Intelligent temperature control ventilation system for facility vegetable greenhouse and control method of intelligent temperature control ventilation system

Abstract

The invention relates to the technical field of greenhouse vegetable planting equipment, in particular to a greenhouse vegetable greenhouse intelligent temperature control ventilation system and a control method thereof, and the system comprises a data acquisition module, a data processing module, a control module and a remote monitoring module; the data acquisition module is used for acquiring temperature, humidity and carbon dioxide concentration data inside the greenhouse body and wind speed data outside the greenhouse body; the data processing module is used for comparing the temperature data, the humidity data, the carbon dioxide concentration data and the wind speed data with a temperature threshold value range, a humidity threshold value range, a carbon dioxide concentration threshold value range and a wind speed threshold value respectively to generate a control instruction; the control module is used for controlling operation of the exhaust assembly according to the control instruction; and the remote monitoring module is used for displaying the temperature data, the humidity data and the carbon dioxide concentration data in the greenhouse body to a user by using an APP client. The ventilation effect in the greenhouse body can be adjusted in a self-adaptive mode according to the wind speed outside the greenhouse body.

Description

Technical Field

[0001] This invention relates to the field of greenhouse vegetable planting equipment technology, specifically to an intelligent temperature control and ventilation system for greenhouse vegetables and its control method. Background Technology

[0002] With the continuous advancement of agricultural modernization, greenhouses play a crucial role in ensuring year-round vegetable supply and improving yield and quality. Proper ventilation within the greenhouse significantly promotes photosynthesis, nutrient absorption, and metabolism in vegetables; conversely, inadequate ventilation can lead to slow growth, increased pests and diseases, and even reduced yields.

[0003] In existing technologies, such as the GL-800 intelligent greenhouse ventilation system, there are certain advantages in ventilation. This ventilation system adopts variable frequency speed control technology, which can automatically adjust the fan speed according to the set temperature parameters to achieve ventilation of different air volumes; at the same time, it is equipped with a simple humidity sensor, which can automatically start ventilation when the humidity is too high, thus achieving a certain degree of coordinated ventilation control of temperature and humidity. Compared with traditional manually controlled ventilation equipment, it improves the timeliness and convenience of ventilation.

[0004] Even so, these intelligent ventilation systems for greenhouses still have certain limitations in practical use. They can only perform single-dimensional control based on simple temperature and humidity thresholds, making it difficult to adapt and adjust the ventilation effect inside the greenhouse in conjunction with the external environment. This results in a significant waste of electricity and increases the operating costs of the greenhouse. Therefore, it is necessary to propose an intelligent temperature control and ventilation system for greenhouse vegetables and its control method to solve the problem that existing intelligent ventilation systems for greenhouses can only perform single-dimensional control based on simple temperature and humidity thresholds, making it difficult to adapt and adjust the ventilation effect inside the greenhouse in conjunction with the external environment, thus increasing the operating costs of the greenhouse. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an intelligent temperature control and ventilation system for greenhouse vegetables and its control method. This system is used to adaptively and intelligently regulate the ventilation within the greenhouse by combining external wind speed. When the external wind is strong, ventilation is achieved through the external wind; when the external wind is weak, the greenhouse's own ventilation system is activated.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: an intelligent temperature control and ventilation system for a greenhouse vegetable facility, comprising a greenhouse body, a plurality of diffusion components at the top of the greenhouse body for drawing air from outside the greenhouse body into the greenhouse body and dispersing it evenly, and a plurality of exhaust components at the bottom of the greenhouse body for using wind energy to expel gas from the greenhouse body; and further comprising a data acquisition module, a data processing module, a control module and a remote monitoring module.

[0007] The data acquisition module includes a sensor group installed inside the greenhouse and a wind speed sensor installed on the top of the greenhouse. The sensor group and the wind speed sensor are used to collect temperature data, humidity data, and carbon dioxide concentration data inside the greenhouse, as well as wind speed data outside the greenhouse.

[0008] The data processing module includes a CNN network learning model. The CNN network learning model is used to learn and generate temperature threshold ranges, humidity threshold ranges, and carbon dioxide concentration threshold ranges for different growth stages of different vegetable varieties using growth data of different vegetable varieties. The temperature data, humidity data, carbon dioxide concentration data, and wind speed data are compared with the temperature threshold range, humidity threshold range, carbon dioxide concentration threshold range, and wind speed threshold range, respectively, to generate control commands.

[0009] The control module is used to control the operation of the exhaust components according to control commands.

[0010] The remote monitoring module includes an APP client. The remote monitoring module is used to display temperature, humidity and carbon dioxide concentration data inside the greenhouse to users through the APP client, and users can manually control the operation of the exhaust components through the control module via the APP client.

[0011] The technical principles of the above solution are as follows:

[0012] Inside the greenhouse, a sensor array captures real-time data on temperature, humidity, and carbon dioxide concentration. A wind speed sensor mounted on the top of the greenhouse monitors wind speed outside, providing crucial parameters for subsequent ventilation control based on external conditions. A CNN network learning model compares the real-time collected data on temperature, humidity, and carbon dioxide concentration inside the greenhouse with corresponding threshold ranges, and also compares the wind speed data outside the greenhouse with wind speed thresholds. Based on the comparison results, the model comprehensively determines whether ventilation is needed, how much ventilation is required, and how to coordinate the operation of the diffusion and exhaust components, thereby generating corresponding control commands. An app client displays real-time environmental data inside the greenhouse to the user, allowing them to monitor the vegetable growth environment at any time. When the user finds that the system's automatic control is not meeting expectations, they can manually send commands to the control module through the app client to control the operation of the exhaust components.

[0013] The above approach has the following beneficial effects:

[0014] 1. This invention achieves multi-dimensional control of greenhouse ventilation by introducing carbon dioxide concentration parameters and external wind speed parameters, combined with multi-dimensional threshold ranges generated by a CNN network learning model for different vegetable varieties and growth stages. This makes ventilation control more closely match the actual environmental needs of vegetables at different growth stages, reducing problems such as slow growth and pest and disease proliferation caused by unsuitable environments, thereby improving vegetable yield and quality.

[0015] 2. In this invention, the exhaust component at the bottom of the greenhouse uses wind energy to expel the gas inside the greenhouse. When the outside wind speed is suitable, natural wind can be used to assist in the exhaust, reducing the dependence on electric drive. At the same time, the coordinated work of the diffusion component and the exhaust component, as well as the precise control commands generated by the control module based on real-time data, can avoid unnecessary energy consumption. Compared with traditional intelligent ventilation systems, it is more energy-efficient and reduces the operating cost of the greenhouse.

[0016] 3. The remote monitoring module equipped in this invention displays real-time environmental data inside the greenhouse to users through an APP client, enabling users to keep track of the situation inside the greenhouse at any time. It also allows users to manually intervene when the system's automatic adjustment does not meet expectations, enhancing users' control and participation in the greenhouse ventilation system.

[0017] Furthermore, the data acquisition module includes a sensor group comprising a temperature sensor, a humidity sensor, and a gas concentration sensor.

[0018] Beneficial effects: The sensor array can collect data on temperature, humidity and carbon dioxide concentration inside the greenhouse, providing accurate basic data for system regulation.

[0019] Furthermore, the diffusion component includes an air pipe fixedly connected to the top wall of the shed, the air pipe being connected to the inside of the shed, a wind hood being connected to the bottom end of the air pipe, and several air nozzles being connected to the bottom of the wind hood.

[0020] Beneficial effects: Traditional straight-through ventilation duct designs can cause excessively strong localized wind speeds inside the greenhouse, making it difficult to simulate the gentle breezes of a real natural environment. The diffuser components draw air from outside the greenhouse through the ducts, then evenly and gently spray it into the greenhouse through several nozzles at the bottom. This ensures timely air renewal in all areas of the greenhouse, avoiding problems such as insufficient ventilation or excessive wind speeds in certain areas. This uniform and gentle airflow method more closely resembles a natural wind environment, reducing the direct impact of strong winds on vegetable leaves and plants, and creating a more suitable airflow environment for vegetable growth.

[0021] Furthermore, the exhaust assembly includes a ventilation duct that runs through and is fixedly connected to the side wall of the shed; the ventilation duct includes an inlet pipe, one end of which is connected to a contraction pipe, the other end of which, away from the inlet pipe, is connected to a diffuser pipe, and an intake pipe is connected to the contraction pipe; both the inlet pipe and the diffuser pipe are connected to the outside of the shed.

[0022] An exhaust fan is fixedly connected to the inner wall of the inlet pipe, and the control module is used to control the operation of the exhaust fan.

[0023] Beneficial effects: When a certain amount of airflow is blown into the inlet pipe, after passing through the contraction pipe, the sudden decrease in the diameter of the contraction pipe causes a Venturi effect, resulting in a decrease in air pressure within the contraction pipe and the formation of negative pressure. This negative pressure draws gas from inside the greenhouse into the contraction pipe through the intake pipe, and then, together with the airflow entering through the inlet pipe, it is discharged outside the greenhouse through the diffuser, significantly enhancing the intake and exhaust capabilities of the exhaust system. Simultaneously, the exhaust fan can actively provide power when natural wind is insufficient, ensuring effective exhaust.

[0024] Furthermore, a Tesla valve is connected inside the intake tube.

[0025] Beneficial effects: The Tesla valve has a one-way flow effect, which can accelerate the air intake effect of the air intake pipe, prevent the gas outside the greenhouse from flowing back into the greenhouse through the air intake pipe, ensure the one-way exhaust, and maintain the stability of the environment inside the greenhouse.

[0026] Furthermore, a protective cover for shielding the air pipes is fixedly connected to the top of the shed.

[0027] Beneficial effects: The protective cover can protect the air tube from damage caused by external wind, rain, debris, etc., extend the service life of the air tube, and ensure the normal operation of the diffusion components.

[0028] Furthermore, in the data processing module, the CNN network learning model optimizes the generation of control commands by combining environmental data and ventilation effect data of the same vegetable variety and growth stage from the same historical period.

[0029] Beneficial effects: Makes the generated control commands more scientific and adaptable, and improves the accuracy and effectiveness of ventilation control.

[0030] Furthermore, the shed is equipped with a storage battery. When the exhaust fan is not running, the control module is also used to use wind power to rotate the exhaust fan and charge the storage battery; at the same time, the control module is also used to use the storage battery to drive the exhaust fan.

[0031] Beneficial effects: When there is sufficient natural wind and no need for the exhaust fan to operate actively, the exhaust fan is driven by wind energy to generate electricity, which is stored in the battery, realizing the effective recovery and utilization of wind energy and improving energy efficiency; when there is insufficient natural wind or when the exhaust fan needs to operate but the external power supply is unstable, the battery can serve as a backup power source to provide power to the exhaust fan, ensuring that the exhaust components can work normally under various conditions, enhancing the reliability and endurance of the system, reducing dependence on the external power grid, and further saving operating costs.

[0032] Furthermore, a camera is fixedly connected to the top wall inside the greenhouse. The camera is used to collect image information of the vegetables inside the greenhouse, and users can view the image information of the vegetables inside the greenhouse through an APP client.

[0033] Beneficial effects: The camera can capture images of the growth status, leaf color, and plant shape of vegetables in the greenhouse in real time. Users can intuitively understand the growth of vegetables through the APP client and promptly detect whether the vegetables have pests, diseases, or abnormal growth.

[0034] Furthermore, a control method for an intelligent temperature control and ventilation system in a greenhouse vegetable facility includes the following steps:

[0035] S1, when the wind speed data exceeds the wind speed threshold:

[0036] If at least one of the following conditions is met: temperature data exceeds the temperature threshold range, humidity data exceeds the humidity threshold range, and carbon dioxide concentration data exceeds the carbon dioxide concentration threshold range, the CNN network learning model generates a control command to "ventilate using natural wind". The control module controls the exhaust fan to turn off, and natural wind is used to draw the gas out of the greenhouse through the ventilation duct and exhaust it to the outside of the greenhouse through the diffuser.

[0037] If the temperature data is within the temperature threshold range, the humidity data is within the humidity threshold range, and the carbon dioxide concentration data is within the carbon dioxide concentration threshold range, the CNN network learning model generates a "stop ventilation" control command, and the control module controls the exhaust fan to remain off.

[0038] S2, when the wind speed data is below the wind speed threshold:

[0039] If at least one of the following conditions is met: temperature data exceeds the temperature threshold range, humidity data exceeds the humidity threshold range, and carbon dioxide concentration data exceeds the carbon dioxide concentration threshold range, the CNN network learning model generates a control command to "control the exhaust fan to exhaust air". The control module sets the operating power of the exhaust fan according to the degree to which the temperature data, humidity data, and carbon dioxide concentration data exceed the threshold range.

[0040] If the temperature data, humidity data, and carbon dioxide concentration data are all within the temperature threshold range, a control command for "exhaust fan running at low speed" is generated. The control module then controls the exhaust fan to run at minimum power to maintain a slight airflow inside the greenhouse.

[0041] Beneficial effects: This control method fully integrates the key environmental factor of external wind speed, enabling intelligent switching of ventilation modes. When the external wind speed is high, natural wind is prioritized for ventilation to minimize power consumption, which aligns with energy-saving principles. When the external wind speed is low, the operation of the exhaust fans is flexibly adjusted according to the specific environmental parameters inside the greenhouse, meeting ventilation needs while avoiding energy waste.

[0042] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0043] Figure 1 This is a structural diagram of the intelligent temperature control and ventilation system for vegetable greenhouses according to the present invention.

[0044] Figure 2 This is a side sectional view of the greenhouse structure in the intelligent temperature control and ventilation system for vegetable greenhouses of the present invention.

[0045] Figure 3 for Figure 2 Enlarged view of section A.

[0046] The reference numerals in the accompanying drawings of the instruction manual include: 1. Shelter; 2. Wind speed sensor; 3. Air pipe; 4. Wind hood; 5. Air nozzle; 6. Protective cover; 7. Inlet pipe; 8. Contraction pipe; 9. Diffuser; 10. Intake pipe; 11. Exhaust fan; 12. Tesla valve. Detailed Implementation

[0047] The following detailed description illustrates the specific implementation method:

[0048] Example 1:

[0049] As attached Figure 1 As shown: An intelligent temperature control and ventilation system for a greenhouse vegetable facility includes a greenhouse body 1. The top of the greenhouse body 1 is provided with several diffusion components that draw air from outside the greenhouse body 1 into the greenhouse body 1 and diffuse it evenly. The bottom of the greenhouse body 1 is provided with several exhaust components that use wind energy to expel the gas inside the greenhouse body 1.

[0050] It also includes the following modules: data acquisition module, data processing module, control module, and remote monitoring module.

[0051] The data acquisition module primarily collects temperature, humidity, and carbon dioxide concentration data inside greenhouse 1 using a sensor array, and collects wind speed data outside greenhouse 1 using an anemometer 2. The data processing module uses a CNN network learning model to generate threshold ranges for temperature, humidity, and carbon dioxide concentration at different growth stages of different vegetables, compares the collected data with these thresholds, and generates control commands. The control module controls the exhaust system according to these commands. The remote monitoring module displays relevant data from inside greenhouse 1 to users via an app and allows users to manually control the exhaust system.

[0052] The functions of each module will be explained in detail below:

[0053] Specifically, the data acquisition module includes a sensor group installed inside the shed 1 and a wind speed sensor 2 installed on the top of the shed 1; the sensor group and the wind speed sensor 2 are used to collect temperature data, humidity data and carbon dioxide concentration data inside the shed 1, and wind speed data outside the shed 1, respectively; in the data acquisition module, the sensor group includes a temperature sensor, a humidity sensor and a gas concentration sensor.

[0054] This embodiment takes a tomato greenhouse as an example. First, a sensor group consisting of a temperature sensor, a humidity sensor, and a gas concentration sensor is installed in different areas inside the greenhouse (greenhouse 1). The temperature sensor monitors the temperature of the tomato growing environment in real time; for example, at 10:00 AM, the temperature data inside greenhouse 1 is 28°C. The humidity sensor records the air humidity simultaneously, and the humidity data at this time is 65%. The gas concentration sensor collects the carbon dioxide concentration, and the current carbon dioxide concentration data is 800 ppm. The wind speed sensor 2 installed at the top of greenhouse 1 captures the wind conditions outside greenhouse 1; for example, if the wind speed outside greenhouse 1 is 3 m / s. These real-time acquired data on temperature, humidity, carbon dioxide concentration inside greenhouse 1, and wind speed outside greenhouse 1, are transmitted to the subsequent data processing module, providing the raw data for the intelligent control of the system.

[0055] Specifically, the data processing module includes a CNN network learning model. This model uses growth data from different vegetable varieties to learn and generate temperature, humidity, and carbon dioxide concentration threshold ranges for different growth stages. It then compares the temperature, humidity, carbon dioxide concentration, and wind speed data with these threshold ranges to generate control commands. Within the data processing module, the CNN network learning model optimizes the generation of control commands by incorporating historical environmental and ventilation data for the same vegetable varieties and growth stages.

[0056] In the greenhouse where tomatoes are grown, the CNN network learning model in the data processing module has learned from a large amount of tomato growth data and generated temperature thresholds of 25-30℃, humidity thresholds of 60-70%, and carbon dioxide concentration thresholds of 700-900ppm for tomatoes in the current fruiting stage. At the same time, the wind speed threshold is preset to 2m / s.

[0057] Upon receiving the data from the data acquisition module at 10:00 AM, including the internal temperature data of 28℃, humidity data of 65%, carbon dioxide concentration data of 800ppm, and external wind speed data of 3m / s for greenhouse 1, the CNN network learning model will compare each data point. The temperature data of 28℃ falls within the 25-30℃ temperature threshold range, the humidity data of 65% falls within the 60-70% humidity threshold range, the carbon dioxide concentration data of 800ppm falls within the 700-900ppm threshold range, while the external wind speed of 3m / s for greenhouse 1 is higher than the preset wind speed threshold of 2m / s.

[0058] In addition, the CNN network learning model will also combine ventilation effect data under similar environmental data during the same period of tomato fruiting in history for optimization analysis. It was found that when the wind speed outside the greenhouse is higher than the wind speed threshold, the exhaust component driven by wind energy can achieve a better ventilation effect.

[0059] Specifically, the remote monitoring module includes an APP client. The APP client displays temperature, humidity, and carbon dioxide concentration data inside the greenhouse 1 to the user, and allows the user to manually control the exhaust system via the APP client. A camera is fixed to the inner top wall of the greenhouse 1 with screws. The camera captures images of the vegetables inside the greenhouse 1, which the user can view through the APP client.

[0060] In the greenhouse where tomatoes are grown, the remote monitoring module's APP client receives and displays the environmental data inside greenhouse 1 transmitted by the data acquisition module in real time. For example, at 10:00 a.m., when the user opens the APP client, they can see that the current temperature data inside greenhouse 1 is 28℃, the humidity data is 65%, and the carbon dioxide concentration data is 800ppm. These data are presented intuitively in the form of numbers and charts, making it convenient for users to keep track of the real-time status of the tomato growing environment at any time.

[0061] If users need to adjust the exhaust intensity through the APP client, they only need to find the "Exhaust Component Control" option in the control interface of the APP client, click "Manual Mode", and then slide the adjustment button to select the operating level, thereby controlling the exhaust component to operate according to the level set by the user.

[0062] by Figure 2For example, specifically, the diffusion assembly includes an air pipe 3 that is bolted to the top wall of the shed 1. The air pipe 3 is connected to the interior of the shed 1, and a wind hood 4 is connected to the bottom of the air pipe 3. Several air nozzles 5 are connected to the bottom of the wind hood 4. A protective cover 6 for shielding the air pipe 3 is fixedly connected to the top of the shed 1.

[0063] When air enters from the air duct 3, passes through the wind hood 4, and then enters the greenhouse 1 through the air nozzles 5, the airflow is evenly diffused and its speed is reduced due to the several air nozzles 5 connected to the bottom of the wind hood 4. Traditional straight-through ventilation duct designs can cause excessively strong winds in some areas of the greenhouse 1, making it difficult to simulate the gentle breeze of a real natural environment. The diffuser component introduces air from outside the greenhouse 1 through the air duct 3 into the greenhouse 1, and then evenly and gently sprays it into the greenhouse 1 through the several air nozzles 5 at the bottom. This ensures that the air in all areas of the greenhouse 1 is refreshed in a timely manner, avoiding problems such as insufficient ventilation or excessive wind speed in some areas. This even and gentle airflow method is closer to a natural wind environment, reducing the direct impact of strong winds on vegetable leaves and plants, and creating a more suitable airflow environment for vegetable growth.

[0064] by Figure 2 and Figure 3 For example, specifically, the exhaust assembly includes a ventilation duct that runs through and is bolted to the side wall of the shed 1; the ventilation duct includes an inlet pipe 7, one end of which is connected to a contraction pipe 8, and the end of the contraction pipe 8 away from the inlet pipe 7 is connected to a diffuser pipe 9; an intake pipe 10 is connected to the contraction pipe 8, and the intake pipe 10 is connected to the inside of the shed 1; both the inlet pipe 7 and the diffuser pipe 9 are connected to the outside of the shed 1; an exhaust fan 11 is bolted to the inner side wall of the inlet pipe 7, and a control module is used to control the operation of the exhaust fan 11. A Tesla valve 12 is connected inside the intake pipe 10.

[0065] The shed 1 is equipped with a storage battery. When the exhaust fan 11 is not running, the control module is also used to use wind power to drive the exhaust fan 11 to rotate and charge the storage battery; at the same time, the control module is also used to use the storage battery to drive the exhaust fan 11 to run.

[0066] Combination Figure 2As shown, when natural wind from outside the shed 1 blows into the inlet pipe 7, after passing through the contraction pipe 8, the diameter of the contraction pipe 8 suddenly decreases. At this time, the airflow in the contraction pipe 8 experiences a Venturi effect, causing the air pressure inside the contraction pipe 8 to decrease, creating a negative pressure. This negative pressure draws the gas inside the shed 1 into the contraction pipe 8 through the suction pipe 10, and then, together with the airflow entering through the inlet pipe 7, it is discharged outside the shed 1 through the diffuser pipe 9, creating a negative pressure inside the shed 1. This negative pressure then draws fresh air from outside the shed 1 into the shed 1 through the air pipe 3, achieving the function of using natural wind to drive ventilation and significantly enhancing the intake and exhaust capabilities of the exhaust assembly. Simultaneously, the exhaust fan 11, when natural wind is insufficient, can actively blow airflow into the inlet pipe 7 by rotating, allowing the suction pipe 10 to also draw air from inside the shed 1, ensuring effective exhaust. The Tesla valve 12 inside the suction pipe 10 has a one-way flow effect and an airflow acceleration effect. It can accelerate the suction effect of the suction pipe 10 and prevent the gas outside the shed 1 from flowing back into the shed 1 through the suction pipe 10, ensuring the one-way exhaust and maintaining the stability of the environment inside the shed 1.

[0067] Meanwhile, when there is sufficient natural wind and the exhaust fan 11 does not need to operate actively, the exhaust fan 11 is driven by wind energy to generate electricity, which is then stored in the battery. In this embodiment, the motor driving the exhaust fan 11 is a motor that can both rotate and generate electricity, which is existing technology and will not be elaborated on in this embodiment. This design achieves effective wind energy recovery and utilization, improving energy efficiency. When natural wind is insufficient or the exhaust fan 11 needs to operate but the external power supply is unstable, the battery can serve as a backup power source to power the exhaust fan 11, ensuring that the exhaust assembly can work normally under various conditions, enhancing the system's reliability and endurance, reducing dependence on the external power grid, and further saving operating costs.

[0068] Example 2:

[0069] As attached Figure 2 As shown, the difference from Embodiment 1 is that a control method for an intelligent temperature control and ventilation system for a greenhouse vegetable facility includes the following steps:

[0070] S1, when the wind speed data exceeds the wind speed threshold:

[0071] If at least one of the following conditions is met: temperature data exceeds the temperature threshold range, humidity data exceeds the humidity threshold range, and carbon dioxide concentration data exceeds the carbon dioxide concentration threshold range, the CNN network learning model generates a control command to "ventilate using natural wind". The control module controls the exhaust fan 11 to turn off, and uses natural wind to draw the gas in the shed 1 out through the air intake pipe 10 and discharge it outside the shed 1 through the diffuser pipe 9.

[0072] If the temperature data is within the temperature threshold range, the humidity data is within the humidity threshold range, and the carbon dioxide concentration data is within the carbon dioxide concentration threshold range, the CNN network learning model generates a "stop ventilation" control command, and the control module controls the exhaust fan 11 to remain off.

[0073] S2, when the wind speed data is below the wind speed threshold:

[0074] If at least one of the following conditions is met: the temperature data exceeds the temperature threshold range, the humidity data exceeds the humidity threshold range, and the carbon dioxide concentration data exceeds the carbon dioxide concentration threshold range, the CNN network learning model generates a control command to "control the exhaust fan 11 to exhaust air". The control module sets the operating power of the exhaust fan 11 according to the degree to which the temperature data, humidity data, and carbon dioxide concentration data exceed the threshold range.

[0075] If the temperature data is within the temperature threshold range, the humidity data is within the humidity threshold range, and the carbon dioxide concentration data is within the carbon dioxide concentration threshold range, a control command for "exhaust fan 11 to run at low speed" is generated. The control module controls exhaust fan 11 to run at minimum power to maintain a slight airflow inside the shed 1.

[0076] The specific implementation process is as follows:

[0077] In a greenhouse where tomatoes are grown, under condition S1, where the wind speed exceeds the wind speed threshold (2 m / s):

[0078] If at 10:00 AM, the wind speed outside shed 1 is 3 m / s, and the temperature inside shed 1 is 32℃ (exceeding the 25-30℃ temperature threshold), humidity is 75% (exceeding the 60-70% humidity threshold), and carbon dioxide concentration is 950 ppm (exceeding the 700-900 ppm concentration threshold), satisfying the condition of "at least one exceeding the threshold range," then the CNN network learning model generates a control command to "utilize natural wind for ventilation." The control module will then control the exhaust fan 11 to shut off. Using the natural wind outside shed 1, the high-temperature, high-humidity, and high-carbon dioxide concentration gas inside shed 1 is extracted through the intake pipe 10 and then discharged outside shed 1 through the diffuser pipe 9, thus regulating the environment inside shed 1.

[0079] If the external wind speed of shed 1 is 3 m / s, but the internal temperature of shed 1 is 28℃, the humidity is 65%, and the carbon dioxide concentration is 800 ppm, all within their respective threshold ranges, then the CNN network learning model will generate a "stop ventilation" control command. The control module will then control the exhaust fan 11 to remain off to avoid wasting electrical energy.

[0080] When in situation S2, i.e., the wind speed data is below the wind speed threshold (2m / s):

[0081] Assuming the external wind speed of shed 1 is 1.5 m / s, and the internal temperature data of shed 1 is 31℃ (exceeding the temperature threshold range), humidity data is 68% (within the humidity threshold range), and carbon dioxide concentration is 920 ppm (exceeding the concentration threshold range), satisfying the condition that "at least one of them exceeds the threshold range," the CNN network learning model generates a control command to "control exhaust fan 11 to exhaust air." The control module sets the operating power of exhaust fan 11 based on the degree to which the temperature and carbon dioxide concentration exceed the thresholds, for example, setting the power to 70% of the rated power, to improve the environment inside shed 1 with stronger exhaust force.

[0082] If the wind speed outside greenhouse 1 is 1.5 m / s, and all data inside greenhouse 1 are within the threshold range (temperature 27℃, humidity 63%, carbon dioxide concentration 850 ppm), a control command for "exhaust fan 11 to run at low speed" will be generated. The control module will then control exhaust fan 11 to run at minimum power (e.g., 30% of rated power) to maintain slight airflow inside greenhouse 1 and ensure a comfortable growing environment for tomatoes.

[0083] This invention achieves multi-dimensional control of greenhouse ventilation by introducing carbon dioxide concentration parameters and external wind speed parameters, combined with multi-dimensional threshold ranges generated by a CNN network learning model for different vegetable varieties and growth stages. This makes ventilation control more closely match the actual environmental needs of vegetables at different growth stages, reducing problems such as slow growth and pest and disease proliferation caused by unsuitable environments, thereby improving vegetable yield and quality. Simultaneously, the exhaust assembly at the bottom of the greenhouse 1 utilizes wind energy to expel gas from inside the greenhouse 1. When the external wind speed is suitable, natural wind can assist in exhaust, reducing reliance on electric power. Furthermore, the coordinated operation of the diffusion and exhaust assemblies, along with the precise control commands generated by the control module based on real-time data, avoids unnecessary energy consumption, making it more energy-efficient than traditional intelligent ventilation systems and reducing greenhouse operating costs.

[0084] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A facility vegetable greenhouse intelligent temperature control ventilation system, comprising a greenhouse body (1), characterized in that, The shed body (1) top is equipped with several diffusion assemblies for sucking air outside the shed body (1) into the shed body (1) and uniformly diffusing; the shed body (1) bottom is equipped with several exhaust assemblies for exhausting air in the shed body (1) by using wind energy; further comprising a data acquisition module, a data processing module, a control module and a remote monitoring module; The data acquisition module comprises a sensor group installed inside the shed body (1) and a wind speed sensor (2) installed on the top of the shed body (1); the sensor group and the wind speed sensor (2) are respectively used to collect temperature data, humidity data and carbon dioxide concentration data inside the shed body (1) and wind speed data outside the shed body (1); The data processing module comprises a CNN network learning model; the CNN network learning model is used to learn and generate temperature threshold range, humidity threshold range and carbon dioxide concentration threshold range of different growth stages of different vegetable varieties by using growth data of different vegetable varieties, and compare temperature data, humidity data, carbon dioxide concentration data and wind speed data with temperature threshold range, humidity threshold range, carbon dioxide concentration threshold range and wind speed threshold range respectively to generate control instructions; The control module is used to control the operation of the exhaust assembly according to the control instructions; The remote monitoring module comprises an APP client, and the remote monitoring module is used to show the temperature data, humidity data and carbon dioxide concentration data inside the shed body (1) to the user through the APP client, and the user manually controls the operation of the exhaust assembly through the APP client.

2. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 1, characterized in that, In the data acquisition module, the sensor group comprises a temperature sensor, a humidity sensor and a gas concentration sensor.

3. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 2, characterized in that, The diffusion assembly comprises an air pipe (3) fixedly connected to the top wall of the shed body (1), the air pipe (3) and the shed body (1) are in communication, the bottom end of the air pipe (3) is communicated with a wind cover (4), and the bottom of the wind cover (4) is communicated with a plurality of air nozzles (5).

4. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 3, characterized in that, The exhaust assembly comprises a ventilation duct penetrating through and fixedly connected to the side wall of the shed body (1); the ventilation duct comprises an inlet pipe (7), one end of the inlet pipe (7) is communicated with a contraction pipe (8), the end of the contraction pipe (8) away from the inlet pipe (7) is communicated with a diffusion pipe (9), the contraction pipe (8) is communicated with a suction pipe (10), the suction pipe (10) and the shed body (1) are in communication, and the inlet pipe (7) and the diffusion pipe (9) are both communicated with the outside of the shed body (1); The exhaust fan (11) is fixedly connected to the inner side wall of the inlet pipe (7), and the control module is used to control the operation of the exhaust fan (11).

5. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 4, characterized in that, The suction pipe (10) is communicated with a Tesla valve (12).

6. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 5, characterized in that, The top of the shed body (1) is fixedly connected with a protective cover (6) for shielding the air pipe (3).

7. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 6, characterized in that, In the data processing module, when generating the control instructions, the CNN network learning model is combined with historical same period environmental data and ventilation effect data of the same vegetable variety and growth stage for optimization.

8. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 7, characterized in that, The shed body (1) is provided with a storage battery, when the exhaust fan (11) is not running, the control module is further used to drive the exhaust fan (11) to rotate by using wind energy to charge the storage battery; at the same time, the control module is further used to drive the exhaust fan (11) to run by using the storage battery.

9. The intelligent temperature control ventilation system for facility vegetable greenhouse according to claim 8, characterized in that, The camera is fixedly connected to the inner top wall of the shed body (1) and is used to collect image information of the vegetables in the shed body (1), and the user can view the image information of the vegetables in the shed body (1) through an APP client.

10. A control method of an intelligent temperature control ventilation system for a facility vegetable greenhouse, according to any one of claims 1-9, wherein The method comprises the following steps: S1, when the wind speed data exceeds the wind speed threshold value: If at least one of the temperature data exceeding the temperature threshold range, the humidity data exceeding the humidity threshold range, and the carbon dioxide concentration data exceeding the carbon dioxide concentration threshold range is true, the CNN network learning model generates a control instruction of "utilizing natural wind ventilation", the control module controls the exhaust fan (11) to be closed, and the natural wind is utilized to pass through the ventilation pipeline so that the suction pipe (10) draws the gas in the shed body (1) out of the shed body (1) by the diffusion pipe (9); If the temperature data is in the temperature threshold range, the humidity data is in the humidity threshold range, and the carbon dioxide concentration data is in the carbon dioxide concentration threshold range, the CNN network learning model generates a control instruction of "stopping ventilation", and the control module controls the exhaust fan (11) to be in a closed state; S2, when the wind speed data is lower than the wind speed threshold value: If at least one of the temperature data exceeding the temperature threshold range, the humidity data exceeding the humidity threshold range, and the carbon dioxide concentration data exceeding the carbon dioxide concentration threshold range is true, the CNN network learning model generates a control instruction of "controlling the exhaust fan (11) to exhaust", and the control module sets the operating power of the exhaust fan (11) according to the degree of the temperature data, the humidity data, and the carbon dioxide concentration data exceeding the threshold range; If the temperature data is in the temperature threshold range, the humidity data is in the humidity threshold range, and the carbon dioxide concentration data is in the carbon dioxide concentration threshold range, a control instruction of "low-speed operation of the exhaust fan (11)" is generated, and the control module controls the exhaust fan (11) to operate at the minimum power to maintain the air flow in the shed body (1).

Images