CN107168263B - A kind of knitting MES Production-Plan and scheduling method excavated based on big data - Google Patents
A kind of knitting MES Production-Plan and scheduling method excavated based on big data Download PDFInfo
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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 method of the present invention includes the following steps, S1, establishes multidimensional knitting creation data model；S2, big data analysis platform is constructed based on Hadoop distributed platform；S3, it excavates under MapReduce frame with constraint of the Apriori association rules mining algorithm to planned dispatching；S4, based on contract demand analysis order planning priority；The constraint of S5, comprehensive scheduled production priority and planned dispatching, obtain order planning Gantt chart；S6, real time monitoring ERP and ZigBee data, when finding the anomalous events such as order variation delivery date, weaving process update, loom catastrophic failure, dynamic adjusts production plan.
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 technique
In the entire production process of knitting enterprise, production planning management and traffic control occupy more consequence,
It is related to the significant datas such as product quality, Yield Grade, delivery cycle and utilization rate of equipment and installations.Currently, most of knitting enterprise
It arranges production according further to the experience of workshop director or Job-Shop person, since order volume is relatively more, kind is turned frequently, institute
With purely by experience come arrange it is single inevitably will appear order dispatch not in time, flexibility is poor, divisions of responsibility is unclear.Most of production
The arrangement of plan is transmitted step by step from higher level department to department, junior 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 the survival and development of knitting enterprise to Guan Chong
It wants.
Be knitted MES as the shop layer between enterprise's upper layer planning management and bottom Shop floor control production management technology and
Real time information system can be solved the above problems by reasonable, efficient planning and scheduling Workshop Production task.
With knitting equipment automation, the fast development of networking, entire weaving process is daily at an unprecedented rate
Magnanimity Product Process, machine energy consumption and production process data are generated, in addition to this further includes sensor sensing data, network biography
The unstructured datas such as transmission of data, defect image data, therefore be knitted production process data and have big data " 4V " substantially
The characteristics of (Volume, Velocity, Varity, Value) is a typical big data.In this context, it is knitted enterprise
" big data era " is entered from " data age ".
Summary of the invention
Enterprise is knitted present invention aims at help and realizes that production process states period real-time tracing entirely, is dug using big data
Office's technology is quickly arranged production plan and dynamic dispatching.
The present invention provides a kind of knitwear production method excavated based on big data, includes the following steps,
S1, multidimensional knitting creation data model is established, including acquires multidimensional knitting data and establishes communication protocol；
S2, ERP data, ZigBee data and MES system historical data are included based on the building of Hadoop distributed platform
Big data analysis platform；
S3, under MapReduce frame with Apriori association rules mining algorithm to the constraint of planned dispatching into
Row excavates, obtain resource constraint factor, process constraint factor, yield and quality constraint and the planned time constraint of corresponding order because
Element；
S4, based on contract demand analysis order planning priority；
The constraint of S5, comprehensive scheduled production priority and planned dispatching, obtain order planning Gantt chart；
S6, real time monitoring ERP and ZigBee data, discovery order variation delivery date, weaving process update, loom burst event
When the anomalous events such as barrier, dynamic adjusts production plan.
Specifically, it includes acquiring the data of following 6 dimensions that multidimensional knitting data is acquired in the step S1:
1) workshop environmental data is acquired: including influencing knitting equipment performance, grey quality, plant personnel health
Environmental parameter.Such as: workshop temperature, humidity, flyings filoplume situation etc..
2) knitting equipment running state parameter is acquired: temperature, oil pressure, acceleration etc. including knitting equipment operation.Pass through
Sensor completes acquisition.
3) knitting equipment operating condition data are acquired: including the knitting equipment speed of mainshaft, operational mode, machine stop times.
4) acquire the maintenance log of knitting equipment: failure cause, maintenance duration including knitting equipment, maintenance are arranged
It applies, replace record.
5) block lathe work operation performance data is acquired: including spinner when class's yield, knitting technology, fault type and quantity note
Record, energy consumption data.
6) weaving process class data are acquired: on practical beginning process time and expected concluding time and machine including order
The current manufacturing schedule of product.
Specifically, multidimensional knitting data is acquired in the step S1, by including complete with the sensor including lower sensor
At: 1. Temperature Humidity Sensor, temperature when acquiring workshop moisture temperature and machine operation, 2. oil pressure sensor, acquires knitting machines
Oil pressure data, 3. velocity sensor, acquires the speed of knitting machines, high, easy for installation using optoelectronic induction principle, precision；④
RFID frequency read/write acquires the identity information of spinner and mechanician；5. dimensional code scanner, the production of acquisition and recording fabric
Amount, quality information；6. intelligent electric meter acquires the energy consumption data of knitting machines.
Specifically, it further includes that at least one is installed in workshop for producing that multidimensional knitting data is acquired in the step S1
The industry of data storage and transmission calculates IPC, and the terminal work for being used for production data acquisition is installed on every knitting machines
Control machine (hereinafter referred to as terminating machine), and WEB service end system is installed in central control room.
Specifically, establishing communication protocol in the step S1 includes:
1) pass through RS485 serial communication between sensor and terminal industrial personal computer；
2) data transmission uses the ZigBee channel radio based on IEEE 802.15.4 standard between terminal industrial personal computer and IPC
Letter technology；
3) the data transmission 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 collection and MES system historical data etc..
Specifically, when excavating in the step S3 to constraint, method when specifically excavating 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 collection is generated in platform；Wherein k is positive integer；
2) beta pruning is carried out to candidate k item collection, obtains Frequent Set, specific beta pruning way is traversal Candidate Set each record
Ti, calculate TiSupport support (Ti), if support (Ti) < min_support then deletes this record；T is traversed simultaneouslyi
In each nonvoid subset, if it exists non-Frequent Set also delete this record；
3) Frequent Set of acquisition is carried out forming the Candidate Set of next round from connecting；
4) step 2) and step 3) are repeated 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_
The item collection of confidence obtains constraint data to generate Strong association rule；
6) obtained constraint data will be excavated and is written to Hbase or edis or NoSQL database, and pass through Web exhibition
Show.
Specifically, the resource constraint factor of order is produced by ERP order management 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 obtain；
The loom knitting technology data mining that process constraint factor is acquired in real time by MES process data and ZigBee obtains；Yield and quality is about
Shu Yinsu is excavated to obtain by the block lathe work operation performance data in MES historical data and multidimensional knitting creation data model；Plan
Time-constrain factor is obtained by the weaving process class data mining in MES planning data and multidimensional knitting creation data model.
Specifically, contract requirements are analyzed in the step S4 and determine scheduled production priority, make a concrete analysis of content as fabric production
Technique 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 require etc..
Specifically, in the step S5, obtained constraint will be excavated as precondition, contract requirements are as target
Function, then relevant parameter is inputted, production plan is finally exported, and show with gunter diagram form at the end Web.
Specifically, in the step S6, real time monitoring ERP and ZigBee data find that order variation or Workshop Production are inclined
Production plan is adjusted from dynamic in time is planned to.
The present invention comprehensively utilizes the emerging technologies such as big data, cloud computing and Internet of Things, designs and realize the knitting more product of MES
The automatic scheduled production of kind small lot order, exception information promptly and accurately capture, produce executive condition real time monitoring and actively perceive, improve
The level of IT application of traditional knitting industry, to automate instead of traditional manual patrol and write by hand report, in big data
On the basis of excavate the resource constraint factor of order, process constraint factor, yield and quality constraint and planned time constraint,
Intelligent arrange production plan and dynamic dispatching are realized, the time that information is transmitted in production process is greatly reduced, improves simultaneously
The production efficiency in knitting workshop, par devices comprehensive utilization ratio improve 8%, ensure that the knitting production process of Agility,
Greatly improve enterprise competitiveness and fine-grained management degree.
Detailed description of the invention
Fig. 1 is the knitting MES Production-Plan and scheduling method excavated in one embodiment of the present invention based on big data
Flow chart.
Fig. 2 is to use Apriori association rule algorithm to data under MapReduce frame in one embodiment of the present invention
The flow chart for being excavated and being analyzed.
Fig. 3 is the platform structure block diagram that big data is excavated in one embodiment of the present invention.
Specific embodiment
The knitting MES Production-Plan and scheduling method excavated shown in Fig. 1 based on big data, specific steps include
S1, multidimensional knitting creation data model (including multidimensional knitting data acquisition technique and communication protocol) is established；S2, it is based on Hadoop
Distributed platform building includes the big data analysis platform of ERP data, ZigBee data and MES system historical data；S3,
It excavates, obtains pair with constraint of the Apriori association rules mining algorithm to planned dispatching under MapReduce frame
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；The constraint of S5, comprehensive scheduled production priority and planned dispatching, it is sweet to obtain order planning
Spy's figure；S6, real time monitoring ERP and ZigBee data, discovery order variation delivery date, weaving process update, loom catastrophic failure
Etc. anomalous events when, dynamic adjust production plan.
Specifically,
STEP1: establishing multidimensional knitting creation data model (including multidimensional knitting data acquisition technique and communication protocol), main
To include the following contents:
One terminal industrial personal computer (hereinafter referred to as terminating machine) for being used for production data acquisition is installed on every knitting machines,
And the industrial computer IPC that various sensors and at least one dress are stored and transmitted for creation data is installed in workshop,
Remote cloud server can also be installed in central control room or use user terminal browser, WEB server and data server
The WEB service end system of three-tier architecture；The sensor includes: 1. Temperature Humidity Sensor, acquires workshop moisture temperature and machine
Temperature when operation, 2. oil pressure sensor, acquires knitting machines oil pressure data, and 3. velocity sensor, acquires the speed of knitting machines,
It is high, easy for installation using optoelectronic induction principle, precision；4. RFID frequency read/write acquires 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 acquires the energy consumption number of knitting machines
According to.
Data transmission by RS485 serial communication between sensor and terminal industrial personal computer, between terminal industrial personal computer and IPC
Data transmission using the ZigBee wireless communication technique based on IEEE 802.15.4 standard, between IPC and remote cloud server
Follow associated internet communication protocol；
1) acquire workshop environmental data: main includes that can influence knitting equipment performance, grey quality, plant personnel body
The environmental parameter of body health, such as: workshop temperature, humidity, flyings filoplume situation etc..Environmental data is conducive to analyze more scientificly
Grey quality and knitting equipment operating status rule affected by environment；
2) knitting equipment running state parameter is acquired: the parameter of the health status including reflection knitting equipment operation, such as:
Temperature, oil pressure, acceleration etc., such data sampling frequency is high, is completed by different sensors；
3) knitting equipment operating condition data are acquired: mainly including the knitting equipment speed of mainshaft, operational mode, shutdown time
Number, the analysis foundation as the reference conditions and equipment production level for carrying out overhaul of the equipments；
4) maintenance log of knitting equipment is acquired: the main failure cause including knitting equipment, maintenance duration, maintenance
Measure, replacement record, these data help to establish the equipment running status prediction model of high quality.
5) it acquires block lathe work operation performance data: mainly including that spinner works as class's yield, knitting technology, fault type sum number
Amount record, energy consumption data, such data are for analyzing employee's average product and skilled operation degree, prediction product final mass effect
Fruit.
6) weaving process class data are acquired: referring mainly to practical beginning process time and expected concluding time and the machine of order
The upper current manufacturing schedule of product.
STEP2: including ERP data, ZigBee data and MES system historical data based on the building of Hadoop distributed platform
Big data analysis platform, structure is as shown in Figure 3；
STEP3: the excavation of constraint is carried out on the basis of STEP1 and STEP2, the specific steps are as follows:
1) as shown in figure 3, ZigBee is collected 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 database.
2) data mining is carried out using MapReduce, the resource constraint factor of order is by ERP order management data and multidimensional
Knitting equipment operating condition data, knitting equipment maintenance log, weaving process class data in knitting creation data model
Excavation obtains；The loom knitting technology data mining that process constraint factor is acquired in real time by MES process data and ZigBee obtains；
Yield and quality constraint is excavated by the block lathe work operation performance data in MES historical data and multidimensional knitting creation data model
It arrives；Planned time constraint is by the weaving process class data mining in MES planning data and multidimensional knitting creation data model
It obtains.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 collection is generated in platform；Wherein k is positive integer；
2. carrying out beta pruning to candidate k item collection, Frequent Set is obtained, specific beta pruning way is traversal Candidate Set each record
Ti, calculate TiSupport support (Ti), if support (Ti) < min_support then deletes this record；T is traversed simultaneouslyi
In each nonvoid subset, if it exists non-Frequent Set also delete this record；
3. carrying out forming the Candidate Set of next round from connecting to the Frequent Set of acquisition；
4. repeating step 2. with step 3. until not new item collection meets minimum support；
5. calculating the confidence level between Frequent Set items according to the final Frequent Set of formation, filtering out less than min_
The item collection of confidence obtains constraint data to generate Strong association rule；
6. will excavate obtained constraint data is written to Hbase or edis or NoSQL database, and passes through Web exhibition
Show.
STEP4: analysis contract requirements determine scheduled production 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 are wanted
It asks.
STEP5: using the constraint excavated in STEP3 as precondition, contract requirements are as target in STEP4
Function, then relevant parameter is inputted, production plan is finally obtained, and show with gunter diagram form at the end Web.
STEP6: it is dynamic in time to find that order variation or Workshop Production deviation are planned to for real time monitoring ERP and ZigBee data
State adjusts production plan.Order variation content is specially that order variation delivery date or rush order (insert list, order priority mentions
It is high), Workshop Production deviation plan specifically includes the events such as knitting equipment failure, knitting technology update, spinner's temporary shift.
The present invention is excavated by constraint of the big data digging technology to knitting planned dispatching, obtains constraint
Accuracy rate and Real time Efficiency than tradition knitting workshop manual patrol, copy report mode and greatly improve；First with big data
Technology mining goes out the constraint of planned dispatching, then analyzes contract requirements and determine scheduled production 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 has been described by way of example and in terms of the preferred embodiments, it is not intended to limit the invention, any to be familiar with this skill
The people of art can do various change and modification, therefore protection model of the invention without departing from the spirit and scope of the present invention
Enclosing subject to the definition of the claims.
Claims (9)
1. a kind of knitting MES Production-Plan and scheduling method excavated based on big data, which is characterized in that include the following steps,
S1, multidimensional knitting creation data model is established, including acquires multidimensional knitting data and establishes communication protocol；
S2, the big number based on the building of Hadoop distributed platform including ERP data, ZigBee data and MES system historical data
According to analysis platform；
S3, it digs under MapReduce frame with constraint of the Apriori association rules mining algorithm to planned dispatching
Pick obtains resource constraint factor, process constraint factor, yield and quality constraint and the planned time constraint of corresponding order；
S4, based on contract demand analysis order planning priority；
The constraint of S5, comprehensive scheduled production priority and planned dispatching, obtain order planning Gantt chart；
S6, real time monitoring ERP and ZigBee data, discovery order variation delivery date, weaving process update, loom catastrophic failure are different
When ordinary affair part, dynamic adjusts production plan；
Wherein, in step S1 acquire multidimensional knitting data be included in workshop install at least one for creation data store and pass
Defeated industrial computer IPC installs the terminal industrial personal computer for being used for production data acquisition on every knitting machines, and in
Entreat control room that WEB service end system is installed；Acquiring multidimensional knitting data includes acquisition workshop environmental data, knitting equipment fortune
Row state parameter, acquisition knitting equipment operating condition data, the maintenance log of knitting equipment, block lathe work operation performance number
According to, weaving process data.
2. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
Be, pass through in the step S1: 1. Temperature Humidity Sensor acquires temperature when workshop moisture temperature and machine are run, 2. oil pressure
Sensor acquires knitting machines oil pressure data, and 3. velocity sensor, acquires the speed of knitting machines；4. RFID frequency read/write,
Acquire the identity information of spinner and mechanician；5. dimensional code scanner, the yield and quality information of acquisition and recording fabric；6. intelligence
Energy ammeter, acquires the energy consumption data of knitting machines.
3. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, establishing communication protocol in the step S1 includes:
1) pass through RS485 serial communication between sensor and terminal industrial personal computer；
2) data transmission uses the ZigBee based on IEEE 802.15.4 standard to wirelessly communicate skill between terminal industrial personal computer and IPC
Art；
3) the data transmission between IPC and WEB service end system follows associated internet communication protocol.
4. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, the data source of the big data analysis platform in the step S2 based on Hadoop is ERP data, BOM data, knitting vehicle
Between terminating machine ZigBee real-time data collection and MES system historical data.
5. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, when excavating in the step S3 to constraint, steps are as follows:
1) minimum support and min confidence are set, candidate's k item collection is generated in big data analysis platform；Wherein k is positive whole
Number；
2) beta pruning is carried out to candidate k item collection, obtains Frequent Set, specific beta pruning way is that traversal Candidate Set each records Ti, meter
Calculate TiSupport support (Ti), if support (Ti) be less than minimum support then delete this record；T is traversed simultaneouslyiIn
Each nonvoid subset, non-Frequent Set also deletes this record if it exists；
3) Frequent Set of acquisition is carried out forming the Candidate Set of next round from connecting；
4) step 2) and step 3) are repeated 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 obtains constraint data to generate Strong association rule；
6) obtained constraint data will be excavated and be written to Hbase or edis or NoSQL database, and shown by Web.
6. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, the resource constraint factor of order is by ERP order management data and multidimensional knitting creation data model 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 acquired in real time by MES process data and ZigBee obtains；Yield and quality constraint is by MES
Block lathe work operation performance data in historical data and multidimensional knitting creation data model excavates to obtain；Planned time constraint
It is obtained by the weaving process data mining in MES planning data and multidimensional knitting creation data model.
7. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, contract requirements is analyzed in the step S4 and determine that scheduled production priority, concrete analysis content are fabric manufacturing technique requirent, base
Cloth production cycle, order total amount, client's important level, delay completion fine and collisional transfer requirement.
8. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
It is, in the step S5, obtained constraint will be excavated as precondition, contract requirements is then defeated as objective function
Enter relevant parameter, finally exports production plan, and show with gunter diagram form at the end Web.
9. a kind of knitting MES Production-Plan and scheduling method excavated based on big data according to claim 1, feature
Be, in the step S6, real time monitoring ERP and ZigBee data, find order variation or Workshop Production deviation be planned to and
When dynamic adjustment production plan.
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