CN112947342A - Data-driven tobacco raw silk moisture control system and control method - Google Patents
Data-driven tobacco raw silk moisture control system and control method Download PDFInfo
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- 241000208125 Nicotiana Species 0.000 title claims abstract description 47
- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 47
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Abstract
The invention discloses a data-driven tobacco raw silk moisture control system, which comprises a result estimation layer, a data screening layer, a result control layer, an automatic water adding control layer and a visual display layer, wherein the result estimation layer is used for estimating the moisture content of raw tobacco; wherein: the result estimation layer is used for obtaining an estimation result according to the acquired real-time data; the data screening layer is used for screening out manual operation data results similar to the given data attributes in the database; the result control layer is used for obtaining a final result after error control is carried out on a manual operation data result and an estimation result; the automatic water adding control layer is used for realizing automatic water adding control by combining the operation experience of manual water adding; and the visual display layer is used for visually and prominently displaying the final result obtained after error control and relevant important data. The invention also discloses a data-driven tobacco raw silk moisture control method, which can meet the tobacco raw silk moisture control requirement of multiple influencing factors which are closely crossed and mutually influenced in a complex environment.
Description
Technical Field
The invention relates to the technical field of big data analysis and tobacco shred production, in particular to a data-driven tobacco raw shred moisture control system and a control method.
Background
The investigation and other people of the Yanjing Gangshan cigarette factory in Jiangxi conclude that the tobacco shred manufacturing process control mainly comprises a leaf moisture regaining process, a flavoring and charging process and an expansion and drying process, and the water control is one of important control points; the Shiyi of Guangxi Zhongyan refers to that the moisture content of tobacco leaves is one of factors needing to be controlled in the key point analysis of the quality control of the tobacco shred making process; the Chengxiang in the Mitsuma cigarette factory in Sichuan says that the moisture control can monitor the moisture condition in real time and control the moisture condition through stem washing, fluidized bed, cut tobacco dampening and feeding dampening; the application of the Zhang summarization big data technology of the cigarette factory in Cheng city in Shaanxi refers to the comparison and analysis of the historical data of the moisture at the cut tobacco drying outlet, and a user can screen the data to be analyzed through self-defined conditions, wherein the conditions comprise brand numbers, production time and the like, but the historical data screening of the moisture control of the raw tobacco shreds according to the data characteristics is not explained, and the descending order is carried out according to the influence importance on the moisture of the raw tobacco shreds to select the optimal result as the reference standard; the stability control of key process parameters in a tobacco shred drying process in the Guangxi tobacco south China cigarette factory and other people is analyzed in batches, so that a strong positive correlation between moisture at a tobacco shred drying inlet and the actual water adding proportion of a tobacco flake processing section is obtained, and the correlation coefficient is 0.66; the water content of the leaf moistening and feeding process is controlled by Xugongqing and the like of a tobacco factory in Liuzhou, Guangxi province in a system mode of increasing stable pressure and stable flow; yangping and the like of Yuxi cigarette factory in Yunnan Hongta group quantitatively process the problem of large fluctuation of the moisture content of outlet tobacco strips by adopting a mode of feedforward-cascade double closed-loop control on the influence factors such as fluctuation of the moisture content of incoming materials, a feeding proportion, direct injection steam, temperature and the like, but a preset error range influencing various important factors and results of the moisture of raw tobacco threads is not obtained through data statistical analysis, manual operation data which is closest to the same brand and is most matched with the same brand in historical data is screened out as a reference standard, difference calculation is carried out on the obtained important factors and results influencing the moisture of the raw tobacco threads, and the results are output after error control; the automatic water adding function modification of a feeder control system by Zhang of Lanzhou cigarette factories in Gansu province adopts a computer direct digital control technology, combines a feedforward-feedback control technology to improve the existing automatic water adding device, adds a moisture meter, properly adds hardware, modifies a PLC program and a complete central control system to control in a combined mode, does not carry out the butt joint of a software system under the condition that the current hardware environment is not changed, and adjusts the water adding flow value to control by summarizing manual operation experience and simulating the manual water adding mode, thereby reducing the great production loss caused by the inconsistency of operation methods among people and shifts.
Disclosure of Invention
Aiming at solving the problems in the prior art, the invention aims to provide a data-driven tobacco raw silk moisture control system and a data-driven tobacco raw silk moisture control method aiming at the raw silk moisture control problem that multiple influencing factors are closely crossed and mutually influenced in a complex environment.
In order to achieve the purpose, the invention adopts the technical scheme that: a data-driven tobacco raw silk moisture control system comprises a result estimation layer, a data screening layer, a result control layer, an automatic water adding control layer and a visual display layer; wherein:
the result estimation layer is used for obtaining an estimation result according to the acquired real-time data;
the data screening layer is used for screening out manual operation data results similar to given data attributes in the database;
the result control layer is used for obtaining a final result after error control is carried out on a manual operation data result and an estimation result;
the automatic water adding control layer is used for realizing automatic water adding control by combining the operation experience of manual water adding;
and the visual display layer is used for visually and prominently displaying the final result obtained after error control and relevant important data.
As a further improvement of the invention, the data screening layer is used for screening results and sorting in a descending order according to the importance of the data characteristics on the influence of the data characteristics on the moisture of the raw tobacco shreds based on the similarity of the data characteristics, so as to obtain a required manual operation data result.
As a further improvement of the present invention, the error control of the result of the manual operation and the estimation result specifically includes:
presetting an error range value epsilon obtained after data statistics for each important data attribute and an estimation result, taking a manual operation data result which is closest to and most matched with the selected brand as a reference standard, calculating a difference value between the estimation result and the manual operation data result, if the difference value is less than or equal to epsilon, selecting the estimation result as a final result, otherwise, selecting the manual operation data result as an output result after control after manual intervention.
As a further improvement of the present invention, the implementation of automatic water adding control by combining with the operation experience of manual water adding specifically comprises:
the calculated water addition amount is firstly reduced by 20 percent, and then ten time periods are divided thirty minutes before the water addition is finished to make up the missing water amount.
The invention also provides a data-driven tobacco raw silk moisture control method, which comprises the following steps:
step S10, obtaining an estimation result according to the collected real-time data, and screening out similar manual operation data results in the database;
s20, setting error range values for each important data attribute and the estimation result, and obtaining a final result after error control is carried out on the manual operation data result and the estimation result;
and step S30, visually displaying the final result and related data obtained after error control, and completing automatic water adding control.
As a further improvement of the invention, in step S10, data docking with the PLC control system of the wire manufacturing plant is first completed, real-time data acquisition is completed, an estimation result is obtained, and then data meeting the conditions in the database is screened out according to relevant rules; the method specifically comprises the following steps:
s11, finishing data butt joint with a PLC control system through an OPC Server, establishing a data transmission channel, connecting corresponding required equipment, further finishing real-time data acquisition and obtaining an estimation result;
and S12, screening out the most recent and matched manual operation data results based on the similarity of the data characteristics, and then performing descending order arrangement according to the importance of the data characteristics on the moisture influence of the raw tobacco shreds, thereby obtaining the manual operation data results.
As a further improvement of the present invention, in step S20, the final result after error control is obtained by obtaining a preset error value epsilon through data statistics and performing error control on the estimation result obtained in step S10 and the result of the manual operation data; the method specifically comprises the following steps:
s21, presetting an error value epsilon obtained through data statistics for each important data attribute and an estimation result, and taking the nearest and most matched manual operation data result obtained after screening as a reference standard;
and S22, calculating the difference between the estimation result in the step S10 and the screened manual operation data result, if the difference is less than or equal to epsilon, selecting the estimation result as a final result, otherwise, selecting a value which is manually modified for manual intervention prognosis as an output result after control.
As a further improvement of the invention, in step S30, the final result obtained in step S20 is visually displayed, and then the value is transmitted through a data transmission channel in combination with the experience of manual water adding operation, so as to complete automatic water adding control; the method specifically comprises the following steps:
s31, carrying out prominent visual display on the data result obtained in the S20 and the workshop temperature and humidity data by adopting a trend graph with data marks;
step S32, combining the total weight of the batch of materials and the set flow rate of the materials, and performing conversion calculation on the final result and the final result, that is, the water addition ratio is the final result/the total weight of the batch of materials, and the water addition flow rate is the set flow rate of the materials and the water addition ratio is obtained;
and step S33, combining the experience of manual water adding operation, adjusting the water adding amount by adopting a mode of simulating manual water adding, namely adding 20% less water, adding the lacked water in ten time periods in the last thirty minutes, automatically converting the adjusted water adding flow value into the opening of the water adding valve after transmitting the adjusted water adding flow value into the PLC control system, and automatically controlling the water adding valve by the water adding valve according to the transmitted valve opening parameter to add water, thereby realizing automatic water adding control.
The invention has the beneficial effects that:
1. the method adopts a similarity calculation method of data characteristics, selects five batches of manual operation data results which meet the conditions and have the same brand and are the latest from a database, performs descending arrangement on the obtained results according to the influence of the data characteristics on the importance of the tobacco raw silk moisture, and lays a foundation for realizing the accurate control of the raw silk moisture by taking the obtained nearest and most matched results as a reference standard;
2. the error range value epsilon obtained after data statistics is preset for each important data attribute and the estimation result, the difference value of the two obtained results is calculated according to the obtained manual operation result reference standard, if the difference value is less than or equal to epsilon, the estimation result is selected as the final result, otherwise, the value of manual modification is selected as the output result after control after manual intervention, and therefore hidden dangers caused by manual experience due to inconsistent manual operation between people and between classes are greatly reduced;
3. according to the invention, by combining with the experience of manual water adding operation, through a data transmission channel established between the system and a PLC control system, the water adding amount calculated at the beginning is firstly reduced by 20%, and then the missing water amount is compensated for in ten time periods thirty minutes before the water adding is finished, so that the water adding amount is controlled within a certain range, and the influence of a large-lag leaf storage process on the water absorption of tobacco leaves is indirectly reduced.
Drawings
FIG. 1 is a schematic structural view of a data-driven tobacco raw-tobacco-shred moisture control system according to example 1 of the present invention;
fig. 2 is a block flow diagram of a data-driven tobacco raw silk moisture control method in embodiment 2 of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1, a data-driven tobacco raw silk moisture control system comprises a result estimation layer, a data screening layer, a result control layer, an automatic water adding control layer and a visual display layer.
The result estimation layer is used for finishing data butt joint between systems through an OPC Server, establishing a data transmission channel, connecting corresponding equipment required, further acquiring real-time data and obtaining an estimation result.
The data screening layer is used for screening out similar results from the database according to conditions by adopting a similarity calculation method based on data characteristics according to the brand, batch number, set water adding amount and other characteristics of the current data, and sorting the screening results in a descending order according to the importance of the influence of the data characteristics on the moisture of the raw tobacco shreds so as to obtain a required data result.
The result control layer is used for presetting an error range value epsilon obtained after data statistics for each important data attribute and estimation result, calculating a difference value of the two results according to a nearest and most matched manual operation data result after screening as a reference standard, selecting the estimation result as a final result if the difference value is less than or equal to epsilon, and otherwise, selecting a value manually modified as an output result after control for manual intervention and prognosis.
The automatic water adding control layer is used for carrying out data communication between the system and a PLC control system of a silk making workshop through an OPC Server, converting a final result after error control into water adding flow through calculation, transmitting the value to the PLC control system, automatically converting the value into the opening of a water adding valve in the system, and combining manual water adding experience to carry out automatic water adding control.
The visual display layer is used for displaying corresponding data in an intuitive visual mode such as a trend graph with data marks.
Example 2
As shown in fig. 2, a data-driven tobacco raw silk moisture control method specifically comprises the following steps:
step 1, carrying out data butt joint of two systems, completing real-time data acquisition, obtaining an estimation result, and obtaining data meeting conditions in a database according to a screening rule; the detailed steps are described as follows:
firstly, completing data butt joint of a cost system and a PLC control system through an OPC Server, establishing a data transmission channel, connecting corresponding required equipment, further completing real-time data acquisition, and obtaining an estimation result; the detailed steps of system docking are as follows:
(1) downloading and successfully installing KEPServerEX6 software (a software and hardware system middleware providing industrial automation data connectivity, the same below);
(2) newly building a project, then building an OPC channel, and connecting and configuring the required corresponding equipment parameters; the corresponding equipment parameters are shown in table 1:
TABLE 1 Equipment parameters Table
| Device ID | Name of label | Address | Access path | Data type | Scanning frequency |
| Device 1 | Label 1 | Address 1 | Route 1 | Type 1 | Frequency 1 |
(3) Establishing and connecting an ODBC data source and a database, inputting an account and a password of the database, and configuring parameters such as a storage path of acquired data, response timeout time and the like;
(4) and starting connection, carrying out a communication test, and carrying out data acquisition after success.
And secondly, screening out similar manual operation data results by adopting a similarity calculation method based on data characteristics, and then performing descending order arrangement according to the importance of the data characteristics on the moisture influence of the raw tobacco shreds, thereby obtaining the required data results. The data are characterized as shown in table 2 below:
TABLE 2 data characteristics Table
Step 2, presetting an error value epsilon obtained through data statistics, and carrying out error control on two data results obtained in the step 1 so as to obtain a final result after error control; the detailed description of the steps is as follows:
step one, presetting an error value epsilon obtained through data statistics for each important data attribute and an estimation result, and taking a manual operation data result which is closest to and most matched with the data characteristics after data screening as a reference standard; the error ranges for setting the respective important attributes and the estimation results are shown in table 3:
TABLE 3 error Range Table for important attributes and estimation results
And secondly, calculating a difference value between the estimation result in the step 1 and the reference standard obtained in the previous step after screening, selecting the estimation result as a final result if the difference value is less than or equal to epsilon, and otherwise, selecting a value which is manually modified for manual intervention prognosis as an output result after control so as to improve the stability of the data result. The control results are shown in table 4 below:
TABLE 4 result control table
| Model prediction results | Optimal artificial results | Actual difference of results | A predetermined difference value epsilon | Results of manual intervention | End result |
| Results 1 | Results 2 | Difference 1 | Difference 2 | Results 3 | Results 4 |
And 3, visually displaying the final result obtained in the step 2, and adjusting the water adding amount by combining the experience of manual water adding operation through a data transmission channel established between the two systems so as to finish automatic water adding control. The detailed description of the steps is as follows:
step one, adopting a trend graph with data marks to visually display the data result obtained in the step 2 and relevant important data such as temperature and humidity of a workshop, so that managers and operators can directly observe conveniently and master the overall production condition;
secondly, combining the total weight of the batch of materials and the set flow rate of the materials, and performing conversion calculation on the final result and the final result (namely, the water adding proportion is the final result/the total weight of the batch of materials, and the water adding flow rate is the set flow rate of the materials and the water adding proportion), thereby obtaining the water adding proportion and the water adding flow rate;
and thirdly, by establishing a data transmission channel between the two systems and combining the manual water adding operation experience, the system adjusts the water adding amount by adopting a mode of simulating manual water adding (adding 20% less water amount at the beginning and gradually compensating for the lacking water amount in ten sections in the last thirty minutes), automatically converts the water adding flow value into the PLC control system and then into the opening of the water adding valve, and automatically controls the water adding valve by the water adding valve according to the transmitted opening parameter of the valve to add water, thereby realizing the automatic water adding control.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (8)
1. A data-driven tobacco raw silk moisture control system is characterized by comprising a result estimation layer, a data screening layer, a result control layer, an automatic water adding control layer and a visual display layer; wherein:
the result estimation layer is used for obtaining an estimation result according to the acquired real-time data;
the data screening layer is used for screening out manual operation data results similar to given data attributes in the database;
the result control layer is used for obtaining a final result after error control is carried out on a manual operation data result and an estimation result;
the automatic water adding control layer is used for realizing automatic water adding control by combining the operation experience of manual water adding;
and the visual display layer is used for visually and prominently displaying the final result obtained after error control and relevant important data.
2. The data-driven tobacco raw silk moisture control system according to claim 1, wherein the data screening layer performs result screening and descending order according to importance of the data characteristics on the influence of the data characteristics on the tobacco raw silk moisture based on similarity of the data characteristics, thereby obtaining a required manual operation data result.
3. The data-driven green tobacco shred moisture control system according to claim 1, wherein the error control of the manually operated data result and the estimation result specifically comprises:
presetting an error range value epsilon obtained after data statistics for each important data attribute and an estimation result, taking a manual operation data result which is closest to and most matched with the selected brand as a reference standard, calculating a difference value between the estimation result and the manual operation data result, if the difference value is less than or equal to epsilon, selecting the estimation result as a final result, otherwise, selecting the manual operation data result as an output result after control after manual intervention.
4. The data-driven raw tobacco shred moisture control system according to claim 1, wherein the implementation of automatic water adding control in combination with the operational experience of manual water adding specifically comprises:
the calculated water addition amount is firstly reduced by 20 percent, and then ten time periods are divided thirty minutes before the water addition is finished to make up the missing water amount.
5. A data-driven tobacco raw silk moisture control method is characterized by comprising the following steps:
step S10, obtaining an estimation result according to the collected real-time data, and screening out similar manual operation data results in the database;
s20, setting error range values for each important data attribute and the estimation result, and obtaining a final result after error control is carried out on the manual operation data result and the estimation result;
and step S30, visually displaying the final result and related data obtained after error control, and completing automatic water adding control.
6. The data-driven tobacco raw silk moisture control method according to claim 5, wherein in step S10, data docking with a PLC control system of a silk-making workshop is first completed, real-time data acquisition is completed, an estimation result is obtained, and then data meeting conditions in a database is screened out according to relevant rules; the method specifically comprises the following steps:
s11, finishing data butt joint with a PLC control system through an OPC Server, establishing a data transmission channel, connecting corresponding required equipment, further finishing real-time data acquisition and obtaining an estimation result;
and S12, screening out the most recent and matched manual operation data results based on the similarity of the data characteristics, and then performing descending order arrangement according to the importance of the data characteristics on the moisture influence of the raw tobacco shreds, thereby obtaining the manual operation data results.
7. The data-driven tobacco raw silk moisture control method according to claim 5, wherein in step S20, the estimation result obtained in step S10 and the result of the manual operation data are error-controlled by a preset error value ε obtained through data statistics, so as to obtain a final result after error control; the method specifically comprises the following steps:
s21, presetting an error value epsilon obtained through data statistics for each important data attribute and an estimation result, and taking the nearest and most matched manual operation data result obtained after screening as a reference standard;
and S22, calculating the difference between the estimation result in the step S10 and the screened manual operation data result, if the difference is less than or equal to epsilon, selecting the estimation result as a final result, otherwise, selecting a value which is manually modified for manual intervention prognosis as an output result after control.
8. The data-driven tobacco raw silk moisture control method according to claim 5, wherein in step S30, the final result obtained in step S20 is visually displayed, and then is transmitted through a data transmission channel in combination with manual water adding operation experience, so as to complete automatic water adding control; the method specifically comprises the following steps:
s31, carrying out prominent visual display on the data result obtained in the S20 and the workshop temperature and humidity data by adopting a trend graph with data marks;
step S32, combining the total weight of the batch of materials and the set flow rate of the materials, and performing conversion calculation on the final result and the final result, that is, the water addition ratio is the final result/the total weight of the batch of materials, and the water addition flow rate is the set flow rate of the materials and the water addition ratio is obtained;
and step S33, combining the experience of manual water adding operation, adjusting the water adding amount by adopting a mode of simulating manual water adding, namely adding 20% less water, adding the lacked water in ten time periods in the last thirty minutes, automatically converting the adjusted water adding flow value into the opening of the water adding valve after transmitting the adjusted water adding flow value into the PLC control system, and automatically controlling the water adding valve by the water adding valve according to the transmitted valve opening parameter to add water, thereby realizing automatic water adding control.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101762146A (en) * | 2010-01-05 | 2010-06-30 | 浙江大学 | Vacuum dryer for screw to convey materials and method thereof |
| WO2012177108A2 (en) * | 2011-10-04 | 2012-12-27 | 주식회사 켐에쎈 | Model, method and system for predicting, processing and servicing online physicochemical and thermodynamic properties of pure compound |
| CN103054152A (en) * | 2012-12-28 | 2013-04-24 | 广东中烟工业有限责任公司 | Tobacco redryer moisture control method based on forecast Product Improve (PI) algorithm control system |
| CN104049074A (en) * | 2014-07-02 | 2014-09-17 | 江苏大学 | Method and device for estimating sludge moisture content in sludge drying process |
| CN106983172A (en) * | 2017-04-24 | 2017-07-28 | 江苏密斯欧智能科技有限公司 | A kind of method of tobacco silk thread intelligence manufacture |
| CN111144667A (en) * | 2020-01-16 | 2020-05-12 | 红云红河烟草(集团)有限责任公司 | Tobacco conditioner discharged material water content prediction method based on gradient lifting tree |
| CN112068507A (en) * | 2020-07-31 | 2020-12-11 | 红塔烟草(集团)有限责任公司 | Tobacco processing moisture big data artificial intelligence control method |
-
2021
- 2021-02-26 CN CN202110215944.0A patent/CN112947342B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101762146A (en) * | 2010-01-05 | 2010-06-30 | 浙江大学 | Vacuum dryer for screw to convey materials and method thereof |
| WO2012177108A2 (en) * | 2011-10-04 | 2012-12-27 | 주식회사 켐에쎈 | Model, method and system for predicting, processing and servicing online physicochemical and thermodynamic properties of pure compound |
| CN103054152A (en) * | 2012-12-28 | 2013-04-24 | 广东中烟工业有限责任公司 | Tobacco redryer moisture control method based on forecast Product Improve (PI) algorithm control system |
| CN104049074A (en) * | 2014-07-02 | 2014-09-17 | 江苏大学 | Method and device for estimating sludge moisture content in sludge drying process |
| CN106983172A (en) * | 2017-04-24 | 2017-07-28 | 江苏密斯欧智能科技有限公司 | A kind of method of tobacco silk thread intelligence manufacture |
| CN111144667A (en) * | 2020-01-16 | 2020-05-12 | 红云红河烟草(集团)有限责任公司 | Tobacco conditioner discharged material water content prediction method based on gradient lifting tree |
| CN112068507A (en) * | 2020-07-31 | 2020-12-11 | 红塔烟草(集团)有限责任公司 | Tobacco processing moisture big data artificial intelligence control method |
Non-Patent Citations (3)
| Title |
|---|
| 孙永刚;吕希胜;: "制丝线实时数据采集与控制系统", 制造业自动化, no. 11 * |
| 应文渊;赵章风;钟江;: "滚筒杀青机系统的控制算法改进研究", 机电工程, no. 12 * |
| 张辉;: "大数据技术在烘丝出口水分预测与控制中的应用", 通讯世界, no. 06 * |
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