Intelligent ventilation system and method applied to crop planting greenhouse

Abstract

The invention discloses an intelligent ventilation system and method applied to a crop planting greenhouse. The intelligent ventilation system comprises a measurement sensing device, a control communication device, a walking and rolling rod balancing device and a power supply part. Wherein the walking and rolling rod balancing device is an executing mechanism, and opening and closing actions of a greenhouse air opening are achieved; the measurement sensing device collects sensor information such as temperature to sense the environment state, and the information is fused and fed back to the control communication device. The control communication device performs intelligent analysis and judgment according to the environment information, sends a control command and controls the walking and rolling rod balancing device in real time; the power supply part is formed by connecting a solar storage battery pack and a power grid in parallel, the power grid provides power under normal conditions, the storage battery pack independently provides power for the whole system under sudden power failure conditions, and stable operation of the system is guaranteed. Intelligent and automatic ventilation of the greenhouse can be realized, the production efficiency is improved, the labor cost is reduced, and the crop growth quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of greenhouse regulation, and in particular relates to an intelligent ventilation system and method applied to a crop planting greenhouse. Background Art

[0002] The most suitable environmental parameters in the greenhouse will change depending on the crop type, planting season, planting time, etc., and the traditional greenhouse control methods are difficult to meet the dynamic needs of various crops for the greenhouse environment. In order to achieve unmanned greenhouse adaptive control, it is necessary to explore the optimal growth environment for crops in facility agriculture, and establish a knowledge expert database of the optimal growth environment for crops with wide adaptability and strong operability based on expert experience and planting technology manuals to achieve the goal of optimal environmental parameter control and pest and disease prediction and early warning.

[0003] The environmental control of crop growth greenhouses currently in use is basically done manually, that is, relying on experience and feeling plus simple instruments to open the greenhouse vents and other operations to improve the environmental conditions in the greenhouse. However, this model cannot achieve accurate and scientific control effects in large-scale crop growth greenhouses, and it is difficult to maintain the greenhouse environment at the optimal level, which seriously restricts the increase in greenhouse crop yields, and the work efficiency is low and the labor intensity of workers is relatively high. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent ventilation system and method for crop planting greenhouses to solve the problems existing in the above-mentioned prior art.

[0005] On the one hand, to achieve the above-mentioned purpose, the present invention provides an intelligent ventilation system for crop planting greenhouses, comprising:

[0006] The vent control unit comprises a rolling rod and a traveling frame, wherein the traveling frame is installed on the side wall inside the greenhouse, a film is laid on the top of the greenhouse, and the film is wound on the rolling rod, and one end of the rolling rod is transmission-connected to a motor, and the motor is slidably installed above the traveling frame through a gear;

[0007] A control unit, comprising a control cabinet, the control cabinet being installed below the traveling frame, a fuzzy neural network control module being installed in the control cabinet, and the fuzzy neural network control module being electrically connected to the motor;

[0008] A sensor unit, the sensor unit is installed in the greenhouse and is in communication connection with the fuzzy neural network control module;

[0009] A power supply module, the fuzzy neural network control module, the motor and the sensor unit are all electrically connected to the power supply module;

[0010] The cloud platform is communicatively connected with the fuzzy neural network control module.

[0011] Optionally, a plurality of elastic gravity reels are also installed on the rod body of the reel.

[0012] Optionally, a communication module is integrated in the fuzzy neural network control module, and the fuzzy neural network control module is communicatively connected with the cloud platform and the sensor unit through the communication module.

[0013] Optionally, the communication module includes one or more of a LoRa module, a 2.5G communication module, a 3G communication module, a 4G communication module, and a 5G communication module.

[0014] Optionally, an antenna is installed on the control cabinet.

[0015] Optionally, the sensing unit includes a temperature sensor, a humidity sensor, a raindrop sensor, a carbon dioxide concentration sensor and a displacement sensor, and each sensor is communicatively connected to the fuzzy neural network control module.

[0016] Optionally, the power supply module includes a battery, one end of the battery is connected to the power grid, and the other end is electrically connected to the fuzzy neural network control module, the motor and each sensor.

[0017] Optionally, the power supply module further includes a solar panel, and the solar panel is electrically connected to the battery.

[0018] On the other hand, to achieve the above-mentioned purpose, the present invention provides an intelligent ventilation method applied to a crop planting greenhouse, which is applied to the intelligent ventilation system applied to the crop planting greenhouse, comprising:

[0019] Install an intelligent ventilation system in the greenhouse and initialize the intelligent ventilation system;

[0020] After the initialization is completed, the greenhouse is monitored by the sensor unit, and the monitoring data is obtained and transmitted to the fuzzy neural network control module, wherein the monitoring data includes the greenhouse environment monitoring data and the vent control unit position data;

[0021] The fuzzy neural network control module is used to calculate the difference between the monitoring data and the monitoring threshold data in the preset expert knowledge base, the difference is input into a pre-trained fuzzy neural network, and a control scheme of the vent control unit is output, and the air intake of the vent of the greenhouse is controlled based on the control scheme; wherein the fuzzy neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer, a normalization layer and an output layer connected in sequence.

[0022] The technical effects of the present invention are:

[0023] The present invention is provided with a measuring sensing device, a control communication device, a walking and rolling rod balancing device and a power supply part, wherein the walking and rolling rod balancing device is an actuator to realize the opening and closing of the greenhouse air outlet; the measuring sensing device collects sensor information such as temperature to perceive the environmental state, and integrates this information and feeds it back to the control communication device; the control communication device performs intelligent analysis and judgment based on the environmental information, sends control commands, and controls the walking and rolling rod balancing device in real time; the power supply part is composed of a solar battery group connected in parallel with the power grid, which is powered by the power grid under normal circumstances. In the event of a sudden power outage, the battery group independently provides power to the entire system to ensure stable operation of the system. This embodiment can realize the intelligence and automation of greenhouse ventilation, improve production efficiency, reduce labor costs, and improve crop growth quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 It is a schematic diagram of the greenhouse structure in an embodiment of the present invention;

[0026] Figure 2 It is a structural diagram of a greenhouse sensor configuration in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of the intelligent control implementation in the embodiment of the present invention;

[0028] Figure 4 Schematic diagram of a walking and rolling rod balancing device in an embodiment of the present invention;

[0029] Figure 5 Schematic diagram of the system principle in an embodiment of the present invention.

[0030] Explanation of reference numerals: 1. Measuring and sensing device; 2. Control and communication device; 3. Traveling and reeling rod balancing device; 4. Power supply part; 5. Reeling rod; 6. Sensor; 7. Elastic gravity reel; 8. Control cabinet; 9. Antenna; 10. Motor; 11. Traveling frame; 12. Battery pack; 13. Solar panel. DETAILED DESCRIPTION

[0031] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0032] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0033] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] Embodiment 1

[0035] like Figure 1 - Figure 5 As shown, this embodiment provides an intelligent ventilation system for crop planting greenhouses, including:

[0036] The vent control unit includes a rolling rod 5 and a traveling frame 11, wherein the traveling frame 11 is installed on the side wall inside the greenhouse, a film is laid on the top of the greenhouse, and the film is wound on the rolling rod 5, and one end of the rolling rod 5 is connected to a motor 10, and the motor 10 is slidably installed above the traveling frame 11 through gears;

[0037] A control unit, comprising a control cabinet 8, wherein the control cabinet 8 is installed below the traveling frame 11, wherein a fuzzy neural network control module is installed in the control cabinet 8, and wherein the fuzzy neural network control module is electrically connected to the motor 10;

[0038] A sensor unit, the sensor unit is installed in the greenhouse and is in communication connection with the fuzzy neural network control module;

[0039] A power supply module, the fuzzy neural network control module, the motor 10 and the sensor unit are all electrically connected to the power supply module;

[0040] The cloud platform is communicatively connected with the fuzzy neural network control module.

[0041] An intelligent ventilation method applied to a crop planting greenhouse is applied to the intelligent ventilation system applied to the crop planting greenhouse, comprising: installing the intelligent ventilation system in the greenhouse and initializing the intelligent ventilation system; after the initialization is completed, the greenhouse is monitored by a sensor unit, and monitoring data is obtained and transmitted to a fuzzy neural network control module, wherein the monitoring data includes greenhouse environment monitoring data and vent control unit position data; the fuzzy neural network control module is used to calculate the difference between the monitoring data and the monitoring threshold data in a preset expert knowledge base, the difference is input into a pre-trained fuzzy neural network, and a control scheme of the vent control unit is output, and the air intake of the vent of the greenhouse is controlled based on the control scheme; wherein the fuzzy neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer, a normalization layer and an output layer connected in sequence.

[0042] This embodiment discloses an intelligent control method for automatically opening and closing the air outlet of a crop planting greenhouse according to the actual environmental physical conditions. This method involves a measuring sensing device 1, a control communication device 2, a walking and rolling rod balancing device 3, and a power supply part 4. The walking and rolling rod balancing device 3 is an actuator to realize the opening and closing of the greenhouse air outlet; the measuring sensing device 1 collects sensor information such as temperature to sense the environmental state, and integrates this information and feeds it back to the control communication device 2; the control communication device 2 performs intelligent analysis and judgment based on the environmental information, sends a control command, and controls the walking and rolling rod balancing device 3 in real time; the power supply part 4 is composed of a solar battery group 12 connected in parallel with the power grid. Under normal circumstances, the power grid provides power. In the event of a sudden power outage, the battery group 12 independently provides power to the entire system to ensure stable operation of the system. This embodiment can realize the intelligence and automation of greenhouse ventilation, improve production efficiency, reduce labor costs, and improve crop growth quality.

[0043] The intelligent ventilation system for crop-growing greenhouses described in this embodiment includes a measuring sensing device 1, a control communication device 2, a walking and rolling rod balancing device 3, and a power supply part 4.

[0044] The following is an implementation process of this embodiment: in the normal power-on working state, the sensor (temperature sensor / humidity sensor / carbon dioxide sensor / displacement sensor) 6 continuously detects the real-time data of the greenhouse environment and the position data of the equipment walking frame 11 and the winding rod 5. All data are transmitted to the control communication device 2 in the control cabinet 8 via wireless transmission. The information is processed by a pre-programmed software algorithm and sent to the cloud server or host computer via the antenna 9. The cloud server or host computer makes the final judgment and then issues an action instruction.

[0045] The operation process of the electromechanical actuator is as follows: when the control communication device 2 in the control cabinet receives the action command transmitted through the communication antenna 9, the AC / DC motor 10 is started, and the rolling rod 5 starts to rotate under the drive of the motor. The AC / DC motor 10 starts to translate on the walking frame 11 by relying on the engagement of the internal gear and the rack, so that the rolling rod 5 rotates while translating to roll up the plastic film, thereby opening or closing the air outlet. At the same time, the elastic gravity reel 7 connected to the rolling rod maintains the appropriate tension and rigidity of the rolling rod, and helps match the speed of the rolling rod's rotation and translation. Through the above air outlet opening and closing actions, the purpose of adjusting the changes in the physical parameters of the greenhouse environment is achieved.

[0046] When the system is connected to the normal power grid, the power supply part connects the battery pack 12 in parallel with the solar panel 13, and adjusts the energy storage and power supply mode through the control communication device 2 in the control cabinet 8. When the light conditions are good, the solar panel 13 charges the battery pack 12. When the normal power grid suddenly loses power, the system will automatically switch to the battery pack 12 power supply state, and the battery pack will ensure the normal operation of the entire system.

[0047] Table 1 Requirements for the arrangement of each sensor

[0048]

[0049] The intelligent ventilation system of the present embodiment applied to crop greenhouses has a simple structure, high reliability, and low manufacturing cost. It can greatly save manpower, realize remote centralized control and management, and achieve intelligent and automated operation, which is conducive to large-scale promotion and use in agricultural production.

[0050] The specific implementation scheme of this embodiment includes:

[0051] This embodiment discloses an automatic ventilation system that can control the movement of the rolling rod 5 device according to the environmental state in the shed and the experience of experts, and control the size of the air outlet in real time. The system includes a measuring sensor device 1, a control communication device 2, a walking and rolling rod balancing device 3, and a power supply part 4.

[0052] 1. The measuring sensing device 1 collects sensor information such as temperature to sense the environmental status, and integrates this information and feeds it back to the control communication device 2. The function of the measuring sensing device 1 is to receive information from temperature sensors, humidity sensors, raindrop sensors, carbon dioxide concentration sensors, and displacement sensors, detect the ambient temperature, soil moisture, carbon dioxide concentration, and the opening position of the greenhouse vents in the greenhouse, and place 6 to 8 temperature sensors, 5 to 6 carbon dioxide concentration sensors, and 8 to 10 humidity sensors at different locations in the greenhouse according to the length, width, height, and dimensions of the greenhouse. 1 to 2 raindrop sensors are installed in the greenhouse roof film, and 1 displacement sensor is installed on the rack of the walking and rolling rod balance device 3, such as Figure 2 As shown. These signals are of many types and have many points of redundancy. Before the original information of the sensor is processed, comprehensive information analysis is performed to retain as much environmental information as possible. The fused temperature, humidity and carbon dioxide concentration values ​​are transmitted to the control communication device 2 as the basis for its control. The CPU of the measurement sensing device 1 uses a low-power MS P430 single-chip microcomputer.

[0053] 2. The control communication device 2 performs intelligent analysis and judgment based on the environmental information, sends control commands, and controls the walking and rolling rod balancing device 3 in real time. The control communication device 2 compares the received information with the standard parameters of the crop growth expert knowledge base, uses the deviation and the differential of the deviation, and adopts the fuzzy expert control theory to control the action time of the walking and rolling rod balancing device 3 to achieve actions such as full opening, half opening, stopping at any position, and full closing.

[0054] The main environmental factors that affect crop growth and development include: temperature (air temperature inside and outside the greenhouse), light (light quality, light intensity and photoperiod), water (air humidity and soil humidity), soil (soil fertility, chemical composition, physical properties and soil solution reaction), air (the content of oxygen and carbon dioxide in the air and soil, the content of toxic gases, wind speed and atmospheric pressure, etc.), biological factors (soil microorganisms, weeds and pests, and self-shading of crops themselves), etc. These environmental factors do not exist in isolation, but are interconnected. The impact on crop growth and development is often comprehensive. Moreover, for different crops, the degree of influence of various factors is different. Therefore, according to the different types of crops, an expert fuzzy knowledge base has been established. The base contains the optimal values ​​of temperature, humidity and carbon dioxide for different types of crops in different growth periods. The optimal values ​​are different during the germination period, flowering period, young fruit to maturity period, daytime and nighttime. In order to create the best growth environment for different plants, an intelligent analysis and control system has been developed. The main environmental factors that affect crop growth and development include: temperature (air temperature and soil temperature), light (light quality, light intensity and photoperiod), water (air humidity and soil humidity), soil (soil fertility, chemical composition, physical properties and soil solution reaction), air (the content of oxygen and carbon dioxide in the air and soil, the content of toxic gases, wind speed and atmospheric pressure, etc.), biological factors (soil microorganisms, weeds and pests, and self-shading of crops themselves), etc. These environmental factors do not exist in isolation, but are interconnected. The impact on crop growth and development is often comprehensive. Moreover, for different crops, the degree of influence of various factors is different. Even for the same species, the optimal values ​​of its environmental requirements are different during the germination period, flowering period, young fruit to maturity period, daytime and nighttime. Therefore, according to the different types of crops, this patent has established an expert knowledge base, which contains the optimal values ​​of temperature, humidity and carbon dioxide for different fruits such as grapes, nectarines, cherries and different vegetables such as tomatoes, green vegetables, beans, etc. in each growth period. In order to achieve the optimal growth environment goal for different crops, this patent has designed a fuzzy neural network controller.

[0055] The fuzzy neural network controller designed by this patent uses genetic algorithms to optimize the fuzzy neural network controller, overcoming the shortcomings of slow convergence of fuzzy neural networks and easy to fall into local minima. At the same time, combined with expert knowledge, the static target value of the system is set, and the adaptive control effect of the greenhouse is achieved through online learning algorithms. The intelligent control implementation block diagram of this system is shown in the figure below. Figure 3 As shown in the figure, y d (t) is the set value of the system environment parameter, y(t) is the actual value of the system environment parameter, e(t) is the error between the actual value and the set value, and Je is the objective function, which is the square of the expert value and the actual value. 0 , G 1 , G 2 They are the quantization factors of temperature error, humidity error and carbon dioxide, respectively. It transforms the error domain so that any error of the system can be quantified as an element in the domain. Temperature error, humidity error and carbon dioxide error are used as the input of the fuzzy neural network controller. Fuzzy rules are formulated based on expert knowledge, and the static target value of the system is set. The fuzzy neural network controller is trained through the online learning algorithm, and the fuzzy neural network weights are adjusted to minimize the objective function. At the same time, the genetic algorithm is used to adjust the global central parameter a in the fuzzy neural network controller. ij and width parameter b ij The fuzzy neural network controller outputs the fuzzy control quantity u of the actuator * , through the quantization factor G of the output 3 The clear quantity u(t) required for actual control is obtained to drive the actuator to act.

[0056] The fuzzy neural network controller designed in this patent is a 5-layer forward network. The first layer is the input layer, and the input is the language variables temperature error, humidity error and carbon dioxide. The second layer is the fuzzification layer. Each node of this layer represents a language variable value. Among them, the temperature error (T) is described by 6 fuzzy states, namely NB (negative large), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PB (positive large); the humidity error (H) is described by 5 fuzzy states, namely NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large), and the carbon dioxide error (C) is described by 5 fuzzy states, namely NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large). Its function is to calculate the membership function value of each input variable (temperature error, humidity error) belonging to the fuzzy set of each language variable value. The third layer is the fuzzy reasoning layer. Each node of this layer represents a fuzzy control rule. The neural network controller has a total of 30 fuzzy control rules, which are derived from the expert knowledge base. The fourth layer is the normalization layer, and the number of nodes is the same as the third layer, which is used to realize the normalization calculation of the fuzzy neural network to synthesize weighted rules with the same results. The fifth layer is the output layer, which is used to defuzzify and realize the clarification operation. The output control quantity is the control state of the actuator. The actuator driven is the power supply voltage of the motor 10 of the vent plastic cloth rolling rod 5. This voltage is a constant value. When it becomes zero, the motor 10 stops rotating, so it is actually the control of the power-on rotation time.

[0057] Some of the rules are as follows:

[0058] (1) The priority order of greenhouse opening and closing parameters is: temperature sensor > light sensor > carbon dioxide sensor > humidity sensor > raindrop sensor.

[0059] (2) The temperature measurement uses the temperature sensor data above the crops in the greenhouse as the standard value, and the temperature sensor data at the inlet and outlet is used as the conversion reference.

[0060] (3) Each type of sensor must have no less than two measurement points in the same greenhouse. If any abnormality is found, it must be reported and replaced immediately.

[0061] (4) The measurement data of the same type of sensors used for decision-making should be averaged, or the data should tend to be consistent, and abnormal data should not be used.

[0062] (5) Establish a fool-proof mechanism so that when a sensor is abnormal, there is a priority action sequence or it switches to manual operation mode for self-protection.

[0063] (6) Establish a prediction and comparison model that correlates local weather forecasts with system measurement data, make judgments based on actual conditions, and open or close the greenhouse.

[0064] (7) For greenhouses that are not dormant, when planting crops, the vents must be opened daily to exchange air inside and outside the greenhouse. During the exchange process, the temperature inside the greenhouse should be kept within the required range. When the temperature is lower than the set temperature, close the vents; when the temperature is higher than the set temperature, open the upper vents; when the temperature is higher than the set temperature by more than 10%, open both the upper and lower vents to quickly ventilate and lower the temperature.

[0065] (8) When the temperature is extremely high, cooling is prioritized, and when the temperature is normal, ventilation is prioritized. A model is established using light data and temperature data for coordinated control.

[0066] (9) In principle, the vents should be closed on rainy days and on windy days.

[0067] (10) Set an ideal median management standard. The parameter management trend in the greenhouse should be adjusted around the median first. The upper and lower limit standards are used for extreme situation management.

[0068] (11) In manual mode, the sensor continues to work and the data is retained for reference for manual decision-making.

[0069] (12) After a power outage, the system automatically retains the last data. When the power is restored, it should be calibrated to the standard settings and then restored to the last state before the power outage.

[0070] The temperature and humidity values ​​of some crops during their growth period are shown in Table 2:

[0071] Table 2 Temperature and humidity values ​​of some crops at different growth stages

[0072]

[0073] 3. The walking and rolling rod balancing device 3 is an actuator to realize the opening and closing of the greenhouse vent. The walking and rolling rod balancing device 3 is the executive link that drives the plastic cloth rolling rod 5 (65 meters long, 81.25 kilograms in weight, with an elastic reel with a weight of 5 kilograms every 5 meters) of the vent. The rolling rod 5 is driven by a DC motor. The motor 10 is a DC motor with a constant magnetic field. The minimum speed of the motor 10 is 2.4 to 3r per minute (settable). The rolling rod 5 is coaxial with the motor 10. The rotational motion of the motor 10 is converted into the walking of the rolling rod 5 axis on the rack through the gear sleeved on the shaft of the motor 10. The rack is designed to be 1.5 meters long, and the maximum width of the greenhouse vent is designed to be 1.2 meters wide, which can meet the maximum walking distance; the rack tooth pitch is 7 .85 mm, the error caused by the inertia of the rolling rod 5 can be controlled within the required range; the gear on the motor 10 shaft and the rack of the rolling rod 5 shaft can synchronize the rotation and translation of the rolling rod 5, solving the problem that the ultra-long rolling rod 5 in the current venting device will produce uncertain sliding while rotating, causing the air outlet to be not tightly closed; at the same time, because the rolling rod 5 has a set of counterweight devices, the controlled object is increased The traction of gravity reduces the electrical and mechanical time constant of the DC motor, avoids the problem of slow translation speed of the rolling rod 5 caused by the small inclination angle of the greenhouse and the flat roof, and ensures the rapidity and real-time control of the air outlet size. The structure of the walking and rolling rod balancing device 3 is as follows Figure 4 shown.

[0074] 4. The power supply part 4 is composed of a solar battery group 12 connected in parallel with the power grid. Under normal circumstances, the power grid provides power. In the case of a sudden power outage, the battery group 12 independently provides power to the entire system to ensure stable operation of the system. There are 1 to 2 solar panels 13 in each greenhouse, which realize the parallel connection of the solar panel 13 plus the battery group 12 and the direct power supply from the power grid. In the case of abnormal power outage of the normal power grid, the solar battery group 12 will be turned on in time to ensure the normal operation of the system.

[0075] 5. The positive effect of this embodiment is that it can realize the intelligence and automation of greenhouse ventilation, improve production efficiency, reduce labor costs, and improve crop growth quality.

[0076] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent ventilation system for crop greenhouses, characterized in that: include: A vent control unit comprises a rolling rod (5) and a traveling frame (11), wherein the traveling frame (11) is installed on the side wall inside the greenhouse, a film is laid on the top of the greenhouse, and the film is wound on the rolling rod (5), one end of the rolling rod (5) is connected to a motor (10) in a transmission manner, and the motor (10) is slidably installed above the traveling frame (11) through a gear; A control unit comprises a control cabinet (8), wherein the control cabinet (8) is installed below the walking frame (11), a fuzzy neural network control module is installed in the control cabinet (8), and the fuzzy neural network control module is electrically connected to the motor (10); A sensor unit, the sensor unit is installed in the greenhouse and is communicatively connected with the fuzzy neural network control module; A power supply module, the fuzzy neural network control module, the motor (10) and the sensor unit are all electrically connected to the power supply module; The cloud platform is communicatively connected with the fuzzy neural network control module.

2. The intelligent ventilation system for crop greenhouses according to claim 1 is characterized in that: A plurality of elastic gravity reels (7) are also installed on the rod body of the reel (5).

3. The intelligent ventilation system for crop greenhouses according to claim 1 is characterized in that: A communication module is integrated in the fuzzy neural network control module, and the fuzzy neural network control module is communicatively connected with the cloud platform and the sensor unit through the communication module.

4. The intelligent ventilation system for crop greenhouses according to claim 3 is characterized in that: The communication module includes one or more of a LoRa module, a 2.5G communication module, a 3G communication module, a 4G communication module, and a 5G communication module.

5. The intelligent ventilation system for crop greenhouses according to claim 1 is characterized in that: An antenna (9) is installed on the control cabinet (8).

6. The intelligent ventilation system for crop greenhouses according to claim 1, characterized in that: The sensing unit includes a temperature sensor, a humidity sensor, a raindrop sensor, a carbon dioxide concentration sensor and a displacement sensor, and each sensor is communicatively connected with the fuzzy neural network control module.

7. The intelligent ventilation system for crop greenhouses according to claim 1, characterized in that: The power supply module comprises a battery, one end of which is connected to the power grid, and the other end of which is electrically connected to the fuzzy neural network control module, the motor (10) and various sensors.

8. The intelligent ventilation system for crop greenhouses according to claim 7, characterized in that: The power supply module also includes a solar panel (13), and the solar panel (13) is electrically connected to the storage battery.

9. An intelligent ventilation method for crop planting greenhouses, applied to an intelligent ventilation system for crop planting greenhouses as claimed in any one of claims 1 to 8, characterized in that: include: Install an intelligent ventilation system in the greenhouse and initialize the intelligent ventilation system; After the initialization is completed, the greenhouse is monitored by the sensor unit, and the monitoring data is obtained and transmitted to the fuzzy neural network control module, wherein the monitoring data includes the greenhouse environment monitoring data and the vent control unit position data; The fuzzy neural network control module is used to calculate the difference between the monitoring data and the monitoring threshold data in the preset expert knowledge base, the difference is input into a pre-trained fuzzy neural network, and a control scheme of the vent control unit is output, and the air intake of the vent of the greenhouse is controlled based on the control scheme; wherein the fuzzy neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer, a normalization layer and an output layer connected in sequence.

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