Greenhouse intelligent gateway control method and system

Abstract

The present application relates to the technical field of greenhouse automation control, and discloses a greenhouse intelligent gateway control method and system, which comprises the following steps: real-time monitoring of key parameters in the greenhouse by using intelligent sensors; reliable transmission of intelligent sensor data is realized through low-power wireless communication technology; advanced data analysis of collected data is carried out by an edge computing and processing subsystem, and control parameters are automatically adjusted by integrating an intelligent algorithm library; a control execution subsystem receives instructions from the edge computing and processing subsystem, and executes a series of actions in real time according to the instructions, so as to realize automatic adjustment of the greenhouse environment; meanwhile, historical data query, report export, fault early warning, equipment maintenance reminder management functions are supported. The method adopts modular design, supports the access of various sensors and actuators, ensures that the system can be compatible with equipment of different manufacturers, and has good expansibility. Advanced data encryption technology is adopted to ensure the safety and privacy protection of data.

Description

Technical Field

[0001] This invention relates to the field of greenhouse automation control technology, and in particular to a greenhouse intelligent gateway control method and system. Background Technology

[0002] In the field of modern agricultural technology, the development of greenhouse automation control systems is progressing rapidly. These systems aim to achieve precise monitoring and regulation of the greenhouse environment through high-tech means, thereby improving crop yield and quality. Early greenhouse automation control systems mainly relied on wired communication and simple environmental monitoring equipment. With advancements in wireless communication and intelligent sensor technologies, modern greenhouse automation control systems can now achieve real-time data acquisition and preliminary processing. However, despite these technological advancements, existing systems still have significant shortcomings in data transmission, processing capacity, security, and equipment compatibility.

[0003] Existing greenhouse automation control systems exhibit several problems in practical applications, limiting their effectiveness in precision agriculture. First, data transmission bottlenecks are significant. Traditional systems rely on the internet to transmit large amounts of data to the cloud, a process limited by network bandwidth and latency, resulting in compromised real-time data processing. Second, the system's over-reliance on cloud servers means that network failures or cloud service interruptions can paralyze the entire greenhouse automation control system. Third, while cloud processing capabilities are powerful, their efficiency and real-time performance are insufficient for the demands of precision agriculture when dealing with massive amounts of high-frequency greenhouse environmental data. Furthermore, privacy and security issues cannot be ignored; the security of sensitive data during transmission and the risk of data leakage from cloud storage pose serious challenges to existing systems. Finally, equipment compatibility and scalability are also prominent issues. Compatibility problems between different manufacturers' equipment and the complexity of system upgrades and expansions significantly increase the maintenance costs and difficulty of greenhouse automation control systems. To address these shortcomings, this invention proposes a more efficient, secure, and compatible greenhouse automation control method and system, which is expected to bring significant benefits in improving real-time data processing, reducing cloud dependence, and enhancing system security. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a greenhouse smart gateway control method that can design and integrate dedicated edge computing hardware to ensure that complex data processing tasks can be completed on-site in the greenhouse. By locally deploying crop growth models and intelligent algorithms for environmental control strategies within the edge computing gateway, data transmission volume is reduced, while processing speed and decision-making efficiency are improved.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a greenhouse intelligent gateway control method, comprising: using intelligent sensors to monitor key parameters within the greenhouse in real time to obtain first raw data; an edge computing and processing subsystem performing advanced data analysis on the first raw data, and simultaneously automatically adjusting control parameters through an integrated intelligent algorithm library to obtain a second dataset, and generating target instructions; and a control execution subsystem receiving the target instructions from the edge computing and processing subsystem, executing the target actions, and realizing automatic adjustment of the greenhouse environment.

[0007] As a preferred embodiment of the greenhouse intelligent gateway control method of the present invention, the first data includes the temperature, humidity, light intensity, CO2 concentration, and substrate moisture in the greenhouse.

[0008] As a preferred embodiment of the greenhouse intelligent gateway control method of the present invention, the advanced data analysis of the first data includes: after receiving the first raw data, the edge computing gateway filters out obviously erroneous readings and fills in missing data through a built-in preprocessing algorithm;

[0009] Calculate the key statistics for each sensor and extract features from the first raw data;

[0010] The classification model trained on the gateway classifies greenhouse environments based on historical data and environmental conditions.

[0011] As a preferred embodiment of the greenhouse intelligent gateway control method of the present invention, the step of automatically adjusting the control parameters by integrating an intelligent algorithm library includes determining the required ideal environmental parameters by setting the index values ​​of various growth elements in the greenhouse for each period of time according to the growth elements required by the growing crops.

[0012] The intelligent adaptation node calculates the execution time of the execution equipment based on the prediction information, controls the stability of the internal environment of the greenhouse, and through the joint action of the intelligent adaptation node and the execution equipment, quickly adjusts the internal environment of the greenhouse. Based on the prediction information, it optimizes the control parameters and obtains the second dataset.

[0013] As a preferred embodiment of the greenhouse intelligent gateway control method of the present invention, the generation of target instructions includes formatting the control parameters in the second dataset into specific temperature control instructions, humidity control instructions, light control instructions and comprehensive environmental adjustment instructions, wherein the instructions include device identifier, operation action, parameter value and execution time;

[0014] Before generating an instruction, the rationality of the instruction is verified, and the device status is checked to see if the instruction can be executed. The instruction is prioritized according to its urgency and importance. The verified and prioritized instructions are packaged into a data format that can be recognized by the control and execution subsystem and sent to the execution subsystem through a secure communication protocol.

[0015] As a preferred embodiment of the greenhouse intelligent gateway control method of the present invention, the execution target action includes: the execution subsystem continuously listens to the communication interface from the edge computing and processing subsystem, waits to receive instructions, verifies the integrity and legality of the instructions after receiving the instructions, parses the received instructions content, extracts the device ID, operation action, parameter value, execution time, and priority, and plans the sequence of actions that the execution device needs to perform according to the instructions content;

[0016] According to the planned sequence of actions, the actuators are controlled in sequence to perform actions such as adjusting the angle of the membrane, switching on and off the ventilator, switching on and off the sunshade net, switching on and off the skylight, and controlling the heating equipment. While performing these actions, the status of each actuator and changes in environmental parameters are monitored in real time.

[0017] After execution, all execution results are fed back to the edge computing and processing subsystem. Operators can send instructions directly through the remote monitoring and management subsystem. In case of system failure or emergency intervention, operators can switch to manual control mode through the manual control panel or interface to directly control each actuator.

[0018] Another objective of this invention is to provide a greenhouse intelligent gateway control system, characterized in that it includes a data acquisition subsystem, an edge computing and processing subsystem, a control execution subsystem, and a remote monitoring and management subsystem.

[0019] As a preferred embodiment of the greenhouse intelligent gateway control system described in this invention, the data acquisition subsystem monitors key parameters in the greenhouse in real time, obtains first data, and achieves reliable data transmission through low-power wireless communication technology.

[0020] The edge computing and processing subsystem performs advanced data analysis on the first data, automatically adjusts control parameters to obtain the second data, and generates target instructions.

[0021] The control execution subsystem receives target instructions from the edge computing and processing subsystem and executes corresponding target actions. It adjusts the greenhouse environment in real time according to the instructions, supports remote control and manual switching functions, and ensures that it can respond flexibly in special circumstances.

[0022] The remote monitoring and management subsystem provides multiple access methods, including PC and mobile APP, to achieve remote monitoring, display greenhouse environmental parameters, equipment operating status, and control commands in real time, support historical data query and report export functions, and set up fault warning and equipment maintenance reminder management functions.

[0023] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a greenhouse smart gateway control method.

[0024] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a greenhouse smart gateway control method.

[0025] The beneficial effects of this invention are as follows: The application of edge computing technology enables faster and more accurate data processing and control decisions, providing a more suitable growth environment for crops. It reduces reliance on cloud servers and network bandwidth consumption, lowering operating and maintenance costs. Data encryption and localized processing reduce the risk of data leakage, improving system security and privacy protection. Fault self-diagnosis and remote maintenance functions reduce system failure rates and downtime, improving system reliability and stability. It promotes the development of intelligent and refined greenhouse management, improving agricultural production efficiency and quality. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0027] Figure 1 This is a schematic flowchart of a greenhouse smart gateway control method provided in one embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the working module of a greenhouse intelligent gateway control system provided in one embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram illustrating the workflow of a greenhouse smart gateway control method according to an embodiment of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0033] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0034] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0036] Example 1, referring to Figure 1 and Figure 3This is the first embodiment of the present invention, which provides a greenhouse smart gateway control method, including:

[0037] S1: Use smart sensors to monitor key parameters inside the greenhouse in real time to obtain the first raw data.

[0038] Furthermore, multiple smart sensors monitor key parameters in the greenhouse in real time, such as temperature, humidity, light intensity, CO2 concentration, and substrate moisture. The smart sensors use low-power wireless communication technologies (such as StarFlash and WIFI) to transmit data, ensuring the reliability and flexibility of data transmission.

[0039] S2: The edge computing and processing subsystem performs advanced data analysis on the first raw data, and at the same time automatically adjusts the control parameters through an integrated intelligent algorithm library to obtain the second dataset and generate target instructions.

[0040] Furthermore, the edge computing gateway of the edge computing and processing subsystem has a built-in high-performance processor and real-time operating system, which performs advanced data analysis such as preprocessing, feature extraction, and pattern recognition on the data collected by the data acquisition subsystem, thereby improving the accuracy and effectiveness of control decisions.

[0041] It should be noted that after receiving data, the edge computing gateway first identifies and removes outliers using a custom anomaly detection algorithm. For missing data, a time-series-based dynamic interpolation method is used to fill in the gaps.

[0042] The custom anomaly detection algorithm uses an improved Z-score method, which introduces a time weighting factor, expressed as:

[0043]

[0044] Among them, X t It is the observed value at time point t, μ t It is the mean at time point t, σ t α is the standard deviation at time point t, α is the time weighting coefficient, and Δt is the time interval.

[0045] The gateway converts the sensor's raw readings into a uniform format and performs timestamp offset correction to accommodate different time zones. A custom timestamp conversion formula is used, taking into account the Earth's rotational inhomogeneity:

[0046] T UTC =T local +ΔT zone +β·sin(ω·(DOY-S))

[0047] Among them, T UTC It is UTC time, T local It is local time, ΔTzone ω is the time zone difference, β is the correction factor, ω is the angular velocity, DOY is the mid-year day, and S is the seasonal energy parameter.

[0048] Key statistics for each sensor are calculated and features are extracted from the initial raw data. A custom lightweight convolutional neural network (CNN) is deployed on the edge computing gateway to extract features.

[0049]

[0050] Among them, Y ij It is the output of the convolutional layer, X ijmn K is a local region of the input data. mn is the weight of the convolution kernel, b is the bias term, i and j are usually the specific positions of the output feature map of the convolutional layer, m and n are the row and column indices of the element in the local region, respectively, and the two indices represent the specific positions of the sliding window of the convolution kernel (or filter) on the input data.

[0051] The classification model trained on the gateway classifies greenhouse environments based on historical data and environmental conditions.

[0052] Furthermore, the edge computing and processing subsystem is configured with an integrated intelligent algorithm library, including crop growth models, environmental prediction algorithms, and control strategy optimization algorithms;

[0053] A crop growth model refers to, for example, when growing strawberries, setting the index values ​​of various growth elements in a greenhouse for each period of time based on the temperature, humidity, light intensity, carbon dioxide concentration, and other growth elements required for the growth of the crop at each stage of the strawberry's growth life cycle.

[0054] The environmental prediction algorithm refers to the automatic adjustment and control based on the external and internal environmental conditions of the greenhouse. For example, if the required temperature of the greenhouse is set to 20 degrees Celsius, the current temperature inside the greenhouse is 21.5 degrees Celsius, and the temperature outside the greenhouse is 19 degrees Celsius, then the intelligent adaptation node 4 calculates the angle and time required to open the film based on the temperature difference between the inside and outside of the greenhouse, and predicts that the cooling can be completed. Then, it sends the instructions on the opening degree and opening time of the film to the execution device 3 (film roller).

[0055] In another scenario, if the external environment of the greenhouse is predicted to cool down and rain in 1 hour, the intelligent adaptation node will calculate the execution time of the execution equipment (film roller, heating equipment, etc.) based on the prediction information to ensure the stability of the internal environment of the greenhouse during rain and cooling.

[0056] The control strategy optimization algorithm refers to a control algorithm that uses multiple execution devices to work together to quickly adjust the internal environment of a greenhouse. For example, if the greenhouse needs to be cooled by 2 degrees Celsius, the intelligent adapter node 4 calculates through the algorithm that the execution device (film roller) needs to open to two-thirds of its angle. At the same time, turning on the execution device (ventilator) will achieve the fastest cooling. At this time, the intelligent adapter node 4 near the film roller will control the film roller to open for ventilation and cooling, and at the same time transmit the current execution action information to other intelligent adapter nodes 4, which will then control the execution device (ventilator) to ventilate.

[0057] It should be noted that the control parameters in the second dataset are formatted into specific temperature control commands, humidity control commands, light control commands, and comprehensive environmental adjustment commands. The commands include device identification, operation actions, parameter values, and execution time.

[0058] Before generating an instruction, the rationality of the instruction is verified, and the device status is checked to see if the instruction can be executed. The instruction is prioritized according to its urgency and importance. The verified and prioritized instructions are packaged into a data format that can be recognized by the control and execution subsystem and sent to the execution subsystem through a secure communication protocol.

[0059] Examples of generated target instructions include:

[0060] Temperature control command: "Adjust the heating equipment, set the temperature to 22 degrees Celsius, and based on the crop growth model and environmental prediction, the estimated running time is 2 hours."

[0061] Humidity control command: "Start the ventilation fan, set the humidity control parameter to 60%, and according to the optimized algorithm, the recommended operating mode is intermittent, turning it on for 10 minutes every 30 minutes."

[0062] Light control command: "Adjust the shade net and set the light intensity to 50%. According to the environmental prediction algorithm, this is expected to be executed between 2 p.m. and 4 p.m.

[0063] Comprehensive environmental control instructions: "In anticipation of changes in the external environment, automatically adjust the parameters of heating equipment, ventilation fans, and shading nets to ensure that the temperature inside the greenhouse is maintained at 20 degrees Celsius, humidity at 60%, light intensity at 50%, for a duration of 4 hours."

[0064] S3: The control execution subsystem receives the target instructions from the edge computing and processing subsystem, executes the target actions, and realizes the automatic adjustment of the greenhouse environment.

[0065] Furthermore, upon receiving instructions from the edge computing and processing subsystem, the control execution subsystem directly executes the corresponding actions. For example, upon receiving an instruction to raise the temperature inside the greenhouse, the actuators in the control execution subsystem will perform a series of actions, such as opening the shading net, closing the skylights, and turning on the heating equipment. The control execution subsystem executes these actions in real time according to the instructions from the edge computing and processing subsystem, achieving automatic adjustment of the greenhouse environment. Simultaneously, each actuator in the control execution subsystem supports remote control and manual switching functions, ensuring flexible response in special circumstances.

[0066] The remote monitoring and management subsystem provides users with a user-friendly remote monitoring interface through multiple access methods, including PCs and mobile apps. It can display real-time information such as greenhouse environmental parameters, equipment operating status, and control commands, and supports historical data query and report export functions. Simultaneously, the remote monitoring and management subsystem includes management functions such as fault early warning and equipment maintenance reminders, reducing operation and maintenance costs and improving management efficiency.

[0067] Example 2, an embodiment of the present invention, provides a greenhouse intelligent gateway control method. To verify the beneficial effects of the invention, scientific demonstration was conducted through experiments. Taking a tomato greenhouse as an example, in cold winter weather, the edge computing gateway determines whether heating and humidification are needed inside the greenhouse based on data from temperature and humidity sensors. If so, it automatically adjusts the operating parameters of the insulation system and humidification equipment to ensure that the temperature and humidity inside the greenhouse are maintained within the optimal range for tomato growth. Simultaneously, based on data from the light intensity sensor, it automatically adjusts the opening and closing degree of the shading net to balance the relationship between light intensity and temperature. Throughout the process, users can view the greenhouse status and control effects in real time through a remote monitoring interface without being physically present at the site.

[0068] The experiment took place during winter and lasted for 7 consecutive days.

[0069] External environmental conditions: Average temperature below 5 degrees Celsius, nighttime temperature may drop below 0 degrees Celsius, and humidity is low (30%-40%).

[0070] Day 1: System initialization, installation and calibration of temperature, humidity, and light intensity sensors. Setting up the edge computing gateway and connecting sensors and actuators (heating equipment, humidifiers, shading net drive units).

[0071] The optimal temperature and humidity range for tomato growth can be set through the remote monitoring and management subsystem 4 (temperature: 18-25 degrees Celsius, humidity: 60%-80%).

[0072] Days 2-6: Data Acquisition and Automatic Adjustment. Temperature, humidity, and light intensity data inside the greenhouse are recorded every 3 hours. The edge computing gateway analyzes the data in real time and automatically adjusts according to the following logic:

[0073] Heating logic: If the temperature is below 18 degrees Celsius, the heating equipment will be activated and the heating power will be adjusted according to the temperature deviation.

[0074] Humidification logic: If the humidity is below 60%, the humidifier will be turned on and the humidification amount will be adjusted according to the humidity deviation.

[0075] Shading net adjustment logic: If the light intensity exceeds the optimal range for tomato growth (for example, greater than 50,000 lux), the opening and closing degree of the shading net will be adjusted according to the light intensity.

[0076] Day 7: Experimental Results Analysis. Data on temperature, humidity, and light intensity from the past six days were collected and are shown in Table 1. The effectiveness of the automatic adjustment system was analyzed to ensure that temperature and humidity remained within the set range. The usage of heating equipment, humidifiers, and shade nets was recorded, and energy consumption was assessed.

[0077] Table 1

[0078]

[0079]

[0080] It can be seen that the temperature and humidity inside the greenhouse remained within the optimal range for tomato growth during the experiment. Light intensity was automatically adjusted by the shading net, without becoming excessively high or low. The remote monitoring and management subsystem recorded all environmental parameters and control commands, and users could check the greenhouse status at any time via a mobile app. Fault warnings and equipment maintenance reminders were not triggered, indicating that the system was operating stably.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0082] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that:

[0083] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0085] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0086] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0087] Example 4, refer to Figure 2 As an embodiment of the present invention, a greenhouse intelligent gateway control system is provided, characterized in that it includes a data acquisition subsystem 1, an edge computing and processing subsystem 2, a control execution subsystem 3, and a remote monitoring and management subsystem 4.

[0088] Data acquisition subsystem 1 monitors key parameters in the greenhouse in real time, obtains initial data, and achieves reliable data transmission through low-power wireless communication technology;

[0089] Edge computing and processing subsystem 2 performs advanced data analysis on the first data, automatically adjusts control parameters to obtain the second data, and generates target instructions;

[0090] The control and execution subsystem 3 receives target instructions from the edge computing and processing subsystem and executes corresponding target actions. It adjusts the greenhouse environment in real time according to the instructions, supports remote control and manual switching functions, and ensures that it can respond flexibly in special circumstances.

[0091] The remote monitoring and management subsystem 4 provides multiple access methods, including PC and mobile APP, to achieve remote monitoring, display greenhouse environmental parameters, equipment operating status, and control commands in real time, support historical data query and report export functions, and set up fault warning and equipment maintenance reminder management functions.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling a greenhouse intelligent gateway, characterized in that: include, Intelligent sensors are used to monitor key parameters inside the greenhouse in real time to obtain the first raw data; The edge computing and processing subsystem performs advanced data analysis on the first raw data, and at the same time automatically adjusts the control parameters through an integrated intelligent algorithm library to obtain the second dataset and generate target instructions; The control execution subsystem receives target instructions from the edge computing and processing subsystem, executes target actions, and realizes automatic adjustment of the greenhouse environment; The first set of raw data includes the temperature, humidity, light intensity, CO2 concentration, and substrate moisture in the greenhouse. The advanced data analysis of the first raw data includes the following steps: after receiving the first raw data, the edge computing gateway filters out obviously erroneous readings and fills in missing data using a built-in preprocessing algorithm. For missing data, a dynamic interpolation method based on time series is used to fill in the gaps. The custom anomaly detection algorithm uses an improved Z-score method, introducing a time weighting factor, and is expressed as: These are the observations at time point t. It is the mean at time point t. It is the standard deviation at time point t. It is a time-weighted coefficient. It is a time interval; The gateway converts the sensor's raw readings into a uniform format and performs offset correction on the timestamps to adapt to different time zones. It uses a custom timestamp conversion formula, taking into account the non-uniformity of the Earth's rotation. It is UTC time. It is local time. It's a time zone difference. It is a correction factor. It's angular velocity. Mid-year, It is a seasonal energy parameter; Calculate key statistics for each sensor and extract features from the initial raw data. Deploy a custom lightweight convolutional neural network (CNN) on the edge computing gateway to extract features. It is the output of the convolutional layer. It is a local area of ​​the input data. is the weight of the convolution kernel, b is the bias term, i and j usually represent specific positions in the output feature map of the convolutional layer, m and n are the row and column indices of the element in the local region, and the two indices represent specific positions of the sliding window of the convolution kernel on the input data; Calculate the key statistics for each sensor and extract features from the first raw data; The classification model trained on the gateway classifies the greenhouse environment based on historical data and environmental conditions; The automatic adjustment of control parameters through an integrated intelligent algorithm library includes setting the index values ​​of various growth elements in the greenhouse for each period of time based on the growth elements required by the growing crops, and determining the required ideal environmental parameters. The intelligent adaptation node calculates the execution time of the execution equipment based on the prediction information, controls the stability of the internal environment of the greenhouse, and through the joint action of the intelligent adaptation node and the execution equipment, quickly adjusts the internal environment of the greenhouse, optimizes the control parameters based on the prediction information, and obtains the second dataset. The generated target instructions include formatting the control parameters in the second dataset into specific temperature control instructions, humidity control instructions, light control instructions, and comprehensive environmental adjustment instructions. The instructions include device identification, operation actions, parameter values, and execution time. Before generating an instruction, the rationality of the instruction is verified, and the device status is checked to see if the instruction can be executed. The instruction is prioritized according to its urgency and importance. The verified and prioritized instructions are packaged into a data format that can be recognized by the control and execution subsystem and sent to the execution subsystem through a secure communication protocol. The execution target action includes the following: the execution subsystem continuously listens to the communication interface from the edge computing and processing subsystem, waits to receive instructions, verifies the integrity and legality of the instructions upon receiving them, parses the received instruction content, extracts the device ID, operation action, parameter value, execution time, and priority, and plans the sequence of actions that the execution device needs to perform based on the instruction content. According to the planned sequence of actions, the actuators are controlled in sequence to perform actions such as adjusting the angle of the membrane, switching on and off the ventilator, switching on and off the sunshade net, switching on and off the skylight, and controlling the heating equipment. While performing these actions, the status of each actuator and changes in environmental parameters are monitored in real time. After execution, all execution results are fed back to the edge computing and processing subsystem. Operators can send instructions directly through the remote monitoring and management subsystem. In case of system failure or emergency intervention, operators can switch to manual control mode through the manual control panel or interface to directly control each actuator.

2. A system employing the greenhouse intelligent gateway control method as described in claim 1, characterized in that: It includes a data acquisition subsystem, an edge computing and processing subsystem, a control and execution subsystem, and a remote monitoring and management subsystem.

3. The system of the greenhouse intelligent gateway control method as described in claim 2, characterized in that: The data acquisition subsystem monitors key parameters in the greenhouse in real time, obtains the first raw data, and achieves reliable data transmission through low-power wireless communication technology. The edge computing and processing subsystem performs advanced data analysis on the first raw data, automatically adjusts control parameters to obtain the second data, and generates target instructions. The control execution subsystem receives target instructions from the edge computing and processing subsystem and executes corresponding target actions. It adjusts the greenhouse environment in real time according to the instructions, supports remote control and manual switching functions, and ensures flexible response in special circumstances. The remote monitoring and management subsystem provides multiple access methods, including PC and mobile APP, to achieve remote monitoring, display greenhouse environmental parameters, equipment operating status, and control commands in real time, support historical data query and report export functions, and set up fault warning and equipment maintenance reminder management functions.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the greenhouse intelligent gateway control method according to claim 1.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the greenhouse intelligent gateway control method according to claim 1.

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