Distributed Intelligent Drip Irrigation Control System Based on the Internet of Things

Abstract

The present invention discloses a distributed intelligent drip irrigation control system based on the Internet of Things, which belongs to the field of agricultural irrigation technology and includes: basic water conservancy facilities: used to provide a certain water pressure for the drip irrigation system; a water tower: designed as a constant pressure water source to ensure the uniformity of water pressure in the entire irrigation area; a water-fertilizer mixing box: used to mix fertilizer and water in proportion to form a water-fertilizer solution suitable for crop absorption. The mixing ratio is determined by the algorithm #imgabs0#, where F is the fertilizer concentration and N is the water-fertilizer solution. req is the amount of nutrients required by crops, N soil The current amount of nutrients in the soil. This invention addresses the problems of irrational resource allocation and inefficient utilization in traditional irrigation methods through efficient and precise water and fertilizer resource management. By integrating advanced sensor technology, wireless communication modules, and intelligent control algorithms, it not only enables precise regulation of water and fertilizer supply to farmland, but also automatically adjusts irrigation strategies based on real-time environmental data and crop growth conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural irrigation, and in particular relates to a distributed intelligent drip irrigation control system based on the Internet of Things. Background Art

[0002] With the continuous advancement of global agricultural modernization, traditional irrigation methods are no longer able to meet the requirements of efficient and precise management in agricultural production. In particular, the application of drip irrigation technology is plagued by problems such as irrational allocation of water and fertilizer resources and low utilization efficiency. These issues not only lead to resource waste but also limit the improvement of crop yield and quality. Therefore, the development of intelligent drip irrigation systems that can automatically adjust water and fertilizer supply based on soil conditions, crop needs, and environmental changes has become particularly urgent and important.

[0003] While some smart irrigation systems currently exist on the market, they often suffer from complex operations, high costs, and poor adaptability. They require extensive manual operation and monitoring, increasing the complexity and cost of agricultural production and hindering widespread adoption. Furthermore, these systems have limited data processing and decision-making capabilities, often failing to achieve truly precise control and optimal resource allocation.

[0004] Based on this, the present invention designs a distributed intelligent drip irrigation control system based on the Internet of Things to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of existing intelligent irrigation systems, which often have complex operation, high cost, poor adaptability, etc., making it difficult to promote and apply them in a wide range of agricultural production. These systems have limited capabilities in data processing and decision-making, and often cannot achieve true precise control and optimal resource allocation. A distributed intelligent drip irrigation control system based on the Internet of Things is proposed.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a distributed intelligent drip irrigation control system based on the Internet of Things, comprising:

[0007] Basic water conservancy facilities: used to provide a certain water pressure for the drip irrigation system;

[0008] Water tower: designed as a constant pressure water source to ensure uniform water pressure throughout the irrigation area;

[0009] Water-fertilizer mixing box: used to mix fertilizer and water in proportion to form a water-fertilizer solution suitable for crop absorption. The mixing ratio is determined by the algorithm. Determine, where F is the fertilizer concentration, N req is the amount of nutrients required by crops, N soil is the current amount of nutrients in the soil;

[0010] Sealed container and air compressor: used to store pressurized water-fertilizer mixture to ensure stable pressure of drip irrigation system;

[0011] Pressure limiting valve and pressure sensor: used together to monitor and adjust the pressure of water-fertilizer mixture in real time to ensure uniformity of drip irrigation;

[0012] Solenoid valve and dripper: As the actuator of the drip irrigation system, the solenoid valve controls the opening and closing of the dripper, which is responsible for evenly dripping the water and fertilizer solution into the roots of the crops;

[0013] Wireless communication module: realizes data transmission and communication between various components within the system, ensuring real-time exchange of information:

[0014] Sub-controller: responsible for data collection, processing and execution of control commands in their respective areas to achieve regional management;

[0015] Main controller: responsible for analyzing global data, formulating control strategies, and issuing control instructions to sub-controllers;

[0016] Environmental monitoring equipment: including temperature and humidity sensors, light intensity sensors, and image sensors, which collect key data on soil and atmospheric environment in real time, providing a basis for intelligent decision-making.

[0017] As a further description of the above technical solution:

[0018] The basic water conservancy facilities include:

[0019] Reservoir: used to store irrigation water to ensure continuous water supply to the system;

[0020] Filter: Installed in front of the water spring to filter impurities in the water and protect the drip irrigation system;

[0021] Water spring: used to draw water from the reservoir to provide the water pressure required for the drip irrigation system.

[0022] As a further description of the above technical solution:

[0023] The analysis of global data adopts an intelligent control algorithm and a disease identification algorithm, and the intelligent control algorithm adopts the following steps:

[0024] Soil moisture monitoring: Soil moisture sensors are used to collect data and the irrigation amount is determined using the algorithm A = f(SH, ETc), where A is the irrigation amount, SH is soil moisture, and ETc is crop evapotranspiration.

[0025] Crop growth model: Develop corresponding irrigation and fertilization strategies based on the growth characteristics and stages of different crops;

[0026] Environmental factor analysis: Comprehensively consider environmental factors such as temperature, humidity, and light to dynamically adjust irrigation and fertilization plans;

[0027] Automatic adjustment mechanism: Automatically adjust the opening time of the solenoid valve, the drip rate of the dripper, and the amount of fertilizer delivered based on real-time data and preset models.

[0028] As a further description of the above technical solution:

[0029] The disease identification algorithm includes the following steps:

[0030] Feature extraction: Extract features based on crop images of different diseases in historical data;

[0031] Establish a recognition model: Use the convolutional neural network algorithm to establish a recognition model, and train and verify the established recognition model;

[0032] Disease identification: Use the recognition model to identify the crop images obtained by the image sensor to obtain the crop disease results.

[0033] As a further description of the above technical solution:

[0034] The sub-controller may perform the following steps:

[0035] Data preprocessing: including filtering and outlier removal to ensure data quality;

[0036] Environmental data analysis: Apply the algorithm P = g(T, H, L) to predict crop growth conditions, where P is the crop growth forecast, T is temperature, H is humidity, and L is light intensity;

[0037] Data fusion: Fusing data from different sensors to obtain more accurate information about the environment and crop status.

[0038] As a further description of the above technical solution:

[0039] The main controller is used for data analysis and decision-making, specifically including:

[0040] Comprehensive data of each sub-region: Use algorithm D = h (A1, ..., A n ) Comprehensive decision, where D is the decision output, A i is the sub-region data;

[0041] Formulate water and fertilizer supply strategies: Automatically adjust drip irrigation and fertilization based on algorithm output and crop growth models;

[0042] Optimize irrigation plan: According to crop water requirement and soil moisture condition, use the algorithm IV = ETc × Area-SW-Peff to calculate irrigation amount, where IV is irrigation amount, ETc is crop water requirement, Area is irrigation area, SW is soil moisture condition, and Peff is effective rainfall.

[0043] As a further description of the above technical solution:

[0044] The control system further includes a user interaction interface, which is used to:

[0045] Real-time data display: Displays key environmental and system parameters calculated by the algorithm V=v(SH,T,H,L,F), where V is the visualization parameter set, SH is soil moisture, T is temperature, H is humidity, L is light intensity, and F is fertilizer concentration;

[0046] Remote monitoring: Users can access the system remotely through the network and monitor the system status in real time;

[0047] Manual adjustment: Users can manually adjust control parameters such as drip irrigation amount and fertilization amount according to their needs;

[0048] User authority management: Provide corresponding operation interface and control functions according to the roles and permissions of different users.

[0049] As a further description of the above technical solution:

[0050] The control system further includes a self-diagnosis and fault alarm module, which includes:

[0051] Fault detection algorithm: Use the algorithm Fd=fd(Sd, Pd) to detect system faults, where Fd is the fault detection result, Sd is the system status data, and Pd is the performance data;

[0052] Self-diagnosis: The system can automatically diagnose the problem and provide fault location information;

[0053] Alarm notification: When a fault is detected, the system will automatically send an alarm message to the user;

[0054] Troubleshooting suggestions: Provide troubleshooting suggestions and steps to help users quickly solve problems.

[0055] As a further description of the above technical solution:

[0056] The control system adopts a modular design, which has the following features:

[0057] According to the specific conditions of the farmland, the modules are configured using the algorithm M = m(S, C, W), where M is the module configuration, S is the soil type, c is the crop type, and W is the water availability;

[0058] Flexible expansion: The system design takes into account future technological developments and changes in farmland conditions, and supports the addition and upgrading of modules.

[0059] As a further description of the above technical solution:

[0060] The control system can be adaptively adjusted to suit different soil types, crop varieties and climatic conditions;

[0061] Adaptive adjustment is achieved through the following algorithm:

[0062] Soil adaptability: Adjust irrigation and fertilization strategies through soil testing and analysis using the algorithm SA = sa(S, W, N), where SA is soil adaptability adjustment, S is soil type, W is water retention capacity, and N is soil nutrient level;

[0063] Crop adaptability: Develop personalized irrigation and fertilization plans based on the growth needs of different crops, using the algorithm CA = ca(C, G, S), where CA is crop adaptability adjustment, c is crop type, G is growth stage, and s is soil condition;

[0064] Climate adaptability: Dynamically adjust irrigation strategies based on climate data, such as rainfall, temperature, and humidity, using the algorithm CLA = cla(T, H, R), where CLA stands for climate adaptability adjustment, T is temperature, H is humidity, and R is rainfall.

[0065] Flexibility: The system design takes into account the diversity of different agricultural environments and can flexibly respond to various conditions and needs.

[0066] The control system adopts a low power consumption design, which specifically includes:

[0067] Efficient energy management: optimize system operation and reduce unnecessary energy consumption;

[0068] Energy-saving components: Select low-power electronic components and equipment;

[0069] Power management: Rationally allocate power to ensure efficient system operation;

[0070] Energy consumption optimization: Energy consumption is optimized through the algorithm E=e(Ld,Ac), where E is the energy, Ld is the load demand, and Ac is the activity period.

[0071] Data exchange and integration between the modular control system and external systems are achieved through the following methods:

[0072] Data interface standardization: ensures data exchange with external systems such as weather stations, using the algorithm Di = di(W, M, O), where Di is the data interface configuration, W is weather data, M is market data, and O is operational data;

[0073] Information integration: Integrate external meteorological data, agricultural experts' advice and other information to provide comprehensive decision-making support;

[0074] Intelligent analysis: Combine external and internal data to conduct in-depth analysis and optimize irrigation strategies.

[0075] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0076] The present invention solves the problems of irrational resource allocation and low utilization efficiency in traditional irrigation methods through efficient and precise management of water and fertilizer resources, reduces agricultural production costs, and thus improves the sustainability and economic benefits of agricultural production. By integrating advanced sensor technology, wireless communication modules, and intelligent control algorithms, it can not only achieve precise regulation of farmland water and fertilizer supply, but also automatically adjust irrigation strategies based on real-time environmental data and crop growth conditions. The disease recognition algorithm used can automatically identify common diseases based on crop appearance images. Personnel can select appropriate medicines according to the symptoms and add them to the drip irrigation system, realizing simultaneous drip irrigation and treatment.

[0077] It can monitor key environmental parameters such as soil moisture, temperature, and light intensity in real time. It can also intelligently adjust drip irrigation water and fertilizer application according to the specific needs of crops and environmental changes. In this way, the system can significantly improve the utilization efficiency of water and fertilizer resources, reduce agricultural production costs, and improve crop growth quality and yield, providing strong technical support for the sustainable development of modern agriculture.

[0078] Integrated soil and crop growth monitoring sensors collect real-time data on soil moisture, nutrient content, and crop growth conditions. Intelligent control algorithms use this data to accurately calculate and allocate water and fertilizer resources, ensuring that every drop of water and fertilizer is used to its maximum effect.

[0079] The intelligent control strategy can dynamically adjust the drip irrigation and fertilization amounts according to the actual needs of crops and environmental changes, significantly improving the utilization efficiency of water and fertilizer resources and reducing resource waste; it achieves fully automated operation, automatically completing data collection, processing and decision-making through wireless communication modules and intelligent control algorithms, greatly reducing the need for human intervention. The distributed structure design improves the stability and reliability of the system. The independent control and monitoring capabilities of each sub-area ensure that local problems will not affect the operation of the entire system; the modular design makes the system easier to install and can be flexibly configured according to the specific conditions of the farmland. The self-diagnosis and fault alarm functions make system maintenance easier and reduce maintenance costs and time. The intelligent algorithm can adjust the drip irrigation strategy according to real-time data, has strong adaptability, and can meet the irrigation needs of different farmlands. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a schematic diagram of the connection structure of the distributed intelligent drip irrigation control system based on the Internet of Things proposed by the present invention;

[0081] Figure 2 This is a control flow diagram of the distributed intelligent drip irrigation control system based on the Internet of Things proposed by the present invention;

[0082] Figure 3 This is a schematic diagram of the disease identification process of the distributed intelligent drip irrigation control system based on the Internet of Things proposed by the present invention. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] Please see the attached Figure 1 -Attached Figure 3 The present invention provides a technical solution: a distributed intelligent drip irrigation control system based on the Internet of Things, comprising:

[0085] Basic water conservancy facilities: used to provide a certain water pressure for the drip irrigation system;

[0086] Water tower: designed as a constant pressure water source to ensure uniform water pressure throughout the irrigation area;

[0087] Water-fertilizer mixing box: used to mix fertilizer and water in proportion to form a water-fertilizer solution suitable for crop absorption. The mixing ratio is determined by the algorithm. Determine, where F is the fertilizer concentration, N req is the amount of nutrients required by crops, Nsoil is the current amount of nutrients in the soil;

[0088] Sealed container and air compressor: used to store pressurized water-fertilizer mixture to ensure stable pressure of drip irrigation system;

[0089] Pressure limiting valve and pressure sensor: used together to monitor and adjust the pressure of water-fertilizer mixture in real time to ensure uniformity of drip irrigation;

[0090] Solenoid valve and dripper: As the actuator of the drip irrigation system, the solenoid valve controls the opening and closing of the dripper, which is responsible for evenly dripping the water and fertilizer solution into the roots of the crops;

[0091] Wireless communication module: realizes data transmission and communication between various components within the system, ensuring real-time exchange of information:

[0092] Sub-controller: responsible for data collection, processing and execution of control commands in their respective areas to achieve regional management;

[0093] Main controller: responsible for analyzing global data, formulating control strategies, and issuing control instructions to sub-controllers;

[0094] Environmental monitoring equipment: including temperature and humidity sensors, light intensity sensors, and image sensors, which collect key data on soil and atmospheric environment in real time, providing a basis for intelligent decision-making.

[0095] Basic water conservancy facilities include:

[0096] Reservoir: used to store irrigation water to ensure continuous water supply to the system;

[0097] Filter: Installed in front of the water spring to filter impurities in the water and protect the drip irrigation system;

[0098] Water spring: used to draw water from the reservoir to provide the water pressure required for the drip irrigation system.

[0099] The intelligent control algorithm and disease identification algorithm are used to analyze the global data. The intelligent control algorithm adopts the following steps:

[0100] Soil moisture monitoring: Use soil moisture sensors to collect data and determine the irrigation amount using the algorithm A=f(SH,ETc), where A is the irrigation amount, S H is soil moisture, ETc is crop evapotranspiration;

[0101] Crop growth model: Develop corresponding irrigation and fertilization strategies based on the growth characteristics and stages of different crops;

[0102] Environmental factor analysis: Comprehensively consider environmental factors such as temperature, humidity, and light to dynamically adjust irrigation and fertilization plans;

[0103] Automatic adjustment mechanism: Automatically adjust the opening time of the solenoid valve, the drip rate of the dripper, and the amount of fertilizer delivered based on real-time data and preset models.

[0104] The disease identification algorithm includes the following steps:

[0105] Feature extraction: Extract features based on crop images of different diseases in historical data;

[0106] Establish a recognition model: Use the convolutional neural network algorithm to establish a recognition model, and train and verify the established recognition model;

[0107] Disease identification: Use the recognition model to identify the crop images obtained by the image sensor to obtain the crop disease results.

[0108] The child controller can perform the following steps:

[0109] Data preprocessing: including filtering and outlier removal to ensure data quality;

[0110] Environmental data analysis: Apply the algorithm P = g(T, H, L) to predict crop growth conditions, where P is the crop growth forecast, T is temperature, H is humidity, and L is light intensity;

[0111] Data fusion: Fusing data from different sensors to obtain more accurate information about the environment and crop status.

[0112] The main controller is used for data analysis and decision-making, including:

[0113] Comprehensive data of each sub-region: Use algorithm D = h (A1, ..., A n ) Comprehensive decision, where D is the decision output, A i is the sub-region data;

[0114] Formulate water and fertilizer supply strategies: Automatically adjust drip irrigation and fertilization based on algorithm output and crop growth models;

[0115] Optimize irrigation plan: According to crop water requirement and soil moisture condition, use the algorithm IV = ETc × Area-SW-Peff to calculate irrigation amount, where IV is irrigation amount, ETc is crop water requirement, Area is irrigation area, SW is soil moisture condition, and Peff is effective rainfall.

[0116] The control system also includes a user interface, which is used to:

[0117] Real-time data display: Displays key environmental and system parameters calculated by the algorithm V=v(SH,T,H,L,F), where V is the visualization parameter set, sH is soil moisture, T is temperature, H is humidity, L is light intensity, and F is fertilizer concentration;

[0118] Remote monitoring: Users can access the system remotely through the network and monitor the system status in real time;

[0119] Manual adjustment: Users can manually adjust control parameters such as drip irrigation amount and fertilization amount according to their needs;

[0120] User authority management: Provide corresponding operation interface and control functions according to the roles and permissions of different users.

[0121] The control system also includes a self-diagnosis and fault alarm module, which includes:

[0122] Fault detection algorithm: Use the algorithm Fd=fd(Sd, Pd) to detect system faults, where Fd is the fault detection result, Sd is the system status data, and Pd is the performance data;

[0123] Self-diagnosis: The system can automatically diagnose the problem and provide fault location information;

[0124] Alarm notification: When a fault is detected, the system will automatically send an alarm message to the user;

[0125] Troubleshooting suggestions: Provide troubleshooting suggestions and steps to help users quickly solve problems.

[0126] The control system adopts modular design, which has the following features:

[0127] According to the specific conditions of the farmland, the modules are configured using the algorithm M = m(S, C, W), where M is the module configuration, s is the soil type, c is the crop type, and W is the water availability;

[0128] Flexible expansion: The system design takes into account future technological developments and changes in farmland conditions, and supports the addition and upgrading of modules.

[0129] The control system can be adapted to suit different soil types, crop varieties and climatic conditions;

[0130] Adaptive adjustment is achieved through the following algorithm:

[0131] Soil adaptability: Adjust irrigation and fertilization strategies through soil testing and analysis using the algorithm SA = sa(S, W, N), where SA is soil adaptability adjustment, S is soil type, W is water retention capacity, and N is soil nutrient level;

[0132] Crop adaptability: Develop personalized irrigation and fertilization plans based on the growth needs of different crops, using the algorithm CA = ca(C, G, S), where CA is crop adaptability adjustment, c is crop type, G is growth stage, and s is soil condition;

[0133] Climate adaptability: Dynamically adjust irrigation strategies based on climate data, such as rainfall, temperature, and humidity, using the algorithm CLA = cla(T, H, R), where CLA stands for climate adaptability adjustment, T is temperature, H is humidity, and R is rainfall.

[0134] Flexibility: The system design takes into account the diversity of different agricultural environments and can flexibly respond to various conditions and needs.

[0135] The control system adopts a low-power design, which specifically includes:

[0136] Efficient energy management: optimize system operation and reduce unnecessary energy consumption;

[0137] Energy-saving components: Select low-power electronic components and equipment;

[0138] Power management: Rationally allocate power to ensure efficient system operation;

[0139] Energy consumption optimization: Energy consumption is optimized through the algorithm E=e(Ld,Ac), where E is the energy, Ld is the load demand, and Ac is the activity period.

[0140] Data exchange and integration between the modularly designed control system and external systems are achieved through the following methods:

[0141] Data interface standardization: ensures data exchange with external systems such as weather stations, using the algorithm Di = di(w, M, O), where Di is the data interface configuration, W is weather data, M is market data, and O is operational data;

[0142] Information integration: Integrate external meteorological data, agricultural experts' advice and other information to provide comprehensive decision-making support;

[0143] Intelligent analysis: Combine external and internal data to conduct in-depth analysis and optimize irrigation strategies.

[0144] First embodiment:

[0145] The above control system is used for actual implementation operation:

[0146] 1. System layout and installation:

[0147] Total farmland area: 100 hectares;

[0148] Sub-area division: each sub-area is 10 hectares;

[0149] Water storage capacity: Each sub-area is equipped with a 50 cubic meter water storage tank;

[0150] Water pump flow: The water pump flow rate of each sub-area is set at 2 cubic meters per hour;

[0151] Water tower height: The water tower height is set at 20 meters to ensure sufficient water pressure;

[0152] 2. Sensor and actuator configuration:

[0153] Soil moisture sensors: 4 sensors per hectare, at depths of 10 cm, 20 cm, 30 cm, and 40 cm;

[0154] Light intensity sensor: one per hectare, 20 cm above the crop canopy;

[0155] Temperature and humidity sensor: one sensor is installed in each sub-area, located in the center of the field;

[0156] Solenoid valve specifications: 1 installed for each dripper, flow control range is 0.1-2 liters / hour;

[0157] Dripper spacing: According to the crop planting density, the dripper spacing is set to 1 meter;

[0158] 3. Control system setting and debugging:

[0159] Control cycle: The data collection and control instruction update cycle is 30 minutes;

[0160] Drip irrigation strategy: Based on the water demand of crops, the initial drip irrigation rate is set at 10 cubic meters per hectare per day;

[0161] Fertilizer ratio: The initial fertilizer ratio is set to 5 kg of fertilizer per cubic meter of water;

[0162] 4. System operation and monitoring:

[0163] After the system is started, the main controller receives environmental data from each sub-area every 30 minutes;

[0164] Based on real-time data and crop growth models, the main controller adjusts drip irrigation volume and fertilizer ratio;

[0165] Adjusted drip irrigation volume: The expected adjusted drip irrigation volume is 8-12 cubic meters per hectare per day, which will be adjusted dynamically based on the actual water requirements of the crop;

[0166] Adjusted fertilizer ratio: The expected adjusted fertilizer ratio is 4-6 kg of fertilizer per cubic meter of water, which will be adjusted dynamically according to soil nutrient conditions;

[0167] 5. User operation and result evaluation:

[0168] Users can monitor the system's operating status in real time through the user interface, including environmental data, drip irrigation volume, and fertilization volume;

[0169] Users can manually adjust control parameters based on crop-specific needs or environmental changes;

[0170] After the system has been running for a period of time, the expected results are a 10-15% increase in crop yields and a 20-30% increase in water and fertilizer resource utilization efficiency;

[0171] Evaluate the actual effectiveness of the system by comparing crop growth, yield and quality, as well as water and fertilizer utilization efficiency before and after implementation;

[0172] Through the above-mentioned specific implementation methods and quantitative parameters, the distributed intelligent drip irrigation control system of the present invention can achieve precise irrigation, improve the efficiency and sustainability of agricultural production, and reduce resource waste and production costs.

[0173] Second embodiment:

[0174] The above control system is used for actual implementation operation:

[0175] 1. System planning and deployment:

[0176] Total farmland area: 200 hectares;

[0177] Number of sub-areas: Divided into 20 sub-areas, each 10 hectares;

[0178] Reservoir design: Each sub-area is equipped with a 100 cubic meter reservoir to ensure sufficient water supply;

[0179] Water pump configuration: Two water pumps with a flow rate of 5 cubic meters per hour are installed in each sub-area to ensure irrigation needs;

[0180] Water and fertilizer mixing tank capacity: The water and fertilizer mixing tank capacity of each sub-area is 20 cubic meters to meet continuous irrigation needs;

[0181] 2. Sensor and actuator layout:

[0182] Soil moisture sensors: 6 sensors are installed per hectare, with depths set to 5cm, 15cm, and 30cm to cover the main distribution areas of crop roots;

[0183] Soil nutrient sensors: 2 sensors are installed per hectare to monitor nitrogen, phosphorus and potassium levels in the soil;

[0184] Weather station: one is installed in each sub-area to monitor meteorological data such as temperature, humidity, wind speed, and rainfall;

[0185] Solenoid valve specifications: Each dripper is equipped with 1, with a flow control range of 0.05-2.5 liters / hour to adapt to different crop needs;

[0186] Drip head layout: According to the crop planting row spacing, the drip head spacing is set at 0.5-1 meter to achieve uniform irrigation;

[0187] 3. Control system configuration and optimization:

[0188] Control cycle: The data acquisition and control instruction update cycle is 15 minutes to achieve faster response;

[0189] Drip irrigation strategy: The initial drip irrigation rate is set at 12 cubic meters per hectare per day, which is adjusted according to crop type and growth stage;

[0190] Fertilizer ratio: The initial fertilizer ratio is set at 3-8 kg of fertilizer per cubic meter of water, and is adjusted dynamically based on soil test results;

[0191] 4. System operation and monitoring:

[0192] After the system is started, the main controller receives and analyzes the environmental and soil data of each sub-area every 15 minutes;

[0193] Based on real-time data analysis and crop water requirement models, the main controller optimizes drip irrigation volume and fertilizer ratio;

[0194] Adjusted drip irrigation rate: The expected adjusted drip irrigation rate is 10-15 cubic meters per hectare per day to ensure that the crops receive appropriate water;

[0195] Adjusted fertilizer ratio: The expected adjusted fertilizer ratio is 2-7 kg of fertilizer per cubic meter of water to meet the nutritional needs of crops;

[0196] 5. User interaction and effect evaluation:

[0197] Users can view the environmental data, drip irrigation status and system operation status of each sub-area in real time through the interactive interface;

[0198] Users can manually adjust drip irrigation plans and fertilizer ratios to respond to sudden climate changes or changes in crop demand;

[0199] After the system has been running for a period of time, the actual effect of the system will be evaluated by comparing the crop growth data, yield and quality before and after implementation, as well as the efficiency of water and fertilizer resource utilization;

[0200] The expected effects are more uniform crop growth, a 15-20% increase in yield, and a 25-35% improvement in water and fertilizer efficiency;

[0201] Through the above-mentioned specific implementation methods, the distributed intelligent drip irrigation control system of the present invention can provide agricultural producers with an efficient and intelligent irrigation solution, achieve precise irrigation, improve the efficiency and sustainability of agricultural production, and reduce resource waste and production costs.

[0202] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The distributed intelligent drip irrigation control system based on the Internet of Things is characterized by: include: Basic water conservancy facilities: used to provide a certain water pressure for the drip irrigation system; Water tower: designed as a constant pressure water source to ensure uniform water pressure throughout the irrigation area; Water-fertilizer mixing box: used to mix fertilizer and water in proportion to form a water-fertilizer solution suitable for crop absorption. The mixing ratio is determined by the algorithm. Determine, where F is the fertilizer concentration, N req is the amount of nutrients required by crops, N soil is the current amount of nutrients in the soil; Sealed container and air compressor: used to store pressurized water-fertilizer mixture to ensure stable pressure of drip irrigation system; Pressure limiting valve and pressure sensor: used together to monitor and adjust the pressure of water-fertilizer mixture in real time to ensure uniformity of drip irrigation; Solenoid valve and dripper: As the actuator of the drip irrigation system, the solenoid valve controls the opening and closing of the dripper, which is responsible for evenly dripping the water and fertilizer solution into the roots of the crops; Wireless communication module: realizes data transmission and communication between various components within the system, ensuring real-time exchange of information: Sub-controller: responsible for data collection, processing and execution of control commands in their respective areas to achieve regional management; The sub-controller may perform the following steps: Data preprocessing: including filtering and outlier removal to ensure data quality; Environmental data analysis: Apply the algorithm P = g(T, H, L) to predict crop growth conditions, where P is the crop growth forecast, T is temperature, H is humidity, and L is light intensity; Data fusion: Fusing data from different sensors to obtain more accurate information about the environment and crop status; Main controller: responsible for analyzing global data, formulating control strategies, and issuing control instructions to sub-controllers; The main controller is used for data analysis and decision-making, specifically including: Comprehensive data of each sub-region: Use algorithm D=h(A1, ..., A n ) Comprehensive decision, where D is the decision output, A i is the sub-region data; Formulate water and fertilizer supply strategies: Automatically adjust drip irrigation and fertilization based on algorithm output and crop growth models; Optimize irrigation plan: Based on crop water requirement and soil moisture status, calculate irrigation amount using the algorithm IV = ETc × Area-SW-Peff, where IV is the irrigation amount, ETc is the crop water requirement, Area is the irrigation area, SW is the soil moisture status, and Peff is the effective rainfall; Environmental monitoring equipment: including temperature and humidity sensors, light intensity sensors, and image sensors, which collect key data on soil and atmospheric environment in real time, providing a basis for intelligent decision-making.

2. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The basic water conservancy facilities include: Reservoir: used to store irrigation water to ensure continuous water supply to the system; Filter: Installed in front of the water spring to filter impurities in the water and protect the drip irrigation system; Water spring: used to draw water from the reservoir to provide the water pressure required for the drip irrigation system.

3. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The analysis of global data adopts an intelligent control algorithm and a disease identification algorithm. The intelligent control algorithm adopts the following steps: Soil moisture monitoring: Soil moisture sensors are used to collect data and the irrigation amount is determined using the algorithm A = f(SH, ETc), where A is the irrigation amount, SH is soil moisture, and ETc is crop evapotranspiration. Crop growth model: Develop corresponding irrigation and fertilization strategies based on the growth characteristics and stages of different crops; Environmental factor analysis: Comprehensively consider environmental factors such as temperature, humidity, and light to dynamically adjust irrigation and fertilization plans; Automatic adjustment mechanism: Automatically adjust the opening time of the solenoid valve, the drip rate of the dripper, and the amount of fertilizer delivered based on real-time data and preset models.

4. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 3 is characterized in that: The disease identification algorithm includes the following steps: Feature extraction: Extract features based on crop images of different diseases in historical data; Establish a recognition model: Use the convolutional neural network algorithm to establish a recognition model, and train and verify the established recognition model; Disease identification: Use the recognition model to identify the crop images obtained by the image sensor and obtain the crop disease results.

5. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The control system further includes a user interaction interface, which is used to: Real-time data display: Displays key environmental and system parameters calculated by the algorithm V=v(SH'T'H'L'F), where V is the visualization parameter set, SH is soil moisture, T is temperature, H is humidity, L is light intensity, and F is fertilizer concentration; Remote monitoring: Users can access the system remotely through the network and monitor the system status in real time; Manual adjustment: Users can manually adjust control parameters such as drip irrigation amount and fertilization amount according to their needs; User authority management: Provide corresponding operation interface and control functions according to the roles and permissions of different users.

6. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The control system further includes a self-diagnosis and fault alarm module, which includes: Fault detection algorithm: Use the algorithm Fd=fd(Sd, Pd) to detect system faults, where Fd is the fault detection result, Sd is the system status data, and Pd is the performance data; Self-diagnosis: The system can automatically diagnose the problem and provide fault location information; Alarm notification: When a fault is detected, the system will automatically send an alarm message to the user; Troubleshooting suggestions: Provide troubleshooting suggestions and steps to help users quickly solve problems.

7. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The control system adopts a modular design, which has the following features: According to the specific conditions of the farmland, the modules are configured using the algorithm M = m(S, C, W), where M is the module configuration, S is the soil type, C is the crop type, and W is the water availability; Flexible expansion: The system design takes into account future technological developments and changes in farmland conditions, and supports the addition and upgrading of modules.

8. The distributed intelligent drip irrigation control system based on the Internet of Things according to claim 1 is characterized in that: The control system can be adaptively adjusted to suit different soil types, crop varieties and climatic conditions; Adaptive adjustment is achieved through the following algorithm: Soil adaptability: Adjust irrigation and fertilization strategies through soil testing and analysis using the algorithm SA = sa(S, W, N), where SA is soil adaptability adjustment, S is soil type, W is water retention capacity, and N is soil nutrient level; Crop adaptability: Develop personalized irrigation and fertilization plans based on the growth needs of different crops, using the algorithm CA = ca(C, C, S), where CA is crop adaptability adjustment, C is crop type, G is growth stage, and S is soil conditions; Climate adaptability: Dynamically adjust irrigation strategies based on climate data, such as rainfall, temperature, and humidity, using the algorithm CLA = cla(T, H, R), where CLA stands for climate adaptability adjustment, T is temperature, H is humidity, and R is rainfall. Flexibility: The system design takes into account the diversity of different agricultural environments and can flexibly respond to various conditions and needs.

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