CN110771940A - Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning - Google Patents
Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning Download PDFInfo
- Publication number
- CN110771940A CN110771940A CN201911200525.9A CN201911200525A CN110771940A CN 110771940 A CN110771940 A CN 110771940A CN 201911200525 A CN201911200525 A CN 201911200525A CN 110771940 A CN110771940 A CN 110771940A
- Authority
- CN
- China
- Prior art keywords
- control system
- information processing
- baking
- processing control
- cloud server
- 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
Images
Classifications
-
- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B3/00—Preparing tobacco in the factory
- A24B3/10—Roasting or cooling tobacco
-
- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24B—MANUFACTURE OR PREPARATION OF TOBACCO FOR SMOKING OR CHEWING; TOBACCO; SNUFF
- A24B9/00—Control of the moisture content of tobacco products, e.g. cigars, cigarettes, pipe tobacco
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention discloses an intelligent tobacco leaf baking control system and method based on Internet of things and deep learning, wherein the baking control system comprises: the system comprises an information processing control system, a video acquisition system, a wireless data transmission module, a cloud server, a mobile terminal and a PC terminal. The system is characterized in that when a video acquisition system acquires video image information of tobacco leaves and transmits the video image information to an information processing control system, the video image information is uploaded to a cloud server through a wireless data transmission module, depth image recognition is carried out, the baking state of the tobacco leaves in a baking room is judged, data uploaded by each sensor is analyzed, processed and matched, then a baking scheme is formed by comparing baking processes, a control command is issued to the information processing control system to control a circulating fan, and when abnormality occurs, abnormal information is sent to a mobile terminal. Meanwhile, data and videos acquired by the sensors are stored, and baking technicians can monitor the data in real time at the mobile terminal to deal with abnormal conditions.
Description
Technical Field
The embodiment of the invention belongs to the field of intelligent information processing, and particularly relates to an intelligent tobacco leaf baking control system and method based on the Internet of things and deep learning.
Background
Tobacco leaf curing is an important step in the tobacco production process, and aims to promote yellowing and drying of tobacco leaves. The baking process generally divides the tobacco leaf baking into 3 stages of yellowing stage, color fixing stage and stem drying stage, and the baking quality can be ensured only by ensuring the temperature and humidity inside the baking room strictly according to the baking process rules and the wind power reaching the rules in the baking process.
In the tobacco leaf production process, tobacco leaf baking is an important link in the tobacco leaf production process, and plays a role in lifting the weight of the quality influence of the tobacco leaves. With the development of modern intelligent technology, the tobacco leaf baking technology in China is greatly improved, and some flue-cured tobacco baking temperature and humidity controllers designed according to flue-cured tobacco baking can monitor the temperature and humidity inside a baking room, so that baking technicians can conveniently observe and adjust the internal temperature, but the flue-cured tobacco baking controllers still adopt artificial dominance, and control the temperature and humidity inside the baking room and judge the stage of the tobacco leaves according to the experience of the baking technicians. The baking mode has high requirements on human experience, complex operation procedures and more uncertain factors, often cannot accurately control the baking process, technicians are easy to make judgment errors in the baking process, serious consequences are often caused, and the overall quality of the tobacco leaves is reduced.
The intelligent tobacco leaf baking control system provided by the invention realizes the intelligentization and automation of the tobacco leaf baking process by applying the Internet of things and the deep learning technology, frees baking technicians in the baking process, digitalizes the whole baking process, intelligently controls, ensures the baking quality and improves the economic benefit.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent tobacco leaf baking control system and method based on the Internet of things and deep learning by applying a deep learning technology which is developed rapidly, and aims to solve the problems that the baking process depends on artificial experience seriously, the internal condition of a baking room is not clear, and the quality of a baked product is low.
The technical scheme adopted by the embodiment is as follows:
in a first aspect, an embodiment of the present invention provides an intelligent tobacco leaf baking control system based on internet of things and deep learning, including:
the information acquisition comprises a video acquisition system for acquiring tobacco leaf images, a temperature and humidity sensor for acquiring the temperature and humidity in the curing barn and a fan rotating speed sensor for acquiring the fan rotating speed in the curing barn;
the information processing control system is connected with the video acquisition system through a 485 bus, and the temperature and humidity sensor and the fan rotating speed sensor are directly connected with the information processing control system;
the wireless data transmission module is used for connecting the cloud server and the information processing control system and uploading data of the information processing control system to the cloud server through the wireless data transmission module;
the cloud server receives the video data from the wireless data transmission module and the data of the temperature, humidity and fan rotating speed sensors, performs depth identification on video data frames, sends a command to the mobile terminal according to an identification result, and sends the command to the information processing control system through the wireless data transmission module;
the mobile terminal and the PC are used for monitoring the temperature and humidity and the wind speed data in the curing barn by accessing the cloud server database, calling monitoring videos to observe the conditions of the tobacco leaves, sending control commands to the cloud server, transmitting the control commands to the information processing control system through the wireless data transmission module, and controlling the rotating speed of the fan and the video acquisition system after the information processing control system analyzes the commands.
Further, the baking room is a cuboid, the space is divided into three layers by a baking support, the video acquisition system and the temperature and humidity sensor are respectively installed on each layer of the center of the three layers of supports, and the baking room is provided with a boiler, a fan, an exhaust hole and a smoke exhaust pipe.
Furthermore, a human-computer interaction module is arranged on the information processing control system.
Further, the wireless data transmission module adopts a 4G module.
In a second aspect, an embodiment of the present invention provides an intelligent tobacco leaf curing control method based on the internet of things and deep learning, where the method is implemented in the intelligent tobacco leaf curing control system based on the internet of things and deep learning in the first aspect, and the method includes the following steps:
s1, a video acquisition system acquires video data and transmits the video data to an information processing control system, and then the video data is uploaded to a cloud server through a wireless data transmission module;
s2, the cloud server performs format conversion on the uploaded video data to obtain pictures with uniform formats;
s3, carrying out depth image recognition on the picture and dividing the tobacco leaves into a color changing period, a color fixing period, a stem drying period and other four types;
s4, when the identification result is a color changing period, a color fixing period and a tendon drying period, matching corresponding temperature and humidity in the curing barn according to the flue-cured tobacco curing process, sending a command of the rotating speed of the fan, sending the command to the wireless data transmission module through the cloud server, sending the command to the information processing control system, and controlling the rotating speed of the fan by the information processing control system after analyzing the command;
and S5, when the cloud server identifies that the result is other, an abnormal command needs to be sent to the mobile terminal to remind baking control personnel to take corresponding emergency measures.
Further, in the step S3, the image recognition uses a deep learning residual error model to recognize the tobacco leaves, that is: firstly, preprocessing an image, then extracting the characteristics of the image by a characteristic extraction algorithm, then classifying the extracted characteristic values by using a softmax classifier, and finally outputting a tobacco leaf identification and classification result.
Furthermore, the feature extraction algorithm at least comprises three structural blocks, and the structural blocks comprise 1 × 1, 3 × 3, 5 × 5 convolution and a pooling module to extract the features of the tobacco leaf pictures collected in the baking process.
Further, the Softmax classification network classifies the characteristic values extracted from the tobacco leaves according to four types, namely a color changing period, a color fixing period, a tendon drying period and the like.
Further, the wireless data transmission module adopts a 4G module.
Furthermore, the information processing control system collects data from the sensor, uploads video data to the cloud server, and analyzes, processes, stores and displays commands of the cloud server;
the analysis process is as follows: the information processing control system mainly analyzes commands issued by the cloud server, the information processing control system analyzes data packets according to a command analysis protocol, the data packets are specifically of the following formats { ID (x) }, type (0-4) { op (0-1), data () } }, wherein the ID specifically corresponds to a baking room, the commands are classified according to type0, type1, type2 and type3, wherein type0 corresponds to a circulating fan, type1 corresponds to a temperature and humidity controller, type2 corresponds to a video acquisition system, and type3 corresponds to an exhaust fan; op corresponds to the opening and closing of the device (0 for closed, 1 for open), and data represents specific corresponding data.
The technical scheme of the embodiment of the invention has the following beneficial effects: the system is characterized in that when a video acquisition system acquires video image information of tobacco leaves and transmits the video image information to an information processing control system, the video image information is uploaded to a cloud server through a wireless data transmission module, depth image recognition is carried out, the baking state of the tobacco leaves in a baking room is judged, data uploaded by each sensor is analyzed, processed and matched, then a baking scheme is formed by comparing baking processes, a control command is issued to the information processing control system to control a circulating fan, and when abnormality occurs, abnormal information is sent to a mobile terminal. Meanwhile, data and videos acquired by the sensors are stored, and baking technicians can monitor the data in real time at the mobile terminal to deal with abnormal conditions. The problems that the baking process depends on human experience seriously, the internal condition of a baking room is not clear, and the quality of baked products is low are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a structural diagram of an intelligent tobacco flue-curing control system based on internet of things and deep learning according to an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent tobacco leaf curing control method based on internet of things and deep learning according to an embodiment of the invention;
FIG. 3 is a flow chart of tobacco leaf image recognition;
FIG. 4 is a diagram of a tobacco leaf image recognition algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a structural diagram of an intelligent tobacco flue-curing control system based on internet of things and deep learning according to an embodiment of the present invention; the embodiment of the invention provides an intelligent tobacco leaf baking control system based on the Internet of things and deep learning, which comprises:
the information acquisition comprises a video acquisition system for acquiring tobacco leaf images, a temperature and humidity sensor for acquiring the temperature and humidity in the curing barn and a fan rotating speed sensor for acquiring the fan rotating speed in the curing barn;
the information processing control system is connected with the video acquisition system through a 485 bus, and the temperature and humidity sensor and the fan rotating speed sensor are directly connected with the information processing control system;
the wireless data transmission module is used for connecting the cloud server and the information processing control system and uploading data of the information processing control system to the cloud server through the wireless data transmission module;
the cloud server receives the video data from the wireless data transmission module and the data of the temperature, humidity and fan rotating speed sensors, performs depth identification on video data frames, sends a command to the mobile terminal according to an identification result, and sends the command to the information processing control system through the wireless data transmission module;
the mobile terminal and the PC are used for monitoring the temperature and humidity and the wind speed data in the curing barn by accessing the cloud server database, calling monitoring videos to observe the conditions of the tobacco leaves, sending control commands to the cloud server, transmitting the control commands to the information processing control system through the wireless data transmission module, and controlling the rotating speed of the fan and the video acquisition system after the information processing control system analyzes the commands.
Further, the baking room is a cuboid, the space is divided into three layers by a baking support, the video acquisition system and the temperature and humidity sensor are respectively installed on each layer of the center of the three layers of supports, and the baking room is provided with a boiler, a fan, an exhaust hole and a smoke exhaust pipe.
In the embodiment of the application, video acquisition system collection device adopts the camera to gather the video, and the camera is installed between the inside tobacco leaf intermediate layer of roast room, and when the timer was regularly to the acquisition time, the camera was together opened with the light source switch, then 5 seconds back camera begin to gather tobacco leaf video image. After the video is collected, the video collection system packs the video data and sends a video data packet to the information processing control system.
In a further implementation manner, the tobacco leaf curing control system is connected with the cloud server by using a 4G communication technology. Namely, the wireless data transmission module adopts a 4G module, and certainly, the wireless data transmission module can also adopt a 5G module.
In the embodiment of the application, the information processing control system adopts a large-capacity ARM processor, the ARM processor is used as a 32-bit high-performance low-cost embedded RISC microprocessor, and the ARM has become the embedded processor with the most extensive application at present. At present, a Cortex-series processor occupies most of middle-high-end product markets, and the ARM high-capacity processor applied in the invention can well meet the requirements of a system on the running performance of a chip.
Furthermore, the information processing control system comprises a key module, a man-machine interaction module and a timer module. The button module is used for setting temperature and humidity and fan rotating speed, and the man-machine interaction module is used for displaying the temperature and humidity in the curing barn. The data processed by the information processing control system comprises data acquired by a sensor, video data uploaded by a video acquisition system and commands issued by a cloud server.
Fig. 2 is a flowchart of an intelligent tobacco leaf curing control method based on the internet of things and deep learning according to an embodiment of the present invention, and the embodiment of the present invention provides an intelligent tobacco leaf curing control method based on the internet of things and deep learning, which is implemented in the above-mentioned intelligent tobacco leaf curing control system based on the internet of things and deep learning, and includes the following steps:
s1, a video acquisition system acquires video data and transmits the video data to an information processing control system, and then the video data is uploaded to a cloud server through a wireless data transmission module;
s2, the cloud server performs format conversion on the uploaded video data to obtain pictures with uniform formats;
further implemented, the step S2 includes the following sub-steps: s21: carrying out deduplication processing on the uploaded picture video data; s22: extracting frames of video data into a plurality of pictures; s23: converting the picture data into a uniform format according to the configuration;
s3, carrying out depth image recognition on the picture and dividing the tobacco leaves into a color changing period, a color fixing period, a stem drying period and other four types;
specifically, in the step S3, the deep learning residual error model is used to identify the tobacco leaves, the camera module is used to collect video images of the tobacco leaves, the video images are uploaded to the cloud server, the identification process is as shown in fig. 3, image data is input, feature extraction is performed on the images by using a depth recognition feature extraction algorithm, then the extracted feature values are classified by using a softmax classifier, and finally, the tobacco leaf identification classification results are output.
In further implementation, the feature extraction algorithm structure block of step S3 is shown in fig. 4, where the structure block is one of the structure blocks of the classification network identification algorithm, the feature extraction algorithm is composed of at least three of the structure blocks, and the algorithm is composed of 1 × 1, 3 × 3, 5 × 5 convolution and pooling for feature extraction on tobacco leaves. And the Softmax is used for classifying the characteristic values extracted from the tobacco leaves according to four types of a color changing period, a color fixing period, a tendon drying period and the like.
S4, when the identification result is a color changing period, a color fixing period and a tendon drying period, matching corresponding temperature and humidity in the curing barn according to the flue-cured tobacco curing process, sending a command of the rotating speed of the fan, sending the command to the wireless data transmission module through the cloud server, sending the command to the information processing control system, and controlling the rotating speed of the fan by the information processing control system after analyzing the command;
and S5, when the cloud server identifies that the result is other, an abnormal command needs to be sent to the mobile terminal to remind baking control personnel to take corresponding emergency measures.
Furthermore, the information processing control system collects data from the sensor, uploads video data to the cloud server, and analyzes, processes, stores and displays commands of the cloud server;
the analysis process is as follows: the information processing control system mainly analyzes commands issued by the cloud server, the information processing control system analyzes data packets according to a command analysis protocol, the data packets are specifically of the following formats { ID (x) }, type (0-4) { op (0-1), data () } }, wherein the ID specifically corresponds to a baking room, the commands are classified according to type0, type1, type2 and type3, wherein type0 corresponds to a circulating fan, type1 corresponds to a temperature and humidity controller, type2 corresponds to a video acquisition system, and type3 corresponds to an exhaust fan; op corresponds to the opening and closing of the device (0 for closed, 1 for open), and data represents specific corresponding data.
And (3) storing: the information processing system mainly stores data which are real-time temperature and humidity and fan rotating speed monitoring data, the timer is set to collect the temperature, the humidity and the fan rotating speed every 5 minutes, and the collected data are stored in the information processing control system.
Displaying: the information processing control system human-computer interaction module comprises a key and a display screen, baking technicians can display the temperature and humidity inside the baking room and the rotating speed of the fan by controlling the display screen, meanwhile, historical temperature and humidity stored inside the system can be inquired, and the rotating speed of the fan is used for evaluating the internal baking condition.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The utility model provides an intelligence tobacco flue-curing control system based on thing networking and deep learning which characterized in that, this system includes:
the information acquisition comprises a video acquisition system for acquiring tobacco leaf images, a temperature and humidity sensor for acquiring the temperature and humidity in the curing barn and a fan rotating speed sensor for acquiring the fan rotating speed in the curing barn;
the information processing control system is connected with the video acquisition system through a 485 bus, and the temperature and humidity sensor and the fan rotating speed sensor are directly connected with the information processing control system;
the wireless data transmission module is used for connecting the cloud server and the information processing control system and uploading data of the information processing control system to the cloud server through the wireless data transmission module;
the cloud server receives the video data from the wireless data transmission module and the data of the temperature, humidity and fan rotating speed sensors, performs depth identification on video data frames, sends a command to the mobile terminal according to an identification result, and sends the command to the information processing control system through the wireless data transmission module;
the mobile terminal and the PC are used for monitoring the temperature and humidity and the wind speed data in the curing barn by accessing the cloud server database, calling monitoring videos to observe the conditions of the tobacco leaves, sending control commands to the cloud server, transmitting the control commands to the information processing control system through the wireless data transmission module, and controlling the rotating speed of the fan and the video acquisition system after the information processing control system analyzes the commands.
2. The intelligent tobacco leaf baking control system based on the Internet of things and deep learning of claim 1 is characterized in that the baking room is a cuboid, the space is divided into three layers by baking supports, a video acquisition system and a temperature and humidity sensor are respectively installed on each layer of the center of the three layers of supports, and the baking room is provided with a boiler, a fan, an exhaust hole and a smoke exhaust pipe.
3. The intelligent tobacco leaf curing control system based on the internet of things and the deep learning of claim 1 is characterized in that a human-computer interaction module is arranged on the information processing control system.
4. The control method of the intelligent tobacco leaf curing control system based on the Internet of things and deep learning according to claim 4, wherein the wireless data transmission module adopts a 4G module.
5. An intelligent tobacco leaf curing control method based on the internet of things and deep learning is characterized in that the method is realized in the intelligent tobacco leaf curing control system based on the internet of things and deep learning of claim 1, and the method comprises the following steps:
s1, a video acquisition system acquires video data and transmits the video data to an information processing control system, and then the video data is uploaded to a cloud server through a wireless data transmission module;
s2, the cloud server performs format conversion on the uploaded video data to obtain pictures with uniform formats;
s3, carrying out depth image recognition on the picture and dividing the tobacco leaves into a color changing period, a color fixing period, a stem drying period and other four types;
s4, when the identification result is a color changing period, a color fixing period and a tendon drying period, matching corresponding temperature and humidity in the curing barn according to the flue-cured tobacco curing process, sending a command of the rotating speed of the fan, sending the command to the wireless data transmission module through the cloud server, sending the command to the information processing control system, and controlling the rotating speed of the fan by the information processing control system after analyzing the command;
and S5, when the cloud server identifies that the result is other, an abnormal command needs to be sent to the mobile terminal to remind baking control personnel to take corresponding emergency measures.
6. The method according to claim 5, wherein in the step S3, the image recognition uses a deep learning residual model to identify the tobacco leaves, namely: firstly, preprocessing an image, then extracting the characteristics of the image by a characteristic extraction algorithm, then classifying the extracted characteristic values by using a softmax classifier, and finally outputting a tobacco leaf identification and classification result.
7. The method according to claim 6, wherein the feature extraction algorithm is composed of at least three structure blocks consisting of 1 x 1, 3 x 3, 5 x 5 convolutions and a pooling module for feature extraction of the tobacco leaf images captured during the curing.
8. The method according to claim 6, wherein the Softmax classification network classifies the extracted characteristic values of the tobacco leaves according to a color change period, a color fixing period, a tendon drying period and other four categories.
9. The method of claim 5, wherein the wireless data transmission module is a 4G module.
10. The method of claim 5, wherein the information processing control system comprises a sensor for collecting data, uploading video data to a cloud server, and parsing, processing, storing and displaying commands of the cloud server;
the analysis process is as follows: the information processing control system mainly analyzes commands issued by the cloud server, the information processing control system analyzes data packets according to a command analysis protocol, the data packets are specifically of the following formats { ID (x) }, type (0-4) { op (0-1), data () } }, wherein the ID specifically corresponds to a baking room, the commands are classified according to type0, type1, type2 and type3, wherein type0 corresponds to a circulating fan, type1 corresponds to a temperature and humidity controller, type2 corresponds to a video acquisition system, and type3 corresponds to an exhaust fan; op corresponds to the opening and closing of the device (0 for closed, 1 for open), and data represents specific corresponding data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911200525.9A CN110771940A (en) | 2019-11-29 | 2019-11-29 | Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911200525.9A CN110771940A (en) | 2019-11-29 | 2019-11-29 | Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110771940A true CN110771940A (en) | 2020-02-11 |
Family
ID=69393260
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911200525.9A Pending CN110771940A (en) | 2019-11-29 | 2019-11-29 | Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110771940A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111213900A (en) * | 2020-03-12 | 2020-06-02 | 北京优创新港科技股份有限公司 | Intelligent image analysis automatic control system and method for tobacco leaf baking |
| CN114002977A (en) * | 2021-10-22 | 2022-02-01 | 珠海格力电器股份有限公司 | Control method and device of tobacco drying unit, electronic equipment and storage medium |
| CN114115394A (en) * | 2021-11-08 | 2022-03-01 | 中国农业科学院烟草研究所 | Intelligent tobacco leaf baking control system and method |
| CN115462550A (en) * | 2022-10-24 | 2022-12-13 | 西昌学院 | Tobacco leaf baking control method and device, electronic equipment and readable storage medium |
| CN116821104A (en) * | 2022-08-18 | 2023-09-29 | 南通泽烁信息科技有限公司 | Industrial Internet data processing method and system based on big data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106509969A (en) * | 2016-11-29 | 2017-03-22 | 福建省烟草公司三明市公司 | Online intelligent tobacco curing system based on cloud platform and curing process |
| US20170083948A1 (en) * | 2015-09-22 | 2017-03-23 | International Business Machines Corporation | Managing electronic olfactory systems |
| CN206292605U (en) * | 2016-12-02 | 2017-06-30 | 福建省烟草公司三明市公司 | A kind of online image recognition control system of tobacco flue-curing house |
-
2019
- 2019-11-29 CN CN201911200525.9A patent/CN110771940A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170083948A1 (en) * | 2015-09-22 | 2017-03-23 | International Business Machines Corporation | Managing electronic olfactory systems |
| CN106509969A (en) * | 2016-11-29 | 2017-03-22 | 福建省烟草公司三明市公司 | Online intelligent tobacco curing system based on cloud platform and curing process |
| CN206292605U (en) * | 2016-12-02 | 2017-06-30 | 福建省烟草公司三明市公司 | A kind of online image recognition control system of tobacco flue-curing house |
Non-Patent Citations (1)
| Title |
|---|
| 刘震等: "基于卷积神经网络的图像编辑传播", 《浙江工业大学学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111213900A (en) * | 2020-03-12 | 2020-06-02 | 北京优创新港科技股份有限公司 | Intelligent image analysis automatic control system and method for tobacco leaf baking |
| CN111213900B (en) * | 2020-03-12 | 2022-01-25 | 北京优创新港科技股份有限公司 | Intelligent image analysis automatic control system and method for tobacco leaf baking |
| CN114002977A (en) * | 2021-10-22 | 2022-02-01 | 珠海格力电器股份有限公司 | Control method and device of tobacco drying unit, electronic equipment and storage medium |
| CN114002977B (en) * | 2021-10-22 | 2023-12-08 | 珠海格力电器股份有限公司 | Control method and device of tobacco dryer unit, electronic equipment and storage medium |
| CN114115394A (en) * | 2021-11-08 | 2022-03-01 | 中国农业科学院烟草研究所 | Intelligent tobacco leaf baking control system and method |
| CN116821104A (en) * | 2022-08-18 | 2023-09-29 | 南通泽烁信息科技有限公司 | Industrial Internet data processing method and system based on big data |
| CN115462550A (en) * | 2022-10-24 | 2022-12-13 | 西昌学院 | Tobacco leaf baking control method and device, electronic equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110771940A (en) | Intelligent tobacco leaf baking control system and method based on Internet of things and deep learning | |
| CN105867245B (en) | A kind of electric power information monitoring system | |
| CN107318534B (en) | Intelligent temperature control method and system of sunlight greenhouse curtain rolling machine with temperature prediction function | |
| CN104729174A (en) | Heat pump drying controller for crop drying and control method thereof | |
| CN113002816B (en) | Intelligent control method for flue-cured tobacco curing process | |
| CN206292605U (en) | A kind of online image recognition control system of tobacco flue-curing house | |
| CN107229838A (en) | A kind of collecting method and data acquisition unit for medical monitoring equipment | |
| CN213092145U (en) | Intelligent management measurement and control system for plant growth | |
| CN112914141B (en) | Intelligent tobacco leaf baking and collecting system based on Internet of things | |
| CN107589769B (en) | Intelligent beehive control system and method | |
| CN114355857A (en) | Intelligent control system, method, medium, equipment and terminal for tobacco leaf baking | |
| CN114331979A (en) | Intelligent decision method and system for tobacco leaf baking | |
| CN212809020U (en) | Intelligent flue-cured tobacco control system device based on image recognition and environmental parameters of tobacco curing room | |
| CN107045304A (en) | Intelligent hatching system | |
| CN114868948A (en) | Tobacco leaf intelligent baking dynamic management method | |
| CN108444056A (en) | Central air-conditioning monitoring system and its monitoring method | |
| CN114115394A (en) | Intelligent tobacco leaf baking control system and method | |
| CN115047821A (en) | Intelligent monitoring system for live pig breeding environment and evaluation method thereof | |
| CN114997463A (en) | Flue-cured tobacco process stage prediction method and system based on model fusion | |
| CN113535233B (en) | Artificial intelligence system for heating and ventilation cloud edge cooperation | |
| CN206270728U (en) | A kind of grape disease identifying system | |
| CN112651273A (en) | AI intelligent camera tracking method | |
| CN209330740U (en) | A kind of early warning system based on machine vision Citrus Huanglongbing pathogen | |
| CN110209228A (en) | One kind controlling switchgear house monitoring system and method based on mobile terminal | |
| CN115616329A (en) | Five little casees fault recognition system of transformer substation based on convolutional neural network |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200211 |
|
| WD01 | Invention patent application deemed withdrawn after publication |