Greenhouse intelligent gateway control method and system

Abstract

The invention relates to the technical field of greenhouse automation control, and discloses a greenhouse intelligent gateway control method and system, and the method comprises the steps: monitoring key parameters in a greenhouse in real time through an intelligent sensor; reliable transmission of data of the intelligent sensor is realized through a low-power-consumption wireless communication technology; the edge calculation and processing subsystem performs advanced data analysis on the acquired data, and automatically adjusts control parameters by integrating an intelligent algorithm library; the control execution subsystem receives an instruction of the edge calculation and processing subsystem and executes a series of actions in real time according to the instruction so as to realize automatic adjustment of the greenhouse environment; and meanwhile, functions of historical data query, report export, fault early warning setting and equipment maintenance reminding management are supported. According to the method, modular design is adopted, access of various sensors and execution mechanisms is supported, it is ensured that the system can be compatible with devices of different manufacturers, and good expansibility is achieved. And an advanced data encryption technology is adopted, so that the security and privacy protection of the data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse automation control, and in particular to a greenhouse intelligent gateway control method and system. Background Art

[0002] In the field of modern agricultural technology, greenhouse automation control systems are developing rapidly, aiming to achieve accurate monitoring and regulation of the internal environment of the greenhouse through high-tech means to improve crop yield and quality. Early greenhouse automation control systems mainly relied on wired communications and simple environmental monitoring equipment. With the advancement of wireless communication technology and intelligent sensor technology, modern greenhouse automation control systems have been able to achieve real-time data collection and preliminary processing. However, despite technological advances, existing systems still have significant deficiencies in data transmission, processing power, security, and equipment compatibility.

[0003] The existing greenhouse automation control system has exposed multiple problems in practical applications, which limit its application effect in precision agriculture. First, the data transmission bottleneck problem is prominent. The traditional system relies on the Internet to transmit a large amount of data to the cloud. This process is limited by network bandwidth and delay, resulting in the inability to guarantee the real-time performance of data processing. Secondly, the system is too dependent on the cloud server. Once a network failure or cloud service interruption occurs, the entire greenhouse automation control system may be paralyzed. Furthermore, although the cloud processing capacity is powerful, its processing efficiency and real-time performance are still difficult to meet the needs of precision agriculture when facing massive and high-frequency greenhouse environmental data. In addition, privacy and security issues cannot be ignored. The security of sensitive data during transmission and the risk of data leakage in cloud storage are both severe challenges faced by existing systems. Finally, the compatibility and scalability issues of equipment are also very prominent. The compatibility issues between equipment from different manufacturers and the complexity of system upgrades and expansions have greatly increased the maintenance cost and difficulty of the greenhouse automation control system. In view of the above shortcomings, the present invention proposes a more efficient, safe and compatible greenhouse automation control method and system system, which is expected to bring significant benefits in improving the real-time performance of data processing, reducing cloud dependence and enhancing system security. Summary of the invention

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

[0005] Therefore, the present invention provides a greenhouse intelligent 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. The crop growth model and environmental control strategy intelligent algorithm are locally deployed in the edge computing gateway to reduce the amount of data transmission and improve processing speed and decision-making efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions, a greenhouse intelligent gateway control method, comprising: using intelligent sensors to monitor key parameters in the greenhouse in real time to obtain first original data; the edge computing and processing subsystem performs advanced data analysis on the first original data, and automatically adjusts the control parameters through an integrated intelligent algorithm library to obtain a second data set, and generates target instructions; the control execution subsystem receives the target instructions of the edge computing and processing subsystem, executes the target action, and realizes automatic adjustment of the greenhouse environment.

[0007] As a preferred solution of the greenhouse intelligent gateway control method described in the present invention, the first data includes the temperature, humidity, light intensity, CO 2 concentration, matrix moisture.

[0008] As a preferred solution of the greenhouse intelligent gateway control method described in the present invention, wherein: the advanced data analysis of the first data includes, after the edge computing gateway receives the first raw data, filtering out obviously erroneous readings through a built-in preprocessing algorithm and filling in missing data;

[0009] Calculating key statistics of each sensor and performing feature extraction on the first raw data;

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

[0011] As a preferred solution of the greenhouse intelligent gateway control method described in the present invention, wherein: the automatic adjustment of control parameters by integrating the intelligent algorithm library includes setting the index values ​​of various growth elements in the greenhouse in each period of time according to the growth elements required for growing crops, and determining the required ideal environmental parameters;

[0012] The intelligent adaptation node calculates the execution time of the execution device according to the prediction information, controls the stability of the internal environment of the greenhouse, and quickly adjusts the internal environment of the greenhouse through the joint action of the intelligent adaptation node and the execution device. Based on the prediction information, the control parameters are optimized to obtain the second data set.

[0013] As a preferred solution of the greenhouse intelligent gateway control method described in the present invention, wherein: the generating target instruction includes formatting the control parameters in the second data set into specific temperature control instructions, humidity control instructions, light control instructions and comprehensive environment adjustment instructions, and the instructions include device identification, operation action, parameter value, and execution time;

[0014] Before generating instructions, verify the rationality of the instructions, check whether the device status supports the execution of the instructions, set priorities for the instructions according to the urgency and importance, package the verified and prioritized instructions into a data format that can be recognized by the control execution subsystem, and send them to the execution subsystem through a secure communication protocol.

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

[0016] According to the planned action sequence, the actuators are controlled in turn to adjust the film angle, turn on and off the ventilator, turn on and off the sunshade net, turn on and off the skylight, and control the heating equipment. While executing the actions, the status of each actuator and the changes in environmental parameters are monitored in real time;

[0017] After execution is completed, all execution results will be fed back to the edge computing and processing subsystem. The operator can directly send instructions through the remote monitoring and management subsystem. When the system fails or requires emergency intervention, the operator switches to manual control mode through the manual control panel or interface to directly control each actuator.

[0018] Another object of the present 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 solution of a greenhouse intelligent gateway control system described in the present invention, wherein: the data acquisition subsystem monitors key parameters in the greenhouse in real time, obtains first data, and realizes 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 second data, and generates target instructions;

[0021] The control execution subsystem receives target instructions from the edge computing and processing subsystem, executes corresponding target actions, 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;

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

[0023] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor implements the steps of a greenhouse intelligent gateway control method when executing the computer program.

[0024] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of a greenhouse intelligent gateway control method when executed by a processor.

[0025] Beneficial effects of the present invention: The application of edge computing technology makes data processing and control decisions faster and more accurate, providing a more suitable growth environment for crops. It reduces dependence on cloud servers and network bandwidth consumption, reducing operating costs and maintenance costs. Data encryption and localized processing reduce the risk of data leakage and improve the security and privacy protection capabilities of the system. Fault self-diagnosis and remote maintenance functions reduce system failure rate and downtime, and improve system reliability and stability. It promotes the development of greenhouse management towards intelligence and refinement, and improves agricultural production efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0027] Figure 1 A schematic flow chart of a greenhouse intelligent gateway control method provided by one embodiment of the present invention.

[0028] Figure 2 A schematic diagram of working modules of a greenhouse intelligent gateway control system provided by one embodiment of the present invention.

[0029] Figure 3 A schematic diagram of the workflow of a greenhouse intelligent gateway control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0033] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0034] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0035] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0036] Example 1, reference Figure 1 and Figure 3, which is the first embodiment of the present invention, and provides a greenhouse intelligent gateway control method, comprising:

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

[0038] Furthermore, multiple intelligent sensors monitor the temperature, humidity, light intensity, CO 2 For key parameters such as concentration and matrix moisture, smart sensors realize smart sensor data transmission through low-power wireless communication technology (such as Star Flash, WIFI, etc.) to ensure 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 automatically adjusts the control parameters through the integrated intelligent algorithm library to obtain the second data set 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 pre-processing, 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 the data, the edge computing gateway first identifies and removes outliers through a custom anomaly detection algorithm. For missing data, a dynamic interpolation method based on time series is used to fill it.

[0042] The custom anomaly detection algorithm uses an improved Z-score method, in which a time weight factor is introduced, expressed as:

[0043]

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

[0045] The gateway converts the raw readings from the sensors into a uniform format and performs offset correction on the timestamps to accommodate time zones in different regions. A custom timestamp conversion formula is used to take into account the unevenness of the earth's rotation:

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

[0047] Among them, T UTC is UTC time, T localis the local time, ΔT zone is the time zone difference, β is the correction coefficient, ω is the angular velocity, DOY is the day of the year, and S is the seasonal energy parameter.

[0048] Calculate the key statistics of each sensor and perform feature extraction on the first raw data. Deploy a customized lightweight convolutional neural network (CNN) on the edge computing gateway to extract features:

[0049]

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

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

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

[0053] The crop growth model refers to, for example, planting strawberries, setting the index values ​​of various growth elements in the greenhouse during each period of time according to the temperature, humidity, light intensity, carbon dioxide concentration and other growth elements required for growing crops at each stage of the strawberry growth life cycle;

[0054] The environmental prediction algorithm refers to automatic adjustment and control according to the external and internal environmental conditions of the greenhouse. For example, the required temperature of the greenhouse is 20 degrees Celsius, the current temperature inside the greenhouse is 21.5 degrees Celsius, and the temperature outside the greenhouse is 19 degrees Celsius. At this time, 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 to complete the cooling, and then sends the film opening and opening time instructions to the execution device 3 (film roll);

[0055] In another scenario, if the external environment of the greenhouse is predicted to cool down and rain in one hour, the intelligent adaptation node will calculate the execution time of the execution equipment (film roll, heating equipment, etc.) based on the prediction information to ensure the stability of the internal environment of the greenhouse when it rains and the temperature drops;

[0056] The control strategy optimization algorithm refers to a control algorithm that can quickly adjust the internal environment of the greenhouse by working together with multiple execution devices. For example, if the temperature in the greenhouse needs to be reduced by 2 degrees Celsius, the intelligent adapter node 4 calculates through the algorithm that the execution device (film roller) needs to be opened by two-thirds of the angle. At the same time, turning on the execution device (ventilator) can 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 pass the currently executed action information to other intelligent adapter nodes 4, and other intelligent adapter nodes 4 control the execution device (ventilator) for ventilation.

[0057] It should be noted that the control parameters in the second data set are formatted into specific temperature control instructions, humidity control instructions, light control instructions, and comprehensive environment adjustment instructions, and the instructions include device identification, operation action, parameter value, and execution time;

[0058] Before generating instructions, verify the rationality of the instructions, check whether the device status supports the execution of the instructions, set priorities for the instructions according to the urgency and importance, package the verified and prioritized instructions into a data format that can be recognized by the control execution subsystem, and send them to the execution subsystem through a secure communication protocol.

[0059] Examples of build target instructions include:

[0060] Temperature control instructions: "Adjust heating equipment to set temperature to 22 degrees Celsius. Based on crop growth model and environmental forecast, the estimated run time is 2 hours."

[0061] Humidity control instruction: "Start the ventilator and set the humidity control parameter to 60%. According to the optimization algorithm, the recommended operation mode is intermittent, and it is turned on for 10 minutes every 30 minutes."

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

[0063] Comprehensive environmental adjustment instruction: "Predict changes in the external environment and automatically adjust the parameters of the heating equipment, ventilators and sunshade nets to ensure that the temperature inside the greenhouse is maintained at 20 degrees Celsius, the humidity is 60%, and the light intensity is 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 automatic adjustment of the greenhouse environment.

[0065] Furthermore, after receiving the instructions from the edge computing and processing subsystem, the control execution subsystem directly executes the corresponding actions. For example, when receiving the instruction to increase the temperature in the greenhouse, the actuators in the control execution subsystem will open the sunshade net, close the skylight, turn on the heating equipment, and so on. The control execution subsystem executes a series of actions in real time according to the instructions of the edge computing and processing subsystem to achieve automatic adjustment of the greenhouse environment. At the same time, each actuator in the control execution subsystem supports remote control and manual switching functions to ensure flexible response in special circumstances.

[0066] The remote monitoring and management subsystem provides users with a good remote monitoring interface through multiple access methods such as PC and mobile APP. It can display greenhouse environmental parameters, equipment operating status, control instructions and other information in real time, and supports historical data query and report export functions. At the same time, the remote monitoring and management subsystem is equipped with management functions such as fault warning and equipment maintenance reminder to reduce operation and maintenance costs and improve management efficiency.

[0067] Example 2 is an embodiment of the present invention, which provides a greenhouse intelligent gateway control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments. Taking a tomato planting greenhouse as an example, in cold winter weather, the edge computing gateway determines whether the interior of the greenhouse needs heating and humidification based on the data from the temperature sensor and the humidity sensor. If necessary, the operating parameters of the insulation system and the humidification equipment are automatically adjusted to ensure that the temperature and humidity inside the greenhouse remain within the optimal range for tomato growth. At the same time, according to the data from the light intensity sensor, the opening and closing degree of the sunshade net is automatically adjusted to balance the relationship between light intensity and temperature. During the whole process, users can view the greenhouse status and control effects in real time through the remote monitoring interface without having to visit the site in person.

[0068] The experiment lasted for 7 consecutive days in winter.

[0069] External environmental conditions: average temperature below 5 degrees Celsius, night temperature may drop below 0 degrees Celsius, low humidity (30%-40%)

[0070] Day 1: Initialize the system, install and calibrate the temperature sensor, humidity sensor, and light intensity sensor. Set up the edge computing gateway and connect the sensors and actuators (heating equipment, humidifier, sunshade net drive).

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

[0072] From the second day to the sixth day: Data collection and automatic adjustment, record the temperature, humidity and light intensity data inside the greenhouse 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 lower than 18 degrees Celsius, the heating device is started and the heating power is adjusted according to the temperature deviation.

[0074] Humidification logic: If the humidity is lower than 60%, the humidifier is started and the humidification amount is adjusted according to the humidity deviation.

[0075] Sunshade 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 sunshade net will be adjusted according to the light intensity.

[0076] Day 7: Analysis of experimental results. Collect the temperature, humidity and light intensity data for the past 6 days, as shown in Table 1. Analyze the working effect of the automatic adjustment system to ensure that the temperature and humidity remain within the set range. Record the use of heating equipment, humidifiers and sunshade nets to evaluate energy consumption.

[0077] Table 1

[0078]

[0079]

[0080] It can be seen that the temperature and humidity inside the greenhouse were kept within the optimal range for tomato growth during the experiment. The light intensity was automatically adjusted by the sunshade net and was not too high or too low. The remote monitoring and management subsystem recorded all environmental parameters and control instructions, and users could check the status of the greenhouse at any time through the mobile phone APP. The fault warning and equipment maintenance reminder functions were not triggered, indicating that the system was running stably.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0082] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0083] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0085] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0086] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0087] Example 4, reference Figure 2 , is an embodiment of the present invention, and provides a greenhouse intelligent gateway control system, which is characterized by: including 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] The data acquisition subsystem 1 monitors the key parameters in the greenhouse in real time, obtains the first data, and realizes reliable data transmission through low-power wireless communication technology;

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

[0090] Control execution subsystem 3, receives target instructions from edge computing and processing subsystem, executes corresponding target actions, adjusts greenhouse environment according to instructions in real time, supports remote control and manual switching functions, and ensures flexible response in special circumstances;

[0091] Remote monitoring and management subsystem 4 provides multiple access methods such as PC and mobile APP to realize remote monitoring, display greenhouse environmental parameters, equipment operating status, and control instructions in real time, support historical data query and report export functions, and set 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 are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A greenhouse intelligent gateway control method, characterized in that: include, Use smart sensors to monitor key parameters in the greenhouse in real time and obtain the first raw data; The edge computing and processing subsystem performs advanced data analysis on the first raw data, and automatically adjusts control parameters through an integrated intelligent algorithm library to obtain a second data set 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.

2. A greenhouse intelligent gateway control method according to claim 1, characterized in that: The first data includes temperature, humidity, light intensity, CO2 concentration, and substrate moisture in the greenhouse.

3. A greenhouse intelligent gateway control method as claimed in claim 2, characterized in that: The performing of advanced data analysis on the first data includes, after the edge computing gateway receives the first raw data, filtering out obviously erroneous readings through a built-in preprocessing algorithm and filling in missing data; Calculating key statistics of each sensor and performing feature extraction on the first raw data; The classification model trained on the gateway classifies the greenhouse environment based on historical data and environmental conditions.

4. A greenhouse intelligent gateway control method as claimed in claim 3, characterized in that: The automatic adjustment of control parameters by integrating the intelligent algorithm library includes setting the index values ​​of various growth elements in the greenhouse in each period of time according to the growth elements required for growing crops, and determining the required ideal environmental parameters; The intelligent adaptation node calculates the execution time of the execution device according to the prediction information, controls the stability of the internal environment of the greenhouse, and quickly adjusts the internal environment of the greenhouse through the joint action of the intelligent adaptation node and the execution device. Based on the prediction information, the control parameters are optimized to obtain the second data set.

5. A greenhouse intelligent gateway control method as claimed in claim 4, characterized in that: Generating the target instruction includes formatting the control parameters in the second data set into specific temperature control instructions, humidity control instructions, light control instructions and comprehensive environment adjustment instructions, where the instructions include device identification, operation action, parameter value and execution time; Before generating instructions, verify the rationality of the instructions, check whether the device status supports the execution of the instructions, set priorities for the instructions according to the urgency and importance, package the verified and prioritized instructions into a data format that can be recognized by the control execution subsystem, and send them to the execution subsystem through a secure communication protocol.

6. A greenhouse intelligent gateway control method as claimed in claim 5, characterized in that: The execution target action includes that the execution subsystem continuously monitors the communication interface from the edge computing and processing subsystem, waits for receiving instructions, verifies the integrity and legality of the instructions after receiving the instructions, parses the received instruction content, extracts the device ID, operation action, parameter value, execution time, priority, and plans the action sequence that the execution device needs to execute according to the instruction content; According to the planned action sequence, the actuators are controlled in turn to adjust the film angle, turn on and off the ventilator, turn on and off the sunshade net, turn on and off the skylight, and control the heating equipment. While executing the actions, the status of each actuator and the changes in environmental parameters are monitored in real time; After execution is completed, all execution results will be fed back to the edge computing and processing subsystem. The operator can directly send instructions through the remote monitoring and management subsystem. When the system fails or requires emergency intervention, the operator switches to manual control mode through the manual control panel or interface to directly control each actuator.

7. A system using a greenhouse intelligent gateway control method as claimed in any one of claims 1 to 6, characterized in that: It includes data acquisition subsystem, edge computing and processing subsystem, control execution subsystem, and remote monitoring and management subsystem.

8. The system of a greenhouse intelligent gateway control method according to claim 7, characterized in that: The data acquisition subsystem monitors the key parameters in the greenhouse in real time, obtains the first data, and realizes reliable data transmission through low-power wireless communication technology; The edge computing and processing subsystem performs advanced data analysis on the first data, automatically adjusts control parameters to obtain second data, and generates target instructions; The control execution subsystem receives target instructions from the edge computing and processing subsystem, executes corresponding target actions, 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 such as PC and mobile phone APP to realize remote monitoring, display greenhouse environmental parameters, equipment operating status, and control instructions in real time, support historical data query and report export functions, and set fault warning and equipment maintenance reminder management functions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a greenhouse intelligent gateway control method described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a greenhouse intelligent gateway control method described in any one of claims 1 to 6 are implemented.

Images