Crop close-type water-saving irrigation equipment and use method thereof

Abstract

The invention provides crop close-type water-saving irrigation equipment and a using method thereof, and relates to the technical field of agriculture, and the crop close-type water-saving irrigation equipment comprises a data collection module which collects various data related to an irrigation decision; the analysis processing module is used for processing the collected data and providing a basis for irrigation decision making; the control module is used for executing an irrigation decision and coordinating the work of each subsystem; the mobile irrigation device is used for executing a specific irrigation decision; and the distributed water storage station is used for providing water source supply and charging services for the mobile irrigation device.

Description

Technical Field

[0001] This invention relates to the field of agricultural technology, and in particular to a water-saving irrigation device for crops that is close to them and its method of use. Background Technology

[0002] Existing technologies suffer from the following problems: With the increasing scarcity of global water resources and the development of precision agriculture, traditional large-area sprinkler irrigation methods can no longer meet the requirements of modern agriculture for efficient water resource utilization. Existing irrigation technologies mainly suffer from the following issues: Low water utilization rate: Traditional flood irrigation and sprinkler irrigation methods have a water utilization rate of only 40-60%, with a large amount of water wasted due to evaporation and runoff. Insufficient irrigation precision: Existing irrigation systems mostly use uniform standards for irrigation, failing to make precise adjustments according to the specific needs of different crops and different growth stages. High dependence on manual labor: Traditional irrigation requires a large amount of manual intervention, including opening and closing valves, moving equipment, and monitoring crop status, resulting in high labor intensity and low efficiency. Outdated monitoring methods: There is a lack of real-time monitoring of soil moisture and crop growth status, leading to irrigation decisions based on experience, which can easily result in over-irrigation or under-irrigation. Limited equipment installation: Existing irrigation equipment is mostly fixed in place, making it difficult to adapt to the irrigation needs of different plots, and the investment cost is high.

[0003] In view of this, the present invention proposes a water-saving irrigation device for crops and its usage method. Summary of the Invention

[0004] The main objective of this invention is to provide a water-saving irrigation device for crops that is close to the crops and its usage method. The technical solution includes the following aspects.

[0005] According to one aspect of the embodiments of this application, a crop-close-to-the-crop water-saving irrigation device is provided, including: a data acquisition module, an intelligent analysis and processing module, an adaptive coordination control module, and an intelligent mobile irrigation device;

[0006] Data acquisition module: Equipped with a multi-layer soil humidity sensor array, crop canopy temperature and humidity sensor, spectral imaging sensor and micro-meteorological monitoring unit to achieve three-dimensional real-time monitoring of farmland environmental parameters;

[0007] Intelligent analysis and processing module: integrates crop growth status recognition algorithm, soil moisture prediction model and dynamic irrigation demand optimization algorithm, and generates personalized irrigation decisions based on multi-source data fusion analysis;

[0008] Adaptive Coordination Control Module: Through a wireless communication network, multiple mobile irrigation devices are uniformly scheduled to achieve intelligent management of task allocation, path planning, and conflict avoidance;

[0009] Intelligent mobile irrigation device: It has autonomous navigation, precise positioning and variable water application functions, and can implement differentiated irrigation for individual crops.

[0010] According to one aspect of an embodiment of this application, the distributed multi-sensor data acquisition module includes:

[0011] Multi-depth soil moisture sensor array: Sensors are deployed at different depths to measure the moisture content of soil layers;

[0012] Crop canopy microenvironment sensor array: measures temperature, humidity, and CO2 concentration around crops;

[0013] Multispectral image acquisition system: Employs visible light and near-infrared dual-band imaging to monitor crop growth and pests and diseases;

[0014] Portable weather station: integrates sensors for wind speed, wind direction, rainfall, atmospheric pressure, and evaporation;

[0015] Soil EC / pH sensor: monitors changes in soil electrical conductivity and pH;

[0016] Wireless data transmission module: LoRa low-power wide area network technology is used to realize remote data transmission.

[0017] According to one aspect of an embodiment of this application, the deep learning-based intelligent analysis and processing module includes:

[0018] Edge computing processing unit: Equipped with a high-performance ARM processor to perform data preprocessing and feature extraction;

[0019] Crop growth status identification subsystem: Based on convolutional neural network algorithm, it identifies crop type, growth stage, nutritional status and pest and disease conditions;

[0020] Soil moisture dynamic prediction model: Combining historical data and real-time monitoring information, the Long Short-Term Memory (LSTM) network is used to predict the trend of soil moisture changes in the next 72 hours;

[0021] Multi-objective optimization decision engine: Taking into account crop water requirements, irrigation costs, energy consumption and environmental impact, it uses a genetic algorithm to optimize irrigation strategies;

[0022] Knowledge graph database: stores growth models, water requirement patterns, and management experience for different crop varieties;

[0023] Self-learning and model update mechanism: Continuously optimize the prediction model and decision-making algorithm based on feedback on irrigation results.

[0024] According to one aspect of an embodiment of this application, the adaptive coordination control module includes:

[0025] Multi-machine coordinated central controller: adopts a distributed control architecture to realize unified scheduling of multiple mobile irrigation devices;

[0026] Dynamic task allocation algorithm: intelligently allocates irrigation tasks based on device location, power supply, water volume, and the urgency of crop needs;

[0027] Real-time path planning system: Combines farmland topography, crop distribution, and obstacle information to generate the optimal movement path;

[0028] Conflict detection and avoidance mechanism: to prevent collisions and duplicate irrigation when multiple devices are operating in the same area;

[0029] 5G / WiFi 6 dual-mode communication system: ensures low-latency transmission of control commands and large-volume data return;

[0030] Fault diagnosis and emergency handling module: Automatically detects system malfunctions and activates backup plans or manual intervention procedures.

[0031] According to one aspect of an embodiment of this application, the adaptive coordination control module further includes:

[0032] Visual monitoring interface: Provides real-time monitoring of farmland, display of equipment status, and historical data analysis functions;

[0033] Mobile APP control system: supports remote monitoring, parameter adjustment and emergency intervention.

[0034] According to one aspect of an embodiment of this application, the intelligent mobile irrigation device includes:

[0035] All-terrain autonomous mobile chassis: adopts tracked or omnidirectional wheel design to adapt to complex farmland terrain, and is equipped with RTK-GPS precise positioning system;

[0036] Multispectral visual recognition system: combines visible light and thermal infrared cameras to achieve accurate crop identification and health status assessment;

[0037] Six-degree-of-freedom intelligent robotic arm: It has spatial positioning and attitude adjustment capabilities, and can precisely adjust the position and angle of the nozzle;

[0038] Variable flow spray system: Equipped with multi-stage adjustable nozzles, it adjusts the spray flow rate, pressure, and droplet size according to the size of the crop and its water requirements;

[0039] Intelligent obstacle avoidance system: integrates lidar and ultrasonic sensors to achieve dynamic obstacle detection and path replanning.

[0040] According to one aspect of the embodiments of this application, the intelligent mobile irrigation device further includes: a liquid mixing system: which can automatically prepare irrigation liquid containing fertilizer or pesticide as needed.

[0041] According to one aspect of an embodiment of this application, the intelligent mobile irrigation device further includes:

[0042] Hybrid power system: Combining lithium batteries and solar panels to extend driving range and reduce charging frequency;

[0043] Energy optimization controller: dynamically adjusts equipment power consumption based on task requirements and power availability.

[0044] According to one aspect of an embodiment of this application, the energy-self-sufficient distributed water storage network includes:

[0045] Large-capacity tiered water storage system: equipped with a clean water tank, a recycled water tank, and an emergency reserve tank, and equipped with real-time water level and water quality monitoring devices;

[0046] Intelligent variable frequency water pump group: Automatically adjusts the working status of the pump group according to the water replenishment demand to achieve energy-saving and efficient water replenishment;

[0047] Rainwater harvesting and purification system: Collects rainwater and treats it through sedimentation, filtration, and disinfection before using it as irrigation water;

[0048] Edge computing servers: Deploy data caching, task scheduling, and communication relay functions.

[0049] A smart control method for a crop-close-proximity water-saving irrigation device, characterized by comprising the following steps:

[0050] S1. Environmental perception and data fusion: The data acquisition module synchronously collects farmland environmental data and fuses multi-source information through the Kalman filter algorithm to generate a high-precision environmental status map;

[0051] S2. Intelligent identification of crop demand: Utilizes deep learning algorithms to analyze crop images and environmental data, identify crop varieties, growth stages, and health conditions, and calculate individualized water requirements;

[0052] S3. Predictive water demand modeling: Based on historical data and real-time monitoring information, a dynamic water demand prediction model is constructed to predict irrigation demand in the next 24-72 hours;

[0053] S4. Multi-objective optimization decision-making: Taking into account water-saving efficiency, energy consumption cost, operation time and crop health, the optimal irrigation strategy is generated by particle swarm optimization algorithm.

[0054] S5. Collaborative Task Scheduling: Based on the location, status, and capabilities of multiple mobile devices, tasks are decomposed and paths are planned to avoid conflicts and improve operational efficiency.

[0055] S6. Adaptive Precision Execution: After the mobile device reaches the target location, the crop status is reconfirmed through machine vision, and the nozzle parameters are adaptively adjusted to implement precise irrigation;

[0056] S7. Effect Evaluation and Learning: Monitor crop response and soil moisture changes after irrigation, evaluate irrigation effects and update prediction models and decision parameters;

[0057] S8. Intelligent resource scheduling: Real-time monitoring of device power and water levels, optimizing charging and water replenishment timing to ensure continuous operation.

[0058] The present invention provides a water-saving irrigation device for crops that is close to the crops and its method of use, which has the following advantages:

[0059] This invention significantly improves irrigation precision: by adjusting the nozzle position with a telescopic arm, it achieves close-range precision irrigation for individual crops, with an accuracy down to the centimeter level; and it also greatly enhances water conservation. It enables intelligent automated operation: based on multi-sensor data fusion and image recognition technology, it automatically identifies crop needs and executes irrigation decisions, reducing manual intervention. This invention improves operational efficiency: the mobile design overcomes the limitations of fixed facilities, allowing for continuous operation and a high daily processing capacity per unit. It reduces operating costs: high automation reduces labor costs; precision irrigation reduces water and fertilizer waste, resulting in lower overall operating costs. It is highly adaptable: suitable for various crop types and planting patterns, and irrigation strategies can be adjusted according to different needs. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the implementation methods of this application will be described in further detail below.

[0061] Example 1

[0062] Please refer to the following embodiment, which illustrates a crop-close-proximity water-saving irrigation device provided in this application, including: a distributed multi-sensor data acquisition module: configured with a multi-layer soil humidity sensor array, a crop canopy temperature and humidity sensor, a spectral imaging sensor and a micro-meteorological monitoring unit, to realize three-dimensional real-time monitoring of farmland environmental parameters;

[0063] Intelligent analysis and processing module: integrates crop growth status recognition algorithm, soil moisture prediction model and dynamic irrigation demand optimization algorithm, and generates personalized irrigation decisions based on multi-source data fusion analysis;

[0064] Adaptive Coordination Control Module: Through a wireless communication network, multiple mobile irrigation devices are uniformly scheduled to achieve intelligent management of task allocation, path planning, and conflict avoidance;

[0065] Intelligent mobile irrigation device: It has autonomous navigation, precise positioning and variable water application functions, and can implement differentiated irrigation for individual crops;

[0066] Energy-self-sufficient distributed water storage network: Equipped with a solar power system, water purification device and intelligent supply scheduling system, providing water and energy security for mobile devices.

[0067] In one embodiment, the distributed multi-sensor data acquisition module includes:

[0068] Multi-depth soil moisture sensor array: Sensors are deployed at depths of 0-20cm, 20-40cm, and 40-60cm to measure soil moisture content in different layers;

[0069] Crop canopy microenvironment sensor array: measures temperature, humidity, and CO2 concentration around crops;

[0070] Multispectral image acquisition system: Employs visible light and near-infrared dual-band imaging to monitor crop growth and pests and diseases;

[0071] Portable weather station: integrates sensors for wind speed, wind direction, rainfall, atmospheric pressure, and evaporation;

[0072] Soil EC / pH sensor: monitors changes in soil electrical conductivity and pH;

[0073] Wireless data transmission module: LoRa low-power wide area network technology is used to realize remote data transmission.

[0074] In one embodiment, the deep learning-based intelligent analysis and processing module includes:

[0075] Edge computing processing unit: Equipped with a high-performance ARM processor to perform data preprocessing and feature extraction;

[0076] Crop growth status identification subsystem: Based on the convolutional neural network (CNN) algorithm, it identifies crop type, growth stage, nutritional status, and pest and disease conditions;

[0077] Soil moisture dynamic prediction model: Combining historical data and real-time monitoring information, the Long Short-Term Memory (LSTM) network is used to predict the trend of soil moisture changes in the next 72 hours;

[0078] Multi-objective optimization decision engine: Taking into account crop water requirements, irrigation costs, energy consumption and environmental impact, it uses a genetic algorithm to optimize irrigation strategies;

[0079] Knowledge graph database: stores growth models, water requirement patterns, and management experience for different crop varieties;

[0080] Self-learning and model update mechanism: Continuously optimize the prediction model and decision-making algorithm based on feedback on irrigation results.

[0081] In one embodiment, the adaptive coordination control module includes:

[0082] Multi-machine coordinated central controller: adopts a distributed control architecture to realize unified scheduling of multiple mobile irrigation devices;

[0083] Dynamic task allocation algorithm: intelligently allocates irrigation tasks based on device location, power supply, water volume, and the urgency of crop needs;

[0084] Real-time path planning system: Combines farmland topography, crop distribution, and obstacle information to generate the optimal movement path;

[0085] Conflict detection and avoidance mechanism: to prevent collisions and duplicate irrigation when multiple devices are operating in the same area;

[0086] 5G / WiFi 6 dual-mode communication system: ensures low-latency transmission of control commands and large-volume data return;

[0087] Fault diagnosis and emergency handling module: Automatically detects system malfunctions and activates backup plans or manual intervention procedures.

[0088] In one embodiment, the adaptive coordination control module further includes:

[0089] Visual monitoring interface: Provides real-time monitoring of farmland, display of equipment status, and historical data analysis functions;

[0090] Mobile APP control system: supports remote monitoring, parameter adjustment and emergency intervention operations;

[0091] Voice interaction module: Supports natural language command input and system status voice broadcast;

[0092] Data visualization and analysis tools: generate irrigation effect reports, water-saving benefit analysis, and crop growth prediction charts.

[0093] In one embodiment, the intelligent mobile irrigation device includes:

[0094] All-terrain autonomous mobile chassis: adopts tracked or omnidirectional wheel design to adapt to complex farmland terrain, and is equipped with RTK-GPS precise positioning system;

[0095] Multispectral visual recognition system: combines visible light and thermal infrared cameras to achieve accurate crop identification and health status assessment;

[0096] Six-degree-of-freedom intelligent robotic arm: It has spatial positioning and attitude adjustment capabilities, and can precisely adjust the position and angle of the nozzle;

[0097] Variable flow spray system: Equipped with multi-stage adjustable nozzles, it adjusts the spray flow rate, pressure, and droplet size according to the size of the crop and its water requirements;

[0098] Intelligent obstacle avoidance system: integrates lidar and ultrasonic sensors to achieve dynamic obstacle detection and path replanning.

[0099] In one embodiment, the intelligent mobile irrigation device further includes:

[0100] The fertilizer / pesticide mixing system can automatically prepare irrigation solutions containing fertilizers or pesticides as needed.

[0101] In one embodiment, the intelligent mobile irrigation device further includes:

[0102] Hybrid power system: Combining lithium batteries and solar panels to extend driving range and reduce charging frequency;

[0103] Energy optimization controller: dynamically adjusts equipment power consumption based on task requirements and power availability.

[0104] In one embodiment, the energy-self-sufficient distributed water storage network includes:

[0105] Large-capacity tiered water storage system: equipped with a clean water tank (5-10m³), a recycled water tank (2-5m³), and an emergency reserve tank, and equipped with real-time water level and water quality monitoring devices;

[0106] Intelligent variable frequency water pump group: Automatically adjusts the working status of the pump group according to the water replenishment demand to achieve energy-saving and efficient water replenishment;

[0107] Rainwater harvesting and purification system: Collects rainwater and treats it through sedimentation, filtration, and disinfection before using it as irrigation water;

[0108] Edge computing servers: Deploy data caching, task scheduling, and communication relay functions.

[0109] In one embodiment, the energy-self-sufficient distributed water storage network further includes:

[0110] Intelligent scheduling and optimization system: Optimizes supply routes and timing based on the resource status and equipment requirements of each site;

[0111] Fault self-diagnosis module: monitors the operating status of key equipment such as water pumps, charging piles, and sensors;

[0112] Remote operation and maintenance interface: supports remote diagnosis, parameter adjustment and software updates by technical personnel;

[0113] Emergency backup system: Automatically switches to backup equipment in the event of a failure of the main equipment to ensure service continuity.

[0114] Example 2

[0115] A smart control method for a crop-close-proximity water-saving irrigation device, characterized by comprising the following steps:

[0116] S1. Environmental perception and data fusion: The data acquisition module synchronously collects farmland environmental data and fuses multi-source information through the Kalman filter algorithm to generate a high-precision environmental status map;

[0117] S2. Intelligent identification of crop demand: Utilizes deep learning algorithms to analyze crop images and environmental data, identify crop varieties, growth stages, and health conditions, and calculate individualized water requirements;

[0118] S3. Predictive water demand modeling: Based on historical data and real-time monitoring information, a dynamic water demand prediction model is constructed to predict irrigation demand in the next 24-72 hours;

[0119] S4. Multi-objective optimization decision-making: Taking into account water-saving efficiency, energy consumption cost, operation time and crop health, the optimal irrigation strategy is generated by particle swarm optimization algorithm.

[0120] S5. Collaborative Task Scheduling: Based on the location, status, and capabilities of multiple mobile devices, tasks are decomposed and paths are planned to avoid conflicts and improve operational efficiency.

[0121] S6. Adaptive Precision Execution: After the mobile device reaches the target location, the crop status is reconfirmed through machine vision, and the nozzle parameters are adaptively adjusted to implement precise irrigation;

[0122] S7. Effect Evaluation and Learning: Monitor crop response and soil moisture changes after irrigation, evaluate irrigation effects and update prediction models and decision parameters;

[0123] S8. Intelligent resource scheduling: Real-time monitoring of device power and water levels, optimizing charging and water replenishment timing to ensure continuous operation.

[0124] Example 3

[0125] The crop-adjacent water-saving irrigation equipment of the present invention includes a data acquisition module, an analysis and processing module, a control module, a mobile irrigation device, and a water storage station.

[0126] The data acquisition module includes:

[0127] Soil moisture sensor: A capacitive soil moisture sensor is used, which is deployed at depths of 10cm, 20cm and 30cm respectively, with a measurement range of 0-100% and an accuracy of ±3%.

[0128] Temperature and humidity sensor: adopts DHT22 digital sensor, temperature measurement range -40℃ to 80℃, accuracy ±0.5℃; humidity measurement range 0-100%RH, accuracy ±2%.

[0129] Image acquisition device: It adopts a 5-megapixel CMOS camera, supports autofocus, and can acquire clear crop images under different lighting conditions.

[0130] Rain gauge: A tipping bucket rain gauge is used, with a measurement accuracy of 0.2mm.

[0131] Data transmission unit: It adopts a 3MHz wireless transmission module, and the transmission distance can reach 1000 meters.

[0132] The analysis and processing module includes:

[0133] Data processor: Uses an ARM Cortex-A7 quad-core processor with a clock speed of 1.2GHz and 1GB of memory.

[0134] Crop recognition unit: Developed based on the OpenCV image processing library, it can identify the growth stages of common crops such as corn, wheat, and soybeans.

[0135] Water requirement calculation unit: The Penman-Monteith evapotranspiration model is used to calculate the crop water requirement based on crop type, growth stage, soil moisture and environmental parameters.

[0136] Decision generation unit: Taking into account crop water requirements, soil moisture conditions and weather forecasts, it generates the optimal irrigation strategy.

[0137] The control module includes:

[0138] Central controller: It adopts an industrial-grade PLC controller with multiple input / output interfaces.

[0139] Path planning unit: Based on the A* algorithm, it performs path planning to avoid obstacles and plan the shortest operation path.

[0140] Communication unit: Employs ZigBee wireless communication technology to ensure reliable command transmission.

[0141] Mobile irrigation devices include:

[0142] Mobile chassis: adopts a tracked structure, adapts to uneven ground, and has a maximum climbing angle of 25°.

[0143] Positioning module: Integrated GPS positioning chip, with a positioning accuracy of 3-5 meters, meeting the needs of farmland operations.

[0144] Telescopic boom: Hydraulically driven, with an extension length of 0.5-2.0 meters, a horizontal rotation angle of 360°, and a pitch angle of -30° to 90°.

[0145] Nozzle: An adjustable flow nozzle is used, with a flow range of 0.5-5L / min and an adjustable spray radius.

[0146] Water storage tank: 100L capacity, made of PE material, equipped with a water level sensor.

[0147] Drive motor: Adopts DC brushless motor, power 1.5kW, with a battery life of 6-8 hours.

[0148] The water storage station includes: a 2000L water storage container made of stainless steel, equipped with water level monitoring and filtration devices; a centrifugal pump with a flow rate of 30L / min and a head of 20m; and a charging device with both 220V AC charging and solar charging modes.

[0149] Work process

[0150] The working process of this invention is as follows:

[0151] Data acquisition phase: The data acquisition module continuously monitors soil moisture, environmental parameters and crop status, collecting data every 15 minutes and transmitting it to the analysis and processing module.

[0152] Data analysis phase: After receiving the data, the analysis and processing module first determines the crop type and growth stage through image recognition, and then calculates the crop water requirement by combining soil moisture and environmental parameters.

[0153] Decision generation stage: Based on crop water requirements and current soil moisture conditions, irrigation decisions are generated, including irrigation time, irrigation amount, and priority.

[0154] Task execution phase: After receiving the irrigation decision, the control module plans the movement path and sends control commands to the mobile irrigation device.

[0155] Precision irrigation stage: After the mobile irrigation device reaches the designated location, adjust the telescopic arm to bring the nozzle close to the target crop, and adjust the flow rate according to the water demand for precision irrigation.

[0156] Replenishment phase: When the water level in the storage tank is below 20% or the battery power is below 30%, the mobile irrigation device will automatically return to the water storage station for replenishment and charging.

[0157] The control algorithms involved include:

[0158] 1. Crop recognition algorithm: The image recognition method based on feature matching is adopted: Image preprocessing: noise reduction and contrast enhancement; Feature extraction: extracting the shape, color and texture features of the crop; Template matching: matching with pre-stored crop templates; Growth stage judgment: judging the growth stage based on crop size and number of leaves.

[0159] 2. Water demand calculation model: The modified Penman-Monteith formula is adopted: ET0 = [0.408Δ(Rn-G) +γ(900 / (T+273))U2(es-ea)] / [Δ + γ(1+0.34U2)]; where: ET0 is the reference crop evapotranspiration, Δ is the slope of the saturated vapor pressure curve, Rn is the net radiation, G is the soil heat flux, γ is the wet / dry constant, T is the average air temperature, U2 is the wind speed at 2m height, es is the saturated vapor pressure, and ea is the actual vapor pressure.

[0160] 3. Path planning algorithm: An improved A* algorithm is adopted: a grid map is built, and the locations of obstacles and crops are marked; the start and end points are set; the heuristic function is calculated: f(n) = g(n) + h(n); the node with the smallest f value is selected for expansion; the process is repeated until the optimal path is found.

[0161] Example 4

[0162] In another embodiment, the system can be improved as follows:

[0163] Multi-machine collaborative operation: Deploy 2-3 mobile irrigation devices and coordinate them through a control module to improve operational efficiency.

[0164] Weather forecast integration: Connect to the weather service API to obtain the weather forecast for the next 3 days and optimize irrigation plans.

[0165] Integrated water and fertilizer application: A fertilizer tank is added to the water storage tank to achieve simultaneous application of water and fertilizer.

[0166] Remote monitoring: Develop a mobile app to enable remote monitoring and parameter adjustment.

Claims

1. A water-saving irrigation device for crops, characterized in that, include: Data acquisition module, intelligent analysis and processing module, adaptive coordination control module, intelligent mobile irrigation device; Data acquisition module: Equipped with a multi-layer soil humidity sensor array, crop canopy temperature and humidity sensor, spectral imaging sensor and micro-meteorological monitoring unit to achieve three-dimensional real-time monitoring of farmland environmental parameters; Intelligent analysis and processing module: integrates crop growth status recognition algorithm, soil moisture prediction model and dynamic irrigation demand optimization algorithm, and generates personalized irrigation decisions based on multi-source data fusion analysis; Adaptive Coordination Control Module: Through a wireless communication network, multiple mobile irrigation devices are uniformly scheduled to achieve intelligent management of task allocation, path planning, and conflict avoidance; Intelligent mobile irrigation device: It has autonomous navigation, precise positioning and variable water application functions, and can implement differentiated irrigation for individual crops.

2. The crop-close water-saving irrigation equipment according to claim 1, characterized in that, The distributed multi-sensor data acquisition module includes: Multi-depth soil moisture sensor array: Sensors are deployed at different depths to measure the moisture content of soil layers; Crop canopy microenvironment sensor array: measures temperature, humidity, and CO2 concentration around crops; Multispectral image acquisition system: Employs visible light and near-infrared dual-band imaging to monitor crop growth and pests and diseases; Portable weather station: integrates sensors for wind speed, wind direction, rainfall, atmospheric pressure, and evaporation; Soil EC / pH sensor: monitors changes in soil electrical conductivity and pH; Wireless data transmission module: LoRa low-power wide area network technology is used to realize remote data transmission.

3. The crop-close water-saving irrigation equipment according to claim 1, characterized in that, The deep learning-based intelligent analysis and processing module includes: Edge computing processing unit: Equipped with a high-performance ARM processor to perform data preprocessing and feature extraction; Crop growth status identification subsystem: Based on convolutional neural network algorithm, it identifies crop type, growth stage, nutritional status and pest and disease conditions; Soil moisture dynamic prediction model: Combining historical data and real-time monitoring information, the Long Short-Term Memory (LSTM) network is used to predict the trend of soil moisture changes in the next 72 hours; Multi-objective optimization decision engine: Taking into account crop water requirements, irrigation costs, energy consumption and environmental impact, it uses a genetic algorithm to optimize irrigation strategies; Knowledge graph database: stores growth models, water requirement patterns, and management experience for different crop varieties; Self-learning and model update mechanism: Continuously optimize the prediction model and decision-making algorithm based on feedback on irrigation results.

4. The crop-close-proximity water-saving irrigation equipment according to claim 1, characterized in that, The adaptive coordination control module includes: Multi-machine coordinated central controller: adopts a distributed control architecture to realize unified scheduling of multiple mobile irrigation devices; Dynamic task allocation algorithm: intelligently allocates irrigation tasks based on device location, power supply, water volume, and the urgency of crop needs; Real-time path planning system: Combines farmland topography, crop distribution, and obstacle information to generate the optimal movement path; Conflict detection and avoidance mechanism: to prevent collisions and duplicate irrigation when multiple devices are operating in the same area; 5G / WiFi 6 dual-mode communication system: ensures low-latency transmission of control commands and large-volume data return; Fault diagnosis and emergency handling module: Automatically detects system malfunctions and activates backup plans or manual intervention procedures.

5. The crop-close-proximity water-saving irrigation equipment according to claim 4, characterized in that, The adaptive coordination control module also includes: Visual monitoring interface: Provides real-time monitoring of farmland, display of equipment status, and historical data analysis functions; Mobile APP control system: supports remote monitoring, parameter adjustment and emergency intervention.

6. The crop-close water-saving irrigation equipment according to claim 1, characterized in that, The intelligent mobile irrigation device includes: All-terrain autonomous mobile chassis: adopts tracked or omnidirectional wheel design to adapt to complex farmland terrain, and is equipped with RTK-GPS precise positioning system; Multispectral visual recognition system: combines visible light and thermal infrared cameras to achieve accurate crop identification and health status assessment; Six-degree-of-freedom intelligent robotic arm: It has spatial positioning and attitude adjustment capabilities, and can precisely adjust the position and angle of the nozzle; Variable flow spray system: Equipped with multi-stage adjustable nozzles, it adjusts the spray flow rate, pressure, and droplet size according to the size of the crop and its water requirements; Intelligent obstacle avoidance system: integrates lidar and ultrasonic sensors to achieve dynamic obstacle detection and path replanning.

7. The crop-close water-saving irrigation equipment according to claim 6, characterized in that, The intelligent mobile irrigation device also includes: a liquid mixing system: which can automatically prepare irrigation liquid containing fertilizer or pesticide as needed.

8. The crop-close water-saving irrigation equipment according to claim 6, characterized in that, The intelligent mobile irrigation device also includes: Hybrid power system: Combining lithium batteries and solar panels to extend driving range and reduce charging frequency; Energy optimization controller: dynamically adjusts equipment power consumption based on task requirements and power availability.

9. The crop-close-proximity water-saving irrigation equipment according to claim 1, characterized in that, The energy-self-sufficient distributed water storage station network includes: Large-capacity tiered water storage system: equipped with a clean water tank, a recycled water tank, and an emergency reserve tank, and equipped with real-time water level and water quality monitoring devices; Intelligent variable frequency water pump group: Automatically adjusts the working status of the pump group according to the water replenishment demand to achieve energy-saving and efficient water replenishment; Rainwater harvesting and purification system: Collects rainwater and treats it through sedimentation, filtration, and disinfection before using it as irrigation water; Edge computing servers: Deploy data caching, task scheduling, and communication relay functions.

10. A smart control method for a crop-proximity water-saving irrigation device according to any one of claims 1-9, characterized in that, Includes the following steps: S1. Environmental perception and data fusion: The data acquisition module synchronously collects farmland environmental data and fuses multi-source information through the Kalman filter algorithm to generate a high-precision environmental status map; S2. Intelligent identification of crop demand: Utilizes deep learning algorithms to analyze crop images and environmental data, identify crop varieties, growth stages, and health conditions, and calculate individualized water requirements; S3. Predictive water demand modeling: Based on historical data and real-time monitoring information, a dynamic water demand prediction model is constructed to predict irrigation demand in the next 24-72 hours; S4. Multi-objective optimization decision-making: Taking into account water-saving efficiency, energy consumption cost, operation time and crop health, the optimal irrigation strategy is generated by particle swarm optimization algorithm. S5. Collaborative Task Scheduling: Based on the location, status, and capabilities of multiple mobile devices, tasks are decomposed and paths are planned to avoid conflicts and improve operational efficiency. S6. Adaptive Precision Execution: After the mobile device reaches the target location, the crop status is reconfirmed through machine vision, and the nozzle parameters are adaptively adjusted to implement precise irrigation; S7. Effect Evaluation and Learning: Monitor crop response and soil moisture changes after irrigation, evaluate irrigation effects and update prediction models and decision parameters; S8. Intelligent resource scheduling: Real-time monitoring of device power and water levels, optimizing charging and water replenishment timing to ensure continuous operation.