CN115442405A - Wisdom agricultural production management service system - Google Patents
Wisdom agricultural production management service system Download PDFInfo
- Publication number
- CN115442405A CN115442405A CN202211073273.XA CN202211073273A CN115442405A CN 115442405 A CN115442405 A CN 115442405A CN 202211073273 A CN202211073273 A CN 202211073273A CN 115442405 A CN115442405 A CN 115442405A
- Authority
- CN
- China
- Prior art keywords
- internet
- cloud platform
- data
- greenhouse
- module
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/72409—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by interfacing with external accessories
- H04M1/72415—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by interfacing with external accessories for remote control of appliances
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/7243—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/10—Small scale networks; Flat hierarchical networks
- H04W84/12—WLAN [Wireless Local Area Networks]
Abstract
The invention relates to the technical field of intelligent agriculture, in particular to an intelligent agricultural production management service system; the system comprises an information acquisition unit, a control module, a data transmission and remote management module, an Internet of things cloud platform, a deep learning model prediction module and an application terminal; the intelligent agricultural production management service system combines deep learning with super-strong learning capacity with the greenhouse, provides the most suitable environmental conditions for healthy growth of crops by predicting various environmental factors, is based on the Internet of things, surrounds agricultural modern production management, and provides the intelligent agricultural production management service system so as to achieve the purposes of high efficiency, high yield, intelligence and automatic control.
Description
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to an intelligent agricultural production management service system.
Background
In recent years, the modern development of agriculture in China is accelerated, and the development of the agriculture of the Internet of things has remarkable effect. The technology of the internet of things has a lot of applications in the agricultural field, and the aspects of agricultural product safety management, agricultural production environment monitoring and the like are covered. Nowadays, the number of greenhouses is rapidly increased year by year, but the degree of intellectualization and informatization is still low, the traditional manual mode is mainly relied on, the environmental data of the greenhouses need to be checked regularly, and light, temperature, humidity and irrigation and fertilization are manually adjusted according to the experience of agricultural production in the past.
In summary, the development of an intelligent agricultural production management service system remains a key problem to be solved urgently in the technical field of intelligent agriculture.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent agricultural production management service system, which combines deep learning with super-high learning capacity with a greenhouse, provides the most suitable environmental conditions for healthy growth of crops by predicting various environmental factors, provides the intelligent agricultural production management service system around agricultural modern production management on the basis of the Internet of things, achieves the purposes of high efficiency, high yield, intelligence and automatic control, can be used for checking the detected real-time data condition directly through a PC (personal computer) end or an APP (application) end, can be used for remotely operating various controllers in the greenhouse to make corresponding protective measures such as shading, irrigation, ventilation, cooling and the like, realizes the intelligent and fine management of agricultural planting, and improves the planting efficiency.
In order to realize the purpose, the invention provides the following technical scheme:
the invention provides an intelligent agricultural production management service system which comprises an information acquisition unit, a control module, a data transmission and remote management module, an internet of things cloud platform, a deep learning model prediction module and an application terminal, wherein the information acquisition unit comprises a data acquisition module, a data transmission and remote management module, the internet of things cloud platform comprises a data acquisition module, a data transmission and remote management module, a deep learning model prediction module and an application terminal, and the intelligent agricultural production management service system comprises:
the information acquisition unit is used for acquiring real-time environmental factor data in the greenhouse, wherein the real-time environmental factor data comprises illumination intensity, temperature, humidity, air pressure and carbon dioxide concentration data;
the control module is used for acquiring environmental factor data and transmitting the environmental factor data to the Internet of things cloud platform, and automatically controlling equipment, and is connected with the information acquisition unit;
the data transmission and remote management module transmits data to the Internet of things cloud platform through WIFI and receives feedback information of the deep learning model prediction module, and the data transmission and remote management module is connected with the control module;
the Internet of things cloud platform is used for reading and displaying data of the information acquisition unit, remotely and automatically or manually controlling corresponding equipment of the greenhouse and displaying an environment variable predicted value, and is connected with the data transmission and remote management module through an MQTT protocol;
the deep learning model prediction module is connected with the Internet of things cloud platform through an MQTT protocol, receives data of an information acquisition unit in real time, sends a prediction result of an environmental factor to the Internet of things cloud platform according to a trained LSTM prediction model, reports the prediction result to the control module through cloud circulation of the Internet of things cloud platform for judgment and control, and is connected with the Internet of things cloud platform and the data transmission and remote management module;
the application terminal is used for remotely receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling the corresponding equipment of the greenhouse, and is connected with the cloud platform of the Internet of things.
The invention is further provided with: the information acquisition unit includes carbon dioxide sensor, baroceptor and temperature and humidity sensor, wherein:
the carbon dioxide sensor is used for detecting the concentration of carbon dioxide in the greenhouse in real time and is connected with the control module;
the air pressure sensor is used for detecting the air pressure value in the greenhouse in real time and is connected with the control module;
the temperature and humidity sensor is used for detecting the temperature and the humidity in the greenhouse in real time and is connected with the control module.
The invention is further arranged as follows: the information acquisition unit further comprises an illumination intensity sensor, the illumination intensity sensor is used for detecting the illumination intensity in the greenhouse in real time, and the illumination intensity sensor is connected with the control module.
The invention is further provided with: the model of the control module is STM32F407ZGT6.
The invention is further provided with: the data transmission and remote management module is an esp8266 module.
The invention is further provided with: the Internet of things cloud platform is an Ali cloud platform.
The invention is further provided with: the deep learning model prediction module is an LSTM long-term and short-term memory network as a prediction model.
The invention is further arranged as follows: the application terminal comprises an APP terminal and a PC terminal, wherein:
the system comprises an APP end and a cloud platform of the Internet of things, wherein the APP end is used for movably receiving and displaying real-time data and predicted values of environmental factors and remotely controlling corresponding equipment of the greenhouse, and is connected with the cloud platform of the Internet of things;
the PC end is used for fixedly receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and is connected with the Internet of things cloud platform.
Advantageous effects
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
(1) The invention combines deep learning with super-strong learning ability with the greenhouse, provides the most suitable environmental conditions for the healthy growth of crops by predicting various environmental factors, and solves the problems of maintaining the relative stability of the growth environment of the crops in the greenhouse by human experience and behavior, huge manpower resource consumption and resource waste;
(2) The intelligent agricultural production management service system is provided based on the Internet of things and surrounds agricultural modern production management, so that the purposes of high efficiency, high yield, intelligence and automatic control are achieved, a user can directly check the detected real-time data condition through a PC (personal computer) end or an APP (application) end, various controllers in a greenhouse can be remotely operated, corresponding protective measures such as shading, irrigation, ventilation, cooling and the like are taken, the intelligent and fine management of agricultural planting is realized, and the planting efficiency is improved.
Drawings
FIG. 1 is a system diagram of an intelligent agricultural production management service system according to the present invention.
The reference numbers in the figures illustrate:
100. an information acquisition unit; 110. a carbon dioxide sensor; 120. an air pressure sensor; 130. a temperature and humidity sensor; 140. an illumination intensity sensor; 200. a control module; 300. a data transmission and remote management module; 400. an Internet of things cloud platform; 500. a deep learning model prediction module; 600. an application terminal; 610. an APP terminal; 620. and a PC terminal.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top/bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like are to be construed broadly, e.g., "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; the two elements can be directly connected or indirectly connected through an intermediate medium, and the two elements can be communicated with each other; the specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Example (b):
as shown in fig. 1, the invention provides an intelligent agricultural production management service system, which includes an information acquisition unit 100, a control module 200, a data transmission and remote management module 300, an internet of things cloud platform 400, a deep learning model prediction module 500, and an application terminal 600, wherein: the information acquisition unit 100 is used for acquiring real-time environmental factor data in the greenhouse, including illumination intensity, temperature, humidity, air pressure and carbon dioxide concentration data; the control module 200 is used for acquiring the environmental factor data and transmitting the environmental factor data to the internet of things cloud platform 400, and automatically controlling the equipment, and the control module 200 is connected with the information acquisition unit 100; the data transmission and remote management module 300 transmits data to the internet of things cloud platform 400 through WIFI and receives feedback information of the deep learning model prediction module 500, and the data transmission and remote management module 300 is connected with the control module 200; the internet of things cloud platform 400 is used for reading and displaying data of the information acquisition unit 100, remotely and automatically or manually controlling corresponding equipment of the greenhouse and displaying an environmental variable predicted value, and the internet of things cloud platform 400 is connected with the data transmission and remote management module 300 through an MQTT protocol; the deep learning model prediction module 500 is connected with the internet of things cloud platform 400 through an MQTT protocol, receives data of the information acquisition unit 100 in real time, sends a prediction result of an environmental factor to the internet of things cloud platform 400 according to a trained LSTM prediction model, reports the prediction result to the control module 200 through cloud circulation of the internet of things cloud platform 400 for judgment and control, and is connected with the internet of things cloud platform 400 and the data transmission and remote management module 300; the application terminal 600 is used for remotely receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the application terminal 600 is connected with the internet of things cloud platform 400.
In this embodiment, the information acquisition unit 100 is disposed in the greenhouse, the information acquisition unit 100 performs data detection on the environment in the greenhouse, the detection information includes light intensity, temperature, humidity, air pressure and carbon dioxide concentration data, the detected information is related to the growth of crops in the greenhouse, the detected information is transmitted to the control module 200 through WIFI after being collected, then the change of the environmental factor at the next moment is predicted by combining the training result of the deep learning model prediction module 500 with the environmental variable in the current greenhouse, and meanwhile, the real-time value and the predicted value can be watched on the internet of things cloud platform 400, the state of the greenhouse is manually controlled or the automatic mode is selected for control through the internet of things cloud platform 400, in addition, the information received by the internet of things cloud platform 400 can also be transmitted to the application terminal 600, and a manager can check and control the corresponding equipment in the greenhouse through the application terminal 600. The intelligent control problem of the greenhouse is effectively solved, and the intelligent control method has important significance and good engineering practical application value for increasing the yield of crops and promoting the intelligent automatic development of agriculture in China.
In the present invention, the information collecting unit 100 includes a carbon dioxide sensor 110, an air pressure sensor 120, and a temperature and humidity sensor 130, wherein: the carbon dioxide sensor 110 is used for detecting the concentration of carbon dioxide in the greenhouse in real time, and the carbon dioxide sensor 110 is connected with the control module 200; the air pressure sensor 120 is used for detecting the air pressure value in the greenhouse in real time, and the air pressure sensor 120 is connected with the control module 200; the temperature and humidity sensor 130 is used for detecting the temperature and the humidity in the greenhouse in real time, and the temperature and humidity sensor 130 is connected with the control module 200.
In addition, the information acquisition unit 100 further includes an illumination intensity sensor 140, the illumination intensity sensor 140 is used for detecting the illumination intensity in the greenhouse in real time, and the illumination intensity sensor 140 is connected to the control module 200.
In this embodiment, adopt high accuracy baroceptor 120, temperature and humidity sensor 130, carbon dioxide sensor 110 and illumination intensity sensor 140, gather indoor real-time environment factor data, information includes illumination intensity, temperature, humidity, atmospheric pressure and carbon dioxide concentration information, transmits it to control module 200, can realize the collection to vegetation relevant data.
As one sensor type, the model of the air pressure sensor 120 is BMP280, the model of the temperature and humidity sensor 130 is SHT30, the model of the carbon dioxide sensor 110 is CCS811, and the model of the light intensity sensor 140 is BH1750.
In the present invention, the control module 200 is model number STM32F407ZGT6.
In this embodiment, the control module 200 receives the information of the information collecting unit 100, and can control the corresponding devices in the greenhouse, as an implementation manner, if the control module 200 sends an instruction to the stepping motor and the stay wire displacement sensor, the control module controls the stepping motor to realize the forward and reverse rolling function, and when the rolling curtain rises or falls to a certain extent, the rolling curtain touches the stay wire displacement sensor, so as to cause the rolling curtain to stop moving.
In the present invention, the data transmission and remote management module 300 is an esp8266 module.
In this embodiment, the data of the information acquisition unit 100 is transmitted to the internet of things cloud platform 400 through the esp8266 serial port in the MQTT communication protocol.
In the present invention, the internet of things cloud platform 400 is an ali cloud platform.
In the embodiment, the Aliskiren cloud is simple in configuration, individuals and enterprises are open, the specification parameters of the examples are more detailed, the millet cloud platform is complex in configuration, only the enterprises are open, and the cloud platform lacks limiting parameters for data circulation on the cloud, so that the Aliskiren cloud platform is adopted in the invention.
In the present invention, the deep learning model prediction module 500 is an LSTM long-short term memory network as the prediction model.
In this embodiment, the LSTM long-short term memory network is a recurrent neural network that is a variation of RNN, and introduces cell states based on RNN, and can determine which states should be retained and which states should be forgotten according to the cell states, so as to solve the gradient disappearance problem to some extent. And the BP neural network has the problems of low convergence speed, local minimization, weak prediction capability and training capability and the like. The greenhouse data volume is redundant, and the greenhouse data volume only has an effect when the data volume has an obvious variation trend, so that the information among the data can not be completely mined when the gentle data is predicted, and the greenhouse data volume is not suitable for processing the greenhouse data; the RNN neural network introduces time sequence, can predict future events according to the previous data, but easily causes the problem of gradient disappearance when processing long-sequence data; in the aspect of predicting trend and precision, the LSTM long-term and short-term memory network is superior to a BP neural network and an RNN recurrent neural network in a prediction model.
As an implementation mode, if the acquisition time is 1-12 months in 2022, due to the fact that greenhouse environment factor conversion is small, if the acquisition time is too short, data are too much and many data are highly consistent, the data are acquired once every 5min, the data are divided into a training set and a testing set according to the proportion of 2. Therefore, for the missing data with small time difference before and after, a linear function is adopted to express the relation between the data:
In the present invention, the application terminal 600 includes an APP end 610 and a PC end 620, wherein: the APP end 610 is used for movably receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the APP end 610 is connected with the Internet of things cloud platform 400; the PC end 620 is used for fixedly receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the PC end 620 is connected with the Internet of things cloud platform 400.
In this embodiment, both the APP end 610 and the PC end 620 may receive information of the internet of things cloud platform 400, and a manager may view real-time data and predicted values through the APP end 610 and the PC end 620.
The invention has the following functions:
1) The method comprises the following steps of obtaining data of various environmental factors and controlling greenhouse equipment:
various sensors such as the high-precision air pressure sensor 120, the temperature and humidity sensor 130, the carbon dioxide sensor 110 and the illumination intensity sensor 140 with high precision are used, various environment variables required by the system are successfully acquired, equipment of the greenhouse can be successfully controlled, and functions such as sprinkling irrigation, ventilation and light supplement can be realized by simulating a real greenhouse.
2) The change of the environmental factor can be predicted:
the growth environment parameters of crops in the greenhouse can be automatically detected in real time, and corresponding hardware equipment is controlled by means of an artificial intelligence control algorithm according to historical data in the growth process of the crops to realize comprehensive decision making, so that factors such as carbon dioxide concentration, temperature and humidity and illumination intensity of the environment in the greenhouse are always kept in the most suitable range of the plants.
3) Visualization of environment variables:
by using the MQTT transmission protocol and the Ali cloud Internet of things platform which have the characteristics of simplicity, strong expandability, high speed, good real-time performance and the like, the history and real-time data of various environments can be checked at the PC webpage end and the mobile phone APP end.
4) Remote control big-arch shelter equipment:
the realization can be at PC webpage end and cell-phone APP end control the hardware equipment of big-arch shelter, carries out equipment control such as sun control, sprinkling irrigation control, air discharge fan control, fluorescent lamp control to the big-arch shelter.
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; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (8)
1. The utility model provides a wisdom agricultural production management service system, its characterized in that includes information acquisition unit (100), control module (200), data transmission and remote management module (300), thing networking cloud platform (400), deep learning model prediction module (500) and application terminal (600), wherein:
the information acquisition unit (100) is used for acquiring real-time environmental factor data in the greenhouse, wherein the real-time environmental factor data comprises illumination intensity, temperature, humidity, air pressure and carbon dioxide concentration data;
the control module (200) is used for acquiring environmental factor data, transmitting the environmental factor data to the Internet of things cloud platform (400), and automatically controlling equipment, and the control module (200) is connected with the information acquisition unit (100);
the data transmission and remote management module (300) transmits data to the Internet of things cloud platform (400) through WIFI, feedback information of the deep learning model prediction module (500) is received, and the data transmission and remote management module (300) is connected with the control module (200);
the Internet of things cloud platform (400) is used for reading and displaying data of the information acquisition unit (100), remotely and automatically or manually controlling corresponding equipment of the greenhouse and displaying an environmental variable predicted value, and the Internet of things cloud platform (400) is connected with the data transmission and remote management module (300) through an MQTT protocol;
the deep learning model prediction module (500) is connected with the Internet of things cloud platform (400) through an MQTT protocol, receives data of the information acquisition unit (100) in real time, sends a prediction result of an environmental factor to the Internet of things cloud platform (400) according to a trained LSTM prediction model, reports the prediction result to the control module (200) for judgment and control through cloud circulation of the Internet of things cloud platform (400), and the deep learning model prediction module (500) is connected with the Internet of things cloud platform (400) and the data transmission and remote management module (300);
the application terminal (600) is used for remotely receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the application terminal (600) is connected with the Internet of things cloud platform (400).
2. The intelligent agricultural production management service system of claim 1, wherein the information acquisition unit (100) comprises a carbon dioxide sensor (110), an air pressure sensor (120) and a temperature and humidity sensor (130), wherein:
the carbon dioxide sensor (110) is used for detecting the concentration of carbon dioxide in the greenhouse in real time, and the carbon dioxide sensor (110) is connected with the control module (200);
the air pressure sensor (120) is used for detecting the air pressure value in the greenhouse in real time, and the air pressure sensor (120) is connected with the control module (200);
the temperature and humidity sensor (130) is used for detecting the temperature and the humidity in the greenhouse in real time, and the temperature and humidity sensor (130) is connected with the control module (200).
3. The wisdom agricultural production management service system of claim 1, wherein the information collection unit (100) further comprises a light intensity sensor (140), the light intensity sensor (140) is used for detecting the light intensity in the greenhouse in real time, and the light intensity sensor (140) is connected with the control module (200).
4. The wisdom agricultural production management service system of claim 1, wherein the control module (200) is model number STM32F407ZGT6.
5. The wisdom agricultural production management service system of claim 1, wherein the data transmission and remote management module (300) is an esp8266 module.
6. The wisdom agricultural production management service system of claim 1, wherein the internet of things cloud platform (400) is an ari cloud platform.
7. The wisdom agricultural production management service system of claim 1, wherein the deep learning model prediction module (500) is a LSTM long and short term memory network as a prediction model.
8. The intelligent agricultural production management service system of claim 1, wherein the application terminal (600) comprises an APP terminal (610) and a PC terminal (620), wherein:
the APP terminal (610) is used for movably receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the APP terminal (610) is connected with the Internet of things cloud platform (400);
the PC end (620) is used for fixedly receiving and displaying real-time data and predicted values of the environmental factors and remotely controlling corresponding equipment of the greenhouse, and the PC end (620) is connected with the Internet of things cloud platform (400).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211073273.XA CN115442405A (en) | 2022-09-02 | 2022-09-02 | Wisdom agricultural production management service system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211073273.XA CN115442405A (en) | 2022-09-02 | 2022-09-02 | Wisdom agricultural production management service system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115442405A true CN115442405A (en) | 2022-12-06 |
Family
ID=84246439
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211073273.XA Pending CN115442405A (en) | 2022-09-02 | 2022-09-02 | Wisdom agricultural production management service system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115442405A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115686110A (en) * | 2023-01-07 | 2023-02-03 | 广州市农业科学研究院 | Greenhouse intelligent control method, system, monitoring device, equipment and medium |
-
2022
- 2022-09-02 CN CN202211073273.XA patent/CN115442405A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115686110A (en) * | 2023-01-07 | 2023-02-03 | 广州市农业科学研究院 | Greenhouse intelligent control method, system, monitoring device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN209517198U (en) | A kind of wisdom agricultural standardization management system | |
| CN205176701U (en) | Intelligence agricultural environment monitored control system based on big data | |
| CN204731617U (en) | A kind of life cycle characteristic analysis system based on greenhouse gardening organic plant | |
| CN104121998B (en) | A kind of temperature pre-warning method in heliogreenhouse ambient intelligence monitoring system | |
| CN205193568U (en) | Wisdom agricultural monitored control system | |
| CN106254476A (en) | Agroecological environment information management based on Internet of Things, big data and cloud computing and monitoring method and system | |
| CN205193529U (en) | Intelligent greenhouse control system | |
| CN104731135A (en) | Control device and method for family farm | |
| CN206115310U (en) | Intelligent agriculture monitored control system based on thing networking | |
| CN203950191U (en) | A kind of plant supervising device based on mobile device | |
| CN107678410A (en) | It is a kind of towards the intelligent control method of greenhouse, system and controller | |
| CN204790651U (en) | Shrimp environment intelligence control system that grows seedlings based on multisensor information fusion | |
| CN109298684A (en) | A kind of long-distance intelligent plant protection monitoring management system based on cloud platform | |
| CN113273449A (en) | Digital twin body construction method for precise monitoring of sunlight greenhouse | |
| CN105159366A (en) | Greenhouse environment monitoring system and user behavior acquisition method | |
| CN205281296U (en) | Vegetation environment monitor control system | |
| CN109213240A (en) | A kind of strawberry greenhouse wireless monitor and control system based on self adaptive control | |
| CN115442405A (en) | Wisdom agricultural production management service system | |
| CN210375194U (en) | Green house environmental monitoring system based on multisensor | |
| CN205105253U (en) | Agricultural service system based on mobile internet | |
| CN112130608A (en) | Wisdom big-arch shelter system | |
| CN209002233U (en) | A kind of agricultural Internet of Things low cost low-power consumption overlength distance wireless transmitting system | |
| CN210534608U (en) | Ground meteorological environment monitoring, early warning and controlgear | |
| CN110865668A (en) | Remote monitoring and intelligent decision-making system for facility gardening | |
| Thirukkuralkani et al. | LabVIEW based greenhouse automation system through wireless protocol |
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 |