CN110188985A - Quality data processing method between a kind of volume hired car - Google Patents
Quality data processing method between a kind of volume hired car Download PDFInfo
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- CN110188985A CN110188985A CN201910314431.8A CN201910314431A CN110188985A CN 110188985 A CN110188985 A CN 110188985A CN 201910314431 A CN201910314431 A CN 201910314431A CN 110188985 A CN110188985 A CN 110188985A
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- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 238000004519 manufacturing process Methods 0.000 claims abstract description 34
- 230000007547 defect Effects 0.000 claims abstract description 30
- 235000019504 cigarettes Nutrition 0.000 claims abstract description 25
- 230000002159 abnormal effect Effects 0.000 claims abstract description 24
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000003860 storage Methods 0.000 claims abstract description 14
- 238000012423 maintenance Methods 0.000 claims abstract description 11
- 239000000463 material Substances 0.000 claims abstract description 9
- 238000006243 chemical reaction Methods 0.000 claims abstract description 5
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 21
- 238000004140 cleaning Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 8
- 230000010354 integration Effects 0.000 claims description 8
- 238000007689 inspection Methods 0.000 claims description 5
- 238000013501 data transformation Methods 0.000 claims description 3
- 241000208125 Nicotiana Species 0.000 abstract description 4
- 235000002637 Nicotiana tabacum Nutrition 0.000 abstract description 4
- 238000011946 reduction process Methods 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 5
- 239000002775 capsule Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000004806 packaging method and process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000003908 quality control method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The present invention relates to quality data processing methods between a kind of volume hired car, it is stored by the data of cigarette physical index combined test stand by socket mode, the information such as equipment relevant parameter, state are stored by OPC mode in production process, and the data such as maintenance of equipment, open defect and auxiliary material information are manually entered storage by MES system.And relatively large deviation can be generated to secondary outcome due to the abnormal data of human operational error and equipment contingency in production process, screening is carried out to data by data rule itself and washes out legal qualitative data；The data more dispersed for memory node integrate it by relation factor；Redundant storage and incoherent data carry out reduction process to it；The data of unstructured storage become the data that can directly use by data conversion, can realize that efficient data calls by the storage mode of cloud data center, accurately to make aid decision to quality tobacco volume hired car.
Description
Technical field
The invention belongs to quality management auxiliary decision technology fields between tobacco volume hired car, particularly relate to a kind of volume hired car interstitial
Measure data processing method.
Background technique
Tobacco leaf production workshop, volume packet device type is numerous, type of product quality problem and producing cause are intricate, matter
It is interrelated that amount problem corresponds to various factors between producing cause, this brings larger difficulty to cigarette hired car interstitial buret control.
Traditional quality management-control method cannot find simultaneously rationally to do good quality control person with foresight's guidance in time.In traditional work
In, existing hidden danger of quality can only be searched by constantly making an inspection tour observation quality condition.Since cigarette technology of the package is more multiple
It is miscellaneous, can not be exhaustive, previous operator and maintenance personal's handling failure are all the experiences for relying on itself, there is careless omission.
It is illustrated in fig. 1 shown below, mass analysis method is mainly passed through by single physical index data between volume hired car at present
Single data source carries out manual analysis, since the physical index in production process is after testing as a result, belonging to a result
Embodiment, can not real time correlation production status to production process carry out Real-Time Evaluation and analyze and judge and position specific matter
Amount problem.
The factor for influencing product quality problem between volume hired car at present is more, the source of data relevant to product quality problem
And quantity is more, how accurately to make aid decision to quality volume hired car, needs between all kinds of and production volume hired car
Quality related data is acquired and stores, but in existing technology, is to analyze single physical index, cannot accomplish
Comprehensive analysis is carried out using multi-party face data.
Summary of the invention
The object of the present invention is to provide quality data processing methods between a kind of volume hired car, to solve the prior art to cigarette vehicle
Between volume packet quality analysis be only capable of the method using single qualitative data.
The present invention is achieved by the following technical solutions:
Quality data processing method between a kind of volume hired car, comprising the following steps:
1) factor that cigarette workshop influences quality problems is classified, includes at least physical index factor, production process
Real-time status factor, maintenance of equipment factor, auxiliary material information factor and open defect factor；
2) data involved in the maintenance of equipment factor, data involved in the auxiliary material information factor, the appearance
Data involved in defect pass through MES system input database；
Data involved in the physical index factor pass to database by socket communication by combined test stand；
Data involved in the production process real-time status factor communicate typing number by the plc communication and OPC of equipment
According to library；
3) data in the database are pre-processed, comprising the following steps:
Data cleansing, set symbol, which is taken a group photo, rings the data cleansing rule of quality problems, to the data in the database by number
Screening is carried out according to cleaning rule, obtains the qualitative data met；
Data regularization determines the multiple features for influencing quality problems, under the premise of keeping data original appearance, by by data
Qualitative data after cleaning is compared with above-mentioned each feature, deletes and the incoherent data of above-mentioned each feature；
The data in data after data regularization, being related to multiple memory nodes are passed through the pass of setting by data integration
Connection factor is integrated；
Data transformation, by the data after data integration, there are the data of unstructured storage to pass through data conversion
At the data that can directly use；
4) data acquisition system is re-stored as by pretreated data by above-mentioned.
Data involved in the physical index factor include at least Cigarette circumference data, cigarette length data, cigarette weight
It measures in data or resistance to suction data and one or more combines.
Data involved in the production process real-time status factor include at least equipment downtime information data, production information
Data shut down one of reason data or equipment efficiency data or more than one combinations.
Data involved in the open defect factor include at least item and fill open defect data, box-packed open defect data
One of cigarette open defect data or more than one combination.
The data cleansing includes abnormal traffic data cleansing and the cleaning of combined test stand physical index detection data；
Involved by the open defect that the abnormal traffic data cleansing is filled in mainly for combined test stand data, MES system
And data, abnormal production data, equipment efficiency abnormal data, shut down duration abnormal data or shut down reason data
Cleaning；
The cleaning of combined test stand physical index detection data include comparison board corresponding with scheduled production on duty trade mark choosing mistake data,
Deviation larger data or field missing data.
The abnormal traffic data, which include at least unreasonable data, field beyond normal value, to be there is null value, is filled in manually
One of inconsistency data under missing or other same states or more than one combinations.
Data cleansing rule includes abnormal traffic data cleansing rule and physical index detection data cleaning rule.
Data cleansing rule uses Inspection method, picks null value method and setting threshold method.
The beneficial effects of the present invention are:
The technical program is stored by the data of cigarette physical index combined test stand by socket mode, production process
The information such as middle equipment relevant parameter, state are stored by OPC mode, and the data such as maintenance of equipment, open defect and auxiliary material information are logical
It crosses MES system and is manually entered storage.
And secondary outcome can be generated due to the abnormal data of human operational error and equipment contingency in production process
Relatively large deviation carries out screening to data by data rule itself and washes out legal qualitative data；For memory node
More dispersed data integrate it by relation factor；Redundant storage and incoherent data carry out at reduction it
Reason；The data of unstructured storage become the data that can directly use by data conversion, pass through the storage of cloud data center
Mode can realize that efficient data calls, accurately to make aid decision to quality tobacco volume hired car.
Detailed description of the invention
Fig. 1 is prior art Analysis of Quality Problem logic chart；
Fig. 2 is data of the present invention acquisition and storage principle figure；
Fig. 3 is data prediction modular concept figure of the present invention.
Description of symbols
1 data cleansing, 2 data regularizations, 3 data integrations, the transformation of 4 data.
Specific embodiment
Carry out the technical solution that the present invention will be described in detail by the following examples, embodiment below is merely exemplary, only
It can be used to explanation and illustration technical solution of the present invention, and be not to be construed as the limitation to technical solution of the present invention.
In order to accurately make aid decision to quality volume hired car, need related between all kinds of qualities of production volume hired car
Data are acquired and store, and since back end and data type are more between volume hired car, need to distinct device using different
Principle carries out data acquisition and is stored, and can realize that efficient data calls by the storage mode of cloud data center.
Quality assistant decision making support volume hired car is held in order to realize, needs to fully understand that cigarette workshop influences product quality and asks
All factors of topic, by long-term statistical analysis, it mainly includes physics that existing cigarette workshop, which influences the factor of product quality problem,
Index factor, production process real-time status factor, maintenance of equipment factor, auxiliary material information factor and open defect factor.
Wherein, physical index factor mainly includes Cigarette circumference, cigarette length, cigarette weight or resistance to suction (referring to draw resistance)
Etc. factors be to influence the principal element of cigarette quality, also include certainly it is other influence physical indexs factor, in the application
Each embodiment in do not propose, others influence physical indexs factors can according to specific circumstances, if a certain other objects
It is larger to the factor weight for influencing product quality to manage index, can according to need the data acquisition for increasing to physical index factor
In, it, can also be from physical index if above-mentioned physical index factor does not occupy certain specific gravity in actual cigarette workshop
It is deleted in the data acquisition of factor.
Moreover, in the physical index factor of the application, can according to need selection it is one such or more than one
The acquisition of influence factor progress data.
In this application, production process real-time status factor mainly includes equipment downtime information, production information, shuts down reason
One of equipment efficiency or more than one combination, specifically need which data acquired in this respect, according to actual
It is set, still, in the data acquisition of production process real-time status factor, when also may include actual production,
Its aspect influences the data of the production process real-time status factor of product quality.
In this application, open defect factor includes at least item dress open defect, box-packed open defect or cigarette appearance and lacks
One of sunken or more than one combinations.Item dress open defect herein refers to the open defect of whole packaging, according to domestic normal
The item of rule fills quantity, and essentially ten capsules are packaged as one；Box-packed open defect herein refers to the open defect of capsule packaging,
In general, the quantity of capsule packaging is that 20 dresses, ten dresses or other quantity, cigarette open defect refer to single cigarette
The open defect of branch.
As shown in Fig. 2, data involved in maintenance of equipment factor are recorded by hand by MES system when carrying out data acquisition
Enter into database, this is primarily due to, and maintenance of equipment factor is abnormal factor.
Data involved in required auxiliary material information factor pass through in MES system craft input database.
Data involved in open defect factor are usually entered into database by quality control department by MES system, other
In embodiment, by the judgement department typing of open defect factor, technical scheme is had no effect on as the department of typing
It realizes.
Data involved in physical index factor pass to database by socket communication by combined test stand, in this Shen
In other embodiments please, the test data of combined test stand can also pass to database again by intermediate link, but with
Database directly is passed to by socket communication to compare, and is increased intermediate link, is easy to appear loss of data, data cannot upload
Or the case where data distortion, moreover, can be realized data backup by socket communication reduces the active operation because of user
Lead to the corrupted or lost situation of data file.
Data involved in production process real-time status factor such as equipment downtime information data, production information data, are shut down
The plc communication or OPC communication that reason data or equipment efficiency data are carried by equipment are directly passed to database.
In this application, data involved in above-mentioned open defect factor, data involved in physical index factor are raw
Data involved in production process real-time status factor, data involved in maintenance of equipment factor, involved in auxiliary material information factor
Data can be stored in different databases respectively.
In process of production, determination result is assisted to quality due to the abnormal data of human operational error or equipment contingency
Biggish deviation can be generated, needs to carry out screening to data by data cleaning rule itself to wash out legal quality
Data, the data more dispersed for memory node carry out data integration to it by relation factor；To redundant storage and non-phase
The data of pass carry out what data regularization processing and becoming to the data of unstructured storage by data conversion can be used directly
Data.
As shown in figure 3, all data of above-mentioned acquisition are pre-processed, with realize data can subject-oriented, integrate
, it changes over time, is the metastable data acquisition system of information itself, for the support to quality management decision process.
The data prediction of the application the following steps are included:
Firstly, carrying out data cleansing, the data cleansing 1 includes that abnormal traffic data cleansing and combined test stand physics refer to
Mark detection data cleaning.
Involved by the open defect that the abnormal traffic data cleansing is filled in mainly for combined test stand data, MES system
And data, abnormal production data, equipment efficiency abnormal data, shut down duration abnormal data or shut down reason data
Cleaning.
The abnormal traffic data, which include at least unreasonable data, field beyond normal value, to be there is null value, is filled in manually
One of inconsistency data under missing or other same states or more than one combinations, these data influence whether product matter
The accuracy of amount aid decision includes Inspection using data cleansing rule in this application therefore, it is necessary to be rejected
Method picks null value method and setting threshold method, and above-mentioned each method is the method that the prior art is often used, herein without to this
A little methods are described in detail.
Combined test stand physical index detection data includes comparison board and scheduled production on duty corresponding trade mark choosing wrong data, deviation
Larger data or field missing data；Its data cleansing rule also uses Inspection method, picks null value method and setting threshold method.
Secondly, carrying out data regularization 2, the multiple features (determining herein according to actual needs) for influencing quality problems are determined,
Under the premise of keeping data original appearance, by the way that the qualitative data after data cleansing to be compared with above-mentioned each feature, delete
Except with the incoherent data of above-mentioned each feature.
Again, data integration 3 is carried out, the data in the data after data regularization, being related to multiple memory nodes are logical
The relation factor for crossing setting is integrated；In the other embodiments of the application, also include will store with multiple databases in
Data be integrated into a database, to improve the service efficiency of data.
Again, data transformation 4 is carried out, by the data after data integration, by calculating, counts or replaces former number
According to the data for being converted into can be used directly.
By it is above-mentioned by pretreated data be re-stored as it is a subject-oriented, integrated, change over time,
It is the metastable data acquisition system of information itself, for the support to quality management decision process.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And deformation, the scope of the present invention is by appended claims and its equivalent limits.
Claims (8)
1. quality data processing method between a kind of volume hired car, which comprises the following steps:
1) factor that cigarette workshop influences quality problems is classified, it is real-time includes at least physical index factor, production process
Status consideration, maintenance of equipment factor, auxiliary material information factor and open defect factor；
2) data involved in the maintenance of equipment factor, data involved in the auxiliary material information factor, the open defect
Related data pass through MES system input database；
Data involved in the physical index factor pass to database by socket communication by combined test stand；
Data involved in the production process real-time status factor communicate input database by the plc communication and OPC of equipment；
3) data in the database are pre-processed, comprising the following steps:
Data cleansing, set symbol, which is taken a group photo, rings the data cleansing rule of quality problems, clear to the data in data in the database
It washes rule and carries out screening, obtain the qualitative data met；
Data regularization determines the multiple features for influencing quality problems, under the premise of keeping data original appearance, by by data cleansing
Qualitative data afterwards is compared with above-mentioned each feature, deletes and the incoherent data of above-mentioned each feature；
Data integration, in the data after data regularization, will be related to the data of multiple memory nodes by the association of setting because
Element is integrated；
Data transformation, by the data after data integration, there are the data of unstructured storage to pass through data conversion into energy
Enough data directly used；
4) data acquisition system is re-stored as by pretreated data by above-mentioned.
2. quality data processing method between volume hired car according to claim 1, which is characterized in that the physical index factor
Related data include at least in Cigarette circumference data, cigarette length data, cigarette weight data or resistance to suction data and a kind of
Or more than one combinations.
3. quality data processing method between volume hired car according to claim 1, which is characterized in that the production process is real-time
Data involved in status consideration include at least equipment downtime information data, production information data, shut down reason data or equipment
One of efficiency data or more than one combinations.
4. quality data processing method between volume hired car according to claim 1, which is characterized in that the open defect factor
Related data include at least one in item dress open defect data, box-packed open defect data or cigarette open defect data
Kind or more than one combinations.
5. quality data processing method between volume according to claim 1 hired car, which is characterized in that the data cleansing includes
Abnormal traffic data cleansing and the cleaning of combined test stand physical index detection data；
Involved in the open defect that the abnormal traffic data cleansing is filled in mainly for combined test stand data, MES system
Data, equipment efficiency abnormal data, shut down duration abnormal data or shut down the clear of reason data at abnormal production data
It washes；
The cleaning of combined test stand physical index detection data includes comparison board and scheduled production on duty corresponding trade mark choosing wrong data, deviation
Larger data or field missing data.
6. quality data processing method between volume hired car according to claim 5, which is characterized in that the abnormal traffic data
There is null value including at least unreasonable data, field beyond normal value, be filled in manually under missing or other same states
One of inconsistency data or more than one combinations.
7. quality data processing method between volume according to claim 1 hired car, which is characterized in that data cleansing rule includes
Abnormal traffic data cleansing rule and physical index detection data cleaning rule.
8. quality data processing method between volume hired car according to claim 7, which is characterized in that data cleansing rule uses
Inspection method picks null value method and setting threshold method.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111581200A (en) * | 2020-05-09 | 2020-08-25 | 江苏博昊智能科技有限公司 | Production management system based on MES |
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| CN110188091A (en) * | 2019-04-18 | 2019-08-30 | 红云红河烟草(集团)有限责任公司 | Quality data preprocessing method between a kind of volume hired car |
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- 2019-04-18 CN CN201910314431.8A patent/CN110188985A/en active Pending
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| CN106649329A (en) * | 2015-10-30 | 2017-05-10 | 深圳市华威世纪科技股份有限公司 | Safety production big data mining system |
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Application publication date: 20190830 |
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