CN107368056A - Streamline product information acquisition method and harvester - Google Patents

Streamline product information acquisition method and harvester Download PDF

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Publication number
CN107368056A
CN107368056A CN201710754962.XA CN201710754962A CN107368056A CN 107368056 A CN107368056 A CN 107368056A CN 201710754962 A CN201710754962 A CN 201710754962A CN 107368056 A CN107368056 A CN 107368056A
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China
Prior art keywords
mrow
data
streamline
mfrac
msub
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CN201710754962.XA
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Inventor
陈烨
葛大伟
陈�峰
孟传瑞
王博然
刘牛
韩文阳
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SAIC Volkswagen Automotive Co Ltd
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SAIC Volkswagen Automotive Co Ltd
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Priority to CN201710754962.XA priority Critical patent/CN107368056A/en
Publication of CN107368056A publication Critical patent/CN107368056A/en
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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], computer integrated manufacturing [CIM]
    • G05B19/4183—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], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
    • 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
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31282—Data acquisition, BDE MDE
    • 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]

Abstract

Present invention is disclosed a kind of streamline product information harvester, including：Data collecting system, data-transmission system, data handling system and information feedback system.Data collecting system collects the identity information of streamline product.Each station on data-transmission system and streamline carries out data exchange, each station collection site data on streamline.Data handling system obtains the field data that each station gathers on streamline by data-transmission system, and field data is handled, and produces result data, and the result data includes establishing based on field data and optimizing quality control chart.Information feedback system feeds back the result data as caused by data handling system to remote terminal.Present invention further teaches a kind of streamline product information acquisition method.The streamline product information harvester and acquisition method of the present invention can improve information gathering efficiency, realize convenient input, statistics, feedback, while lift once report and hand over qualification rate, reduce rework rate, reduce process costs.

Description

Streamline product information acquisition method and harvester
Technical field
The present invention relates to the field of automobile making line, information collecting method more specifically to streamline product and Harvester.
Background technology
In country, " under the strategical planning of made in China 2025 ", a new manufacturing industry revolution tide sweeps over.It is real Existing industry 4.0, first has to solve the problems, such as the production automation.The automation of production, it is the precondition for realizing intelligence manufacture, is real The important foundation that existing intelligent product, management become more meticulous.The production automation is realized, not only needs to use smart machine, it is also necessary to Solve problem of detached island of information, realize interconnecting in production, and the timely intercommunication of data is an important ring therein.
For automobile manufacturing field, occur " paint free pass " (application zero-fault) in painting dressing automobiles field Quality requirement, and rely on traditional Concepts of Quality Management and method, be difficult to realize this target.
The common product quality information management process of coating workshop is at present：Each post operation personnel record mass letter Breath card, live vehicle body defect inspection assistant director inspect report by random samples and hand over vehicle analysis blemish in paint information, carried out after manual evaluation to upper track Feedback.Per tour is manually counted after terminating to general defect data, is carried out quality analysis within second day, is completed quality control. Under this flow, three aspect problems are primarily present：
1) operating efficiency is low.Worker's hand filling defect information table, artificial statistical analysis.There is upper thousand sheets quality information daily Table, data volume is big, and time-consuming, efficiency is low for artificial statistics, and exists and describe lack of standardization, statistics situation not in place.
2) feedback of the information efficiency is low and lags., could be to being lacked caused by hundreds of chassis of this class processing after production terminates Sunken information is counted, and could be fed back afterwards to upper track, and the hysteresis of feedback is caused to improve and lagged.At present up and down between road Feedback of the information is mainly by " shouting ", and feedback quality information record table is mainly by " walking ".
3) information improper use.The paint on report intersection checks that card information is stored on paper at present, decentralized storage, only uses In retrospect, and deep data mining is not carried out to historical data, therefore prevention sex work can not be carried out.
Therefore, under existing quality management system, defect problem is relatively laterly exposed, and defects count is relatively more, The quality management and control work of painting workshop is not only have impact on, and rework cost increase, potential production efficiency are reduced and also brought along Loss.
The content of the invention
In order to overcome above-mentioned the problem of referring in the prior art, the present invention proposes a kind of streamline product information harvester And acquisition method.
According to one embodiment of the invention, a kind of streamline product information harvester is proposed, including：Data acquisition system System, data-transmission system, data handling system and information feedback system.Data collecting system collects the identity letter of streamline product Breath.Each station on data-transmission system and streamline carries out data exchange, each station collection site number on streamline According to.Data handling system obtains the field data that each station gathers on streamline by data-transmission system, to field data Handled, produce result data, the result data includes establishing based on field data and optimizing quality control chart.Information is anti- Feedback system feeds back the result data as caused by data handling system to remote terminal.
In one embodiment, streamline product information harvester is arranged in vehicle report intersection, and collection vehicle report was handed over The information of journey.
In one embodiment, data collecting system includes：Plc communication module and identity information server.Plc communication mould Block obtains the identity information of streamline product.Identity information server communicates with plc communication module, is set on identity information server Identity information database is equipped with, the identity information for the streamline product that plc communication module obtains is saved to identity information data In storehouse.
In one embodiment, identity information includes：Vehicle and/or color and/or vehicle body number and/or report intersection sequence number And/or timestamp.
In one embodiment, each station on streamline is disposed with information collecting device, information collecting device collection Field data, field data are qualitative datas.
In one embodiment, data handling system includes：Data categorization module, data preprocessing module and process control Module.The field data of each station collection on streamline is identified and classified by data categorization module.Data prediction mould Identified and classification field data is carried out collect statistics and sequence by block, and field data is saved in spot database and made For historical data.Process control module sets waving interval to establish based on the study to the historical data in spot database Quality control chart, quality control chart is optimized using kernel density estimation method.
In one embodiment, the kernel density estimation method that process control module uses is based on：
Gaussian kernel function K (u)：
With a width of：
Density Estimator function is obtained according to gaussian kernel function and bandwidth：
Wherein n is sample size.
In one embodiment, process control module is based on Density Estimator function calculating process alarm threshold value point W；
P1 is the normal probability density of running status, and P2 is the abnormal probability density of running status；
Failing to report alarm probability Pm is：
Wherein f (a) is the probability density curve under normal operating condition, and μ is average, and h is bandwidth, and n is sample size；
False alarm probability Pn is：
Wherein f (b) is the probability density curve under abnormal operating condition, and μ is average, and h is bandwidth, and n is sample size；
Process alarm threshold point W make it that fail to report alarm probability minimizes with false alarm probability, that is, make it that P0 is minimum, wherein P0 For：P0=Pm+Pn。
In one embodiment, information feedback system includes：Webpage feedback device and mobile terminal feedback device.Webpage is anti- Result data is converted into the form for being adapted to web displaying by feedback equipment, is sent to the terminal of operation browser, is passed through by browser Web displaying result data.Result data is converted into the form for being adapted to mobile terminal application display by mobile terminal feedback device, Mobile terminal is sent to, result data is shown by application by mobile terminal.
According to one embodiment of the invention, a kind of streamline product information acquisition method is proposed, using foregoing streamline Product information harvester, the streamline product information acquisition method include：
Data collection steps, collect the identity information of streamline product；
Data transfer step, each station collection site data on streamline are simultaneously transmitted；
Data processing step, the field data of each station collection on streamline is obtained, field data is handled, produced Raw result data, the result data include establishing based on field data and optimizing quality control chart；
Feedback of the information step, to remote terminal feedback result data as caused by data handling system.
The streamline product information harvester and acquisition method of the present invention can increase substantially information gathering efficiency, realize Convenient input, statistics, feedback, while lift once report and hand over qualification rate, rework rate is reduced, reduces process costs.Quickly realize The special equipment for being arranged in report intersection hands over process to carry out real-time data acquisition, monitoring, feedback vehicle report, realizes above and below production line Information mutual communication between trip department.
Brief description of the drawings
The present invention the above and other feature, property and advantage will pass through description with reference to the accompanying drawings and examples And become apparent, identical reference represents identical feature all the time in the accompanying drawings, wherein：
Fig. 1 discloses the structured flowchart of the streamline product information harvester according to one embodiment of the invention.
Fig. 2, which discloses data handling system in the streamline product information harvester according to one embodiment of the invention, to be made The curve map of Density Estimator function.
Embodiment
With reference to shown in figure 1, Fig. 1 discloses the knot of the streamline product information harvester according to one embodiment of the invention Structure block diagram.As illustrated, the streamline product information harvester includes：Data collecting system 102, data-transmission system 104, Data handling system 106 and information feedback system 108.Data collecting system 102 collects the identity information of streamline product.Data Each station on transmission system 104 and streamline carries out data exchange, each station collection site data on streamline.Number The field data that each station gathers on streamline is obtained by data-transmission system 104 according to processing system 106, to field data Handled, produce result data, result data includes establishing based on field data and optimizing quality control chart.Feedback of the information system System 108 feeds back the result data as caused by data handling system to remote terminal.
In one embodiment, the streamline product information harvester is arranged in vehicle report intersection, and collection vehicle report is handed over The information of process.
Data collecting system 102 therein includes：Plc communication module 122 and identity information server 124.Plc communication mould Block 122 obtains the identity information of streamline product.In one embodiment, identity information includes：Vehicle, color, vehicle body number, report Intersection sequence number, timestamp.Table 1 below lists identity information a example.
Table 1
Field Data type
Model (vehicle) char(20)
ModelID (vehicle numbering) int
Color (color) char(10)
ColorID (color code) int
CarBody (vehicle body number) char(20)
LineID (report intersection sequence number) int
CreatTime (timestamp) datetime
Plc communication module 122 is arranged on the porch of report intersection, reports the vehicle of intersection to carry out data acquisition to entering, The identity information of collection vehicle.The identity information that plc communication module 122 gathers is provided to identity information server 124.Identity Information server 124 is communicated with plc communication module, and identity information database is provided with identity information server 124, and PLC leads to Believe the identity information for the streamline product that module 122 obtains, that is, report the identity information of vehicle on intersection to be saved to identity information In database.In one implementation, LAN, identity information server 124 and plc communication module all configurations are established to LAN In.Plc communication module is arranged on report intersection porch, if subsequently increasing new plc communication equipment, can be added to LAN In, it is established that newly-increased communication function, plc communication module and identity information between plc communication module and original plc communication module Communication function between server 124, to realize data acquisition function.When vehicle enters report intersection, body information is by plc communication Module 122 is read, and is stored in the identity information database of identity information server 124.According to International Electrotechnical Commission The Industry Control Programming language standard (IEC1131-3) of formulation, plc communication module have five kinds of standard programming languages：The trapezoidal graphic language Say (LD), repertoire la nguage (IL), functional module language (FBD), order functional sequence graph-based language (SFC), structure text language Say (ST).The present invention, which is not intended to limit, specifically utilizes any programming language, can utilize any feasible functional module language Speech is programmed to realize above-mentioned function.Identity information server 124 can use distributed configuration, i.e., match somebody with somebody in the porch of report intersection A terminal, such as a computer are put, long-range server is then connected to by LAN.When vehicle enters report intersection, Identity information can be sent to computer identity information after the completion of the collection of plc communication module can include above-mentioned vehicle, vehicle volume Number, color, color code, vehicle body number, report intersection sequence number, content, the computer such as timestamp can include part important information On computer screen.The identity information of collection can also be output to the identity information database on remote server automatically by computer simultaneously In.In one embodiment, identity information database is SQL Server databases.One implementation method is in global script VBS is write, and selects trigger of the Boolean type variable as perform script, when plc communication module has gathered data, this Individual value is 1, and 0 is become again immediately again after data transfer is complete.Therefore, as long as there is vehicle to enter report intersection, data will be reached automatically Computer on production line is simultaneously exported into remote data base.
In one embodiment, each station on streamline is disposed with information collecting device, information collecting device collection Field data.In one embodiment, information collecting device is tablet personal computer.The field data of information collecting device collection is matter Measure data.Operating personnel on streamline on each station are checked the quality of vehicle, such as coating quality, if finding to lack Fall into, defect kind and quantity can be selected on tablet personal computer, finally click on " friendship of vehicle report " button, complete adopting for qualitative data Collection.The qualitative data that collection is locked by tablet personal computer on each station is transmitted by data-transmission system 104.Accordingly, identity Information server 124 transmits car also by data-transmission system 104 to the information collecting device on each station, i.e. tablet personal computer Identity information.The identity information of vehicle passes along the station of streamline successively according to the order of technological process according to sequence It is defeated, first it is transferred on the tablet personal computer of first station, after first station acquisition quality end of data, qualitative data passes through number Data handling system 106 is sent to according to transmission system 104.Then the identity information of vehicle passes to the flat board electricity of next station Brain continues the collection of qualitative data.Until the quality data collection of all stations on streamline finishes.
In the embodiment shown in fig. 1, data handling system 106 includes：Data categorization module 162, data prediction mould Block 164 and process control module 166.Data categorization module 162 is by the field data of each station collection on streamline, i.e. quality Data are identified and classified.Data preprocessing module 164 pre-processes to qualitative data, in one embodiment, pre- place Reason includes identified and classification field data carrying out collect statistics and sequence, and field data is saved in into spot database It is middle to be used as historical data.Process control module 166 sets waving interval based on the study to the historical data in spot database To establish quality control chart, quality control chart is optimized using kernel density estimation method.
The basic control principle of process control module 166 is the statistics Process Control Theory based on classics.Classical system Meter learns Process Control Theory (SPC-Statistical Process Control) by the Xiu Hate in the U.S. in nineteen twenty-four in last century It is proposed, first control figure is depicted according to " 6 σ " (σ of μ ± 3) judgment principle of mathematical statistics.It was completed in 1931 has The works of milestone significance《Manufacture the economic control of product quality》, indicate quality control with diagnosing the beginning in epoch.
The basic parameter of Shewhart control chart has two, respectively mean μ and standard deviation δ.By analyzing the two parameters Judge whether production status is controlled with the correlation of actual production service data.In general Shewhart control chart uses 3 δ methods Then, it is assumed that the domain of walker bound of normal data and the distance of center line are 3 δ, i.e., using center of the mean μ as control figure Line, the bound of Shewhart control chart is respectively set to μ+3 δ, μ -3 δ.It can be sent out by the normal distribution in computational statistics Existing, the probability that control data falls in control figure is 99.73%.Quality control chart has much at present, common are two classes 8 Kind：Four kinds of metering type be respectively mean-range chart (X-R figures), mean-standard deviation control figure (X-s figures), median- Range chart (Me-R figures), Individual-moving range control chart (X-Rs figures)；Four kinds of count value are percent defective control respectively Chart (p figures), defective work numerical control is charted (np figures), unit number of non-compliances control figure (u figures), number of non-compliances control figure (c figures).
Shewhart control chart is monitored in real time by sample investigation to production run process, saved it is substantial amounts of monitoring into This, but certain risk in practice be present, wherein most commonly seen mistake has two classes.One is the mistake for alarm of shooting without hitting the target By mistake, i.e., false alarm, also referred to as Error type I, probability of happening are designated as α.Normal situation is maintained in production run process Under, for some point being under production normal condition due to accidentalia out-of-bounds, the probability that the situation occurs is very small.Secondly it is exactly The mistake of activating alarm is leaked, that is, fails to report police, also referred to as error type II, probability of happening is designated as β.Occur in production run process different In the case of often, there is deviation in the mass property of the product largely produced, deviate from conventional section.Even so, still The mass property for having the product of fraction is still maintained in original control bound.Therefore, still control is maintained according to point It is thus regarded that it is exactly typical error type II that production run process, which is in state of a control and does not make appropriate behavior, in system limit.
The process control module 166 of the present invention sets wave zone based on the study to the historical data in spot database Between to establish quality control chart, quality control chart is optimized using kernel density estimation method.
Traditional quality control chart thinks that sample data is similar to normal distribution.According to the property of normal distribution, if raw Production running is in slave mode, then qualitative character value, which falls the probability outside the control limit of upper and lower three standard deviations, is 0.0027, i.e. α=0.0027.Now, error type II β probability is relevant with mean shift coefficient t.Wherein, mean shift system Number t is represented by：
Then error type II β probability is：
Wherein, n is sample size, and K is control limit parameter, value 3.φ refers to normal distyribution function.
Assuming that Multilayer networks function is f (x), then the probability that xi values fall in bandwidth h is：
P=∫ f (x) dx ≈ f (x) × h；
So draw：
If there are k to fall in bandwidth h in n value, then can obtain：
Bringing above formula into can obtain：
When it is x ∈ [x-h/2, x+h/2] that xi, which is in bandwidth h,：
The conventional gaussian kernel function of its Kernel Function K (u) selections：
When sample size is enough, estimation function precision depends on kernel function and bandwidth factor.Bandwidth factor is that cuclear density is estimated An important parameter in meter, it determines influence degree of the different distance sample to dot density.Bandwidth factor is typically with sample Number increases and reduced, if bandwidth is excessive, local detail feature unobvious equalize excessive velocities, and estimation obtains result error Greatly；If bandwidth is too small, randomness increase, it is in irregular shape that estimation obtains density function.Density Estimator Function Optimization bandwidth Generally：
Thus, can obtain Density Estimator function is
The curve of the Density Estimator function is as shown in Fig. 2 Fig. 2 discloses the streamline according to one embodiment of the invention The curve map for the Density Estimator function that data handling system uses in product information harvester.In fig. 2, curve f (a) is Probability density curve under normal operating condition, from A points to C points.Curve f (b) is that the probability density under abnormal operating condition is bent Line, from B points to D points.Assuming that W is pointed out as process alarm threshold point, then alarm is not triggered when running status is on the left of W points, when Alarm is triggered when running status is on the right side of W points.
It is thus appreciated that may be in abnormal when running status is located between B point W points, but this alarm It is not triggered, therefore probability density stool and urine therebetween is to fail to report alarm probability.Relative, between running status is located at W point C points Shi Keneng is still in normal state, but this alarm has been triggered, therefore probability density stool and urine therebetween is false alarm Probability.
Assuming that P1 is the normal probability density of running status, P2 is the abnormal probability density of running status, therefore can be pushed away Export fails to report alarm probability Pm and is：
False alarm probability Pn is
The alarm flow efficiency is maximized, then to make to fail to report alarm probability and false alarm probability minimum, it is therefore desirable to Obtain optimal alarm threshold value W0 so that P0 is minimum, and wherein P0 is：
P0=Pm+Pn；
Find a process alarm threshold point W so that fail to report alarm probability and minimized with false alarm probability, that is, cause P0 most It is small.During the use of reality, in order to improve the simplicity used, the above method has been used to be carried in initial setting Take.In follow-up adjustment, technician can rule of thumb be summarized or relatively simple classic control drawing method is adjusted.
Fig. 1 is returned to, information feedback system 108 includes：Webpage feedback device 182 and mobile terminal feedback device 184.Webpage Result data is converted into the form for being adapted to web displaying by feedback device 182, the terminal of operation browser is sent to, by browser Pass through web displaying result data.Result data is converted into being adapted to mobile terminal application display by mobile terminal feedback device 184 Form, be sent to mobile terminal, by mobile terminal pass through application show result data.In one embodiment, mobile terminal IOS or Android operation system can be used, accordingly, mobile terminal application is under IOS or android system APP.Either webpage feedback device 182 or mobile terminal feedback device 184, all provide following functions：System administration work( Energy, including the system such as information department management, user management, rights management, Role Management, safety management, log management are normally transported The required management of row sets function.Data query, statistics, analytic function, by defect information according to different automobile types, different parts, Different type carries out statistic of classification, and pictorial statement in a variety of forms is shown, in order to which leadership is to oil paint production line Comprehensive grasp of defect information statistical analysis situation.Warning function, according to data analysis module result, according to the actual requirements, if Determine early warning target, daily, day, the moon or other conditions warning information is shown with figure and text mode.
The present invention also proposes a kind of streamline product information acquisition method, and dress is gathered using foregoing streamline product information Put, the streamline product information acquisition method includes：
Data collection steps, collect the identity information of streamline product；
Data transfer step, each station collection site data on streamline are simultaneously transmitted；
Data processing step, the field data of each station collection on streamline is obtained, field data is handled, produced Raw result data, the result data include establishing based on field data and optimizing quality control chart；
Feedback of the information step, to remote terminal feedback result data as caused by data handling system.
The specific implementation details of this method is identical with foregoing streamline product information harvester, is not repeated to retouch herein State.
The streamline product information harvester and acquisition method of the present invention can increase substantially information gathering efficiency, realize Convenient input, statistics, feedback, while lift once report and hand over qualification rate, rework rate is reduced, reduces process costs.
Specific benefit measuring and calculating is as follows：
1st, produce ray examination post and reduce 3, check that post reduces 2；
2nd, the saving to paper auxiliary material with no paper at all, former annual 9600 yuan of four sets of ink-cases of printers, it is annual to print first-class auxiliary material 3200 yuan, 0.2 yuan of printing paper/, produce 300000 per year, totally 6 ten thousand yuan, add up to annual 7.28 ten thousand yuan of saving；
3rd, the rework rate in current workshop is in 5%-6%, and by this project implementation, product vehicle rework rate is controlled in 3%- Between 4%, according to current/class of productive temp 682, it can reduce by 27 cars of doing over again daily, be done over again according to every needed for car Material Cost 500 yuan of calculating of average RMB, can save RMB13500 members daily, and paint material that is annual therefore saving is about More than 3510000 yuan.
Above-described embodiment, which is available to, is familiar with person in the art to realize or using the present invention, be familiar with this area Personnel can make various modifications or change, thus this to above-described embodiment without departing from the present invention in the case of the inventive idea The protection domain of invention is not limited by above-described embodiment, and should meet inventive features that claims are mentioned most On a large scale.

Claims (10)

  1. A kind of 1. streamline product information harvester, it is characterised in that including：
    Data collecting system, data collecting system collect the identity information of streamline product；
    Data-transmission system, each station on data-transmission system and streamline carry out data exchange, each on streamline Station collection site data；
    Data handling system, the field data that each station gathers on streamline is obtained by data-transmission system, to live number According to being handled, result data is produced, the result data includes establishing based on field data and optimizing quality control chart；
    Information feedback system, to remote terminal feedback result data as caused by data handling system.
  2. 2. streamline product information harvester as claimed in claim 1, it is characterised in that the streamline product information is adopted Acquisition means are arranged in vehicle report intersection, and collection vehicle report hands over the information of process.
  3. 3. streamline product information harvester as claimed in claim 2, it is characterised in that the data collecting system bag Include：
    Plc communication module, plc communication module obtain the identity information of streamline product；
    Identity information server, identity information server are communicated with plc communication module, and identity is provided with identity information server Information database, the identity information for the streamline product that plc communication module obtains are saved in identity information database.
  4. 4. streamline product information harvester as claimed in claim 3, it is characterised in that the identity information includes：Car Type and/or color and/or vehicle body number and/or report intersection sequence number and/or timestamp.
  5. 5. streamline product information harvester as claimed in claim 2, it is characterised in that each station cloth on streamline Information collecting device is equipped with, information collecting device collection site data, the field data is qualitative data.
  6. 6. streamline product information harvester as claimed in claim 5, it is characterised in that the data handling system bag Include：
    Data categorization module, the field data of each station collection on streamline is identified and classified；
    Data preprocessing module, identified and classification field data is subjected to collect statistics and sequence, and field data is protected It is stored in spot database and is used as historical data；
    Process control module, based on the study to the historical data in spot database, waving interval is set to establish quality control Drawing, is optimized using kernel density estimation method to quality control chart.
  7. 7. streamline product information harvester as claimed in claim 6, it is characterised in that the process control module uses Kernel density estimation method be based on：
    Gaussian kernel function K (u)：
    With a width of：
    Density Estimator function is obtained according to gaussian kernel function and bandwidth：
    <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>h</mi> <mi>n</mi> </mrow> </mfrac> <mo>&amp;times;</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mi>h</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    Wherein n is sample size.
  8. 8. streamline product information harvester as claimed in claim 7, it is characterised in that the process control module is based on Density Estimator function calculating process alarm threshold value point W；
    P1 is the normal probability density of running status, and P2 is the abnormal probability density of running status；
    Failing to report alarm probability Pm is：
    <mrow> <msub> <mi>P</mi> <mi>m</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mi>w</mi> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>b</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mi>w</mi> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mi>w</mi> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>a</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mi>w</mi> </msubsup> <mfrac> <mn>1</mn> <mrow> <mi>h</mi> <mi>n</mi> </mrow> </mfrac> <mo>&amp;times;</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>&amp;mu;</mi> </mrow> <mi>h</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> <mo>;</mo> </mrow>
    Wherein f (a) is the probability density curve under normal operating condition, and μ is average, and h is bandwidth, and n is sample size；
    False alarm probability Pn is：
    <mrow> <msub> <mi>P</mi> <mi>n</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mi>w</mi> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>a</mi> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mi>w</mi> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>a</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mn>2</mn> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mo>&amp;Integral;</mo> <mi>w</mi> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </msubsup> <mi>f</mi> <mrow> <mo>(</mo> <mi>b</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <mi>w</mi> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </msubsup> <mfrac> <mn>1</mn> <mrow> <mi>h</mi> <mi>n</mi> </mrow> </mfrac> <mo>&amp;times;</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> </mfrac> <mi>exp</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>&amp;mu;</mi> </mrow> <mi>h</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> <mo>;</mo> </mrow>
    Wherein f (b) is the probability density curve under abnormal operating condition, and μ is average, and h is bandwidth, and n is sample size；
    Process alarm threshold point W make it that fail to report alarm probability minimizes with false alarm probability, that is, make it that P0 is minimum, wherein P0 is：P0= Pm+Pn。
  9. 9. streamline product information harvester as claimed in claim 2, it is characterised in that described information reponse system bag Include：
    Webpage feedback device, result data is converted into the form for being adapted to web displaying, is sent to the terminal of operation browser, by Browser passes through web displaying result data；
    Mobile terminal feedback device, result data is converted into the form for being adapted to mobile terminal application display, is sent to mobile whole End, result data is shown by application by mobile terminal.
  10. 10. a kind of streamline product information acquisition method, it is characterised in that using as claimed in any one of claims 1-9 wherein Streamline product information harvester, the streamline product information acquisition method include：
    Data collection steps, collect the identity information of streamline product；
    Data transfer step, each station collection site data on streamline are simultaneously transmitted；
    Data processing step, the field data of each station collection on streamline is obtained, field data is handled, produces knot Fruit data, the result data include establishing based on field data and optimizing quality control chart；
    Feedback of the information step, to remote terminal feedback result data as caused by data handling system.
CN201710754962.XA 2017-08-29 2017-08-29 Streamline product information acquisition method and harvester Pending CN107368056A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108398918A (en) * 2018-02-11 2018-08-14 湖南三德科技股份有限公司 A kind of PLC information flows method, memory, PLC and system
CN109583929A (en) * 2018-07-02 2019-04-05 兰溪金服金融信息服务有限公司 Market of farm produce Synthesis Data Collection System Based and its collecting method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103197623A (en) * 2013-03-04 2013-07-10 张舒 Method and device of assembly line monitoring management
CN105812466A (en) * 2016-03-14 2016-07-27 上海东铸智能科技有限公司 Garment production line data collection system based on Ethernet protocol
CN107016456A (en) * 2017-03-14 2017-08-04 上海迈聘企业管理咨询有限公司 A kind of discreteness based on state-driven produces the prediction analysis method of trace platform

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103197623A (en) * 2013-03-04 2013-07-10 张舒 Method and device of assembly line monitoring management
CN105812466A (en) * 2016-03-14 2016-07-27 上海东铸智能科技有限公司 Garment production line data collection system based on Ethernet protocol
CN107016456A (en) * 2017-03-14 2017-08-04 上海迈聘企业管理咨询有限公司 A kind of discreteness based on state-driven produces the prediction analysis method of trace platform

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
何耀东: "基于密度分析的回溯期非参数变点识别研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 *
倪霖: "基于RFID的汽车生产线信息集成模式及关键技术研究", 《中国博士学位论文全文数据库 信息科技辑》 *
刘恒等: "基于数据驱动的化工过程参数报警阈值优化", 《化工学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108398918A (en) * 2018-02-11 2018-08-14 湖南三德科技股份有限公司 A kind of PLC information flows method, memory, PLC and system
CN109583929A (en) * 2018-07-02 2019-04-05 兰溪金服金融信息服务有限公司 Market of farm produce Synthesis Data Collection System Based and its collecting method

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