Integrated intelligent monitoring and processing system and method for smart irrigation district

Abstract

The application discloses an integrated intelligent monitoring and processing system for a smart irrigation area, which comprises a cloud intelligent management and control platform and a vertical rod frame; the vertical rod frame is provided with a power supply unit, an edge computing control box, a communication unit, a soil integrated monitoring unit, a video monitoring and pest intelligent acquisition unit, a meteorological integrated monitoring unit and an unmanned aerial vehicle automatic inspection unit; the integrated intelligent monitoring and processing system realizes intelligent decision-making of water and fertilizer integrated regulation and control, intelligent early warning of diseases and insect pests and auxiliary decision-making of agricultural disaster assessment; and provides an innovative solution of 'equipment integration, data intelligence and precise management and control' for agricultural six-condition monitoring and analysis in the irrigation area.

Description

Technical Field

[0001] This invention relates to the construction and management of agricultural irrigation areas, specifically to an integrated intelligent monitoring and processing system and method for smart irrigation areas. Background Technology

[0002] In the construction and management of modern agricultural irrigation districts, real-time monitoring and analysis of six agricultural conditions (meteorological conditions, soil moisture, insect infestation, seedling growth, disaster conditions, and land conditions) is crucial for achieving the digital and intelligent development of irrigation districts. However, existing agricultural six-condition monitoring technologies generally have the following shortcomings: (1) Dispersed equipment deployment leads to management inefficiency and resource waste: In traditional monitoring models, monitoring equipment for meteorology, soil moisture, insect infestation, seedling growth, disasters, and land conditions is often deployed independently, with each device requiring its own support frame, power supply system, and communication unit. This decentralized hardware architecture not only increases the complexity of installing field equipment but also leads to inefficient use of land resources—various devices are scattered throughout the fields, hindering agricultural machinery operations and resulting in excessively high infrastructure costs due to redundant construction. Furthermore, the independent operation of multiple devices makes subsequent maintenance work arduous, requiring frequent inspections of different equipment by management personnel, consuming significant manpower and resources. Moreover, it is difficult to quickly locate and repair equipment malfunctions, severely impacting the continuity and reliability of data collection, and potentially leading to equipment damage or loss due to inadequate monitoring and management.

[0003] (2) Data silos hinder systematic decision analysis: Due to the lack of a unified data integration architecture, the data interfaces, transmission protocols, and storage formats of different monitoring devices vary significantly, creating "data silos." Inconsistent transmission protocols among multiple devices lead to difficulties in data interaction, requiring irrigation district managers to switch between multiple independent systems or platforms to query data. This process is cumbersome and time-consuming, making it difficult to quickly obtain an overall picture of the irrigation district's environment. More importantly, isolated single-point data cannot achieve multi-dimensional correlation analysis. For example, the separation between meteorological data and pest and disease occurrence patterns results in a lack of comprehensive environmental factor analysis for pest and disease early warning; the disconnect between soil moisture data and crop growth monitoring means that water and fertilizer management relies solely on experience, making precise regulation difficult. This inefficiency in data utilization severely restricts the scientific and timely nature of irrigation district management decisions.

[0004] (3) Weak intelligent processing capabilities lead to a lack of precise control: Existing monitoring systems are generally limited to the "data acquisition-visualization" stage, lacking the support of artificial intelligence algorithms, and exhibiting significant shortcomings, particularly in real-time processing and early warning. In pest and disease monitoring, traditional equipment relies on manual insect identification or simple image comparison, lacking sufficient real-time identification capabilities for migratory pests and emerging pests, resulting in delayed early warning responses. Regarding water and fertilizer management, fixed threshold control strategies are often employed, failing to dynamically adjust based on crop variety characteristics, growth stages, and short-term weather forecasts. This easily leads to problems such as over- or under-irrigation and unreasonable fertilizer ratios, affecting resource utilization efficiency and crop yield and quality. Furthermore, in the face of emergencies such as extreme weather, existing systems lack the ability to simulate and extrapolate based on historical data and domain knowledge, failing to provide targeted emergency management solutions and struggling to meet the demands of modern irrigation districts for precise and intelligent management.

[0005] (4) Insufficient intelligence in the integrated solution restricts the overall effectiveness of monitoring and analysis: While some technologies attempt to integrate multiple sensor devices through physical integration, these efforts merely amount to a simple hardware stacking, failing to achieve deep collaboration in data acquisition, transmission, and processing. Such solutions suffer from drawbacks such as data transmission congestion, weak intelligent analysis capabilities, and poor system scalability. For example, sharing transmission channels between video and sensor data leads to excessive latency; software platforms only support basic data display and lack AI-driven decision-making capabilities; adding new monitoring functions requires significant hardware and software modifications, making it difficult to adapt to the personalized needs of different irrigation district scenarios. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated intelligent monitoring and processing system and method for smart irrigation districts, which solves the problems of scattered equipment, inefficient decision-making, and extensive regulation in traditional agricultural monitoring and management.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The intelligent irrigation district integrated intelligent monitoring and processing system of the present invention includes a cloud-based intelligent management and control platform and a pole frame; the pole frame is equipped with a power supply unit, an edge computing control box, a communication unit, an integrated soil monitoring unit, a video monitoring and insect infestation intelligent collection unit, an integrated meteorological monitoring unit, and an unmanned aerial vehicle (UAV) automatic inspection unit.

[0008] Alternatively, the power supply unit, consisting of an off-grid power supply system comprising a solar photovoltaic panel, a maximum power point tracking control unit, a charging management unit, and a battery, provides continuous and stable independent power support for the intelligent monitoring and processing system. The edge computing control box is used to preprocess and perform preliminary identification on the acquired images and video streams, including image cropping, noise reduction, brightness correction, target region extraction, and key frame filtering. The communication unit uses the ESP32 series chip as the main control core, integrating 5G, LoRa, NB-IoT, and Ethernet multi-mode communication technologies. It adopts a dual-link transmission mechanism, combined with frequency division multiplexing and channel coding technology, to ensure low-latency and high-reliability data transmission in high-concurrency scenarios. Video data is directly connected to the cloud-based intelligent management and control platform through an independent 4G / 5G channel, while structured data is transmitted via Ethernet or IoT cards after edge computing, and frequency division multiplexing and channel coding technology are used to ensure data transmission quality. The integrated soil monitoring unit uses a multi-sensor that includes soil temperature, humidity, electrical conductivity, pH, nitrogen, phosphorus and potassium, which is directly deployed in the soil at the bottom of the pole frame to collect soil moisture and nutrient data in real time. The video monitoring and intelligent pest collection unit is equipped with an infrared night vision artificial intelligence camera to identify pest species, density and crop leaf morphology in real time, and simultaneously monitor the operating status of each unit of the support frame, triggering an early warning when abnormalities occur. The integrated meteorological monitoring unit includes a multi-sensor array for monitoring environmental parameters such as air temperature, air humidity, wind speed, wind direction, and rainfall. The unmanned aerial vehicle (UAV) automatic inspection unit is equipped with a UAV base station and a take-off and landing platform, which supports the UAV's autonomous take-off and landing, charging and task scheduling. It acquires normalized vegetation index, crop growth and disaster images through multispectral remote sensing, and works in conjunction with the ground pole unit to form an integrated "sky-air-ground" monitoring network. The cloud-based intelligent management and control platform is used to receive, store, and integrate multi-source data from various units of the pole erection frame and drones, enabling intelligent data analysis and decision support.

[0009] Optionally, the edge computing control box embeds a lightweight convolutional neural network (CNN) model to identify and classify crop seedling conditions, leaf morphology, pest targets, and abnormal areas, and outputs structured recognition results.

[0010] Furthermore, the structured recognition results include, but are not limited to, target category, target quantity, target area percentage, suspected pest and disease level, seedling growth level, crop coverage, recognition confidence level, and corresponding image frame number.

[0011] Furthermore, a lightning rod is installed at the top of the pole frame, and the lightning rod is connected to the grounding device through a wire.

[0012] A method for implementing the integrated intelligent monitoring and processing system of the smart irrigation district includes intelligent decision-making for integrated water and fertilizer regulation, intelligent early warning of pests and diseases, and auxiliary decision-making for agricultural disaster assessment. The intelligent decision-making process for integrated water and fertilizer management includes the following steps: Step 1.1, Multi-source data fusion processing: The cloud-based intelligent management and control platform, based on soil moisture and nutrient data, crop growth data acquired from UAV remote sensing images (e.g., Normalized Difference Vegetation Index, NDVI), and crop water requirements (e.g., water requirements reflected by leaf morphology) and seedling condition data obtained from video monitoring and intelligent insect collection units, comprehensively and accurately constructs a dynamic model of "soil moisture - crop water requirements - irrigation threshold" through data fusion, feature extraction, and correlation analysis (the coupling relationship between soil moisture as a water requirement and environmental factors). Combined with crop growth cycles (e.g., tillering stage, heading stage) and short-term weather forecasts, it provides rich and reliable data support for intelligent decision-making. The soil moisture and nutrient data are acquired in real-time by the integrated soil monitoring unit, and the meteorological data comes from the integrated meteorological monitoring unit. Step 1.2, Precise Control: Based on the soil multi-sensor detection data and crop variety characteristics, the cloud-based intelligent management and control platform uses the least squares method to fit the nutrient demand curve and construct a dynamic "nutrient demand" model to dynamically adjust the fertilizer ratio. According to the dynamic model of "soil moisture - crop water requirement - irrigation threshold", it determines the irrigation timing and amount, controls the precise operation of the integrated water and fertilizer equipment in the field, and realizes precise irrigation and fertilization in the field by "supplementing what is lacking and how much is lacking", thereby improving the efficiency of water and fertilizer resource utilization and promoting healthy crop growth. The intelligent early warning system for pests and diseases includes the following steps: Step 2.1, Multi-source image recognition: The images of pests captured by the infrared night vision AI camera are initially identified by edge computing and then sent to the cloud-based intelligent management and control platform. The cloud-based intelligent management and control platform uses a lightweight convolutional neural network model to identify the types, densities, and leaf morphology of pests in real time, and performs secondary verification based on the YOLOv8 deep learning model. Combining historical pest and disease data with the temperature, humidity, and wind speed data collected in real time by the multi-sensor array, it generates a pest and disease occurrence risk level and prevention and control recommendations. Step 2.2, Closed-loop management: The cloud-based intelligent management and control platform constructs a three-dimensional model of the irrigation area's terrain, canal system, crops, and facilities using digital twin technology. This model dynamically maps crop growth, environmental changes, and disaster status in real time. By linking drone inspections with infrared night vision AI cameras, it identifies the spread of pests and diseases in the fields, generates prevention and control suggestions, and pushes them to agricultural machinery terminals, achieving closed-loop management of "monitoring-diagnosis-prevention." This forms a full-chain intelligent management system encompassing "data collection-intelligent analysis-decision execution-effect feedback." The agricultural disaster assessment-assisted decision-making process includes the following steps: Step 3.1, Disaster Monitoring: The cloud-based intelligent management and control platform acquires satellite remote sensing imagery and data sent by each unit of the pole support frame, as well as UAV remote sensing images. Based on historical disaster data (floods, droughts, frost damage, lodging), and combined with crop distribution, topography, and field conditions, it constructs a three-dimensional disaster assessment model ("sky-air-ground") through data fusion, feature extraction, and correlation analysis with disasters (floods, droughts, frost damage, lodging) to identify the risk of floods, droughts, frost damage, and lodging in real time. Step 3.2, Intelligent Early Warning: Based on the aforementioned three-dimensional disaster assessment model of "sky-air-ground" and the geographical information of the irrigation area, disaster early warning levels and response plans (such as drainage scheduling, emergency irrigation, and crop reinforcement suggestions) are dynamically generated and pushed to management personnel via SMS and APP to improve the disaster resistance and mitigation capabilities of the irrigation area.

[0013] This invention achieves efficient integration of multi-dimensional monitoring equipment through an innovative hardware architecture, overcoming the resource waste and management inefficiency problems of traditional decentralized deployment; it establishes a multi-source data correlation analysis model through data fusion, realizing intelligent processing from data acquisition to decision execution; and through a flexible and scalable system design, it meets the differentiated monitoring needs of different crops and regions. The invention aims to provide an innovative solution for monitoring and analyzing six agricultural conditions in irrigation areas, characterized by "equipment integration, data intelligence, and precise control." Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the integrated intelligent monitoring and processing system for smart irrigation districts described in this invention.

[0015] Figure 2 This is a structural block diagram of the three-dimensional model of "irrigation area topography, canal system, crops and facilities" described in this invention.

[0016] Figure 3 This is a structural block diagram of the dynamic model of "nutrient demand" described in this invention.

[0017] Figure 4This is a structural block diagram of the dynamic model of "soil moisture-crop water requirement-irrigation threshold" described in this invention.

[0018] Figure 5 This is a structural block diagram of the "sky-air-ground" three-dimensional disaster assessment model described in this invention. Detailed Implementation Plan

[0019] The intelligent irrigation district integrated intelligent monitoring and processing system of the present invention includes a cloud-based intelligent management and control platform and a pole frame; the pole frame is equipped with a power supply unit, an edge computing control box, a communication unit, an integrated soil monitoring unit, a video monitoring and insect infestation intelligent collection unit, an integrated meteorological monitoring unit, and an unmanned aerial vehicle (UAV) automatic inspection unit.

[0020] like Figure 1 As shown, firstly, concrete foundation construction is carried out at selected locations in the irrigation area (such as the center of the plot or key nodes of the canal system), and the erector frame 1 is hoisted and fixed.

[0021] Then, the soil multi-sensor 2 (soil moisture detector, model JXBS-3001-TR) is deployed in the soil under the pole frame 1 and uploaded through the communication unit at a preset cycle (such as once per hour). The soil multi-sensor 2 integrates monitoring units for soil temperature, humidity, conductivity, pH value, nitrogen, phosphorus and potassium, etc., to realize the real-time collection and transmission of soil moisture and nutrient data, providing a comprehensive and accurate data foundation for monitoring the soil fertility status of the irrigation area.

[0022] The landing platform 8 of the meteorological integrated monitoring unit and the UAV automatic inspection unit is deployed on the top platform of the pole frame 1. The UAV base station of the UAV automatic inspection unit is deployed near the pole frame 1 or next to the irrigation area management station to ensure that it is interconnected with the communication unit 12 and the cloud intelligent control platform network.

[0023] The integrated meteorological monitoring unit consists of an air temperature sensor 3 (model YF-WD-1A), an air humidity sensor 4 (model YF-XD-1A), a wind speed sensor 5 (model YF-FS-1A), a wind direction sensor 6 (model YF-FX-1A), and a rainfall sensor 7 (model YF-YL-1A). It collects data such as air temperature, humidity, wind speed, wind direction, and rainfall in real time and transmits them to the cloud-based intelligent management and control platform in real time or near real time.

[0024] The power supply unit, video monitoring and intelligent insect collection unit 10, edge computing control box 11, and communication unit 12 are deployed on the pole frame 1 located below the top platform.

[0025] The power supply unit consists of an off-grid power supply system comprising a solar photovoltaic panel 9.1, an MPPT controller 9.2 (Maximum Power Point Tracking controller), and a battery 9.3. The solar photovoltaic panel 9.1 converts light energy into electrical energy, which is then efficiently stored in the battery 9.3 via the MPPT controller 9.2 to provide power to each unit.

[0026] The video monitoring and intelligent pest data acquisition unit 10 quantitatively acquires field image information according to a preset acquisition strategy, which includes timed acquisition and event-triggered acquisition. The time interval for timed acquisition can be set to a fixed period based on crop growth stage, monitoring needs, or communication bandwidth. Event-triggered acquisition can be triggered by conditions such as abnormal light changes, abnormal crop morphology, suspected pest occurrence, or soil moisture or meteorological parameters exceeding preset thresholds. The acquired data includes high-definition field images and continuous video streams, and the acquired data includes at least metadata such as acquisition time, acquisition location, image resolution, video frame rate, and target area number.

[0027] The acquired images and video streams are first preprocessed and initially identified by the edge computing control box 11, including image cropping, noise reduction, brightness correction, target region extraction, and keyframe filtering. The edge computing control box 11 deploys a lightweight CNN model to identify and classify crop seedling conditions, leaf morphology, pest targets, and abnormal areas, and outputs structured recognition results. These structured recognition results include, but are not limited to, target category, target quantity, target area percentage, suspected pest / disease level, seedling growth level, crop coverage, recognition confidence level, and corresponding image frame number.

[0028] When the identification results meet the preset upload conditions, the video monitoring and pest intelligent acquisition unit 10 uploads the structured analysis results and corresponding high-priority images to the cloud platform via an independent 4G / 5G communication link. The preset upload conditions include an identification confidence level higher than a set threshold, an abnormal area ratio exceeding a set threshold, a pest target quantity exceeding a set threshold, a seedling condition level lower than a set threshold, or a call command issued by the cloud intelligent management platform. Based on the received structured data and key images, the cloud intelligent management platform further analyzes and stores the field crop growth status, pest risk, and abnormal events.

[0029] A lightning rod 13 is installed at the top of the pole frame 1, and the lightning rod 13 is connected to the grounding device 14 through a conductor.

[0030] After installation, the system is started and the power supply unit begins to work. Each unit (soil, meteorology, video) is powered on in sequence to perform initialization self-test and reports the status information to the cloud intelligent management and control platform via communication unit 12.

[0031] The communication unit 12 prioritizes wired Ethernet; if this is not possible, it will enable 5G / 4G or NB-IoT wireless network to ensure a stable connection with the cloud-based intelligent management and control platform.

[0032] The intelligent decision-making system for integrated water and fertilizer management described in this invention includes the following steps: Step 1.1, Multi-source data fusion processing: The cloud-based intelligent management and control platform, based on soil moisture and nutrient data, crop growth data (such as NDVI values) acquired from UAV remote sensing imagery, and crop water requirements (such as water requirements reflected in leaf morphology) and seedling condition data obtained from video monitoring and intelligent insect pest collection units, constructs a dynamic model of "soil moisture - crop water requirements - irrigation threshold" through data fusion, feature extraction, and correlation analysis (the coupling relationship between soil moisture, crop water requirements, and environmental factors). Figure 4 As shown; combining crop growth cycles (such as tillering stage and heading stage) and short-term weather forecasts provides rich and reliable data support for intelligent decision-making; soil moisture and nutrient data are collected in real time by the integrated soil monitoring unit, and meteorological data comes from the integrated meteorological monitoring unit.

[0033] Step 1.2, Precise Control: The cloud-based intelligent management platform, based on soil multi-sensor detection data and crop variety characteristics, uses the least squares method to fit nutrient requirement curves and constructs a dynamic "nutrient requirement" model, such as... Figure 3 As shown, real-time soil nitrogen, phosphorus, and potassium data are matched with the fertilizer requirements of the current crop variety and growth stage to dynamically adjust the fertilizer ratio. Based on the dynamic model of "soil moisture - crop water requirement - irrigation threshold," the timing and amount of irrigation are determined, and the integrated water and fertilizer equipment in the field is controlled for precise operation. This achieves precise irrigation and fertilization in the field, ensuring that "what is lacking is supplemented, and how much is lacking," thereby improving the efficiency of water and fertilizer resource utilization and promoting healthy crop growth.

[0034] The intelligent early warning system for pests and diseases described in this invention includes the following steps: Step 2.1, Multi-source image recognition: The images of pests captured by the infrared night vision AI camera are initially identified through edge computing and then sent to the cloud-based intelligent management and control platform. The cloud-based intelligent management and control platform uses a lightweight convolutional neural network model to identify pest species, density, and crop leaf morphology in real time, and performs secondary verification based on the YOLOv8 deep learning model. Combining historical pest and disease data with real-time temperature, humidity, and wind speed data collected by a multi-sensor array, it generates a pest and disease occurrence risk level and control recommendations. When the risk exceeds a set threshold, the cloud-based intelligent management and control platform generates early warning information and control recommendations (such as "High risk of rice blast in plot B, it is recommended to spray fungicide within 3 days"), which is pushed to the administrator via APP or SMS. At the same time, drones are dispatched to conduct detailed investigations of suspected areas and generate precise pesticide application work orders, which are then issued to plant protection drones or ground machinery.

[0035] Step 2.2, Closed-loop management: The cloud-based intelligent management and control platform constructs a three-dimensional model of the irrigation district's topography, canal system, crops, and facilities using digital twin technology. Figure 2 As shown, it dynamically maps crop growth, environmental changes, and disaster status in real time; it identifies the spread of pests and diseases in the field through drone inspections and infrared night vision AI cameras, generates prevention and control suggestions and pushes them to agricultural machinery terminals, realizing closed-loop management of "monitoring-diagnosis-prevention"; and forms a full-chain intelligent management system of "data collection-intelligent analysis-decision execution-effect feedback".

[0036] The agricultural disaster assessment and decision support method described in this invention includes the following steps: Step 3.1, Disaster Monitoring: The cloud-based intelligent management and control platform acquires satellite remote sensing imagery and data transmitted from each unit of the pole erection system, as well as UAV remote sensing imagery. Based on historical disaster data (floods, droughts, frost damage, lodging), and combined with crop distribution, topography, and field conditions, it constructs a three-dimensional "sky-air-ground" disaster assessment model through data fusion, feature extraction, and correlation analysis with disasters (floods, droughts, frost damage, lodging). Figure 5 As shown.

[0037] When the integrated meteorological monitoring unit detects continuous heavy rainfall, the cloud-based intelligent management and control platform immediately triggers the disaster assessment process. The disaster assessment process integrates real-time rainfall, soil saturation moisture content, images of waterlogged areas taken by drones, and historical topographic data. For example, if there is a risk of waterlogging in the downstream area of ​​the C canal system, the level is yellow, and it is recommended to open the No. XX drainage gate and notify relevant personnel to take action to identify the risks of flooding, drought, frost damage, and lodging disasters in real time. Step 3.2, Intelligent Early Warning: Based on the "sky-air-ground" three-dimensional disaster assessment model and the geographical information of the irrigation area, the system dynamically generates disaster early warning levels and response plans (such as drainage scheduling, emergency irrigation, and crop reinforcement suggestions), which are then pushed to management personnel via SMS and APP to improve the disaster resistance and mitigation capabilities of the irrigation area.

[0038] Through the above specific implementation methods, the present invention realizes a complete closed loop from hardware integration and data perception to intelligent decision-making and execution, providing an efficient, accurate and reliable intelligent solution for smart irrigation districts.

Claims

1. An integrated intelligent monitoring and processing system for smart irrigation districts, characterized in that: It includes a cloud-based intelligent management and control platform and a pole frame; the pole frame is equipped with a power supply unit, an edge computing control box, a communication unit, an integrated soil monitoring unit, a video monitoring and intelligent insect collection unit, an integrated meteorological monitoring unit, and an automatic drone inspection unit.

2. The integrated intelligent monitoring and processing system for smart irrigation districts according to claim 1, characterized in that: The power supply unit is an off-grid power supply system consisting of solar photovoltaic panels, a maximum power point tracking control unit, a charging management unit, and a battery. The edge computing control box is used to preprocess and perform preliminary identification on the acquired images and video streams, including image cropping, noise reduction, brightness correction, target region extraction, and key frame filtering. The communication unit uses the ESP32 series chip as the main control core and integrates 5G, LoRa, NB-IoT and Ethernet multi-mode communication technologies, and adopts a dual-link transmission mechanism. The integrated soil monitoring unit uses a multi-sensor that includes soil temperature, humidity, electrical conductivity, pH, nitrogen, phosphorus, and potassium. The video monitoring and intelligent pest collection unit is equipped with an infrared night vision artificial intelligence camera to identify pest species, density and crop leaf morphology in real time. The integrated meteorological monitoring unit includes a multi-sensor array for monitoring environmental parameters such as air temperature, air humidity, wind speed, wind direction, and rainfall. The unmanned aerial vehicle (UAV) automatic inspection unit is equipped with a UAV base station and a landing platform. The cloud-based intelligent management and control platform is used to receive, store, and integrate data transmitted from various units of the pole erection frame and drones, enabling intelligent data analysis and decision support.

3. The integrated intelligent monitoring and processing system for smart irrigation districts according to claim 1, characterized in that: The edge computing control box embeds a lightweight CNN model to identify and classify crop seedling conditions, leaf morphology, pest targets, and abnormal areas, and outputs structured recognition results.

4. The integrated intelligent monitoring and processing system for smart irrigation districts according to claim 3, characterized in that: The structured recognition results include target category, target quantity, target area percentage, suspected pest and disease level, seedling growth level, crop coverage, recognition confidence level, and corresponding image frame number.

5. The integrated intelligent monitoring and processing system for smart irrigation districts according to claim 1, characterized in that: A lightning rod is installed at the top of the pole frame, and the lightning rod is connected to the grounding device through a wire.

6. A method for implementing the integrated intelligent monitoring and processing system for smart irrigation districts as described in claim 1, characterized in that: This includes intelligent decision-making for integrated water and fertilizer management, intelligent early warning of pests and diseases, and auxiliary decision-making for agricultural disaster assessment. The intelligent decision-making process for integrated water and fertilizer management includes the following steps: Step 1.1, Multi-source data fusion processing: The cloud-based intelligent management and control platform constructs a dynamic model of "soil moisture-crop water requirement-irrigation threshold" based on soil moisture and nutrient data, crop growth data obtained from UAV remote sensing images, and crop water requirement and seedling condition data obtained from video monitoring and intelligent insect pest collection units. This model is achieved through data fusion, feature extraction, and correlation analysis. Combined with crop growth cycles and short-term weather forecasts, it provides reliable data support for intelligent decision-making. The soil moisture and nutrient data are collected in real time by the integrated soil monitoring unit, and the meteorological data comes from the integrated meteorological monitoring unit. Step 1.2, Precise Control: Based on the soil multi-sensor data and crop variety characteristics, the cloud-based intelligent management and control platform uses the least squares method to fit the nutrient demand curve, constructs a dynamic "nutrient demand" model, and dynamically adjusts the fertilizer ratio; it also determines the irrigation timing and amount based on the dynamic "soil moisture-crop water requirement-irrigation threshold" model. The intelligent early warning system for pests and diseases includes the following steps: Step 2.1, Multi-source image recognition: After the insect images collected by the infrared night vision AI camera are initially identified by edge computing, the cloud-based intelligent management and control platform uses a lightweight convolutional neural network model to identify the insect species, density and crop leaf morphology in real time, and performs secondary verification based on the YOLOv8 deep learning model. Combining historical pest and disease data with the temperature, humidity and wind speed data collected in real time by the integrated multi-sensor array, the platform generates pest and disease occurrence risk levels and prevention and control suggestions. Step 2.2, Closed-loop management: The cloud-based intelligent management and control platform constructs a three-dimensional model of the irrigation area's topography, canal system, crops, and facilities using digital twin technology. This model dynamically maps crop growth, environmental changes, and disaster status in real time. By linking drone inspections with infrared night vision AI cameras, it identifies the spread of pests and diseases in the field, generates prevention and control suggestions, and achieves closed-loop management of "monitoring-diagnosis-prevention". The agricultural disaster assessment-assisted decision-making process includes the following steps: Step 3.1, Disaster Monitoring: The cloud-based intelligent management and control platform acquires satellite remote sensing image information and data sent by each unit of the pole support frame, as well as UAV remote sensing images. Based on historical disaster data and combined with crop distribution, topography and field status information, it constructs a three-dimensional disaster assessment model of "sky-air-ground" through data fusion, feature extraction and disaster correlation analysis, and identifies the risks of floods, droughts, frost damage and lodging disasters in real time. Step 3.2, Intelligent Early Warning: Based on the aforementioned "sky-air-ground" three-dimensional disaster assessment model and irrigation area geographic information, disaster early warning levels and response plans are dynamically generated and pushed to management personnel via SMS and APP to improve the irrigation area's disaster resistance and mitigation capabilities.

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