CN110816938A - Big data analysis method based on comprehensive detection platform of cigarette packaging machine - Google Patents
Big data analysis method based on comprehensive detection platform of cigarette packaging machine Download PDFInfo
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- CN110816938A CN110816938A CN201911120706.0A CN201911120706A CN110816938A CN 110816938 A CN110816938 A CN 110816938A CN 201911120706 A CN201911120706 A CN 201911120706A CN 110816938 A CN110816938 A CN 110816938A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65B—MACHINES, APPARATUS OR DEVICES FOR, OR METHODS OF, PACKAGING ARTICLES OR MATERIALS; UNPACKING
- B65B19/00—Packaging rod-shaped or tubular articles susceptible to damage by abrasion or pressure, e.g. cigarettes, cigars, macaroni, spaghetti, drinking straws or welding electrodes
- B65B19/28—Control devices for cigarette or cigar packaging machines
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65B—MACHINES, APPARATUS OR DEVICES FOR, OR METHODS OF, PACKAGING ARTICLES OR MATERIALS; UNPACKING
- B65B19/00—Packaging rod-shaped or tubular articles susceptible to damage by abrasion or pressure, e.g. cigarettes, cigars, macaroni, spaghetti, drinking straws or welding electrodes
- B65B19/28—Control devices for cigarette or cigar packaging machines
- B65B19/30—Control devices for cigarette or cigar packaging machines responsive to presence of faulty articles, e.g. incorrectly filled cigarettes
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Abstract
The invention relates to the technical field of cigarette detection, in particular to a big data analysis method based on a comprehensive detection platform of a cigarette packaging machine. This big data analysis method based on cigarette packagine machine comprehensive testing platform, through dispose big data analysis software on cigarette packagine machine comprehensive testing platform, the operating information can be concentrated, abundant demonstration, the analysis result of detected data can in time inform the user to solve current trouble, and can fix a position to specific fault point through specific analysis, big data analysis software's application can in time discover the problem, fix a position the trouble fast, reduce the rejection rate, improve the qualification rate, thereby continuous improvement manufacturing level, promote the promotion of production efficiency, reduce waste product consumption, continuous improvement lean production management level, make a step forward to intelligent manufacturing.
Description
Technical Field
The invention relates to the technical field of cigarette detection, in particular to a big data analysis method based on a comprehensive detection platform of a cigarette packaging machine.
Background
Cigarette packaging is a very important process in cigarette production, and products with unqualified packaging quality flow into the market to bring very serious influence on the reputation of enterprises. At present, cigarette production enterprises add a plurality of detection devices on a packaging machine for improving the product quality, such as cigarette appearance detection, cigarette end missing mouth detection, aluminum foil paper paperboard detection, small package appearance detection, stay wire detection, large strip appearance detection, missing package detection and the like, wherein the detection devices can be integrated on a comprehensive detection platform of the cigarette packaging machine, the comprehensive detection platform of the cigarette packaging machine can obtain a large amount of detection data, but the data collection application and mining analysis are still in a primary stage, and how to deeply utilize the detection data resources by means of a data mining analysis technology is an important supporting means for improving the production management level and the information construction level of the enterprises and also for improving the production and manufacturing level.
Currently, the installed detection devices only realize the function of removing or alarming after detecting defects, do not analyze the reasons of abnormal products produced, and cannot guide users to improve the production level and reduce the proportion of defective products, thereby not really improving the production rate and the production and manufacturing level. In view of the above, we propose a big data analysis method based on a cigarette packing machine comprehensive detection platform.
Disclosure of Invention
The invention aims to provide a big data analysis method based on a comprehensive detection platform of a cigarette packaging machine, which aims to solve the problems that the reason of abnormal products cannot be analyzed and the productivity and the production and manufacturing level are not really improved in the background technology.
In order to achieve the purpose, the invention provides a big data analysis method based on a comprehensive detection platform of a cigarette packaging machine, which comprises the following analysis steps:
s1, acquiring original data: acquiring a large amount of detection data through a plurality of detection devices of a comprehensive detection platform of the cigarette packaging machine;
s2, establishing a database: building a logistics private cloud big data center by using a cloud computing technology, and pooling resource data detected by cigarettes by using a virtualization technology to form a total database;
s3, cleaning data: cleaning each acquired detection data to remove the data lower than or exceeding a preset value;
s4, analysis data: analyzing the cleaned data, establishing different analysis models according to the types of the detected data, and processing different data by the different analysis models;
s5, displaying the result: and a foreground display module is adopted to realize data interaction between the total database and the data receiving terminal and display data.
Preferably, the raw data acquired in S1 includes system operating status, alarm information, display information, rejection information, detection method type, detection number, defect proportion, detection image, and detection object data.
Preferably, the detection target data includes a type, a parameter, a method, a position, a detection time, an operation state, and a detection ratio of the detection target.
Preferably, the database established in S2 is built and modified by SQLserver 2005 software.
Preferably, the cleaning data in S3 includes a defect number module, a proportion data module, a time data module, a normal detection image module, and a detection object data module;
the defect number module is used for removing data lower than a preset defect number;
the proportion data module is used for removing data lower than a preset detected proportion;
the time data module is used for removing data which is lower than preset detection time;
the normal detection image module is used for removing normal detection images exceeding a preset value;
the detection object data module is used for removing the detection object data which is not enabled.
Preferably, the analysis data comprises a model building module, a sample mapping module, a production discrimination model module and an analysis comparison module.
Preferably, the foreground display module comprises a user interaction module and a data display module;
the user interaction module is used for realizing data interaction between the total database and the data receiving terminal;
and the data display module is used for receiving the detected data by the data receiving terminal and displaying the detected data in a chart form.
Preferably, the graph in the data display module comprises a real-time curve, a histogram, a pie chart and a statistical table.
Preferably, the foreground display module further comprises an alarm module, and the alarm module is used for giving an alarm prompt when error data occurs.
Preferably, the foreground display module further comprises a data storage module, and the data storage module is used for storing the displayed data.
Compared with the prior art, the invention has the beneficial effects that:
1. in the big data analysis method based on the cigarette packer comprehensive detection platform, one-line real-time detection data directly reflects the current production condition and the packaging quality, the big data analysis method is applied to the data to carry out centralized extraction and analysis, the analysis result can timely inform a user of solving the current fault, a specific fault point can be positioned through specific analysis, and the technical requirements and the working intensity of the user and a maintainer can be greatly reduced. The quality control mode in the cigarette packaging process is improved by reasonably planning, storing and utilizing big data and utilizing an information technology, so that on one hand, the human experience intervention can be reduced, and the workload is reduced; on the other hand, the accuracy and the stability of quality control can be improved to a certain extent by adopting a continuous self-learning prediction control model, the monitoring, the prediction and the control of the tobacco production process data are carried out by utilizing an informatization means, the method has important significance for improving the quality of the tobacco finished products, and the continuous improvement of the production and manufacturing level is finally promoted.
2. According to the big data analysis method based on the comprehensive detection platform of the cigarette packing machine, the big data analysis software is deployed on the comprehensive detection platform of the cigarette packing machine, the operation information can be displayed in a centralized, sufficient and abundant mode, the alarm information is notified and displayed in a centralized mode, the problem that notification and display are inconvenient due to the fact that a plurality of detectors are deployed in a scattered mode is avoided, the analysis result of the detection data can notify a user of the current fault in time, the specific fault point can be located through specific analysis, and the technical requirements and the working intensity of a user and a maintainer can be greatly reduced. The application of big data analysis software can find problems in time, locate faults quickly, reduce the rejection rate and improve the qualified product rate, thereby improving the production and manufacturing level continuously, promoting the improvement of the production efficiency, reducing the waste consumption, improving the lean production management level continuously and advancing to intelligent manufacturing.
Drawings
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a database framework architecture diagram of the present invention;
FIG. 3 is a block diagram of a cleaning data module of the present invention;
FIG. 4 is a flow chart of a neural network algorithm of the present invention;
FIG. 5 is a diagram of an analytical data block according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The invention provides a big data analysis method based on a comprehensive detection platform of a cigarette packaging machine, which comprises the following analysis steps as shown in figure 1:
s1, acquiring original data: acquiring a large amount of detection data through a plurality of detection devices of a comprehensive detection platform of the cigarette packaging machine;
s2, establishing a database: building a logistics private cloud big data center by using a cloud computing technology, and pooling resource data detected by cigarettes by using a virtualization technology to form a total database;
s3, cleaning data: cleaning each acquired detection data to remove the data lower than or exceeding a preset value;
s4, analysis data: analyzing the cleaned data, establishing different analysis models according to the types of the detected data, and processing different data by the different analysis models;
s5, displaying the result: and a foreground display module is adopted to realize data interaction between the total database and the data receiving terminal and display data.
In this embodiment, the cigarette packaging machine may select a full-automatic L-type heat shrink packaging machine of model BTS-450/450a + BM-500L produced by shanghai quanli machinery limited company, and a CCD camera may be set on the cigarette packaging machine to detect the appearance of a cigarette, the lack of a mouth of a cigarette, the jam of aluminum foil paper, the appearance of a small packet, a pull wire, the appearance of a large strip, and the lack of a packet.
Further, the original DATA acquired in S1 include system running status, alarm information, display information, rejection information, detection method type, detection quantity, defect proportion, detection image, and detection object DATA, where the acquired original DATA may adopt a DATA-6131 type 4GDTU DATA transmission module, which is fully compatible with 4G, 3G, and 2G networks, and collects serial device DATA, and implements device networking through a 4G/3G/2G wireless network, and supports remote communication with 1-4 monitoring centers, so as to transmit the detected information to the cloud for storage and processing in real time.
Specifically, the detection object data comprises the type, parameters, methods, positions, detection time, operation conditions and detection proportion of the detection object, the type of detection information can be recorded comprehensively, the detection data resources are deeply utilized, and the production management level and the information construction are improved.
Example 2
As a second embodiment of the present invention, in order to facilitate analysis of detected data through big data, the present invention further provides a total database, and as a preferred embodiment, as shown in fig. 2, the database established in S2 is built and modified by SQLserver 2005 software.
In this embodiment, SQL is a computer language dedicated to a database, and can be used to Access and modify the content of the database regardless of Oracle, MS SQL, Access, MySQL, or a database created by itself, or regardless of whether the database is built on a mainframe or a personal computer, thereby providing strong compatibility for building the database.
Further, Windows XP can be used as an optimal system platform for running SQL Sserver2005, and the steps for establishing the database are as follows:
if exists(select * from sysdtabases where name ='binbin')
drop database binbin
----------
create database bin- -creation of database
on primary-primary database file
(
name = bin, -primary database file name
filename = 'D:/bin.mdf', -path of primary database file deposit
size =5, size at initialization (default unadditive unit is MB)
maxsize =20, -max (default unit of not adding is MB)
filegrowth = 10-file growth speed (default unadditive unit is MB)
)，
filegroup jay-secondary database file group
(
name = jay _ 1' - -secondary database file
filename='D:/jay_1.ndf'，
size=5，
maxsize=20，
filegrowth=10
)，
(
name = jay _ 2' - -secondary database file
filename='D:/jay_2.ndf'，
size=5，
maxsize=20，
filegrowth=10
)
log on
(
name=ziji，
filename='D:/ziji.ldf'，
size=5，
maxsize=20，
filegrowth=10
Specifically, the main data comprises a login module, an entry module, a deletion module, a mobilization module and a query module, wherein the login module is used for logging in a main interface of a main database for operation, the entry module is used for adding data to be added into the main database, the deletion module is used for deleting error data in the main database, the mobilization module is used for updating information in the main database, and the query module is used for querying information in the database so as to complete the logging, deletion and mobilization of data information.
Example 3
As a third embodiment of the present invention, in order to facilitate a cleaning operation on each acquired inspection data and remove data that is lower than or exceeds a preset value, the personnel sets a data cleaning step, as shown in fig. 3, in S3, the cleaning data includes a defect number module, a proportion data module, a time data module, a normal inspection image module and an inspection object data module, the defect number module is configured to remove data that is lower than a preset defect number, the proportion data module is configured to remove data that is lower than a preset inspection proportion, the time data module is configured to remove data that is lower than a preset inspection time, the normal inspection image module is configured to remove a normal inspection image that is higher than a preset value, and the inspection object data module is configured to remove inspection object data that is not enabled.
In this embodiment, the data cleansing step includes data integration, detecting and eliminating data anomalies, and detecting and eliminating approximate duplicate records.
Data integration is mainly to map structures and data in a data source to target structures and domains.
The method comprises the steps of detecting and eliminating data difference, detecting numerical attributes by adopting a statistical method, calculating the mean value and the standard deviation of a field value, and identifying abnormal fields and records by considering confidence intervals of all the fields. Data mining methods are introduced into data cleaning, such as clustering methods are used for detecting abnormal records, model methods find abnormal records which do not conform to the existing mode, association rule methods find abnormal data which do not conform to rules with high confidence level and support degree in data sets.
The detection and elimination of the approximate duplicate record is used for judging whether the two data are approximately duplicated or not and deleting the data from the database.
Specifically, the data cleaning is designed based on a feedforward neural network algorithm and comprises an input layer, a hidden layer and an output layer, wherein each layer has a plurality of points, and neurons pass through weights
Connected, each neuron node input, 1 output, and its mathematical model is:
in the formula
Is the output value of node j on a certain layer
so that it is illustrated that the neural network algorithm flow is shown in fig. 4, and the steps are as follows:
1. initializing parameters: the initial topology of the input neural network is: m-n-k, m is the number of nodes of the input layer, n is the number of nodes of the hidden layer, h is the number of nodes of the output layer, input population scale t = population, cross probability
Probability of variation；
2. Initializing a population: because the capability of genetic algorithm for adjusting weight is weak, in order to avoid the condition that the convergence of the algorithm is too slow due to the fact that the weight is over-small, random decimal (decimal real number) uniformly distributed on [ -3, 3) is used for initializing population in experiment
Setting selection probabilities for chromosomes in a population
The selection probability is defined as:
in the formula
Typically 0.05, i =1, 2, … …, t
3. And (3) iterative judgment: the method comprises the steps of reducing the chromosome in the current generation into a network model in a graph, training a sample by a generation person, calculating the total error E and fitness of the chromosomeJudging whether the network error reaches the specified error or the maximum number of times of selection, if so, ending the iteration; otherwise, continuing to sort the chromosomes from high to low according to the fitness;
4. selecting operation: for each chromosome i, its cumulative probability is calculated
Is defined as:
using a roulette algorithm, one is generated for each roundA random number r uniformly distributed on the random number, if
Then the chromosome in the round is selected as a parent chromosome;
5. crossing and mutation:
crossing, using t roulette algorithms to obtain t parent chromosomes and form them into pairs……
It is formed into pairs, e.g. < CHEM >, < CHEM >
,
)，(,
) Etc. for each pair of chromosomes, with cross probabilities
Determining a crossing position k, interchanging two parents
Numbering of the genes between each other, thereby obtaining two new chromosomes
；
Mutation: by the probability of variation
Determining u mutation positions
,
. The genes at these positions are mutated by simply adding a value of-1.1 to the original gene]Random decimal evenly distributed between the two chromosomes of the new filial generation
，
；
6. Generating a new generation: and repeating the selection, crossing and mutation operations to continuously generate new offspring chromosomes until the population scale of the new generation is the same as that of the parent generation, so as to obtain a new offspring, and changing to 3 dimension continuation.
In the big data analysis method based on the comprehensive detection platform of the cigarette packaging machine, when data is cleaned, data lower than a preset defect number is removed through the defect number module, data lower than a preset detection proportion is removed through the proportion data module, data lower than preset detection time is removed through the time data module, a normal detection image exceeding the preset value is removed through the normal detection image module, and detection object data which is not started is removed through the detection object data module.
Example 4
As a fourth embodiment of the present invention, in order to facilitate data analysis, the present invention further provides a step of analyzing data, specifically as shown in fig. 5, where the step of analyzing data includes establishing a model module, a mapping sample module, a production discrimination model module and an analysis comparison module, the foreground display module includes a user interaction module and a data display module, the user interaction module is used for implementing data interaction between the total database and the data receiving terminal, the data display module is used for the data receiving terminal to receive the detected data and display the detected data in a graph form, a graph in the data display module includes a real-time curve, a histogram, a pie graph and a statistical table, the foreground display module further includes an alarm module, and when error data occurs, and the foreground display module also comprises a data storage module which is used for storing the displayed data.
In this embodiment, a generator model is directly built by using a building model module for collected detection data, a mapping sample module adopts a network structure of a multilayer perceptron, a guidable mapping generator model is represented by using parameters of an MLP, an input space is mapped to a sample space, qualified data and samples mapped by the generator model are input, a discriminator model is built, and finally, a final discrimination result of the discriminator model is represented by "0" and "1" through "Sigmoid function" transformation.
Further, the final discrimination algorithm of the discriminator model is as follows:
wherein D is a discriminator model, G is a generator model, G (z) is a generator model of mapping, and D (x) is a multi-layer perceptron discriminator model with parameters.
The optimization target of the discriminator model D is as follows:
the optimization target of the generator model G is as follows:
specifically, different analysis models are established according to the types of the detection data, and the different analysis models process different data; updating the system state, alarming abnormity and displaying corresponding treatment in time according to the system running state, alarming information, display information and rejection information; establishing curve data and abnormal data according to the type, the detection quantity, the defect quantity and the defect proportion of the detection method; analyzing according to the data of the detection object, determining the position of a product according to the position of the detection object, further determining the fault position and reason of equipment, determining whether the setting is proper or not and whether the equipment runs in an overload mode or not according to the average detection time of the detection object, and extracting the data of the running process according to the type, parameters, methods and running conditions of the detection object.
It is worth to be noted that the data display module displays the data in 4 levels, normal operation data is displayed on a display screen through characters and diagrams, and the color is mainly green; the data reaching the early warning level is displayed on a display screen through characters and charts, and the color is mainly orange; the data of the alarm level is displayed on a display screen through a red picture with display information in a flashing way, and simultaneously, the data of the alarm level is reported to a monitoring center through an alarm lamp; the result data is stored in a file according to a designed format so as to facilitate the subsequent backtracking extraction.
In the big data analysis method based on the comprehensive detection platform of the cigarette packaging machine, data are analyzed, data interaction between a total database and a data receiving terminal is achieved through a user interaction module, the data receiving terminal receives detected data and displays the data in a chart form through a data display module, meanwhile, different analysis models are established according to the types of the detected data, and the different analysis models process different data; updating the system state, alarming abnormity and displaying corresponding treatment in time according to the system running state, alarming information, display information and rejection information; establishing curve data and abnormal data according to the type, the detection quantity, the defect quantity and the defect proportion of the detection method; analyzing according to the data of the detection object, determining the position of a product according to the position of the detection object, further determining the fault position and reason of equipment, determining whether the setting is proper or not and whether the equipment runs in an overload mode or not according to the average detection time of the detection object, and extracting the data of the running process according to the type, parameters, methods and running conditions of the detection object.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A big data analysis method based on a cigarette packaging machine comprehensive detection platform is characterized by comprising the following steps: the method comprises the following analysis steps:
s1, acquiring original data: acquiring a large amount of detection data through a plurality of detection devices of a comprehensive detection platform of the cigarette packaging machine;
s2, establishing a database: building a logistics private cloud big data center by using a cloud computing technology, and pooling resource data detected by cigarettes by using a virtualization technology to form a total database;
s3, cleaning data: cleaning each acquired detection data to remove the data lower than or exceeding a preset value;
s4, analysis data: analyzing the cleaned data, establishing different analysis models according to the types of the detected data, and processing different data by the different analysis models;
s5, displaying the result: and a foreground display module is adopted to realize data interaction between the total database and the data receiving terminal and display data.
2. The big data analysis method based on the comprehensive detection platform of the cigarette packing machine according to claim 1, characterized in that: the original data acquired in S1 includes system operating status, alarm information, display information, reject information, detection method type, detection number, defect proportion, detection image, and detection object data.
3. The big data analysis method based on the cigarette packing machine comprehensive detection platform as claimed in claim 2, characterized in that: the detection object data comprises the type, parameters, methods, positions, detection time, running conditions and detection proportion of the detection object.
4. The big data analysis method based on the comprehensive detection platform of the cigarette packing machine according to claim 1, characterized in that: and establishing a database in the S2, and constructing and modifying a total database by adopting SQLserver 2005 software.
5. The big data analysis method based on the comprehensive detection platform of the cigarette packing machine according to claim 1, characterized in that: the cleaning data in the S3 comprises a defect number module, a proportion data module, a time data module, a normal detection image module and a detection object data module;
the defect number module is used for removing data lower than a preset defect number;
the proportion data module is used for removing data lower than a preset detected proportion;
the time data module is used for removing data which is lower than preset detection time;
the normal detection image module is used for removing normal detection images exceeding a preset value;
the detection object data module is used for removing the detection object data which is not enabled.
6. The big data analysis method based on the comprehensive detection platform of the cigarette packing machine according to claim 1, characterized in that: the analysis data comprises a model building module, a mapping sample module, a production discrimination model module and an analysis comparison module.
7. The big data analysis method based on the comprehensive detection platform of the cigarette packing machine according to claim 1, characterized in that: the foreground display module comprises a user interaction module and a data display module;
the user interaction module is used for realizing data interaction between the total database and the data receiving terminal;
and the data display module is used for receiving the detected data by the data receiving terminal and displaying the detected data in a chart form.
8. The big data analysis method based on the cigarette packing machine comprehensive detection platform as claimed in claim 7, wherein: the chart in the data display module comprises a real-time curve, a histogram, a pie chart and a statistical table.
9. The big data analysis method based on the cigarette packing machine comprehensive detection platform as claimed in claim 7, wherein: the foreground display module also comprises an alarm module, and the alarm module is used for giving an alarm prompt when error data occurs.
10. The big data analysis method based on the cigarette packing machine comprehensive detection platform as claimed in claim 7, wherein: the foreground display module also comprises a data storage module, and the data storage module is used for storing the displayed data.
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| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| US11681283B1 (en) | 2022-05-17 | 2023-06-20 | Chengdu Qinchuan Iot Technology Co., Ltd. | Intelligent manufacturing industrial Internet of Things based on distributed control, control methods and media thereof |
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Denomination of invention: A big data analysis method based on comprehensive detection platform of cigarette packaging machine Effective date of registration: 20220819 Granted publication date: 20210409 Pledgee: Bank of Hangzhou Limited by Share Ltd. Nanjing branch Pledgor: NANJING DASHU INTELLIGENT SCIENCE AND TECHNOLOGY Co.,Ltd. Registration number: Y2022980013088 |