CN113673144A - Prediction system and method for data of tobacco stem drying equipment - Google Patents
Prediction system and method for data of tobacco stem drying equipment Download PDFInfo
- Publication number
- CN113673144A CN113673144A CN202110788637.1A CN202110788637A CN113673144A CN 113673144 A CN113673144 A CN 113673144A CN 202110788637 A CN202110788637 A CN 202110788637A CN 113673144 A CN113673144 A CN 113673144A
- Authority
- CN
- China
- Prior art keywords
- drying equipment
- parameters
- algorithm
- target parameters
- predicting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001035 drying Methods 0.000 title claims abstract description 131
- 241000208125 Nicotiana Species 0.000 title claims abstract description 61
- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 60
- 238000013528 artificial neural network Methods 0.000 claims abstract description 20
- 238000004140 cleaning Methods 0.000 claims abstract description 17
- 239000002245 particle Substances 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 13
- 239000002994 raw material Substances 0.000 claims description 19
- 238000007637 random forest analysis Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 241000196324 Embryophyta Species 0.000 claims 3
- 238000012423 maintenance Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 230000008092 positive effect Effects 0.000 abstract description 3
- 230000008447 perception Effects 0.000 abstract 1
- 230000008569 process Effects 0.000 description 7
- 238000004519 manufacturing process Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- SNICXCGAKADSCV-JTQLQIEISA-N (-)-Nicotine Chemical compound CN1CCC[C@H]1C1=CC=CN=C1 SNICXCGAKADSCV-JTQLQIEISA-N 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 235000019504 cigarettes Nutrition 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000013072 incoming material Substances 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229960002715 nicotine Drugs 0.000 description 1
- SNICXCGAKADSCV-UHFFFAOYSA-N nicotine Natural products CN1CCCC1C1=CC=CN=C1 SNICXCGAKADSCV-UHFFFAOYSA-N 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B5/00—Stripping tobacco; Treatment of stems or ribs
- A24B5/16—Other treatment of stems or ribs, e.g. bending, chopping, incising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Manufacture Of Tobacco Products (AREA)
Abstract
The invention provides a system and a method for predicting data of tobacco cut stem drying equipment, wherein the method for predicting the data of the tobacco cut stem drying equipment comprises the following steps: collecting target parameters of drying equipment, and cleaning the target parameters; and processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment. The invention realizes the perception, analysis and processing of target parameters, provides an optimization model of key quality from the operation angle of drying equipment, has positive effects on the stability and the excellent rate of the quality of tobacco cut stem products and the intelligent level of the operation and maintenance of the equipment, ensures the stability of moisture at the outlet of the drying equipment and ensures the quality of the products.
Description
Technical Field
The invention relates to the technical field of tobacco stem drying, in particular to a system and a method for predicting data of tobacco stem drying equipment.
Background
The cut stem drying machine is a terminal of each cut stem making process, the quality of products is irreversible, the product is the most important weight in the cut stem making process, and the cut stem drying machine has the functions of controlling the water content of cut stems, enabling the cut stems to reach a loose, fluffy and elastic physical state, reducing free nicotine and ammonia substances and the like, enabling the cut stems to have better chemical quality, and finally achieving the purposes of improving the filling value of the cut stems and having better sensory quality.
In the drying process, the key parameters are hot air temperature, negative pressure value and the like, the parameters are realized to reach target values through PID closed-loop control, and the method is the working mode of the main stem shred drying machine at present. However, in actual operation, the control target of the PID and the actual outlet water content cannot be completely matched, and there is a certain fluctuation. The main reasons include fluctuation of the moisture content of the supplied material of the cut stem drying machine, unreasonable set values of the process parameters of the cut stem drying machine, unstable control of the moisture at the cut stem outlet and great influence on the cigarette quality.
Disclosure of Invention
The invention provides a system and a method for predicting data of tobacco cut stem drying equipment, which are used for solving the defect that parameters of the tobacco cut stem drying equipment cannot be predicted in the prior art, so that abnormity is generated in the drying process of the drying equipment, predicting and optimizing parameters of the tobacco cut stem drying equipment, which influence outlet moisture, and ensuring the product quality.
The invention provides a method for predicting data of tobacco cut stem drying equipment, which comprises the following steps:
collecting target parameters of drying equipment, and cleaning the target parameters;
and processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
According to the method for predicting the data of the tobacco cut stem drying equipment provided by the invention, the step of collecting the target parameters of the drying equipment and cleaning the target parameters further comprises the following steps:
obtaining raw material parameters of tobacco cut stems to be dried, and cleaning the raw material parameters;
and processing the cleaned raw material parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
According to the prediction method of the data of the tobacco cut stem drying equipment provided by the invention, the raw material parameters comprise the initial moisture content and batch type of the cut stems to be dried.
According to the prediction method of the data of the tobacco cut stem drying equipment, provided by the invention, the target parameters comprise inlet moisture, hot air temperature, inlet temperature, cylinder wall temperature, environment temperature, steam flow, air door opening and steam saturation of the drying equipment.
According to the prediction method of the data of the tobacco cut stem drying equipment provided by the invention, the step of collecting the target parameters of the drying equipment and cleaning the target parameters comprises the following steps:
and screening the influence factors of the target parameters by adopting a random forest algorithm.
According to the prediction method of the data of the tobacco cut stem drying equipment provided by the invention, the step of processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment specifically comprises the following steps:
and establishing a prediction model of the outlet moisture of the drying equipment and an operation state model of the drying equipment under the influence of the cleaned target parameters by adopting a neural network algorithm.
The method for predicting the data of the tobacco cut stem drying equipment further comprises the following steps of:
and acquiring the actual outlet moisture of the drying equipment, comparing and checking the actual outlet moisture with the prediction parameters, and calculating a deviation index to obtain an evaluation index level.
The invention also provides a system for predicting data of the tobacco cut stem drying equipment, which comprises the drying equipment, a controller and a target parameter acquisition module, wherein the target parameter acquisition module is installed on the drying equipment, the target parameter module is connected with the controller, and a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm are implanted into the controller in advance.
According to the prediction system for the data of the tobacco cut stem drying equipment, provided by the invention, the target parameter acquisition module comprises a temperature sensor, a humidity sensor, a steam flowmeter and a steam quality detector.
The system for predicting the data of the tobacco cut stem drying equipment further comprises a touch display screen, and the touch display screen is connected with the controller.
The invention provides a system and a method for predicting data of tobacco cut stem drying equipment, which are characterized in that target parameters of the drying equipment are collected and cleaned; the neural network algorithm, the particle swarm algorithm and the multivariate time sequence algorithm are adopted to process the cleaned target parameters to obtain the prediction parameters of the outlet moisture of the drying equipment, so that the target parameters are sensed, analyzed and processed, an optimization model of key quality is provided in the operation angle of the drying equipment, positive effects are provided on the stability and the excellent rate of the quality of tobacco cut stem products and the intelligent level of the operation and maintenance of the equipment, the stability of the outlet moisture of the drying equipment is ensured, and the product quality is guaranteed.
Further, in the system for predicting cut rolled tobacco stems drying device data according to the present invention, since the method for predicting cut rolled tobacco stems drying device data as described above can be implemented, various advantages as described above can be also provided.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting data of a cut rolled tobacco stem drying device provided by the invention;
fig. 2 is a second schematic flow chart of the method for predicting data of the cut rolled tobacco stem drying equipment provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
The method for predicting the data of the cut rolled tobacco stems drying device according to the invention is described below with reference to fig. 1 and 2, and comprises the following steps:
collecting target parameters of drying equipment, and cleaning the target parameters;
and processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
According to the prediction method of the data of the tobacco cut stem drying equipment provided by the invention, the steps of collecting the target parameters of the drying equipment and cleaning the target parameters further comprise:
obtaining raw material parameters of tobacco cut stems to be dried, and cleaning the raw material parameters;
and processing the cleaned raw material parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
According to the prediction method of the data of the tobacco cut stem drying equipment provided by the invention, the raw material parameters comprise the initial moisture content and batch type of the cut stems to be dried.
According to the prediction method of the data of the tobacco cut stem drying equipment, provided by the invention, the target parameters comprise inlet moisture, hot air temperature, inlet temperature, cylinder wall temperature, environment temperature, steam flow, air door opening and steam saturation of the drying equipment.
According to the prediction method of the data of the tobacco cut stem drying equipment, provided by the invention, the steps of collecting the target parameters of the drying equipment and cleaning the target parameters comprise the following steps:
and screening the influence factors of the target parameters by adopting a random forest algorithm.
According to the prediction method of the data of the tobacco cut stem drying equipment, provided by the invention, the step of processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment specifically comprises the following steps:
and establishing a prediction model of the outlet moisture of the drying equipment and an operation state model of the drying equipment under the influence of the cleaned target parameters by adopting a neural network algorithm.
The method for predicting the data of the tobacco cut stem drying equipment further comprises the following steps of:
and acquiring the actual outlet moisture of the drying equipment, comparing and checking the actual outlet moisture with the prediction parameters, and calculating a deviation index to obtain an evaluation index level.
The invention provides a method for predicting data of tobacco cut stem drying equipment, which specifically comprises the following steps:
collecting target parameters of drying equipment and raw material parameters of cut tobacco stems to be dried, wherein the target parameters comprise inlet moisture, hot air temperature, inlet temperature, cylinder wall temperature, environment temperature, steam flow, air door opening and steam saturation of the drying equipment, and the raw material parameters comprise initial moisture content and batch type of the cut tobacco stems to be dried and production place information of the cut tobacco stems;
the method comprises the steps of identifying and cleaning the target parameters and the raw material parameters in demand, carrying out production process historical data collection and cleaning work aiming at key quality indexes of water at an outlet of drying equipment (taking a cut stem drying machine as an example in the embodiment), establishing a target parameter cleaning rule for the collected target parameters, cleaning and analyzing the target parameters in correlation, analyzing the influence of the target parameters and the raw material parameters on the quality of the drying process by using an FMEA (failure mode analysis) tool, enumerating all related data by using an exhaustion method, and refining a related data list, wherein the target parameters and the raw material parameters are subjected to data-driven model research; the method comprises the following steps of analyzing the reasons of problems from two angles, namely, the angle of the working mechanism of a cut stem drying machine and the angle of incoming material moisture or other factors; in particular, the influence of the internal and external working conditions of the cut rolled stem drying machine on the outlet moisture and the influence of inlet moisture fluctuation need to be researched. Searching for an influence factor in the cut stem processing process, evaluating the importance of the influence factor, and providing a basis for model training;
influence factors are screened for the target parameters by adopting a random forest algorithm, a classical algorithm in random forest ensemble learning can represent the influence of a certain characteristic on the whole system based on a method for reducing average uncertainty in the random forest algorithm, and the ordering of the influence factors can be realized;
establishing a prediction model of the outlet moisture content of the cut stem drying machine and an operation state model of the cut stem drying machine under the influence of the cleaned target parameters and the raw material parameters by utilizing a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm, adjusting model parameters through the actual outlet moisture of the cut stem drying machine in actual production, perfecting the model and improving the model precision;
establishing a key index of the stability of outlet moisture of the cut stem drying machine and a key index of the diagnosis of the running state of the cut stem drying machine; the method comprises the following steps of recommending and setting key target parameters of a cut stem drying machine according to the operation mode of the cut stem drying machine, improving the operation state of the cut stem drying machine and improving the stability of outlet moisture;
comparing and checking the actual outlet moisture of the cut stem drying machine with the prediction parameters, calculating a deviation index, and evaluating the index level according to an index model and an iteration model to reduce the moisture SD value fluctuation of the cut stem drying machine and enable the SD value of the cut stem drying machine to be less than or equal to 0.17;
and displaying the prediction result type in real time through a touch display screen.
The prediction system of the data of the cut rolled tobacco drying device provided by the present invention is described below, and the prediction system of the data of the cut rolled tobacco drying device described below and the prediction method of the data of the cut rolled tobacco drying device described above may be referred to in correspondence with each other.
The invention also provides a system for predicting the data of the tobacco cut stem drying equipment, which comprises the drying equipment, a controller and a target parameter acquisition module, wherein the target parameter acquisition module is installed on the drying equipment, the target parameter module is connected with the controller, and a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm are implanted into the controller in advance. It is understood that the drying device in this embodiment is embodied as a cut stem drying machine. The target parameter acquisition module is used for acquiring related target parameters of the cut stem drying machine and sending the target parameters to the controller, and the controller analyzes and processes the target parameters through a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm.
According to the prediction system for the data of the tobacco cut stem drying equipment, provided by the invention, the target parameter acquisition module comprises a temperature sensor, a humidity sensor, a steam flowmeter and a steam quality detector. It can be understood that the temperature sensors are provided in plurality, including a first temperature sensor for detecting the temperature of hot air, a second temperature sensor for detecting the inlet temperature of the cut stem drying machine, a third temperature sensor for detecting the temperature of the cylinder wall of the cut stem drying machine, and a fourth temperature sensor for detecting the ambient temperature. Humidity transducer sets up two, including the second humidity transducer who is used for detecting the first humidity transducer who dries by the fire entry moisture of stalk silk machine and export moisture. The steam flow meter is used for detecting the steam flow at the inlet of the cut stem drying machine. The steam quality detector is used for detecting the steam saturation. The opening degree of the air door can be directly obtained manually.
The system for predicting the data of the tobacco cut stem drying equipment further comprises a touch display screen, and the touch display screen is connected with the controller. It can be understood that the touch display screen is used for inputting instructions and related parameters directly acquired to the controller, and the related parameters include raw material parameters and air door opening degree. Meanwhile, the touch display screen can display the processing result information of the controller.
The invention provides a system and a method for predicting data of tobacco cut stem drying equipment, which are characterized in that target parameters of the drying equipment are collected and cleaned; the neural network algorithm, the particle swarm algorithm and the multivariate time sequence algorithm are adopted to process the cleaned target parameters to obtain the prediction parameters of the outlet moisture of the drying equipment, so that the target parameters are sensed, analyzed and processed, an optimization model of key quality is provided in the operation angle of the drying equipment, positive effects are provided on the stability and the excellent rate of the quality of tobacco cut stem products and the intelligent level of the operation and maintenance of the equipment, the stability of the outlet moisture of the drying equipment is ensured, and the product quality is guaranteed.
Further, in the system for predicting cut rolled tobacco stems drying device data according to the present invention, since the method for predicting cut rolled tobacco stems drying device data as described above can be implemented, various advantages as described above can be also provided.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A prediction method of tobacco stem drying equipment data is characterized by comprising the following steps:
collecting target parameters of drying equipment, and cleaning the target parameters;
and processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
2. The method for predicting cut rolled tobacco stems drying equipment data according to claim 1, wherein the step of collecting target parameters of the drying equipment and cleaning the target parameters further comprises:
obtaining raw material parameters of tobacco cut stems to be dried, and cleaning the raw material parameters;
and processing the cleaned raw material parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time sequence algorithm to obtain the prediction parameters of the outlet moisture of the drying equipment.
3. The method of predicting cut rolled tobacco stem drying equipment data as set forth in claim 2, wherein the raw material parameters comprise an initial moisture content and a batch type of cut rolled tobacco stems to be dried.
4. The method of predicting cut rolled tobacco stems drying plant data as recited in claim 1, wherein the target parameters include inlet moisture, hot air temperature, inlet temperature, drum wall temperature, ambient temperature, steam flow, damper opening, and steam saturation of the drying plant.
5. The method for predicting cut rolled tobacco stems drying device data according to claim 1, wherein the step of collecting target parameters of the drying device and cleaning the target parameters comprises:
and screening the influence factors of the target parameters by adopting a random forest algorithm.
6. The method for predicting the data of the cut rolled tobacco stems drying equipment according to claim 1, wherein the step of processing the cleaned target parameters by adopting a neural network algorithm, a particle swarm algorithm and a multivariate time series algorithm to obtain the predicted parameters of the outlet moisture of the drying equipment specifically comprises the following steps:
and establishing a prediction model of the outlet moisture of the drying equipment and an operation state model of the drying equipment under the influence of the cleaned target parameters by adopting a neural network algorithm.
7. The method for predicting cut rolled tobacco stems drying plant data according to claim 1, further comprising the steps of:
and acquiring the actual outlet moisture of the drying equipment, comparing and checking the actual outlet moisture with the prediction parameters, and calculating a deviation index to obtain an evaluation index level.
8. A prediction system for implementing the prediction method of the cut rolled tobacco stems drying equipment data according to any one of claims 1 to 7, characterized by comprising a drying equipment, a controller and a target parameter acquisition module, wherein the target parameter acquisition module is installed on the drying equipment, the target parameter module is connected with the controller, and the controller is pre-implanted with a neural network algorithm, a particle swarm algorithm and a multivariate time series algorithm.
9. The system for predicting cut rolled tobacco stems drying equipment data according to claim 8, wherein the target parameter acquisition module comprises a temperature sensor, a humidity sensor, a steam flow meter and a steam quality detector.
10. The system for predicting cut rolled tobacco stems drying equipment data according to claim 8, further comprising a touch display screen, wherein the touch display screen is connected with the controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110788637.1A CN113673144A (en) | 2021-07-13 | 2021-07-13 | Prediction system and method for data of tobacco stem drying equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110788637.1A CN113673144A (en) | 2021-07-13 | 2021-07-13 | Prediction system and method for data of tobacco stem drying equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113673144A true CN113673144A (en) | 2021-11-19 |
Family
ID=78538980
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110788637.1A Pending CN113673144A (en) | 2021-07-13 | 2021-07-13 | Prediction system and method for data of tobacco stem drying equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113673144A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114595888A (en) * | 2022-03-10 | 2022-06-07 | 广西中烟工业有限责任公司 | Method and device for predicting process control parameters of tobacco shred production line |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102072922A (en) * | 2009-11-25 | 2011-05-25 | 东北林业大学 | Particle swarm optimization neural network model-based method for detecting moisture content of wood |
| CN108986470A (en) * | 2018-08-20 | 2018-12-11 | 华南理工大学 | The Travel Time Estimation Method of particle swarm algorithm optimization LSTM neural network |
| CN109858714A (en) * | 2019-04-03 | 2019-06-07 | 杭州安脉盛智能技术有限公司 | Based on pipe tobacco quality inspection index prediction technique, the apparatus and system for improving neural network |
| CN111199305A (en) * | 2018-11-20 | 2020-05-26 | 上海烟草集团有限责任公司 | Neural network-based production energy consumption prediction method and system, electronic terminal and storage medium |
| CN111597729A (en) * | 2020-05-27 | 2020-08-28 | 北京天泽智云科技有限公司 | Method and system for optimizing technological parameters of processing equipment |
| CN112021626A (en) * | 2020-07-10 | 2020-12-04 | 张家口卷烟厂有限责任公司 | Intelligent control system and method for tobacco shred making link |
| CN112021641A (en) * | 2020-07-10 | 2020-12-04 | 张家口卷烟厂有限责任公司 | On-line moisture meter calibration system and method for tobacco shred production link |
| CN112749211A (en) * | 2021-01-18 | 2021-05-04 | 国网福建省电力有限公司 | Novel tea yield prediction method based on electric power big data |
-
2021
- 2021-07-13 CN CN202110788637.1A patent/CN113673144A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102072922A (en) * | 2009-11-25 | 2011-05-25 | 东北林业大学 | Particle swarm optimization neural network model-based method for detecting moisture content of wood |
| CN108986470A (en) * | 2018-08-20 | 2018-12-11 | 华南理工大学 | The Travel Time Estimation Method of particle swarm algorithm optimization LSTM neural network |
| CN111199305A (en) * | 2018-11-20 | 2020-05-26 | 上海烟草集团有限责任公司 | Neural network-based production energy consumption prediction method and system, electronic terminal and storage medium |
| CN109858714A (en) * | 2019-04-03 | 2019-06-07 | 杭州安脉盛智能技术有限公司 | Based on pipe tobacco quality inspection index prediction technique, the apparatus and system for improving neural network |
| CN111597729A (en) * | 2020-05-27 | 2020-08-28 | 北京天泽智云科技有限公司 | Method and system for optimizing technological parameters of processing equipment |
| CN112021626A (en) * | 2020-07-10 | 2020-12-04 | 张家口卷烟厂有限责任公司 | Intelligent control system and method for tobacco shred making link |
| CN112021641A (en) * | 2020-07-10 | 2020-12-04 | 张家口卷烟厂有限责任公司 | On-line moisture meter calibration system and method for tobacco shred production link |
| CN112749211A (en) * | 2021-01-18 | 2021-05-04 | 国网福建省电力有限公司 | Novel tea yield prediction method based on electric power big data |
Non-Patent Citations (3)
| Title |
|---|
| 刘德胜;张佳薇;: "粒子群优化神经网络在木材干燥动态建模中的应用", 佳木斯大学学报(自然科学版), no. 01, 30 March 2007 (2007-03-30), pages 46 - 48 * |
| 朱光耀;谢方平;陈凯乐;代振维;: "基于PSO-BP算法的油菜籽干燥工艺参数的优化", 湖南农业大学学报(自然科学版), no. 02, 25 April 2017 (2017-04-25), pages 222 - 225 * |
| 胡宇航;何毅;李凡;陈之辉;曲锋;杨进国;王哲;: "烘丝过程出口水分控制数据分析方法应用研究", 电脑与信息技术, no. 04, 15 August 2016 (2016-08-15), pages 13 - 15 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114595888A (en) * | 2022-03-10 | 2022-06-07 | 广西中烟工业有限责任公司 | Method and device for predicting process control parameters of tobacco shred production line |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112034095B (en) | System and method for analyzing structure of cut tobacco after online drying | |
| CN107219827B (en) | Rice food full-industry chain information system, establishment method and application | |
| AU2015293548B9 (en) | Method for detecting anomalies in a distribution network, in particular for drinking water | |
| CN111144667A (en) | Tobacco conditioner discharged material water content prediction method based on gradient lifting tree | |
| CN111949700A (en) | Intelligent safety guarantee real-time optimization method and system for petrochemical device | |
| CN107851294B (en) | State-based preventive maintenance device and method for large-scale operation system | |
| CN103487558A (en) | Detection method for abnormal samples in mode identification and analysis of tea quality through intelligent sensory signals | |
| CN110244664A (en) | The method of tobacco preparation system and running equipment for tobacco industry | |
| CN113673144A (en) | Prediction system and method for data of tobacco stem drying equipment | |
| CN114115393A (en) | Method for controlling moisture and temperature at outlet of cut tobacco dryer for sheet cut tobacco making line | |
| CN106951359A (en) | A kind of system health degree determination method and device | |
| CN112132316A (en) | System and method for monitoring abnormality of on-line equipment in silk making link | |
| CN103499613A (en) | Selection method of intelligent sensory spectrum feature sensors in electronic nose Longjing tea quality detection system | |
| CN111879726B (en) | Tobacco hot processing strength and volatility online monitoring method based on synchronous near-infrared analysis before and after processing | |
| CN118014115A (en) | Production line loss monitoring method based on long-short-term memory network | |
| CN116602435A (en) | Machine learning-based method for analyzing moisture change of cut tobacco in cut tobacco making machine | |
| CN111950166A (en) | Cost optimization method for household paper making machine based on data mining | |
| CN117073360A (en) | Method and equipment for controlling water content of Chinese herbal medicine drying based on LSTM model | |
| CN103499665A (en) | Optimizing West Lake Longjing tea tree species detection method on basis of genetic algorithm | |
| CN103499616A (en) | Selection method of sensors in producing area models for quality detection of Longjing tea on basis of genetic algorithm | |
| CN107220705A (en) | Atmospheric and vacuum distillation unit Atmospheric Tower does Forecasting Methodology | |
| CN118076929A (en) | Monitoring production of material boards, in particular engineered wood boards, in particular using self-organizing maps | |
| CN103499615A (en) | Selection method of sensors in tree species model for quality detection of Longjing tea based on genetic algorithm | |
| CN113836813A (en) | Blast furnace tuyere water leakage detection method based on data analysis | |
| Sarveswaran et al. | MilkSafe: A Hardware-Enabled Milk Quality Prediction using Machine Learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |