CN111667139A - Tobacco shred manufacturing quality detection early warning module and method - Google Patents
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Abstract
The invention provides a tobacco shred manufacturing quality detection and early warning module, which comprises an Oracle database, a tobacco shred manufacturing system, a data acquisition sub-module, a distributed middleware, a real-time database, a detection and early warning sub-module and a data transmission sub-module, wherein the Oracle database is used for storing tobacco shred manufacturing quality data; the data acquisition submodule transmits a data source acquired from the tobacco shred manufacturing system to the distributed middleware; the distributed middleware realizes data interaction with an Oracle database and a real-time database; the detection early warning submodule carries out detection early warning on the quality of the cut tobacco according to data in the real-time database; and the Oracle database and the detection early warning submodule return data to the tobacco shred manufacturing system through the data transmission submodule. The invention also provides an early warning method, which realizes the real-time acquisition and calculation processing of key parameters of each batch of each section of the tobacco shred manufacturing system, fully excavates and analyzes a large amount of data generated in the tobacco shred production process, fully utilizes the data and realizes the real-time judgment of the quality abnormal mode in the tobacco shred production process.
Description
Technical Field
The invention relates to the technical field of tobacco shred manufacturing, in particular to a tobacco shred manufacturing quality detection early warning module and a method.
Background
The quality management of the silk making process carries out posterior statistical analysis according to the data collected by the current batch at present, and the data variation of the production process cannot be systematically and timely analyzed, so that the quality control effect of the production process is not obvious enough, and the production process of the silk making is particularly obvious.
In the prior art system, the data flow of the filament making process is shown in fig. 1. The data are directly stored in an Oracle database in the batch production process and cannot meet the real-time requirement of the business on the data, the data are not beneficial to other analysis and calculation, and in the production process of the silk making, the changes of the temperature, the moisture content, the feeding and the perfuming and other related data can affect the product quality. In the face of a large amount of silk making production process data, the data processing system based on the relational database has the defects of high construction cost, low analysis capability and the like when storing, processing and analyzing data, so that the data utilization rate in the silk making process is not high, and the statistics and quality early warning of the system are not facilitated.
Disclosure of Invention
The invention provides a tobacco shred manufacturing quality detection early warning module and method for overcoming the technical defects that the existing tobacco shred manufacturing system is low in data utilization rate and not beneficial to system statistics and quality early warning in the tobacco shred manufacturing process.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a tobacco shred manufacturing quality detection and early warning module comprises an Oracle database, a tobacco shred manufacturing system, a data acquisition sub-module, a distributed middleware, a real-time database, a detection and early warning sub-module and a data transmission sub-module; wherein:
the data acquisition submodule is used for acquiring a data source in a tobacco shred manufacturing system and transmitting the acquired data to the distributed middleware;
the distributed middleware realizes data interaction with the Oracle database and the real-time database;
the detection early warning submodule carries out detection early warning on the quality of the cut tobacco according to data in a real-time database;
and the Oracle database and the detection early warning submodule return data to the tobacco shred manufacturing system through the data transmission submodule.
In the scheme, the distributed middleware is constructed by adopting a Kafka cluster, the Kafka is a high-throughput distributed publishing and subscribing information system, all action flow data can be processed, thousands of clients can be supported to read and write at the same time, hundreds of thousands of messages can be processed per second, and the delay is only a few milliseconds at the lowest; the real-time database is a Redis database.
The detection early warning sub-module comprises a data preprocessing unit, an early warning threshold setting unit, a detection unit and an early warning unit; wherein:
the data preprocessing unit preprocesses original data in a real-time database;
the early warning threshold setting unit is used for presetting a data threshold;
the detection unit compares the preprocessed data with a data threshold value of an early warning threshold value setting unit, if no abnormity occurs, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit to carry out early warning on the tobacco shred quality;
and the early warning unit returns data to the tobacco shred manufacturing system through the data transmission submodule according to the obtained early warning information.
The early warning unit comprises a data processing subunit and a quality control chart processing subunit; wherein:
the data processing subunit respectively performs calculation processing on each data item of the received preprocessing data to obtain a key parameter CPK value of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns data to the cut tobacco manufacturing system through the data transmission submodule according to a judgment result.
The process of the key parameter CPK value of each data item specifically includes:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
The quality control chart processing subunit generates a quality control chart, and the process of judging abnormal data specifically comprises the following steps:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to 8 abnormal inspection criteria, and sending the early warning information to the tobacco shred manufacturing system, wherein the early warning information specifically comprises the following steps:
when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
when 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information; when each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
when 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
when 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
when 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
when the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
and when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
A tobacco shred manufacturing quality detection early warning method comprises the following steps:
s1: collecting a data source in the tobacco shred manufacturing system through the data collection submodule, and transmitting the collected data to the distributed middleware;
s2: synchronizing the real-time database to the data from the distributed middleware;
s3: and the detection and early warning submodule carries out detection and early warning on the tobacco shred quality on the data synchronized by the real-time database.
Wherein the step S3 specifically includes:
s31: the data preprocessing unit preprocesses original data in a real-time database;
s32: the early warning threshold setting unit is used for presetting a data threshold, the detection unit compares the preprocessed data with the data threshold of the early warning threshold setting unit, if no abnormity exists, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit for early warning of the tobacco shred quality;
s33: the early warning unit returns the data to the tobacco shred manufacturing system through the data transmission submodule.
In step S32, the process of performing the early warning on the tobacco shred quality by the early warning unit specifically includes:
the data processing subunit respectively calculates and processes each data item of the received preprocessing data to obtain a CPK value of a key parameter of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns the data to the tobacco shred manufacturing system through the data transmission submodule according to the judgment result.
The process of the key parameter CPK value of each data item specifically includes:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
The quality control chart processing subunit generates a quality control chart, and the process of judging abnormal data specifically comprises the following steps:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to the 8 abnormal inspection criteria, and sending the early warning information to a tobacco shred manufacturing system, wherein the method specifically comprises the following steps:
(1) when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
(2) when 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information;
(3) when each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
(4) when 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
(5) when 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
(6) when 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
(7) when the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
(8) and when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the tobacco shred manufacturing quality detection early warning module and method, the distributed middleware, the real-time database and the detection early warning sub-module are arranged, so that the real-time acquisition and calculation processing of key parameters of each batch of each section of a tobacco shred manufacturing system are realized, a large amount of data generated in the tobacco shred production process are sufficiently mined and analyzed, the data are sufficiently utilized, the real-time judgment of 8 quality abnormal modes in the tobacco shred production process is realized, and the quality control capability of the production process is effectively improved.
Drawings
FIG. 1 is a flow chart of data transmission in a conventional system;
FIG. 2 is a schematic connection diagram of a tobacco shred manufacturing quality detection and early warning module;
FIG. 3 is a diagram illustrating quality control map zoning;
FIG. 4 is a flow chart of a tobacco shred manufacturing quality detection early warning method;
FIG. 5 is a schematic diagram of the anomaly detection of item (1);
FIG. 6 is a schematic diagram of the anomaly detection of item (2);
FIG. 7 is a schematic diagram of the anomaly detection of item (3);
FIG. 8 is a schematic diagram of the anomaly detection of item (4);
FIG. 9 is a schematic diagram of the abnormality detection of item (5);
FIG. 10 is a schematic diagram showing abnormality detection in item (6);
FIG. 11 is a schematic diagram of the abnormality detection of item (7);
FIG. 12 is a schematic view of abnormality detection in item (8);
fig. 13 is a diagram of a real-time rainbow.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 2, a tobacco shred manufacturing quality detection and early warning module comprises an Oracle database, a tobacco shred manufacturing system, a data acquisition sub-module, a distributed middleware, a real-time database, a detection and early warning sub-module and a data transmission sub-module; wherein:
the data acquisition submodule is used for acquiring a data source in a tobacco shred manufacturing system and transmitting the acquired data to the distributed middleware;
the distributed middleware realizes data interaction with the Oracle database and the real-time database;
the detection early warning submodule carries out detection early warning on the quality of the cut tobacco according to data in a real-time database;
and the Oracle database and the detection early warning submodule return data to the tobacco shred manufacturing system through the data transmission submodule.
In a specific real-time process, the distributed middleware is constructed by adopting a Kafka cluster, the Kafka is a high-throughput distributed publishing and subscribing information system, all action flow data can be processed, thousands of clients can be supported to read and write at the same time, hundreds of thousands of messages can be processed per second, and the delay is only a few milliseconds at the lowest; the real-time database is a Redis database. The tobacco shred manufacturing quality detection early warning module realizes real-time acquisition and calculation processing of key parameters of each batch of each section of a tobacco shred manufacturing system, fully excavates and analyzes a large amount of data generated in the tobacco shred production process, fully utilizes the data, and realizes real-time judgment of quality abnormal modes in the tobacco shred production process.
More specifically, the detection and early warning sub-module comprises a data preprocessing unit, an early warning threshold setting unit, a detection unit and an early warning unit; wherein:
the data preprocessing unit preprocesses original data in a real-time database;
the early warning threshold setting unit is used for presetting a data threshold;
the detection unit compares the preprocessed data with a data threshold value of an early warning threshold value setting unit, if no abnormity occurs, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit to carry out early warning on the tobacco shred quality;
and the early warning unit returns data to the tobacco shred manufacturing system through the data transmission submodule according to the obtained early warning information.
In the specific implementation process, the data preprocessing unit eliminates invalid data influencing a calculation result by identifying the states of a stub bar, stable production, postpartum cleaning, material breakage and the like in the production process, so as to realize preprocessing of original data in a real-time database and real-time analysis; the early warning threshold setting unit sets an early warning line according to the process index control requirement, compares the analysis result with the early warning threshold in real time, and realizes early warning judgment on the abnormal result, thereby ensuring the quality control of the production process.
More specifically, the early warning unit comprises a data processing subunit and a quality control chart processing subunit; wherein:
the data processing subunit respectively performs calculation processing on each data item of the received preprocessing data to obtain a key parameter CPK value of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns data to the cut tobacco manufacturing system through the data transmission submodule according to a judgment result.
More specifically, the process of the CPK value of the key parameter of each data item specifically includes:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
More specifically, as shown in fig. 3, the quality control chart processing subunit generates a quality control chart, and the process of performing abnormal data judgment specifically includes:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to 8 abnormal inspection criteria, and sending the early warning information to the tobacco shred manufacturing system, wherein the early warning information specifically comprises the following steps:
when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
when 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information; when each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
when 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
when 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
when 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
when the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
and when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
In the specific implementation process, the statistical process control of the system data is mainly used for analyzing the production process and evaluating the production condition, and measures are taken in time to keep the process stable and ensure the product quality after finding the abnormal condition. Control charts are one of the most effective tools and are the technical basis for statistical process control. The control chart is that two control lines and a central line are drawn on a common grid coordinate paper by calculation by utilizing the principle of probability statistics, a horizontal axis is used as a time axis, numerical values of all samples obtained according to a specified time sequence are sequentially drawn on a coordinate graph, and two adjacent points are sequentially connected by a straight line. The time is used as an abscissa, the ordinate is a product quality characteristic value or a sample statistic, a central line is marked as CL (Cnoorl Limit), two control limits are represented by dotted lines, and a control limit line above the central line is an upper control line and is marked as UCL (upper control Limit); the control limit line below the centerline is the lower control line, denoted as LCL (LowerContorl Limit).
In the specific implementation process, the theoretical basis for carrying out early warning through the quality detection early warning module is as follows:
1) assumption of normality
There will always be some degree of fluctuation in any production process. When there is no cause for the abnormal behavior, these fluctuations come primarily from small common variations of 5M1E (i.e., human, machine, material, method, measurement, and environment), causing some degree of random error. At this time, the quality characteristic value of the product follows or approximates to a normal distribution, which is called a normal assumption. On the basis of this assumption, the regular features of a normal distribution can be used for modeling.
2)3 sigma criterion
On the premise that the normality assumption is established, the area included in the range of 3 σ (i.e., ± 3 σ) from both sides of the distribution center μ is 99.73%. As shown in fig. 3, if the production process is affected only by random common causes, the product quality characterization data for the process will have a value of 99.73% that falls within this range, namely: p { μ -3 σ < X < μ +3 σ } -, 99.73%, if the point exceeding 0.27% falls outside the range of ± 3 σ, it can be determined that some abnormality has occurred in the production process.
3) Definition of the principle of small probability, i.e.It is believed that a small probability event will not generally occur. From the 3 σ criterion, when X obeys normal distribution N (μ, σ)2) The probability that the product quality characteristic value of the process falls outside the control limits is only 0.27%. Namely: 1-P { mu-3 sigma<X<μ+3σ}＝0.27％
Thus, X generally does not exceed the operator defined control limit of 3 σ, assuming no particular cause of the abnormality has occurred.
In a specific implementation, the 8 abnormal inspection criteria come from 8 abnormal fluctuation inspection criteria in GB/T4901 conventional control chart.
In the specific implementation process, the data of the silk production process has the following characteristics that the production data has a process (material tail) of continuously rising to be stable and finally continuously falling in the process from the beginning to the end of the batch production. The data is a fluctuating process, the data requirement in the silk production process is to analyze the quality level of batch data in a stable state, and meanwhile, the real-time production data approximately meets normal distribution, so that the data acquisition program is required to meet the following functions:
(1) and real-time data acquisition, wherein the data acquisition program can acquire real-time data information of each parameter.
(2) The head and tail material removing function is realized, the system needs to realize the function of removing the head and tail material, and meanwhile, the filtering of abnormal data is ensured.
(3) And the calculation function of the quality indexes related to the batch data of each parameter is realized.
According to the requirement of the system on data, the functions of establishing batch information, removing head and tail materials, grouping data and the like are realized in a data processing thread:
(1) establishing batch information:
the method comprises the steps that a flow type parameter exists in each process of silk making, such as loose moisture regain cut tobacco flow, leaf charging cut tobacco flow, cut stem blending cut tobacco flow and the like, the collection and judgment of batch data of each process are carried out according to the numerical value of the flow type parameter, so that the silk making data collection program carries out judgment processing according to the business requirements, and the flow reference value is set to be used as the judgment of the starting time and the ending time of batch information. The specific implementation mode is as follows: when the flow data value reaches a reference value from 0, determining that the time is the batch starting time, starting to collect effective data, when the flow begins to decrease and is lower than the reference value, determining that the time is the batch ending time, and automatically eliminating the data after the batch is ended without entering a system.
(2) Function is rejected to stub bar material tail:
the data acquisition program establishes a cache array for each parameter, the array length is defined, the array length is the length of the tailing cache, when real-time data starts to be acquired, the data preferentially enters the tailing cache, normal logic processing is carried out only after the tailing cache is full of data, abnormal data can be guaranteed to be always processed in the tailing cache when batch data is finished, the tailing data is prevented from participating in calculation of quality indexes such as CPK (continuous tone keying) in normal rainbow diagram drawing, and when the acquisition program acquires a batch finishing signal, the data in the cache is marked as a tailing state and is stored in a database. The processing of the head material is relatively simple, corresponding data are removed by the system according to the number of the head material set by a user at the beginning of collection, and then normal data are processed, so that the function of removing the head material and the tail material is realized.
After data are collected by the data collection submodule, the data are sent to a server side of a distributed middleware Kafka, service data are collected from the Kafka in a unified mode, a client side directly stores the data into a distributed database Hbase, the client side calculates the data in real time and stores the data into a real-time database Redis and the real-time database Hbase, and the client side is used for sending the data to an MES system, so that the consistency of the data is guaranteed. Because the built environment is multi-node fault-tolerant, the whole system can work normally as long as all nodes are out of order in a certain link, and the problem of data loss caused by a certain service fault is avoided.
Example 2
As shown in fig. 4, a tobacco shred manufacturing quality detection and early warning method comprises the following steps:
s1: collecting a data source in the tobacco shred manufacturing system through the data collection submodule, and transmitting the collected data to the distributed middleware;
s2: synchronizing the real-time database to the data from the distributed middleware;
s3: and the detection and early warning submodule carries out detection and early warning on the tobacco shred quality on the data synchronized by the real-time database.
More specifically, the step S3 specifically includes:
s31: the data preprocessing unit preprocesses original data in a real-time database;
s32: the early warning threshold setting unit is used for presetting a data threshold, the detection unit compares the preprocessed data with the data threshold of the early warning threshold setting unit, if no abnormity exists, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit for early warning of the tobacco shred quality;
s33: the early warning unit returns the data to the tobacco shred manufacturing system through the data transmission submodule.
More specifically, in step S32, the process of performing the early warning on the tobacco shred quality by the early warning unit specifically includes:
the data processing subunit respectively calculates and processes each data item of the received preprocessing data to obtain a CPK value of a key parameter of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns the data to the tobacco shred manufacturing system through the data transmission submodule according to the judgment result.
In the specific implementation process, the key parameters of different silk making processes are different, for example, a loosening and moisture regaining process, outlet moisture and outlet temperature are key parameters, a leaf moistening and feeding process has outlet moisture, outlet temperature, feeding precision and the like. For the key parameters, the quality control condition in the production process is conveniently monitored by operators through parameters such as statistics, average values, maximum values, minimum values, standard deviations and the like, and related algorithms are needed to execute the parameters.
More specifically, the process of the CPK value of the key parameter of each data item specifically includes:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
More specifically, the quality control chart processing subunit generates a quality control chart, and the process of performing abnormal data judgment specifically includes:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to the 8 abnormal inspection criteria, and sending the early warning information to a tobacco shred manufacturing system, wherein the method specifically comprises the following steps:
(1) when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
as shown in fig. 5, when the production process is in the statistical control state, the probability that the point falls within the control limit is 99.73%, that is, the probability that the point appears outside the limit is 1-99.73% — 0.27%, which is a small probability event, and it can be determined as abnormal.
(2) When 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information;
as shown in FIG. 6, the points that appear on the same side of the control chart centerline are referred to as "chains," and the number of points contained in a chain is referred to as chain length. When the production process is in a statistical control state, the probability that the 9 continuous points fall on the same side of the central line is 2 (0.9973/2)90.3906% is a small probability event, that is, when the chain length is equal to or more than 9, it is determined that an abnormality occurs in the production process.
(3) When each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
as shown in FIG. 7, when the production process is under statistical control, the probability of 6 successive points increasing or decreasing is 2X (0.9973)6And (6 |) > 0.2733% are small probability events and can be determined as abnormal. The state of the points ascending or descending gradually is called 'trend', the descending trend is reduced, the later points are necessarily lower than the former points, otherwise, the descending trend is interrupted and needs to be recalculated, and the same is true for the ascending trend.
(4) When 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
as shown in fig. 8, it is found from the test results of monte carlo that the probability of false alarm is about 0.4%, and the probability is not much different from the probability that the point falls outside the region a, and it is a small probability event and can be determined as abnormal.
(5) When 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
as shown in FIG. 9, when the process is under statistical control, the probability that 2 of the consecutive 3 points fall outside the B region on the same side of the centerline is 2 × C2 3*(1-0.9772)20.9772 is 0.3048%, which is a small probability event, and can be determined as abnormal.
(6) When 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
as shown in fig. 10, when the process is in the statistical control state, the probability that 4 of the continuous 5 points fall outside the C region on the same side of the centerline is: 2 x C4 5*(1-0.8413)40.0.8413 is 0.5331%, is a small probability event, and can be determined to be abnormal.
(7) When the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
as shown in FIG. 11, when the process is in the statistical control state, the probability that the continuous 15 points fall within the C region on both sides of the centerline is: (1-0.1587*2)150.326%, which is a small probability event, may be determined to be abnormal.
(8) And when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
As shown in FIG. 12, when the process is in the statistical control state, the probability that the continuous 8 points fall on both sides of the centerline and none are in the C region is: (0.1587*2)80.0103%, which is a small probability event, can be determined as abnormal.
In the specific implementation process, the tobacco shred manufacturing quality detection and early warning module and the method provided by the invention realize the real-time acquisition and calculation processing of key parameters of each batch of each section of the tobacco shred manufacturing system by arranging the distributed middleware, the real-time database and the detection and early warning submodule, fully excavate and analyze a large amount of data generated in the tobacco shred production process, fully utilize the data, realize the real-time judgment of 8 quality abnormal modes in the tobacco shred production process and effectively improve the quality control capability of the production process.
In the specific implementation process, as shown in fig. 13, the method completes real-time acquisition of key parameters of each batch of each working section, and counts parameters such as an average value, a maximum value, a minimum value, a standard deviation and the like in real time to form a real-time rainbow diagram; by combining with the statistical process control concept, a large amount of data generated in the silk making production process is fully mined and analyzed, and the real-time judgment of 8 process quality abnormal modes in the silk making production process is realized; the modular programming mode is noted in the development process, and parameters of each part provide personalized setting functions, so that portable modules are provided for upgrading and reconstructing other similar systems.
In the specific implementation process, the invention fully excavates and analyzes a large amount of data generated in the wire-making production process, assists an operator in judging the operation condition of the production process and finally improves the quality control capability of the production process. Provides an effective solution for mining and analyzing big data in the production process, and can be referred by the implementation of other systems.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A tobacco shred manufacturing quality detection and early warning module comprises an Oracle database and a tobacco shred manufacturing system, and is characterized by further comprising a data acquisition sub-module, a distributed middleware, a real-time database, a detection and early warning sub-module and a data transmission sub-module; wherein:
the data acquisition submodule is used for acquiring a data source in a tobacco shred manufacturing system and transmitting the acquired data to the distributed middleware;
the distributed middleware realizes data interaction with the Oracle database and the real-time database;
the detection early warning submodule carries out detection early warning on the quality of the cut tobacco according to data in a real-time database;
and the Oracle database and the detection early warning submodule return data to the tobacco shred manufacturing system through the data transmission submodule.
2. The cut tobacco manufacturing quality detection and early warning module according to claim 1, wherein the detection and early warning submodule comprises a data preprocessing unit, an early warning threshold setting unit, a detection unit and an early warning unit; wherein:
the data preprocessing unit preprocesses original data in a real-time database;
the early warning threshold setting unit is used for presetting a data threshold;
the detection unit compares the preprocessed data with a data threshold value of an early warning threshold value setting unit, if no abnormity occurs, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit to carry out early warning on the tobacco shred quality;
and the early warning unit returns data to the tobacco shred manufacturing system through the data transmission submodule according to the obtained early warning information.
3. The tobacco shred manufacturing quality detection and early warning module according to claim 2, wherein the early warning unit comprises a data processing subunit and a quality control chart processing subunit; wherein:
the data processing subunit respectively performs calculation processing on each data item of the received preprocessing data to obtain a key parameter CPK value of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns data to the cut tobacco manufacturing system through the data transmission submodule according to a judgment result.
4. The tobacco shred manufacturing quality detection and early warning module according to claim 3, wherein the process of the CPK value of the key parameter of each data item specifically comprises the following steps:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
5. The tobacco shred manufacturing quality detection and early warning module according to claim 3, wherein the quality control chart processing subunit generates a quality control chart, and the process of judging abnormal data specifically comprises the following steps:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to 8 abnormal inspection criteria, and sending the early warning information to the tobacco shred manufacturing system, wherein the early warning information specifically comprises the following steps:
when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
when 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information; when each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
when 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
when 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
when 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
when the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
and when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
6. The tobacco shred manufacturing quality detection early warning method applying the tobacco shred manufacturing quality detection early warning module according to any one of claims 1 to 5, is characterized by comprising the following steps of:
s1: collecting a data source in the tobacco shred manufacturing system through the data collection submodule, and transmitting the collected data to the distributed middleware;
s2: synchronizing the real-time database to the data from the distributed middleware;
s3: and the detection and early warning submodule carries out detection and early warning on the tobacco shred quality on the data synchronized by the real-time database.
7. The cut tobacco manufacturing quality detection and early warning method according to claim 6, wherein the step S3 specifically comprises the following steps:
s31: the data preprocessing unit preprocesses original data in a real-time database;
s32: the early warning threshold setting unit is used for presetting a data threshold, the detection unit compares the preprocessed data with the data threshold of the early warning threshold setting unit, if no abnormity exists, the corresponding preprocessed data is returned to the tobacco shred manufacturing system through the data transmission sub-module, and if abnormity occurs, the corresponding preprocessed data is transmitted to the early warning unit for early warning of the tobacco shred quality;
s33: the early warning unit returns the data to the tobacco shred manufacturing system through the data transmission submodule.
8. The cut tobacco manufacturing quality detection and early warning method according to claim 7, wherein in the step S32, the process of the early warning unit for early warning of cut tobacco quality specifically comprises:
the data processing subunit respectively calculates and processes each data item of the received preprocessing data to obtain a CPK value of a key parameter of each data item, and transmits the obtained CPK value to the quality control chart processing subunit;
and the quality control chart processing subunit generates a quality control chart, performs processing analysis according to the obtained CPK value, performs abnormal data judgment, and returns the data to the tobacco shred manufacturing system through the data transmission submodule according to the judgment result.
9. The cut tobacco manufacturing quality detection and early warning method according to claim 8, wherein the process of the CPK value of the key parameter of each data item specifically comprises the following steps:
setting all aggregation point lists of the data item in the current production batch as point 1[ n ], and setting the aggregation point list after removing the invalid point as point [ n ], wherein the average value avg is (point [0] +. + point [ n-1 ])/n; if point [ n ] is empty, returning the result as empty;
then, the upper limit value upper and the lower limit value lower in the aggregation point list are calculated in real time, that is, there are:
upper＝max(point[n])；
lower＝min(point[n])；
and finally, calculating a key parameter CPK in real time, specifically comprising the following steps:
CPK＝(1.0-k)*(upper-lower)/(6.0*std)；
wherein, the coefficient k is 2 (| (upper + lower)/2-avg |)/| upper-lower |); std represents the standard deviation of the data.
10. The tobacco shred manufacturing quality detection and early warning module according to claim 8, wherein the quality control chart processing subunit generates a quality control chart, and the process of judging abnormal data specifically comprises the following steps:
the quality control chart is divided into 6 cells, the labels of the 6 cells are respectively marked as A, B, C, C, B and A, namely the quality control chart is distributed from the middle to two sides, and the two C regions, the two B regions and the two A regions are respectively symmetrical through a central line;
generating early warning information according to the 8 abnormal inspection criteria, and sending the early warning information to a tobacco shred manufacturing system, wherein the method specifically comprises the following steps:
when the CPK value appears outside the area A of the quality control chart, judging that the production process is possible to be abnormal, calculating the CPK value of X data items in front of the data item, judging whether all the X data items are outside the area A, if so, judging that the data items are abnormal, and sending out early warning information; otherwise, judging the result to be false, and not sending out early warning information;
when 9 continuous CPK values fall on the upper side or the lower side of the central line, judging that the CPK values are abnormal, and sending out early warning information; when each value is larger than the previous value or each value is smaller than the previous value in the 6 continuous CPK values, judging that the CPK values are abnormal, and sending out early warning information;
when 14 continuous CPK values and each adjacent CPK value appear up and down alternately, judging that the CPK values are abnormal, and sending out early warning information;
when 2 CPK values in the continuous 3 CPK values fall outside the B area, judging that the CPK values are abnormal, and sending out early warning information;
when 4 CPK values in the 5 continuous CPK values fall outside the C area on the same side, judging that the CPK values are abnormal, and sending out early warning information;
when the continuous 15 CPK values all fall on the C areas on the two sides of the central line, judging that the CPK values are abnormal, and sending out early warning information;
and when all the continuous 8 CPK values fall on two sides of the central line and none of the continuous 8 CPK values fall on the C area, judging that the CPK values are abnormal and sending out early warning information.
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