Intelligent precise irrigation system and method based on Internet of Things and fuzzy control

Abstract

The invention relates to the technical field of water conservancy irrigation, and discloses an intelligent precise irrigation system and method based on the Internet of Things and fuzzy control, and the system comprises a sensing and execution module which is used for collecting environment data and executing irrigation operation, and a platform application module which is used for receiving the environment data and executing the irrigation operation and comprises an environment data collection unit and an irrigation execution unit. The sensing and execution module is used for storing and analyzing data in the environment data acquisition unit and then generating an irrigation instruction, the platform application module comprises a data storage unit, a data analysis unit and a twin experiment unit, and the network transmission module is used for being in communication connection with the sensing and execution module and the platform application module. The system solves the problems that a traditional irrigation mode is serious in water resource waste, low in automation degree and poor in adaptability, and high efficiency, intelligence and automation of agricultural irrigation are achieved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and irrigation technology, and in particular to an intelligent precision irrigation system and method based on the Internet of Things and fuzzy control. Background Technology

[0002] Traditional irrigation methods, such as flood irrigation and furrow irrigation, have low water resource utilization rates and serious waste. Existing automated irrigation systems, such as timer-based systems, lack the ability to perceive the actual water needs of crops and environmental changes in real time, and cannot achieve truly precise irrigation. Although some systems have introduced soil moisture sensors, they are mostly simple threshold controls. When the humidity is below the set value, irrigation is turned on, and when the set value is reached, irrigation is turned off. This method is slow to react and cannot cope with drastic changes in environmental factors such as weather. It also cannot achieve on-demand water supply, which may lead to insufficient or excessive irrigation. Therefore, there is an urgent need for an intelligent precision irrigation system. Summary of the Invention

[0003] The purpose of this invention is to overcome one or more of the above-mentioned existing technical problems and provide an intelligent precision irrigation system and method based on the Internet of Things and fuzzy control.

[0004] To achieve the above objectives, the present invention provides an intelligent precision irrigation system based on the Internet of Things and fuzzy control, comprising: The sensing and execution module is used to collect environmental data and execute irrigation operations; The sensing and execution module includes an environmental data acquisition unit and an irrigation execution unit. The environmental data acquisition unit includes a multi-source sensor array for collecting environmental and crop data. The irrigation execution unit is used to receive specific irrigation instructions and irrigate crops; The platform application module is used to store and analyze the data in the environmental data acquisition unit and then generate irrigation instructions; The platform application module includes a data storage unit, a data analysis unit, and a twin experiment unit. The data storage unit is used to store environmental and crop data and irrigation instructions. The data analysis unit is used to analyze environmental and crop data based on fuzzy control algorithms and generate irrigation instructions; Twin experiment unit, used to simulate crops and predict crop growth results based on irrigation instructions; The network transmission module is used for communication connections between the sensing and execution module and the platform application module.

[0005] According to one aspect of the present invention, the sensing and execution module further includes an edge node unit, the edge node module including a plurality of edge node devices, the plurality of edge node devices being communicatively connected to the environmental data acquisition unit and the irrigation execution unit; The edge node unit is used to perform preliminary cleaning of the data, remove obviously abnormal sensor data, and aggregate high-frequency data to generate a data integration package, which is then transmitted to the platform application module via the network transmission module. The edge node unit receives and backs up the data from the irrigation execution unit, generates local edge decision commands for storage, and uses the stored local edge decision commands to make real-time decisions, generate irrigation instructions, and control the irrigation execution unit when the system is offline. When the system is online, the edge node unit automatically uploads all local operation records from the offline state to the platform application module for data synchronization and analysis.

[0006] According to one aspect of the present invention, in the data analysis unit, the difference between the expected humidity and the actual humidity in the soil and the rate of change of the deviation are used as input variables, and calculations are performed using a preset membership function and a fuzzy rule base, with irrigation amount as the output variable, to generate corresponding irrigation instructions.

[0007] According to one aspect of the present invention, in the data analysis unit, the input soil moisture deviation is processed based on the Gaussian membership function, and it is transformed into the membership degree of a fuzzy set, wherein the formula is: ; Represents the membership degree of a fuzzy set; Indicates soil moisture deviation; Represents the center value of a fuzzy set; Indicates the width of the fuzzy set; The activation strength of the current rule is obtained by multiplying the membership degrees of multiple fuzzy sets under the same rule, where the formula is: ; This represents the activation strength of the j-th rule; It represents the membership degree of another fuzzy set under the same rule; ; Indicates irrigation volume; This represents the weight value of the j-th rule; Let represent the center value of the fuzzy set of the j-th rule.

[0008] According to one aspect of the present invention, in a twin experimental unit, a three-dimensional terrain mesh model containing information on field boundaries, slope, aspect, and elevation is generated based on satellite detection and UAV oblique photogrammetry. It also includes soil profile models and crop growth models. Based on the soil profile model, the soil is divided into multiple layers in the vertical direction, and the physical properties of each layer are defined. Irrigation instructions are optimized based on the soil profile model. Based on the crop growth model, crop growth is divided into germination, seedling stage, vegetative growth stage, flowering stage, fruiting stage and maturity stage, and irrigation instructions are optimized for different stages.

[0009] According to one aspect of the invention, in response to an irrigation command generated by a data analysis unit, this irrigation command is simulated in a twin experimental unit to map the dynamic changes in soil moisture, and a soil moisture distribution heat map is generated based on the dynamic changes, wherein the formula is: ; Indicates the volumetric water content of the soil; Indicates the elapsed time; Indicates the depth of the soil; Indicates soil hydraulic conductivity; Indicates soil water potential; This indicates the root system's ability to absorb water.

[0010] According to one aspect of the present invention, the platform application module further includes a weather forecasting unit, which is used to obtain weather conditions for future times, compare the weather conditions with irrigation instructions, and optimize the irrigation instructions.

[0011] According to one aspect of the invention, it also includes a user interface for users to remotely monitor field data in real time, increase or decrease irrigation amount based on user experience, and view historical records and alarm information in real time.

[0012] To achieve the above objectives, the present invention provides an intelligent precision irrigation method based on the Internet of Things and fuzzy control, comprising: Based on actual field data, a twin simulation test field was created; According to the sensor settings, environmental data is collected periodically, and the collected data is uploaded to the platform application module through the network transmission module; Based on the data received by the platform, the data is analyzed using a fuzzy control algorithm to generate irrigation instructions; Experiments were conducted in a twin-simulation test field based on irrigation instructions. Soil moisture distribution heat maps were generated and analyzed. If the results did not meet the requirements, the irrigation instructions were optimized. If the results met the requirements, the optimal irrigation instructions were generated. The platform application module sends irrigation commands to the irrigation execution unit to control the designated valve controllers and water pumps to open or close. The execution status is monitored in real time, and the execution results are sent back to the platform application module. The platform application module stores and analyzes irrigation results, and optimizes fuzzy rules and parameters through machine learning algorithms.

[0013] According to one aspect of the present invention, if the system is offline, the edge node unit uses stored local edge decision commands to make real-time decisions, generate irrigation instructions, and control the irrigation execution unit. When the network is restored, all local operation records in the offline state are uploaded to the platform application module for data synchronization and analysis.

[0014] Based on this, the beneficial effects of the present invention are as follows: through multi-sensor fusion and intelligent decision-making, on-demand irrigation is achieved, avoiding water waste in traditional methods and achieving significant water-saving effects; By employing fuzzy control algorithms, the imprecision and complexity of environmental information can be addressed, enabling the system to respond more promptly and rationally to weather and environmental changes. Users can remotely monitor and control the entire system via mobile phone or computer, which greatly reduces labor costs and management difficulty. The system supports the addition of different types of sensors and actuators, and its functions can be easily expanded, such as integrating a fertilization system to achieve precise control of water and fertilizer integration; Based on long-term operational data, the system can learn and optimize itself, and the irrigation strategy becomes more and more accurate over time. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating an intelligent precision irrigation system based on the Internet of Things and fuzzy control according to an exemplary embodiment; Figure 2 This is a flowchart illustrating an intelligent precision irrigation method based on the Internet of Things and fuzzy control, according to an exemplary embodiment. Detailed Implementation

[0016] The invention will now be discussed with reference to exemplary embodiments. It should be understood that the described embodiments are merely intended to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0017] As used herein, the term “comprising” and its variations are to be interpreted as open-ended terms meaning “including but not limited to”. The term “based on” is to be interpreted as “at least partially based on”, and the terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment”.

[0018] According to one embodiment of the present invention, Figure 1 This is a schematic diagram illustrating an intelligent precision irrigation system based on the Internet of Things and fuzzy control, according to an exemplary embodiment. Figure 1 As shown, to achieve the above objectives, the present invention provides an intelligent precision irrigation system based on the Internet of Things and fuzzy control, comprising: The sensing and execution module is used to collect environmental data and execute irrigation operations; The sensing and execution module includes an environmental data acquisition unit and an irrigation execution unit. The environmental data acquisition unit includes a multi-source sensor array for collecting environmental and crop data. The irrigation execution unit is used to receive specific irrigation instructions and irrigate crops; The platform application module is used to store and analyze the data in the environmental data acquisition unit and then generate irrigation instructions; The platform application module includes a data storage unit, a data analysis unit, and a twin experiment unit. The data storage unit is used to store environmental and crop data and irrigation instructions. The data analysis unit is used to analyze environmental and crop data based on fuzzy control algorithms and generate irrigation instructions; Twin experiment unit, used to simulate crops and predict crop growth results based on irrigation instructions; The network transmission module is used for communication connections between the sensing and execution module and the platform application module.

[0019] According to one embodiment of this application, the sensing and execution module further includes an edge node unit. The edge node module includes multiple edge node devices, which are communicatively connected to the environmental data acquisition unit and the irrigation execution unit. The edge node unit is used to perform preliminary cleaning of the data, remove obviously abnormal sensor data, and aggregate high-frequency data to generate a data integration package, which is then transmitted to the platform application module via the network transmission module. The edge node unit receives and backs up the data from the irrigation execution unit, generates local edge decision commands for storage, and uses the stored local edge decision commands to make real-time decisions, generate irrigation instructions, and control the irrigation execution unit when the system is offline. When the system is online, the edge node unit automatically uploads all local operation records from the offline state to the platform application module for data synchronization and analysis.

[0020] According to one embodiment of this application, in the data analysis unit, the difference between the expected humidity and the actual humidity in the soil and the rate of change of the deviation are used as input variables. The calculation is performed using a preset membership function and a fuzzy rule base, and the irrigation amount is used as the output variable to generate the corresponding irrigation instruction.

[0021] According to one embodiment of this application, in the data analysis unit, the input soil moisture deviation is processed based on the Gaussian membership function, and converted into the membership degree of a fuzzy set, wherein the formula is: ; Represents the membership degree of a fuzzy set; Indicates soil moisture deviation; Represents the center value of a fuzzy set; Indicates the width of the fuzzy set; The activation strength of the current rule is obtained by multiplying the membership degrees of multiple fuzzy sets under the same rule, where the formula is: ; This represents the activation strength of the j-th rule; It represents the membership degree of another fuzzy set under the same rule; ; Indicates irrigation volume; This represents the weight value of the j-th rule; According to one embodiment of this application, in a twin experimental unit, a three-dimensional terrain mesh model containing information on field boundaries, slope, aspect, and elevation is generated based on satellite detection and UAV oblique photogrammetry. It also includes soil profile models and crop growth models. Based on the soil profile model, the soil is divided into multiple layers in the vertical direction, and the physical properties of each layer are defined. Irrigation instructions are optimized based on the soil profile model. Based on the crop growth model, crop growth is divided into germination, seedling stage, vegetative growth stage, flowering stage, fruiting stage and maturity stage, and irrigation instructions are optimized for different stages.

[0022] According to one embodiment of this application, in response to an irrigation command generated by a data analysis unit, this irrigation command is simulated in a twin experimental unit to map the dynamic changes in soil moisture, and a soil moisture distribution heat map is generated based on the dynamic changes, wherein the formula is... ; Indicates the volumetric water content of the soil; Indicates the elapsed time; Indicates the depth of the soil; Indicates soil hydraulic conductivity; Indicates soil water potential; This indicates the root system's ability to absorb water.

[0023] According to one embodiment of this application, the platform application module further includes a weather forecasting unit, which is used to obtain the weather conditions for future times, compare the weather conditions with irrigation instructions, and optimize the irrigation instructions.

[0024] According to one embodiment of this application, it also includes a user interface for users to remotely monitor field data in real time, increase or decrease irrigation amount based on user experience, and view historical records and alarm information in real time.

[0025] According to one embodiment of this application, the sensor array includes a soil moisture sensor, a soil pH sensor, a temperature sensor, a humidity sensor, a light intensity sensor, and a rainfall sensor, etc., for comprehensively collecting environmental data.

[0026] According to one embodiment of this application, the irrigation execution unit includes a solenoid valve, a water pump, a fertilizer injection pump, etc., and is responsible for receiving instructions and executing specific irrigation and fertilization operations.

[0027] According to one embodiment of this application, the user interface also includes displaying the soil moisture, equipment on / off status, and current irrigation mode of each field in the form of a map or list on the mobile phone homepage.

[0028] According to one embodiment of this application, the system is deployed in a 10-hectare farmland to establish a virtual farmland. Each 0.5-hectare area is a control unit. Each unit is equipped with a soil temperature and humidity composite sensor, a light sensor, a rainfall sensor, and an edge node device. A LoRa wireless gateway is deployed in the field to cover all devices. The user selects "corn" as the crop to be planted on the APP, and the current growth stage is "tasseling stage". The cloud platform automatically calls the ideal soil moisture range required by the crop at this stage (such as 70%-85% of field capacity). The system collects data once per minute. At 2:00 PM one day, the platform received data from a certain unit: soil moisture was 65%, temperature was 35℃, sunlight was strong, and the weather forecast indicated no rain was expected. Based on input conditions such as "low humidity" and "strong evaporation," the fuzzy control algorithm triggered the corresponding irrigation rules and calculated that a moderate to large amount of irrigation was needed. The system was then tested in a virtual field. If the test was successful, the solenoid valve in that area was instructed to open for 12 minutes. After irrigation, the system monitored that the soil moisture had recovered to 78%, which met expectations. The system recorded the data from this successful irrigation for subsequent algorithm optimization. The entire process required no manual intervention, achieving fully automatic intelligent irrigation.

[0029] Furthermore, to achieve the aforementioned objectives, this invention also provides an intelligent and precise irrigation method based on the Internet of Things and fuzzy control. Figure 2 This is a flowchart illustrating an intelligent precision irrigation method based on the Internet of Things and fuzzy control, according to an exemplary embodiment. Figure 2 As shown, an intelligent precision irrigation method based on the Internet of Things and fuzzy control in this invention includes: Based on actual field data, a twin simulation test field was created; According to the sensor settings, environmental data is collected periodically, and the collected data is uploaded to the platform application module through the network transmission module; Based on the data received by the platform, the data is analyzed using a fuzzy control algorithm to generate irrigation instructions; Experiments were conducted in a twin-simulation test field based on irrigation instructions. Soil moisture distribution heat maps were generated and analyzed. If the results did not meet the requirements, the irrigation instructions were optimized. If the results met the requirements, the optimal irrigation instructions were generated. The platform application module sends irrigation commands to the irrigation execution unit to control the designated valve controllers and water pumps to open or close. The execution status is monitored in real time, and the execution results are sent back to the platform application module. The platform application module stores and analyzes irrigation results, and optimizes fuzzy rules and parameters through machine learning algorithms.

[0030] According to one embodiment of this application, if the system is offline, the edge node unit uses stored local edge decision commands to make real-time decisions, generate irrigation instructions, and control the irrigation execution unit. When the network is restored, all local operation records in the offline state are uploaded to the platform application module for data synchronization and analysis.

[0031] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0032] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0033] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0034] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0035] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0036] If the aforementioned functions are implemented as software functional modules 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 this invention, essentially, or the part that contributes to the prior art, or a portion 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 energy-saving signal transmission / reception methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0037] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0038] It should be understood that the sequence number of each step in the invention and embodiments of the present invention does not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Claims

1. An intelligent precision irrigation system based on Internet of Things and fuzzy control, characterized in that, The system comprises: a perception and execution module for collecting environmental data and performing irrigation operations; the perception and execution module comprises an environmental data collection unit and an irrigation execution unit, the environmental data collection unit comprises a multi-source sensor array for collecting environmental and crop data; the irrigation execution unit is used for receiving specific irrigation instructions and irrigating crops; a platform application module for storing and analyzing data in the environmental data collection unit to generate irrigation instructions; the platform application module comprises a data storage unit, a data analysis unit and a twin experiment unit, the data storage unit is used for storing environmental and crop data and irrigation instructions; the data analysis unit is used for analyzing the environmental and crop data based on a fuzzy control algorithm to generate irrigation instructions; the twin experiment unit is used for simulating crops, predicting crop growth results based on irrigation instructions; a network transmission module for communication connection between the perception and execution module and the platform application module.

2. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 1, wherein, The perception and execution module further comprises an edge node unit, the edge node module comprises a plurality of edge node devices, and the plurality of edge node devices are in communication connection with the environmental data collection unit and the irrigation execution unit; the edge node unit is used for preliminary cleaning of data, elimination of obviously abnormal sensor data and data aggregation of high-frequency data, and generation of a data integration package transmitted to the platform application module based on the network transmission module; the edge node unit receives and backs up the data of the irrigation execution unit, generates local edge decision commands for storage, uses the stored local edge decision commands for real-time decision-making in an offline state of the system, generates irrigation instructions and controls the irrigation execution unit; when the system is online, the edge node unit automatically uploads all local operation records in the offline state to the platform application module for data synchronization and analysis.

3. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 2, wherein, In the data analysis unit, the difference between the expected soil humidity and the actual soil humidity and the deviation change rate are used as input variables, a preset membership function and a fuzzy rule base are used for calculation, and the irrigation amount is used as an output variable to generate corresponding irrigation instructions.

4. The intelligent precision irrigation system based on the Internet of Things and fuzzy control according to claim 3, characterized in that, In the data analysis unit, the soil humidity deviation is processed based on a Gaussian membership function to convert it into the membership degree of a fuzzy set, wherein the formula is ； membership function representing a fuzzy set; represents the soil moisture bias; representing a center value of the fuzzy set; represents the width of the fuzzy set; The membership degrees of multiple fuzzy sets under the same rule are multiplied to obtain the activation strength of the current rule, wherein the formula is ； represents the activation strength of the jth rule; membership degree representing another fuzzy set under the same rule; ； represents the irrigation amount; Wj represents a weight value of the jth rule; Cj represents the center value of the fuzzy set representing the jth rule.

5. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 4, wherein, In the twin experiment unit, a three-dimensional terrain grid model containing field boundary, slope, aspect and elevation information is generated based on satellite detection and unmanned aerial vehicle oblique photogrammetry; It also includes a soil profile model and a crop growth model, the soil is divided into multiple layers in the vertical direction based on the soil profile model, and the physical properties of each layer are defined, and the irrigation instructions are optimized based on the soil profile model; Based on the crop growth model, the growth of crops is divided into germination, seedling stage, vegetative growth stage, flowering stage, fruiting stage and maturation stage, and the irrigation instructions are optimized for different stages.

6. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 5, wherein, In response to the generation of irrigation instructions by the data analysis unit, the irrigation instructions are simulated in the twin experiment unit to map the dynamic changes of soil moisture, and a soil moisture distribution thermodynamic map is generated according to the dynamic changes, wherein the formula is ； where VSWC represents the volumetric soil water content; represents elapsed time; represents the depth of the soil; K represents the soil hydraulic conductivity; Ψ represents soil water potential; Root water uptake capacity 7. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 6, wherein, The platform application module further comprises a weather forecast unit, which is configured to obtain weather conditions at a future time, compare the weather conditions with the irrigation instruction, and optimize the irrigation instruction.

8. The intelligent precision irrigation system based on Internet of Things and fuzzy control as claimed in claim 7, wherein, The platform application module further comprises a user interaction interface, which is configured to allow a user to remotely monitor field data in real time, increase or decrease the irrigation amount based on the user's experience, and view historical records and alarm information in real time.

9. An Internet of Things and fuzzy control based smart precision irrigation method using the Internet of Things and fuzzy control based smart precision irrigation system as claimed in any of claims 1-8. The platform application module comprises the following steps: Based on the actual field data, a twin simulation test field is created; Periodic collection of environmental data is performed according to the sensor settings, and the collected data is uploaded to the platform application module through the network transmission module; Based on the data received by the platform, the data is analyzed based on a fuzzy control algorithm to generate an irrigation instruction; Based on the irrigation instruction, an experiment is performed in the twin simulation test field, a soil moisture distribution and heat map is generated and analyzed, if it does not meet the requirements, the irrigation instruction is optimized, if it meets the requirements, an optimal irrigation instruction is generated; The platform application module sends the irrigation instruction to the irrigation execution unit to control the specified valve controller and water pump to open or close; The execution state is monitored in real time, and the execution result is fed back to the platform application module; The platform application module stores and analyzes the irrigation result, and optimizes the fuzzy rules and parameters through a machine learning algorithm.

10. The intelligent precision irrigation method based on the Internet of Things and fuzzy control according to claim 9, characterized in that, If it is in an offline state, real-time decision-making is performed using the stored local edge decision command through the edge node unit to generate an irrigation instruction and control the irrigation execution unit, and when the network is restored, all local operation records in the offline state are uploaded to the platform application module for data synchronization and analysis.

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