Candidate Set
Ti, calculate TiSupport support (Ti), if support (Ti)<Min_support then deletes this record；T is traveled through simultaneouslyi
In each nonvoid subset, if exist non-Frequent Set also delete this record；
3. the Frequent Set of acquisition is carried out, from connecting, forming the Candidate Set of next round；
4. repeat step is 2. with step 3. until not new item collection meets minimum support；
5. according to the final Frequent Set of formation, the confidence level between Frequent Set items is calculated, is filtered out less than min_
Confidence item collection, so as to generate Strong association rule, obtains constraint data；
6. constraint data excavation obtained are written to Hbase or edis or NoSQL databases, and pass through Web exhibitions
Show.
STEP4：Analysis contract requirements determine scheduling priority, and concrete analysis content includes fabric manufacturing technique requirent, base
Cloth production cycle (including draw a design and put into serial production), order total amount, client's important level, delay completion fine and collisional transfer will
Ask.
STEP5：Obtained constraint will be excavated in STEP3 as precondition, contract requirements are used as target in STEP4
Function, then relevant parameter is inputted, the production schedule is finally obtained, and show with gunter diagram form at Web ends.
STEP6：Monitoring ERP and ZigBee data, it is found that order variation or Workshop Production deviation are planned to dynamic in time in real time
State adjusts the production schedule.Order variation content is specially that order variation delivery date or rush order (insert single, order priority is carried
It is high), Workshop Production deviation plan specifically includes the events such as knitting equipment failure, knitting technology renewal, spinner's temporal shift.
The present invention is excavated by big data digging technology to the constraint for being knitted planned dispatching, obtains constraint
Accuracy rate and Real time Efficiency ratio tradition knitting workshop manual patrol, copy form mode and greatly improve；First with big data
Technology mining goes out the constraint of planned dispatching, then analyzes contract requirements and determine scheduling priority, realizes knitting machines, keeps off a car
The reasonable arrangement of work, order, saves planned time, improves equipment efficiency of usage.
Although the present invention is disclosed as above with preferred embodiment, it is not limited to the present invention, any to be familiar with this skill
The people of art, without departing from the spirit and scope of the present invention, can do various changes and modification, therefore the protection model of the present invention
Enclose being defined of being defined by claims.
Claims (9)
1. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data, it is characterised in that comprise the following steps,
S1, multidimensional knitting creation data model is set up, including collection and sets up communication protocol at multidimensional knitting data；
S2, based on Hadoop distributed platforms build include the big number of ERP data, ZigBee data and MES system historical data
According to analysis platform；
S3, with Apriori association rules mining algorithms the constraint of planned dispatching is dug under MapReduce frameworks
Pick, obtains resource constraint factor, process constraint factor, yield and quality constraint and the planned time constraint of correspondence order；
S4, based on contract demand analysis order planning priority；
S5, comprehensive scheduling priority and planned dispatching constraint, obtain order planning Gantt chart；
S6, in real time monitoring ERP and ZigBee data, find order variation delivery date, weaving process renewal, loom catastrophic failure etc.
During anomalous event, the dynamic adjustment production schedule；
Wherein, in step S1 gather multidimensional knitting data be included in workshop install at least one be used for creation data store and pass
Defeated industrial computer IPC, installs a terminal industrial computer for production data acquisition on every knitting machines, and in
Entreat control room that WEB service end system is installed；Gathering multidimensional knitting data includes collection workshop environmental data, knitting equipment fortune
Row state parameter, collection knitting equipment operating condition data, the maintenance log of knitting equipment, block lathe work operation performance number
According to, weaving process class data.
2. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, passes through in the step S1：Temperature when 1. Temperature Humidity Sensor, collection workshop moisture temperature and machine operation, 2. oil pressure
Sensor, gathers knitting machines oil pressure data, 3. velocity sensor, gathers the speed of knitting machines；4. RFID frequency read/writes,
Gather the identity information of spinner and mechanician；5. dimensional code scanner, the Yield and quality information of acquisition and recording fabric；6. intelligence
Can ammeter, the energy consumption data of collection knitting machines.
3. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, setting up communication protocol in the step S1 includes：
1) RS485 serial communications are passed through between sensor and terminal industrial computer；
2) data transfer uses the ZigBee radio communication skills based on IEEE 802.15.4 standards between terminal industrial computer and IPC
Art；
3) data transfer between IPC and WEB service end system follows associated internet communication protocol.
4. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, the data source of the big data analysis platform based on Hadoop is ERP data, BOM data, knitting car in the step S2
Between terminating machine ZigBee real-time data collections and MES system historical data etc..
5. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, when being excavated in the step S3 to constraint, step is as follows：
1) setting minimum support and min confidence, produce candidate's k item collections in big data analysis platform；Wherein k is just whole
Number；
2) beta pruning is carried out to candidate k item collections, obtains Frequent Set, specific beta pruning way is each record T of traversal Candidate Seti, meter
Calculate TiSupport support (Ti), if support (Ti) then delete this record less than minimum support；T is traveled through simultaneouslyiIn
Each nonvoid subset, if there is non-Frequent Set also deletes this record；
3) Frequent Set of acquisition is carried out, from connecting, forming the Candidate Set of next round；
4) repeat step 2) and step 3) minimum support is met until not new item collection；
5) according to the final Frequent Set of formation, the confidence level between Frequent Set items is calculated, is filtered out less than min confidence
Item collection, so as to generate Strong association rule, obtains constraint data；
6) the constraint data for obtaining excavation are written to Hbase or edis or NoSQL databases, and are shown by Web.
6. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, the resource constraint factor of order is knitted in creation data model by ERP order managements data and multidimensional in the step S3
Knitting equipment operating condition data, knitting equipment maintenance log, weaving process class data mining obtain；Process constraint because
The loom knitting technology data mining that element is gathered in real time by MES process datas and ZigBee is obtained；Yield and quality constraint is by MES
Block lathe work operation performance data in historical data and multidimensional knitting creation data model, which is excavated, to be obtained；Planned time constraint
Obtained by the weaving process class data mining in MES planning datas and multidimensional knitting creation data model.
7. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, contract requirements is analyzed in the step S4 and determine scheduling priority, concrete analysis content is fabric manufacturing technique requirent, base
Cloth production cycle, order total amount, client's important level, delay completion fine and collisional transfer requirement etc..
8. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
It is, in the step S5, obtained constraint will be excavated as precondition, contract requirements is then defeated as object function
Enter relevant parameter, finally export the production schedule, and show with gunter diagram form at Web ends.
9. a kind of knitting MES Production-Plan and scheduling methods excavated based on big data according to claim 1, its feature
Be, in the step S6, monitoring ERP and ZigBee data in real time, find order variation or Workshop Production deviate be planned to and
When dynamically adjust the production schedule.
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108710677A (en) * | 2018-05-18 | 2018-10-26 | 中国兵器工业新技术推广研究所 | Pass through the multigroup solution for knitting multiple view of NoSQL database realizing BOM datas |
| CN108803521A (en) * | 2018-06-26 | 2018-11-13 | 康赛妮集团有限公司 | A kind of careless wool spinning continuity intelligent flexible production method |
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| CN108710677A (en) * | 2018-05-18 | 2018-10-26 | 中国兵器工业新技术推广研究所 | Pass through the multigroup solution for knitting multiple view of NoSQL database realizing BOM datas |
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| CN108803521A (en) * | 2018-06-26 | 2018-11-13 | 康赛妮集团有限公司 | A kind of careless wool spinning continuity intelligent flexible production method |
| CN108803521B (en) * | 2018-06-26 | 2020-03-06 | 康赛妮集团有限公司 | Continuous intelligent flexible production method for carded wool spinning |
| CN109507966A (en) * | 2018-11-20 | 2019-03-22 | 东华大学 | A kind of intelligent managing and control system towards Weft Knitted Shell Fabric production |
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| CN110289049A (en) * | 2019-06-19 | 2019-09-27 | 江南大学 | A method of analysis communication path and the heat-resisting sexual intercourse of lipase |
| CN110289049B (en) * | 2019-06-19 | 2021-06-25 | 江南大学 | Method for analyzing heat resistance relation between communication path and lipase |
| CN110647126A (en) * | 2019-10-17 | 2020-01-03 | 任羲 | Cloud intelligent manufacturing system based on public cloud |
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| CN111311090A (en) * | 2020-02-13 | 2020-06-19 | 北京轩宇信息技术有限公司 | Intelligent scheduling method and device based on big data calculation and analysis |
| CN111401629A (en) * | 2020-03-13 | 2020-07-10 | 常州机电职业技术学院 | Production management algorithm and production management method for warp knitting workshop of intelligent knitting factory |
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| CN111882151A (en) * | 2020-06-16 | 2020-11-03 | 杭州未名信科科技有限公司 | Production scheduling method and system for discrete manufacturing industry based on reinforcement learning |
| CN112465454A (en) * | 2020-11-25 | 2021-03-09 | 宁波金田铜业(集团)股份有限公司 | Scheduling system and method applied to order production process |
| CN112465454B (en) * | 2020-11-25 | 2022-06-03 | 宁波金田铜业(集团)股份有限公司 | Scheduling system and method applied to order production process |
| CN112348415A (en) * | 2020-12-01 | 2021-02-09 | 北京理工大学 | MES production scheduling delay correlation analysis method and system |
| CN112348415B (en) * | 2020-12-01 | 2023-05-09 | 北京理工大学 | MES production scheduling delay association analysis method and system |
| CN113971216A (en) * | 2021-10-22 | 2022-01-25 | 北京百度网讯科技有限公司 | Data processing method and device, electronic equipment and memory |
| CN114677048A (en) * | 2022-04-22 | 2022-06-28 | 北京阿帕科蓝科技有限公司 | Demand area mining method |
| CN114677048B (en) * | 2022-04-22 | 2024-01-16 | 北京阿帕科蓝科技有限公司 | Method for excavating demand area |
| CN115142187A (en) * | 2022-07-06 | 2022-10-04 | 圣东尼（上海）针织机器有限公司 | Knitting circular knitting machine manufacturing quality optimization control system |
| CN115099706A (en) * | 2022-07-27 | 2022-09-23 | 广州春晓信息科技有限公司 | Distributed production management system and method based on Internet of things |
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