NBIOT-based gobi tomato greenhouse intelligent measurement and control system and method

Abstract

The invention discloses a gobi tomato greenhouse tuyere intelligent measurement and control system and method based on NBIOT, the tomato greenhouse tuyere intelligent measurement and control system is designed according to a six-domain infrastructure of the Internet of Things, and the method comprises the following steps: collecting greenhouse environment and tomato growth information which are completed by a tomato greenhouse intelligent monitoring system; collecting greenhouse internal and external environment data in the current environment and growth data in the current tomato growth state; and judging the growth period, selecting the regulation and control model and generating prediction data by a greenhouse control system, judging the growth period according to the tomato growth data, selecting the control model and generating the prediction data. According to the system and the method, greenhouse microenvironment prediction and system control are performed according to different growth periods and current environment data of tomatoes, so that the data transmission capability and the greenhouse environment control effect are effectively improved, the greenhouse environment change better meets the growth requirements of the tomatoes, and the yield and the quality of the tomatoes are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural informatization, and more specifically to an intelligent measurement and control system and method for Gobi tomato greenhouses based on NBIoT. Background Art

[0002] With the rapid development of NBIoT in the agricultural field, measurement and control systems relying on NBIoT wireless transmission have gradually been widely used.

[0003] Tomatoes play a crucial role among bulk vegetables in China. Tomato greenhouse cultivation is a high-yield and efficient agricultural production method. Currently, greenhouse tomatoes have become the main cultivation mode in the market, and greenhouse intelligent control has gradually become an important part of the intelligent management of tomato planting.

[0004] The environmental data sensed by the current greenhouse environment monitoring system is gradually becoming more refined and intelligent, but the control system has poor adaptability and mostly uses threshold control or complex model control, which is not suitable for actual production. A scientific and reliable intelligent measurement and control system for greenhouse tomatoes is of great significance for reducing management costs, increasing tomato yields, achieving regular market supply, and enhancing the economic benefits of tomatoes. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent measurement and control system and method for Gobi tomato greenhouses based on NBIoT.

[0006] In the first aspect of the present invention, an intelligent measurement and control method for Gobi tomato greenhouses based on NBIoT is provided. The method includes: collecting greenhouse environment and tomato growth information; judging the growth period, selecting a regulation model, and generating prediction data; adjusting the greenhouse environment in real time according to the prediction data; and providing intelligent early warning data feedback.

[0007] In combination with the first aspect, in the first possible implementation manner, the collection of greenhouse environment and tomato growth information includes the internal and external environment information of the greenhouse and the growth information of tomatoes in different growth periods. The internal greenhouse environment information includes air temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, and soil conductivity; the external greenhouse environment information includes air temperature and humidity, light intensity, carbon dioxide concentration, wind speed, and wind direction; the tomato growth information includes plant height, stem diameter, leaf area, fruit transverse diameter, and fruit longitudinal diameter.

[0008] Combined with the first possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, the determination of the growth period, the selection of the regulation model, and the generation of the prediction data include: the tomato growth information collected by the tomato greenhouse environment monitoring system is used to determine the tomato growth period. The control stage is divided into three periods: the seedling stage, the flowering and fruit-setting stage, and the fruiting stage. With the goal of increasing tomato yield, achieving regular market supply, and enhancing the economic benefits of tomatoes, the flowering and fruit-setting stage and the fruiting stage are selected for analysis. Relying on the determined growth period, the greenhouse control models under different growth periods are selected. The regulation models include the tomato flowering and fruit-setting period air vent closing regulation model, the tomato flowering and fruit-setting period air vent opening regulation model, the tomato fruiting period air vent closing regulation model, and the tomato fruiting period air vent opening regulation model.

[0009] Further, after the air vent is closed, the temperature change in the greenhouse is relatively stable during the day. The main heat source in the greenhouse at night comes from the heat accumulation during the day, and it is also affected by the change of the outside air temperature and the characteristics of the tomatoes themselves. Therefore, the indoor temperature drop amplitude at night is mainly affected by the base temperature when the air vent is closed and the change of the outside air temperature. The tomato flowering and fruit-setting period air vent closing regulation model includes the regression equation of the base temperature (y g ) when the air vent is closed during the tomato flowering and fruit-setting period and the highest outdoor temperature (α1) of the current day, the lowest outdoor temperature (α3) in the early morning of the next day, and the lowest indoor temperature (α4) in the early morning of the next day:

[0010] y g = 0.0267α3 2 + 0.0267α4 2 - 0.0534α3·α4 + 0.3899α1 + 0.3723α3 + 4.5821 (1)

[0011] Further, the regulation model when the air vent is closed during the tomato fruiting period includes the regression equation obtained from the base temperature (y g ) when the air vent is closed during the tomato fruiting period, the highest outdoor temperature (α1) of the current day, the highest indoor temperature (α2) of the current day, the lowest outdoor temperature (α3) in the early morning of the next day, and the lowest indoor temperature (α4) in the early morning of the next day:

[0012] y g = 0.5154α1 - 0.2122α2 - 0.4156α4 + 16.8337 (2)

[0013] Further, the base temperature when the air vent is opened is affected by the night temperature accumulation, the change of the external environment, and the requirements of tomato growth and development. The regulation model when the air vent is opened during the tomato flowering and fruit-setting period includes: the base temperature (y k) The regression equation obtained from the highest outdoor temperature on the same day (β1), the highest indoor temperature on the same day (β2), the lowest indoor temperature in the early morning on the same day (β3), and the lowest outdoor temperature in the early morning on the same day (β4) is as follows:

[0014]

[0015] Further, the regulation model when the air vent is opened during the tomato fruiting period includes the basic temperature (y k ) The regression equation obtained from the highest outdoor temperature on the same day (β1), the highest indoor temperature on the same day (β2), the lowest indoor temperature in the early morning on the same day (β3), and the lowest outdoor temperature in the early morning on the same day (β4) is as follows:

[0016] y k = 0.8136β1 - 0.6106β2 + 2.633β3 - 0.8136β4 - 11.722 (4)

[0017] Further, the generation of the prediction includes generating predicted temperature based on the indoor and outdoor environmental data, regulating the greenhouse environment in real time according to the prediction data, selecting the predicted greenhouse temperature (Y), indoor humidity (X1), indoor illuminance (X2), soil temperature (X3), soil humidity (X4), outdoor temperature (X5), outdoor humidity (X6), outdoor illuminance (X7), outdoor wind speed (X8), outdoor wind direction (X9), and 10 environmental factors. After standardizing the data, collinearity and correlation analysis are carried out. After obtaining the relationship model, a T-test is performed on the model to obtain the relationship model:

[0018] Y = 0.447X1 + 0.455X2 + 0.306X3 + 0.640X5 + 0.035X6 + 0.043X7 - 0.063X8 - 0.032X9 - 0.015X4 3 + 0.00524 (5)

[0019] Combined with the second possible implementation manner of the first aspect, in the third possible implementation manner of the first aspect, the greenhouse environment is regulated in real time according to the prediction data. The prediction data is obtained every 5 minutes. Based on the prediction and relying on the tomato flowering and fruit - setting period air vent closing regulation model, tomato flowering and fruit - setting period air vent opening regulation model, tomato fruiting period air vent closing regulation model, and tomato fruiting period air vent opening regulation model, the opening, closing, and opening degree of the air vent are controlled.

[0020] Combined with the third possible implementation of the first aspect, in the fourth possible implementation of the first aspect, the intelligent warning data feedback, including intelligent warning data feedback, is completed relying on the intelligent warning system and transmitted through NBIoT. The intelligent warning system is deployed in the upper computer software. The upper computer software generates logs by using the operation data of the tomato greenhouse intelligent monitoring system and the greenhouse control system, and detects the system operation status every 5 minutes to determine whether the operation is normal. The intelligent warning system adds Hall current sensors and Hall switch sensors to detect whether the issued instructions are successfully executed. If not, the control instructions are resent.

[0021] The second aspect of the application provides an intelligent measurement and control system for Gobi tomato greenhouses based on NBIoT. The system includes a tomato greenhouse intelligent monitoring system, a tomato greenhouse intelligent control system and an upper computer system.

[0022] Combined with the second aspect, in the first possible implementation of the second aspect, the data collected by the tomato greenhouse intelligent monitoring system includes indoor and outdoor environmental information, crop growth information, system control information and warning information, including air temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, soil conductivity, wind speed, wind direction, Hall current sensor, Hall counter signal, plant height, stem diameter, leaf area, fruit transverse diameter, fruit longitudinal diameter, etc.

[0023] Combined with the first possible implementation of the second aspect, in the second possible implementation of the second aspect, the tomato greenhouse intelligent control system realizes control by relying on control instructions to control the forward and reverse rotation of the air vent motor, including controlling the opening and closing of the greenhouse air vent and the opening degree. In addition, Hall current sensors and Hall switch sensors are used to detect whether the control is successful and timely feedback the control status.

[0024] Combined with the second possible implementation of the second aspect, in the third possible implementation of the second aspect, after the upper computer system receives the growth information collected by the greenhouse intelligent monitoring system, it selects a greenhouse regulation model, and based on the indoor and outdoor environmental information, historical environmental information and the air vent switch control model of different growth periods of tomatoes, sends control instructions and feedback detection instructions to realize the control of the opening and closing time of the greenhouse air vent; based on the indoor and outdoor environmental information, predicted environmental information and the real-time regulation of the opening degree of the greenhouse air vent in different growth periods of tomatoes, to realize the overall control of the greenhouse air vent.

[0025] The technical effects and advantages of the present invention:

[0026] 1. Based on the different growth stages of tomatoes and the current environmental data, it makes predictions and system controls for the greenhouse microenvironment, and transmits data through NBIOT, effectively improving the data transmission ability and the greenhouse environment control effect, making the greenhouse environment change more in line with the growth needs of tomatoes, thereby effectively improving the yield and quality of tomatoes.

[0027] 2. The research and development of the intelligent measurement and control system for Gobi tomato greenhouses is based on the "Internet of Things six-domain model" and combines precise monitoring technology and intelligent regulation models to achieve intelligent measurement and control of greenhouse air vents. After verification, the system operates stably and reliably, can create a more suitable environment for the growth of greenhouse tomatoes, significantly reduce the labor intensity and labor costs, and provide support for the digitalization of the tomato industry. Brief Description of the Drawings

[0028] Figure 1 It is a flowchart of the method described in this application;

[0029] Figure 2 It is a schematic diagram of the intelligent monitoring system for tomato greenhouses described in this application;

[0030] Figure 3 It is a schematic diagram of the intelligent control system for tomato greenhouses described in this application. Detailed Embodiments

[0031] Next, the technical solutions in the embodiments of the present invention will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] Next, this solution will be elaborated in combination with the drawings and specific embodiments.

[0033] Figure 1 A method and system for intelligent measurement and control of Gobi tomato greenhouses based on NBIOT provided by an embodiment of this application, the method includes:

[0034] S1. Collect greenhouse environment and tomato growth information.

[0035] Collecting greenhouse environment and tomato growth information includes collecting greenhouse environment information and tomato growth information. The collection of greenhouse environment information includes collecting greenhouse interior environment information and greenhouse exterior environment information. The interior environment information of the greenhouse includes indoor air temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, soil conductivity; the exterior environment information of the greenhouse includes outdoor air temperature and humidity, light intensity, carbon dioxide concentration, wind speed, wind direction; the tomato growth information includes plant height, stem diameter, leaf area, fruit transverse diameter, fruit longitudinal diameter.

[0036] Specifically, the overall system information collection and intelligent control are realized by combining the upper computer and the lower computer. After the system is powered on, the system program will be automatically activated, and the real-time monitoring of the whole growth season, all-round and all-weather of greenhouse tomatoes and the intelligent control of the greenhouse air vents will start.

[0037] By setting a cyclic acquisition instruction in the lower computer, an acquisition instruction is sent to the acquisition card every 5 minutes. After the sensors collect the indoor and outdoor environmental data and tomato growth data, the data is transmitted back to the main control unit of the lower computer through the acquisition card. The lower computer transmits the data to the upper computer through the NBIOT communication module and stores it in the database. Then, the upper computer calls the data model to analyze the indoor and outdoor environmental data and growth data, and sends control instructions to the control unit of the lower computer according to the analysis results. Finally, the lower computer transmits the instructions through the acquisition card to achieve the purpose of control.

[0038] S2. Judge the growth period, select the regulation model, and generate prediction data.

[0039] The judgment of the growth period in S2 includes judging whether the tomato is in the seedling stage, flowering and fruit-setting stage or fruiting stage based on the tomato growth information transmitted back by the lower computer, combined with the planting time and days. After determining different growth periods, different growth models are selected for different growth periods.

[0040] The selection of the regulation model in S2 includes: the regulation model when the air vent is closed during the flowering and fruit-setting period of tomatoes, the regulation model when the air vent is closed during the fruiting period of tomatoes, the regulation model when the air vent is opened during the flowering and fruit-setting period of tomatoes, and the regulation model when the air vent is opened during the fruiting period of tomatoes.

[0041] The establishment of the greenhouse air vent switch model for different growth periods of tomatoes needs to consider the impact of the greenhouse air vent switch time on tomato growth. After the air vent is closed, the temperature change in the greenhouse is relatively stable during the day and night. The main source of heat in the greenhouse at night is the heat accumulation during the day, and it is also affected by the change of the external air temperature and the characteristics of the tomatoes themselves. Therefore, the temperature drop amplitude in the greenhouse at night is mainly affected by the base temperature when the air vent is closed and the change of the external air temperature. The selection of the greenhouse air vent closing time is based on the lowest suitable growth temperature that tomatoes can withstand at each growth stage. Therefore, in this paper, the base temperature (y g ) when the air vent is closed during the flowering and fruit-setting period of tomatoes, the highest outdoor temperature (α1) of the day, the highest indoor temperature (α2) of the day, the lowest outdoor temperature (α3) in the early morning of the next day, and the lowest indoor temperature (α4) in the early morning of the next day are selected for multiple regression analysis. After performing correlation analysis, the highest outdoor temperature (α1) of the day and the base temperature (y g ) when the air vent is closed, the outdoor temperature difference of the day (α1 - α3), and the square of the indoor and outdoor minimum temperature difference (α4 - α3) 2, the temperature difference (α2 - α1) between the highest indoor and outdoor temperatures is significantly correlated. After T-test and F-test, the model is corrected, and the regulation model when the air vent is closed during the tomato flowering and fruit-setting period is as follows:

[0042] y g = 0.0267α3 2 + 0.0267α4 2 - 0.0534α3·α4 + 0.3899α1 + 0.3723α3 + 4.5821 (6)

[0043] Similarly, select the base temperature (y g ) when the air vent is closed during the tomato fruiting period, the highest outdoor temperature (α1) on the same day, the highest indoor temperature (α2) on the same day, the lowest outdoor temperature (α3) in the early morning of the next day, and the lowest indoor temperature (α4) in the early morning of the next day for correlation analysis. Select the variables with significant correlation to participate in model analysis and correction. After T-test and F-test, the regulation model when the air vent is closed during the tomato fruiting period is as follows:

[0044] y g = 0.5154α1 - 0.2122α2 - 0.4156α4 + 16.8337 (7)

[0045] Among them, the base temperature when the air vent is opened is affected by night temperature accumulation, external environment changes, and the requirements of tomato growth and development. The tomato flowering and fruit-setting period air vent opening model conducts correlation analysis on the base temperature (yk) when the air vent is opened, the highest outdoor temperature (β1) on the same day, the highest indoor temperature (β2) on the same day, the lowest indoor temperature (β3) in the early morning of the same day, and the lowest outdoor temperature (β4) in the early morning of the same day. After selecting the variables with significant correlation to participate in model analysis and correction, and after T-test and F-test, the regulation model when the air vent is opened during the tomato flowering and fruit-setting period is as follows:

[0046]

[0047] Similarly, analyze the opening time of the greenhouse air vent during the tomato fruiting period. By conducting correlation analysis on the base temperature (y k ) when the air vent is opened, the highest outdoor temperature (β1) on the same day, the highest indoor temperature (β2) on the same day, the lowest indoor temperature (β3) in the early morning of the same day, and the lowest outdoor temperature (β4) in the early morning of the same day. After selecting the variables with significant correlation to participate in model analysis and correction, and after T-test and F-test, the regulation model when the air vent is opened during the tomato fruiting period is as follows:

[0048] y k = 0.8136β1 - 0.6106β2 + 2.633β3 - 0.8136β4 - 11.722 (9)

[0049] The generation of prediction data described in S2 includes the generation of greenhouse temperature change data. The natural ventilation of the greenhouse is mainly affected by the combined action of wind pressure and thermal pressure. Wind pressure is mainly affected by the combined influence of wind speed and wind direction, and thermal pressure mainly changes with heat. Therefore, 10 environmental factors, namely indoor temperature, indoor humidity, indoor illuminance, soil temperature, soil humidity, outdoor temperature, outdoor humidity, outdoor illuminance, outdoor wind speed, and outdoor wind direction, are selected as the main environmental factors for tomato greenhouse ventilation research. The greenhouse temperature (Y) is selected as the dependent variable, and 9 variables, namely indoor humidity (X1), indoor illuminance (X2), soil temperature (X3), soil humidity (X4), outdoor temperature (X5), outdoor humidity (X6), outdoor illuminance (X7), outdoor wind speed (X8), and outdoor wind direction (X9), are selected as independent variables for correlation and multicollinearity tests.

[0050] Table 1 Correlation Coefficient Test among Environmental Factors

[0051]

[0052] Table 2 Multicollinearity Test Results

[0053]

[0054] From the VIF values, it is found that all the independent variables participating in the test are less than 10, indicating that there is no multicollinearity among this group of variables. Therefore, a regression model can be directly established between the variables.

[0055] Through multiple linear regression analysis, a greenhouse temperature prediction model is obtained. Significance tests and T-tests are carried out on each environmental factor, and the environmental factors that do not meet the tests are corrected. After correction, the prediction model of multiple linear regression is:

[0056] Y = 0.447X1 + 0.455X2 + 0.306X3 + 0.640X5 + 0.035X6 + 0.043X7 - 0.063X8 - 0.032X9 - 0.015X4 3 + 0.00524 (10)

[0057] Prediction data is generated through the above multiple linear regression prediction model, and based on this prediction data as the target, the opening degree of the air vents is controlled.

[0058] S3 Real-time regulation of the greenhouse environment according to the prediction data. Based on the prediction data, the size of the greenhouse air vents is regulated in real time. Based on the predicted temperature of the air vent switch model, the air vent switch is controlled. The control of the air vent is mainly achieved by the upper computer sending control instructions to the lower computer. The lower computer controls the pin level of the control chip to pull up and pull down, so as to control the opening and closing of the air vent and its opening degree.

[0059] S4 Intelligent Early Warning and Data Feedback. The lower computer uses Hall current sensors and Hall counting sensors to record the motor reception and movement conditions after the control command is issued. The Hall current sensor records the command sending and receiving conditions. If the feedback is 0, the retransmission program is started to send the control command again until the reception is successful. If the feedback is 1, the Hall counting sensor is used to record the number of motor rotations, and the packed fields are transmitted back to the lower computer, and then the lower computer transmits them to the upper computer system through the NBIOT communication module. After the upper computer stores the data in the database by field, it feeds back the information that the information storage is completed to the lower computer.

[0060] The composition of a Gobi tomato greenhouse intelligent measurement and control system based on NBIOT follows the architecture model of the Internet of Things' six domains, including the target object domain, perception and control domain, service provision domain, operation and maintenance management domain, resource exchange domain, and user domain.

[0061] The target object domain mainly includes two types: environmental perception objects and intelligent control objects. The perception objects, as shown Figure 2 mainly include environmental factors inside and outside the greenhouse, such as air temperature and humidity, light intensity, CO2 concentration, etc.; the intelligent control objects, as shown Figure 3 mainly include control factors such as the switch state of the ventilation openings in the greenhouse, the air volume of the openings, the opening degree of the openings, and the limit of the openings. The perception and control domain, as a collection of software and hardware systems for perception objects and control objects, is the core part of the Internet of Things system. It realizes functions such as data perception, intelligent control, NBIOT wireless transmission, intelligent storage, and data preprocessing of target objects through the Internet of Things main control system, and transmits perception data, control data, and system operation data to the service provision domain through the data service interface, and provides remote management and interface services for other domains. Service Provision Domain. The service provision domain provides basic services for the Internet of Things. The service provision domain processes the data from the device side and jointly provides data services by visualization tools such as servers, databases, upper computers, data processing systems, mobile phone APPs, and Web terminals, realizing data sending and receiving, data processing, data storage, and providing functions such as data management, service management, and business management. User Domain. The user domain provides services for greenhouse users and greenhouse administrators. According to different user needs, the system permissions are divided into four categories: greenhouse users, greenhouse administrators, management services, and comprehensive management, which is convenient for users to use the service system.

[0062] Analyzing from the system structure, the Gobi tomato greenhouse intelligent measurement and control system can be divided into two parts: the upper computer and the lower computer. The lower computer includes the tomato greenhouse intelligent monitoring system and the tomato greenhouse intelligent control system.

[0063] As shown Figure 2As shown in the figure, the intelligent monitoring system of the tomato greenhouse is responsible for monitoring the internal and external environment of the greenhouse. Its main control unit uses the MSP430F5438A single-chip microcomputer, equipped with an ADAM Advantech acquisition card and a hub with the SDI-12 protocol. After receiving the acquisition instruction, it realizes the acquisition of the internal and external environment information of the greenhouse through signals such as current type, voltage type, 485 protocol output type, and pulse type. After collecting the environmental data, it transmits the data packet back to the lower computer, and transmits the collected data packet to the upper computer through the NBIOT communication module by the main control unit.

[0064] As Figure 3 shown in the figure, the intelligent control system of the tomato greenhouse is responsible for controlling the opening and closing of the greenhouse air vents and their opening degrees. After its main control unit receives the control instruction sent by the upper computer, it realizes the high and low setting of the pin level through the level conversion chip SN74LVC4245. A total of 8 control circuits are designed for the control chip, and the external solid-state DC relay (SSR) amplifies the DC control circuit. 4 solid-state relays are set to control the normally open and normally closed states of motor 1 (M1) and motor 2 (M2) respectively. The solid-state DC relays are externally connected to electromagnetic relays and controllers respectively to realize the opening and closing of the 220V voltage, and the interlock of the normally open and normally closed states of the air vents is realized by using the suction and closing principle of the controller. The forward and reverse rotations of the DC motor are realized through a transformer. At the same time, travel switches and manual control switches are set to prevent abnormal operation trajectories of the air vents, forming a triple protection mechanism for the air vent control.

[0065] In the design of the tomato greenhouse control system, when the Hall current sensor detects that the motor has working current, it returns a signal to the main control unit MSP4305438A through the A / D circuit conversion. At the same time, the Hall switch sensor records the number of positive and reverse rotations of the air vent according to the principle of electromagnetic induction. After the main control unit receives the feedback information, the data transmission system transmits the data to the upper computer and the database, and then the upper computer makes a judgment on the control status according to the greenhouse air vent regression model.

[0066] After receiving the environmental data collected by the lower computer, the upper computer realizes the intelligent control of the greenhouse air vents by calling the Python control model. The user interface of the Qt application is responded to through the Signal&Slot mechanism. When the signal is triggered, the slot, as the receiving and processing function, will receive the triggered signal and give corresponding operations. After receiving the data information and status information, the upper computer is connected to the MYSQL database through settings. After parsing, the data is stored in the specified data table ws_data. By analyzing the greenhouse environmental data and control data, different types of data and the collection time are stored in the ws_data data table. The data uploaded by the lower computer is of string type, and in the upper computer, the data is set to VARCHAR(20) type and the time is set to DateTime type. The upper computer realizes the control of the opening and closing of the greenhouse air vents and the opening degree by calling the regulation models for closing the air vents during the tomato flowering and fruit-setting periods, the regulation models for opening the air vents during the tomato fruit-setting period, and the prediction model.

[0067] In this implementation, the data transmission between the upper computer and the lower computer is completed by the NBIOT wireless communication module. The communication module establishes communication with the lower computer through the RS485 serial port and accesses the national communication network through the wireless network to realize the data interaction between the lower computer and the upper computer.

[0068] In this implementation, the upper computer is built in the Alibaba Cloud server.

[0069] Based on the temperature change, the intelligent measurement and control system and method for the gobi tomato greenhouse described in this implementation selects environmental factors such as the highest outdoor temperature on the current day, the highest indoor temperature on the current day, the lowest outdoor temperature in the early morning of the next day, the lowest indoor temperature in the early morning of the next day, the lowest outdoor temperature in the early morning of the current day, and the lowest indoor temperature in the early morning of the current day to establish a multiple regression model to predict the base temperature when opening / closing the air vents, determine the opening and closing time of the greenhouse air vents, and output the desired temperature. At the same time, based on the indoor and outdoor environmental factors and with the desired temperature as the goal, a greenhouse temperature prediction model is established to gradually adjust the opening degree of the greenhouse air vents. The verification results show that the air vent regulation model effectively adjusts the day-night temperature difference in the tomato greenhouse and makes the daily temperature change more in line with the growth requirements of tomatoes.

[0070] The research and development of the intelligent measurement and control system for the gobi tomato greenhouse described in this implementation is based on the "Internet of Things six-domain model" and combines precise monitoring technology and intelligent regulation models to realize the intelligent measurement and control of the greenhouse air vents. After verification, the system runs stably and reliably, can create a more suitable environment for the growth of greenhouse tomatoes, significantly reduces the labor intensity and labor cost, and provides support for the digitalization of the tomato industry.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entity operations. Moreover, the terms "comprising", "including" or variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0072] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent measurement and control method for Gobi tomato greenhouse based on NBIOT, characterized in that: The method comprises: Collect greenhouse environment and tomato growth information; Determine the growth period, select the regulation model, and generate forecast data; Real-time control of greenhouse environment based on forecast data; Intelligent early warning data feedback.

2. According to claim 1, a Gobi tomato greenhouse intelligent measurement and control system and method based on NBIOT is characterized by: Relying on the tomato greenhouse environmental monitoring system, the greenhouse environment information, outdoor environment information and tomato growth information are collected, including: The greenhouse environment information includes air temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, and soil conductivity; The greenhouse external environment information includes air temperature and humidity, light intensity, carbon dioxide concentration, wind speed, and wind direction; The tomato growth information includes plant height, stem thickness, leaf area, horizontal stems of the fruit, and vertical stems.

3. The Gobi tomato greenhouse intelligent measurement and control method based on NBIOT according to claim 2 is characterized in that: Determine the growth period, select the control model, generate forecast data, and rely on the greenhouse control system to complete, including: The tomato growth period is determined based on the collected tomato growth information, and the control stage is divided into three periods: seedling stage, flowering and fruiting stage, and fruiting stage. Based on the determined growth period, the greenhouse control model under different growth periods is selected; The air vent closing control model for tomato flowering and fruiting period includes the basic temperature (y g ) with the highest outdoor temperature of the day (α1), the lowest outdoor temperature of the next morning (α3), and the lowest indoor temperature of the next morning (α4): y g ＝0.0267α3 2 +0.0267α4 2 -0.0534α3·α4+0.3899α1+0.3723α3+4.5821 (1) The control model for closing the air vents during the tomato fruiting period includes the basic temperature (y g ), the highest outdoor room temperature on that day (α1), the highest indoor temperature on that day (α2), the lowest outdoor temperature in the early morning of the next day (α3), and the lowest indoor temperature in the early morning of the next day (α4). The regression equation is: y g ＝0.5154α1-0.2122α2-0.4156α4+16.8337 (2) The regulation model when the air outlet is opened during the tomato flowering and fruiting period includes: the basic temperature (y k ), the highest outdoor temperature of the day (β1), the highest indoor temperature of the day (β2), the lowest indoor temperature in the early morning of the day (β3), and the lowest outdoor temperature in the early morning of the day (β4). The regression equation is: The regulation model when the air vent is opened during the tomato fruiting period includes the basic temperature (y k ), the highest outdoor temperature of the day (β1), the highest indoor temperature of the day (β2), the lowest indoor temperature in the early morning of the day (β3), and the lowest outdoor temperature in the early morning of the day (β4). The regression equation is: y k ＝0.8136β1-0.6106β2+2.633β3-0.8136β4-11.722 (4) After judging the tomato growth period based on the tomato growth information collected by the tomato greenhouse environment monitoring system, the control model is selected, and control instructions are generated based on the control model. The control instructions are sent by the main control unit to the control module to control the vent switch.

4. The method for intelligent measurement and control of Gobi tomato greenhouse based on NBIOT according to claim 1, characterized in that: The greenhouse environment is controlled in real time according to the predicted data, including: using the environmental information collected by the tomato greenhouse environmental monitoring system, selecting the predicted greenhouse temperature (Y), indoor humidity (X1), indoor light intensity (X2), soil temperature (X3), soil humidity (X4), outdoor temperature (X5), outdoor humidity (X6), outdoor light intensity (X7), outdoor wind speed (X8), and outdoor wind direction (X9) 10 environmental factors, standardizing the data, and performing collinearity and correlation analysis. After obtaining the relationship model, the model is subjected to T test to obtain the relationship model: Y=0.447X1+0.455X2+0.306X3+0.640X5+0.035X6+0.043X7-0.063X8-0.032X9-0.015X4 3 +0.00524 (5)。 5. The method for intelligent measurement and control of Gobi tomato greenhouse based on NBIOT according to claim 4 is characterized in that: Relying on the relationship model, the temperature in the greenhouse is predicted. After obtaining the predicted data, the size of the air vent switch is adjusted by referring to the highest / lowest temperature of the greenhouse predicted by right 3. If the temperature is too high, the air vent is increased. If the temperature is too low, the air vent is decreased. The control command is generated based on the control model and sent to the control module by the main control unit to control the size of the air vent.

6. The method for intelligent measurement and control of Gobi tomato greenhouse based on NBIOT according to claim 4 is characterized in that: The greenhouse environment is regulated in real time according to the predicted data, and the predicted data is obtained every 5 minutes. Relying on the prediction and the closed control model of the tomato flowering and expiration vents, the opened control model of the tomato flowering and expiration vents, the closed control model of the tomato fruiting period vents, and the opened control model of the tomato fruiting period vents, the opening and closing of the vents and the size of the opening are controlled.

7. The Gobi tomato greenhouse intelligent measurement and control system and method based on NBIOT according to claim 1, characterized in that: Intelligent early warning data feedback is completed based on the intelligent early warning system and NBIOT transmission. The intelligent early warning system is deployed in the host computer software. The host computer software generates logs of the operating data of the tomato greenhouse intelligent monitoring system and the greenhouse control system, and sets a detection of the system operating status every 5 minutes to determine whether the operation is normal; the intelligent early warning system adds Hall current sensors and Hall switch sensors to detect whether the issued instructions are successfully executed. If not, the control instructions are resent.

8. An intelligent measurement and control system for Gobi tomato greenhouse based on NBIOT, characterized in that: include: Greenhouse intelligent monitoring system, greenhouse control system, greenhouse environment control system, intelligent early warning system, including: The greenhouse intelligent monitoring system is completed: collecting the environmental data inside and outside the greenhouse under the current environment and the growth data of the current tomato growth status; Greenhouse control system: Determine the growth period and select the control model based on tomato growth data to generate prediction data; Greenhouse environment control system: after receiving the prediction data, generating control instructions to control the greenhouse environment; The intelligent early warning system has been completed, which monitors the operating status of the tomato greenhouse intelligent monitoring system and greenhouse control system in real time, monitors and controls the data, and provides data feedback for emergency data warnings.

9. The Gobi tomato greenhouse intelligent measurement and control system based on NBIOT according to claim 8, characterized in that: The greenhouse intelligent monitoring system adopts an MSP430F5438A single-chip microcomputer as the main control unit, and is equipped with an ADAM Advantech acquisition card and an SDI-12 protocol hub. The main control unit communicates with NBIOT, adopts the protocol to realize long-distance data transmission, and sends the environmental data collected in the tomato greenhouse to the server for storage; after receiving the collection instruction, the environmental information inside and outside the greenhouse is collected through current type, voltage type, 485 protocol output type and pulse type signals. After the environmental data is collected, the data packet is transmitted back to the lower computer, and the collected data packet is transmitted to the upper computer through the main control unit through the NBIOT communication module.

10. The Gobi tomato greenhouse intelligent measurement and control system based on NBIOT according to claim 9, characterized in that: The greenhouse control system transmits the control command sent by the host computer through NBIOT, which is received by the MSP4305438A main control unit, and the voltage of the pin level is set high or low through the level conversion chip SN74LVC4245. At the same time, a travel switch and a manual control switch are set to prevent abnormal operation trajectory of the air outlet, forming a triple protection mechanism for air outlet control.

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