An all-link management and control system for intelligent irrigation of farmland

Abstract

The application provides a full-link management and control system for intelligent irrigation of farmland. A basic object access module of the system establishes an object relationship model, generates spatial boundaries of plots, device association and historical diagnosis information. A perception and collection module collects environmental, soil state, remote sensing analysis and device operation state data. An intelligent analysis and decision module generates irrigation parameters and sends them to an irrigation execution control module. The irrigation execution control module generates a valve control scheme and sends instructions to the device to execute zoned irrigation or automatic rotation irrigation. A running monitoring and early warning module collects pressure, flow, device state and control feedback data during irrigation, generates abnormal early warning results according to preset early warning rules and feeds back to the irrigation execution control module. A device management and data interaction module is used for storing object data, perception data, analysis results, irrigation control data, execution records and early warning records. A multi-end display and record tracing module is used for displaying the full-process irrigation record.

Description

Technical Field

[0001] This invention relates to the field of agricultural informatization and irrigation automation technology, and in particular to a full-chain management and control system for smart irrigation of farmland. Background Technology

[0002] As agricultural production shifts from extensive to precision, and from experience-based to data-driven, farmland irrigation management has evolved from traditional timed irrigation or manual valve operation to a precision irrigation model that dynamically regulates water requirements based on crop water needs, soil moisture dynamics, weather conditions, target yields, and equipment operating status. Especially against the backdrop of water scarcity, rising labor costs, higher standards for agricultural product quality, and the expansion of agricultural operations, traditional irrigation methods are increasingly unable to meet the demands of modern agricultural production.

[0003] Currently, several irrigation control platforms, equipment networking systems, or farmland information systems exist on the market. However, most systems are limited to a single aspect, such as equipment networking, remote sensing display, pump station control, or farmland data visualization, and have not yet been able to construct a complete irrigation closed loop. Analysis of existing technologies and industry applications reveals the following main technical deficiencies in existing systems: Irrigation and fertilization decisions rely excessively on human experience, resulting in a low level of intelligence. Most existing solutions only provide simple threshold prompts, or rely on managers to judge irrigation needs, amounts, timing, and fertilization coordination based on experience, failing to comprehensively consider multiple factors such as crop type, plot size, planting stage, target yield, soil nutrient distribution, soil moisture content, evapotranspiration, meteorological conditions, and historical irrigation records. Therefore, this often leads to problems such as insufficient or excessive irrigation, uneven fertilization, resource waste, and unstable crop growth.

[0004] The spatial relationship management between plots and equipment is weak. The control objects of irrigation systems are not limited to a single pump or valve, but are closely related to specific farms, plots, zones, and pipeline topology. However, existing systems often lack unified modeling and map visualization capabilities for plot boundaries, irrigation zones, pump station locations, valve locations, and equipment relationships. This makes it difficult for managers to clearly identify the pump station corresponding to a specific plot, the valve corresponding to a specific zone, and the irrigation range, thus restricting the level of refined management.

[0005] The existing systems suffer from coarse-grained control, making them ill-suited for complex irrigation scenarios. They typically only support start-stop control, lacking functions such as valve opening adjustment, irrigation duration setting, group irrigation rotation, and zone-specific differentiated control. Modern farmland, especially large-scale planting bases, exhibits variations in soil conditions, crop growth, and water scarcity levels across different areas. This necessitates setting different valve openings and irrigation times for different regions, and implementing automatic rotation irrigation based on a time sequence. Existing systems are significantly inadequate in this regard.

[0006] Irrigation process monitoring is weak, and real-time feedback and anomaly handling mechanisms are lacking. During irrigation, problems such as excessively high or low pressure, abnormal flow, equipment offline, valve command execution failure, pipeline blockage, and hardware failure occur frequently. If the system cannot collect operating parameters in real time, display trend curves, and provide timely warnings, irrigation anomalies can only be identified through manual inspections, which can easily lead to irrigation failures, crop damage, and increased maintenance costs.

[0007] Data is fragmented, failing to establish an integrated closed loop for analysis, control, monitoring, and recording. In existing systems, remote sensing analysis data, equipment ledgers, valve configurations, control records, irrigation records, and early warning records are often scattered across different pages, databases, or systems, lacking a unified connection. This makes it difficult for managers to answer questions such as: what analysis results triggered irrigation, valve parameter configuration details, real-time pressure and flow status, early warning occurrences, and the final irrigation effect. The inability to form a complete data loop also hinders subsequent review and optimization.

[0008] The early warning rules are rigid and lack object-oriented configuration capabilities. Different equipment types, operating periods, and irrigation scenarios have different requirements for early warning thresholds. For example, the pressure threshold of a specific valve differs between irrigation and standby states; some equipment only needs monitoring during operating periods, while others require 24 / 7 monitoring. Existing solutions mostly only support fixed threshold alarms and cannot flexibly configure alarms for specific equipment, time periods, data indicators, and comparison conditions.

[0009] There is a lack of multi-role collaboration capabilities. Smart irrigation involves multiple roles, including managers, agricultural technicians, equipment maintenance personnel, and on-site implementers. Managers focus on overview, statistics, and risk; technicians focus on analytical models, target yields, and fertilization and irrigation recommendations; maintenance personnel pay attention to equipment status, pump station and valve operating curves; and implementers focus on control entry points and task results. Existing systems typically cannot accommodate the needs of multiple roles in a unified interface, leading to inefficient information transmission and a disconnect between decision-making and execution. Summary of the Invention

[0010] This invention aims to at least solve the technical problems existing in the prior art, and innovatively proposes a full-link management and control system for smart irrigation of farmland.

[0011] To achieve the above-mentioned objectives of the present invention, the present invention provides a full-link management and control system for smart irrigation of farmland, the system including a basic object access module, a sensing and acquisition module, an intelligent analysis and decision-making module, an irrigation execution control module, an operation monitoring and early warning module, an equipment management and data interaction module, and a multi-terminal display and recording traceability module; The basic object access module is used to establish an object relationship model between farms, plots, irrigation zones, pumping stations, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment, and to generate spatial boundary information, equipment association information, and historical diagnostic information corresponding to the target plot. The sensing and acquisition module is used to collect environmental data, soil condition data, remote sensing analysis data, and irrigation equipment operation status data corresponding to the target plot. It also cleans, aligns, and standardizes the collected data to form sensing data in a unified format. The intelligent analysis and decision-making module is used to perform joint analysis based on target yield parameters, spatial boundary information, equipment association information, historical diagnostic information, and remote sensing analysis data, environmental data, and soil condition data in the sensing data to generate irrigation parameters for the corresponding target plot and send the irrigation parameters to the irrigation execution control module. The irrigation execution control module is used to determine the corresponding water pump, fertigation unit, butterfly valve and electric valve according to irrigation parameters and object relationship model, generate valve control scheme, and send control commands to the corresponding irrigation equipment to execute zoned irrigation or automatic rotation irrigation tasks. The operation monitoring and early warning module is used to collect pressure data, flow data, equipment operating status data and control feedback data during the irrigation process, generate abnormal early warning results according to preset early warning rules, and feed back the abnormal early warning results to the irrigation execution control module; The equipment management and data interaction module is used to associate and store object data, sensing data, analysis results, irrigation control data, execution records, and early warning records; The multi-terminal display and record traceability module is used to display the entire irrigation process record.

[0012] The beneficial effects of this invention are as follows: By constructing a basic object access module, a sensing and acquisition module, an intelligent analysis and decision-making module, an irrigation execution control module, an operation monitoring and early warning module, an equipment management and data interaction module, and a multi-terminal display and recording traceability module, this invention achieves closed-loop management of the entire farmland irrigation process, from object modeling, data acquisition, intelligent analysis, control execution, process monitoring to result traceability. By establishing an object relationship model between farms, plots, irrigation zones, pump houses, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment, a correlation mapping is formed between plot spatial information and irrigation equipment, improving farmland spatial management and equipment collaborative control capabilities. By integrating target yield parameters, remote sensing analysis data, environmental data, soil condition data, and historical diagnostic information for joint analysis, irrigation parameters that meet the actual needs of the target plot can be automatically generated, reducing the problems of insufficient or excessive irrigation and resource waste caused by traditional irrigation relying on manual experience. By directly transmitting the intelligent analysis results to the irrigation execution control module, and combining valve parameter configuration, zoned irrigation control, and automatic rotation irrigation tasks, the invention achieves control over water pumps, fertigation machines, and butterfly valves. Precise control of actuators such as electric valves improves the automation and precision of irrigation control in complex farmland scenarios. The operation monitoring and early warning module collects pressure, flow, equipment operating status, and control feedback data in real time, and generates abnormal warning results based on configurable early warning rules. This enables dynamic monitoring and anomaly handling during irrigation, reducing irrigation risks caused by equipment failure, pipeline malfunctions, or execution failures. Simultaneously, the equipment management and data interaction module associates and stores object data, sensing data, analysis results, irrigation control data, execution records, and early warning records, and provides unified display through a multi-terminal display and record traceability module, enabling full-process tracking and historical review of each irrigation task. Therefore, this invention solves the problems of low decision-making intelligence, insufficient management of plot and equipment association, coarse control granularity, weak process monitoring capabilities, and inability to form closed-loop data in existing smart irrigation systems, improving the accuracy, automation, operational reliability, and intelligent management level of farmland irrigation.

[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic diagram of the structure of a full-link management and control system for smart irrigation of farmland according to the present invention. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0016] This embodiment provides a full-chain management and control system for smart irrigation of farmland. The system is designed based on the overall business chain of "plot-zone-equipment-strategy-execution-monitoring-recording". The system focuses on the needs of refined plot management and automated irrigation control, combining multi-source sensing and data acquisition, remote sensing analysis, rule calculation, and IoT-based linkage control capabilities to build a comprehensive platform that can be used for farmland irrigation, water and fertilizer co-management, automatic rotational irrigation scheduling, and anomaly risk control.

[0017] The technical solution of this embodiment can be summarized as follows: First, establish an object relationship model between farms, plots, zones, pumping stations, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment; then, collect farmland environmental conditions, soil conditions, and equipment operating conditions through environmental monitoring equipment, remote sensing diagnostic data, and equipment network data; next, perform irrigation and fertilization analysis based on target yield and multi-source data, and output irrigation and fertilization parameters for plots or zones; then, formulate valve control schemes based on analysis results or existing rotational irrigation strategies, and issue control commands to actual hardware equipment; continuously monitor pressure, flow rate, equipment status, and abnormal information during control execution; finally, archive the analysis results, execution process, and operation results in a unified manner to achieve full-process visualization and traceability management.

[0018] The overall system structure of this invention is based on farmland spatial objects and uses the business decision-making chain as the main line. Its bottom layer consists of basic objects and equipment objects, including farms, plots, zones, pump houses, water pumps, water and fertilizer machines, valves, and monitoring equipment; the middle layer is the data acquisition, intelligent analysis, and control execution capability layer; and the top layer is the interface for overview display, map display, business operation, record traceability, and early warning management.

[0019] Among them, a farm is used to represent a complete agricultural business entity or management area; a plot of land is used to represent a specific cultivated area within a farm; an irrigation zone is used to represent a sub-region within a plot of land that can be used as an independent unit for irrigation control; pump houses, water pumps, fertigation machines, butterfly valves, and electric valves constitute a specific irrigation execution equipment system; meteorological stations, soil moisture stations, cameras, and other environmental sensing equipment constitute a monitoring system; and remote sensing analysis results constitute a source of macroscopic analysis data oriented towards the plot scale.

[0020] The aforementioned objects are coded and managed using a unified data structure. Each object can correspond to a unique identifier, affiliation, spatial location, equipment status, and business attributes. For example, an electric valve object can be associated with its pumping station, pump, plot of land, zone, valve type, terminal serial number, online status, most recent opening time, and historical warning records; a plot of land object can be associated with plot boundaries, multi-period remote sensing results, historical irrigation records, and target yield parameters. This unified modeling approach lays the foundation for subsequent data association, strategy calculation, and execution traceability.

[0021] The system data flow in this embodiment mainly includes the following categories: 1. Basic Object Data Flow. This consists of information on farms, plots, zones, and equipment objects entered or maintained by users through the system, forming the master data.

[0022] 2. Monitoring data stream. Status and monitoring data are continuously reported by hardware equipment such as weather stations, soil moisture stations, valves, water pumps, and fertigation machines.

[0023] 3. Remote sensing analysis data stream. Nutrient and water deficiency distribution data at the plot scale are provided by external remote sensing diagnostic results or analysis services within the platform.

[0024] 4. Decision Data Flow. The intelligent analysis and decision-making module calculates irrigation and fertilization parameters based on target yield, plot information, and monitoring data.

[0025] 5. Control data flow. The irrigation execution control module translates the irrigation strategy into specific hardware control commands and sends them to IoT devices.

[0026] 6. Feedback Data Stream. The operation monitoring and early warning module feeds back the equipment execution results, real-time pressure and flow, and abnormal events to the recording system and display interface.

[0027] Through the aforementioned overall structure, this embodiment not only achieves "visible devices, calculated strategies, issued instructions, monitored processes, and retrieved records," but also realizes direct communication between the analysis and execution ends, significantly different from traditional systems that only possess single-point control or simple information display capabilities. The specific system structure is as follows: like Figure 1 As shown, a full-chain management and control system for smart irrigation of farmland includes a basic object access module, a sensing and acquisition module, an intelligent analysis and decision-making module, an irrigation execution and control module, an operation monitoring and early warning module, an equipment management and data interaction module, and a multi-terminal display, recording and traceability module. The basic object access module connects to the intelligent analysis and decision-making module, the irrigation execution and control module, and the equipment management and data interaction module to establish object relationship models between farms, plots, irrigation zones, pump houses, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment. It also generates spatial boundary information, equipment association information, and historical diagnostic information for the target plot. The output of the basic object access module mainly includes target object context information, spatial boundary information, equipment association list, and historical background data. These outputs are directly transmitted to the intelligent analysis and decision-making module and the irrigation execution and control module.

[0028] The basic object access module includes a farm plot selection submodule, an irrigation zone modeling submodule, a historical diagnosis access submodule, and an object relationship mapping submodule. The basic object access module is the business entry point of the entire system, and its core function is to establish static structural relationships and business relationships between farms, plots, zones, and related equipment objects. The farm plot selection submodule is used to acquire target farms, target plots, and target irrigation zones, and load corresponding equipment association information, spatial boundary information, area information, planted crop information, growth period information, and equipment association information. This submodule is also used to select target farms, target plots, and target zones within the system and pass the selected objects to the subsequent analysis and control modules. This submodule supports not only conventional methods such as dropdown selection and list search, but also allows switching between target plots or target zones by directly clicking on them in the map interface. After selection, the system automatically loads the corresponding area, planted crops, growth period, responsible person, historical records, and associated equipment information.

[0029] The farm plot selection submodule is adaptable to agricultural production scenarios of varying scales. Whether it's a small, individually-operated farm or a large-scale, contiguous agricultural production base, it enables rapid location and loading of target objects without issues like retrieval lag or incomplete information loading due to the size of the farm or plot. Furthermore, after completing target object selection and information processing, this submodule automatically pushes the complete target object data to subsequent business modules such as the sensing and acquisition module and the intelligent analysis and decision-making module. This eliminates the need for manual secondary information transfer, reducing the probability of information errors during manual operations and accelerating the entire irrigation decision-making and control process, thereby improving the overall system efficiency.

[0030] The irrigation zoning modeling submodule is used to divide target plots according to plot boundary information and zoning rules, generating multiple irrigation zoning objects and configuring the name, number, and spatial boundaries of each irrigation zoning object. This submodule is also used for plot zoning management. Since a single plot often contains different soil conditions, planting conditions, or irrigation network coverage, it is necessary to further divide a single plot into multiple irrigation zones. This submodule supports region segmentation based on north-south or east-west rules and allows users to manually mark points, assign numbers, automatically connect lines, and divide boundaries on satellite base maps. The system can automatically generate multiple zoning objects based on plot boundaries and user-input zoning rules, and generate a name, number, and spatial boundaries for each zoning.

[0031] Each irrigation zone, defined by the irrigation zoning modeling submodule, is an independent unit for subsequent irrigation decisions, control execution, and record tracking. It can be individually matched with crop planting information, water requirement parameters, and corresponding execution equipment, adapting to the differentiated planting and irrigation conditions within the plot. This submodule also supports manual fine-tuning of the automatically generated zoning results. Users can adjust and optimize the automatically generated zoning boundaries based on the actual pipeline layout, terrain differences, and variety distribution of the plot, ensuring that the zoning results perfectly match the actual field production layout. After zoning is completed, the information of all irrigation zones is automatically synchronized to the object relational mapping submodule, establishing associations and bindings with the corresponding plots and associated equipment.

[0032] The historical diagnostic access submodule is used to acquire historical weather data, historical remote sensing analysis results, historical irrigation records, and historical fertilization recommendations corresponding to the target plot. This submodule is also used to access historical diagnostic information related to the target plot. Historical diagnostic information includes at least historical weather, temperature, weather conditions, accumulated temperature data, historical remote sensing analysis results, historical irrigation records, and historical fertilization recommendations. By accessing historical data, the system can provide agricultural technicians with background information on the plot and also provide historical references for subsequent model analysis.

[0033] Historical data accessed by the historical diagnostic access submodule is archived uniformly under the corresponding plot object according to the time dimension. This forms a complete time-series data set with the real-time monitoring data of the current plot. When the intelligent analysis and decision-making module calculates irrigation parameters, it can fully incorporate information such as the plot's historical irrigation effects and long-term soil moisture variation patterns, further improving the accuracy and adaptability of irrigation decision results. Simultaneously, this submodule supports format adaptation for multi-source historical data, enabling standardized conversion of historical data from different systems and formats. Users are no longer required to manually adjust the data format before importing, lowering the barrier to historical data access and enhancing the system's ability to reuse existing agricultural data resources.

[0034] The Object Relationship Mapping submodule is used to establish an object relationship model between plots of land, irrigation zones, pump houses, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment. This submodule establishes multi-level mapping relationships between plots, zones, and equipment. For example, a pump house can be associated with one or more water pumps, a water pump can be associated with one or more fertigation machines and butterfly valves, a butterfly valve can be associated with multiple electric valves, and these electric valves can be mapped to different zones. This relationship mapping is used not only for equipment display but also for determining the path of control commands. For example, when a user initiates irrigation for a specific zone, the system can automatically generate a set of control objects based on the butterfly valves, electric valves, and associated water pumps of that zone.

[0035] The object relationship mapping submodule automatically identifies hierarchical relationships between objects. When the affiliation of a device changes, only the binding relationship between that device and its corresponding partition or parent device needs to be modified. The system can automatically update the entire link's association mapping without rebuilding the entire object model, significantly reducing the maintenance workload after device changes. Simultaneously, this submodule supports association verification, automatically identifying idle devices without bound partitions and blank irrigation partitions lacking execution device bindings. It automatically generates reminder messages and pushes them to system administrators, helping users promptly improve object relationship configurations and avoid issues with missing control objects during subsequent irrigation task execution. Furthermore, the object relationship model established by this submodule is synchronously stored in the data storage unit of the device management and data interaction module, ensuring that the entire business process can call upon this association to perform business processing, achieving global unified sharing of object relationship data.

[0036] The sensing and acquisition module is connected to the intelligent analysis and decision-making module, the operation monitoring and early warning module, and the equipment management and data interaction module respectively. It is used to collect environmental data, soil condition data, remote sensing analysis data, and irrigation equipment operation status data corresponding to the target plot. The collected data is cleaned, time-aligned, and standardized to form sensing data in a unified format. The sensing and acquisition module includes an environmental equipment access submodule, a remote sensing data access submodule, an equipment status acquisition submodule, and a map point coding submodule. This module is used to uniformly collect data on farmland environmental conditions, equipment operating status, and macroscopic analysis results of the land plots, serving as the core data input for the entire system. This module includes environmental equipment access submodules, remote sensing data access submodules, equipment status acquisition submodules, and map point coding submodules. In terms of data processing, the sensing and acquisition module may also include data cleaning, missing data completion, time alignment, and unit standardization. For example, pressure units uploaded from different terminals may be inconsistent; the system can uniformly convert them to MPa. Different monitoring devices may have different reporting cycles; the system can resample under a unified time axis. Standardized data can be directly used by the intelligent analysis and decision-making module, and can also be displayed and judged in real time by the monitoring and early warning modules.

[0037] The environmental equipment access submodule is used to collect environmental data and soil condition data output from meteorological stations, soil moisture stations, and other environmental monitoring equipment. It also connects to monitoring devices such as meteorological stations, soil moisture stations, cameras, and insect monitoring lamps. Meteorological stations can collect environmental parameters such as temperature, humidity, weather conditions, wind speed, and rainfall; soil moisture stations can collect data such as soil moisture and soil temperature; cameras can provide supplementary on-site image information; and other extended monitoring equipment can also be connected to this submodule. The system can receive, store, and time-series process environmental data according to the device reporting cycle and establish associations with plots or zones.

[0038] The remote sensing data access submodule is used to acquire remote sensing analysis data corresponding to the target plot. This data includes soil nutrient distribution results and soil water shortage status results. The submodule also accesses plot-scale remote sensing analysis results, which at least include soil nutrient distribution results and soil water shortage status results. Soil nutrient distribution data reflects the spatial distribution of nutrients such as nitrogen, phosphorus, and potassium within the plot and can be displayed using a heatmap. Water shortage status analysis reflects soil moisture content distribution, water-deficient areas, stress levels, risk levels, and water balance status. These remote sensing results can be provided by an external remote sensing platform or generated by the system's internal analysis service after processing remote sensing imagery.

[0039] The equipment status acquisition submodule is used to collect operational status data of irrigation equipment. This data includes the online status, operating status, pressure data, flow data, current opening degree data, and control feedback data of pumps, fertigation units, butterfly valves, and electric valves. The submodule also collects operational status information from equipment such as pump houses, pumps, fertigation units, butterfly valves, and electric valves. This status information includes at least online status, operating status, last opening time, last warning time, current pressure, current flow rate, current opening degree, opening records, and warning records. This submodule can receive data reports from terminal devices via IoT communication protocols and can also obtain execution result information from control feedback messages.

[0040] The map location coding submodule is used to bind each monitoring and irrigation device to its corresponding spatial location. This submodule binds all device objects to their spatial locations. Each device object can have its latitude and longitude or map location set during data entry, and the system generates a device icon on a satellite base map or other map base map accordingly. Different types of equipment can be managed using different layers, such as weather station layers, soil moisture station layers, camera layers, valve layers, pump room layers, and fertigation machine layers. Users can select, deselect, zoom, and position layers to achieve a spatial panoramic view.

[0041] The map point coding submodule supports manual adjustment of equipment points. When there is a discrepancy between the actual installation location and the entered location of field equipment, or when equipment is relocated, users only need to drag the corresponding equipment icon to the target location on the map interface. The system will automatically update the spatial coordinate information of the equipment, eliminating the need to manually input and modify latitude and longitude parameters, greatly improving operational convenience. Simultaneously, the bound point information is automatically synchronized to the corresponding object relationship model. When users click on any equipment point on the map interface, they can view the equipment's basic attributes, real-time operating status, historical operation records, and associated irrigation zone information with a single click. This achieves a closed-loop integration of spatial positioning, equipment query, and business operations. Compared to the traditional management method that only displays equipment information in a list format, the spatial visualization is higher, helping managers quickly grasp the overall layout of field equipment and effectively improving operational efficiency in scenarios such as equipment fault diagnosis and on-site inspection. Furthermore, this submodule supports coordinate transformation adaptation across multiple coordinate systems, allowing it to interface with coordinate system standards of different map base maps without requiring users to manually convert coordinates, further reducing the workload of system access and configuration.

[0042] The intelligent analysis and decision-making module is connected to the basic object access module, the sensing and acquisition module and the irrigation execution control module respectively. It is used to perform joint analysis based on the target yield parameters, spatial boundary information, equipment association information, historical diagnostic information and remote sensing analysis data, environmental data and soil condition data in the sensing data, generate irrigation parameters for the corresponding target plot, and send the irrigation parameters to the irrigation execution control module. The intelligent analysis and decision-making module includes a target yield configuration submodule, a remote sensing nutrient analysis submodule, a remote sensing water shortage analysis submodule, a parameter calculation submodule, and a decision suggestion generation submodule. This module is the core of the system in this embodiment, primarily used to generate irrigation and fertilization decisions based on target yield, land parcel attributes, multi-source sensing data, and historical records. A key technical feature of the intelligent analysis and decision-making module is that the analysis results are not confined to the display layer but can be directly used as input to the irrigation execution control module. For example, after a user clicks "I want to irrigate" on the analysis page, the system can directly import the suggested irrigation parameters generated from this analysis into the valve configuration and rotation irrigation execution pages, thus connecting analysis and execution.

[0043] The target yield configuration submodule is used to obtain the target yield parameters corresponding to the target crop. This submodule also receives the target yield per acre input by the user and uses it as an important input parameter for irrigation and fertilization analysis. This submodule supports manual input of the target yield per acre value, such as inputting the target yield in kg / acre. The system performs validity checks on the input value, such as checking if it is empty, exceeds a reasonable range, or matches the crop type. After the user clicks "Start Calculation," the system generates a new analysis task.

[0044] The target yield configuration submodule also supports batch configuration. When multiple plots or zones are planted with the same crop and have the same target yield, users can select multiple target objects to import target yield parameters in batches, eliminating the need for manual entry and further improving system configuration efficiency. This submodule binds and stores the configured target yield parameters with the corresponding plots or irrigation zones. When conducting a new round of intelligent analysis and decision-making, historical configuration parameters can be directly retrieved. Modification is only required when the user needs to adjust the target yield, reducing repetitive workload. Furthermore, the target yield configuration submodule includes built-in target yield reference ranges for different crops and growth stages. Users can directly refer to these ranges when configuring parameters, avoiding significant deviations in irrigation and fertilization decisions due to unreasonable target yield settings. This also provides guidance for planting managers lacking production experience.

[0045] The remote sensing nutrient analysis submodule is used to determine soil nutrient requirements based on soil condition data and target yield parameters of a plot. It analyzes the nutrient status of target plots or zones. This submodule receives basic plot information, soil nutrient heat maps, and target yield information, extracts nitrogen, phosphorus, and potassium nutrient levels in different areas within the plot, and performs spatial aggregation, fertility level determination, and demand estimation. The system can output the current soil NPK content, corresponding fertility level, and the total nitrogen, phosphorus, and potassium fertilizer requirements derived based on the target yield. This submodule also supports displaying historical fertilization decision records, including historical growth periods, historical target yields, historical nutrient usage, and historical fertilization recommendations, enabling technicians to make longitudinal comparisons.

[0046] The remote sensing nutrient analysis submodule in this embodiment employs a combined analysis logic of "remote sensing macroscopic inversion + ground-based measurement correction." It does not rely solely on remote sensing inversion results to output conclusions; instead, it utilizes nutrient monitoring data from soil moisture stations within the same period, input from the sensing and acquisition module, to spatially correct the nutrient distribution results obtained from remote sensing inversion. This eliminates analysis errors caused by factors such as atmosphere, cloud cover, and vegetation cover in the remote sensing image, further improving the accuracy of the nutrient analysis results. For input remote sensing images of different resolutions, this submodule can automatically adapt to the corresponding pixel scale. When the spatial area of ​​an irrigation zone is smaller than the original resolution of the remote sensing image, resulting in insufficient pixels in a single zone, it can automatically fuse surrounding related pixel information and perform spatial interpolation to complete the analysis by combining historical nutrient data of the target zone, ensuring the reliability of the nutrient analysis results for small-scale zones. After completing the analysis, this submodule organizes the nutrient analysis results at the zone level into a standard output format. This output is passed to the parameter calculation submodule as the core input for fertilization parameter calculation and simultaneously generates a visually displayable nutrient distribution heatmap, which is stored in the equipment management and data interaction module for subsequent display and traceability.

[0047] The specific analysis method for the remote sensing nutrient analysis submodule is as follows: The first step is to extract the input dataset, including the spatial boundary information of the target plot and each irrigation zone, the target yield parameters, and the current period remote sensing inversion nutrient spatial data and the measured nutrient data of the ground monitoring equipment transmitted by the sensing and acquisition module. The second step is to carry out the correction of the remote sensing inversion results. Based on the ground measured nutrient data, the existing spatial correction algorithm is used to correct the global bias of the remote sensing inversion nutrient data of the whole plot, eliminate the analysis error caused by external environmental interference such as atmosphere, cloud layer, and vegetation cover, and obtain the corrected spatial distribution results of nutrient with the required accuracy. The third step is to calculate the nutrient statistics and demand of each irrigation zone. According to the spatial boundary of each irrigation zone, the corrected nutrient data within the zone is extracted, and the average nitrogen, phosphorus and potassium nutrient content of the zone is calculated. Then, combined with the fertilizer requirements of the corresponding crops in the current growth stage and the preset target yield, the total nitrogen, phosphorus and potassium demand required for this irrigation cycle of the zone is estimated. The fourth step is result output and storage. The analysis results of nutrient requirements and fertility levels of each irrigation zone are organized into structured standard data and output to the parameter calculation submodule. At the same time, an interactive nutrient distribution heat map is generated and stored synchronously in the data storage unit of the equipment management and data interaction module.

[0048] The remote sensing water shortage analysis submodule is used to determine water shortage status information based on environmental data, remote sensing analysis data, historical diagnostic information, remote sensing water shortage status results, soil moisture data, soil temperature data, historical evapotranspiration, and weather conditions. This submodule is used to analyze the water shortage status of target plots or zones. It combines remote sensing water shortage heatmaps, soil moisture monitoring values, historical evapotranspiration, soil temperature, and weather conditions to comprehensively assess the current water status. Output may include soil moisture content distribution, stress level, equilibrium state, risk level, current soil moisture, current soil temperature, historical ET0, and recommended irrigation parameters. This submodule can also display historical irrigation volume curves to help users understand water change trends.

[0049] The analysis principle of the remote sensing water shortage analysis submodule in this embodiment is as follows: The first step is to extract the input dataset, including the spatial boundary information of the target plot and each irrigation zone, the crop type and current growth period parameters corresponding to the target zone, the current period remote sensing inversion soil moisture spatial data transmitted by the sensing and acquisition module, the measured soil moisture content and temperature data of the ground moisture monitoring equipment, the historical regional reference evapotranspiration data, and the weather forecast data for the next 10 days. The second step is to correct the remote sensing inversion water results. Based on the soil moisture content data measured by the ground soil moisture station, a spatial correction algorithm is used to correct the global bias of the remote sensing inversion water data of the entire plot. This eliminates the analysis errors caused by atmospheric cloud cover and vegetation canopy cover interference, and obtains the corrected spatial distribution results of soil moisture content with the required accuracy. The third step is to assess the water shortage status and calculate the water demand of each irrigation zone. The corrected water data within the zone is extracted according to the spatial boundary of each irrigation zone. The average soil moisture content of the zone is calculated. Combined with the suitable water range for the current growth stage of the corresponding crop, the reference evapotranspiration, and the rainfall and temperature trends in the future weather forecast, the current water shortage stress level of the zone is determined, and then the net irrigation water required for the current irrigation cycle of the zone is calculated. The fourth step involves organizing the analysis results of water shortage status, stress level, and calculated water demand for each irrigation zone into structured standard data and outputting it to the parameter calculation submodule. At the same time, an interactive heat map of water shortage status distribution is generated and synchronously stored in the data storage unit of the equipment management and data interaction module.

[0050] The parameter calculation submodule generates irrigation parameters based on spatial boundary information, equipment association information, nutrient requirement information, and water shortage status information. These parameters include irrigation time, irrigation flow rate, zonal irrigation duration, and irrigation sequence. The submodule also converts the nutrient and water shortage analysis results into parameters directly applicable to irrigation execution and fertilization decisions. For irrigation, it calculates total irrigation time, total flow rate, recommended zonal irrigation duration, recommended irrigation sequence, and key monitoring indicators. For fertilization, it calculates total nitrogen, total phosphorus, total potassium, recommended fertilization frequency, and critical fertilization periods. This submodule also supports manual fine-tuning; after the system provides default suggested parameters, users can adjust them based on agricultural experience. The system records the adjusted values ​​and incorporates them into the decision-making results. For example, when agricultural technicians predict effective rainfall within the next three days based on their field production experience, they can manually adjust the total irrigation duration calculated by the system. Similarly, when a crop in a specific zone experiences mild pest or disease requiring controlled water and fertilizer application, the irrigation and fertilizer amounts for that zone can be adjusted individually. The adjusted parameters are automatically synchronized to subsequent decision-making results, adapting to the user's personalized production management needs. After parameter calculation and manual adjustment, this submodule combines the partition-to-equipment relationships stored in the object-relationship mapping module to match the irrigation and fertilization parameters for each zone to the specific execution equipment, providing a foundation for generating executable irrigation control commands. All calculated parameters are synchronously output to the decision suggestion generation submodule for generating the final decision results and control scheme.

[0051] The decision recommendation generation submodule is used to visualize irrigation parameters. It outputs analysis results in a user-understandable manner. This submodule can generate irrigation recommendation text, fertilization recommendation text, parameter overview, chart results, and analysis status prompts. For example, it displays a "Pending Analysis" or "Calculating" status when the analysis is not yet complete; after the analysis is completed, it outputs natural language recommendations such as "Irrigation is recommended at a future time," "Monitor pressure changes," and "Fertilization should be done in two stages." The system also supports switching between remote sensing nutrient maps, remote sensing water shortage maps, and other analysis charts.

[0052] The irrigation execution control module is connected to the intelligent analysis and decision-making module, the basic object access module, the equipment management and data interaction module, and the operation monitoring and early warning module. It is used to determine the corresponding water pump, fertigation machine, butterfly valve and electric valve according to irrigation parameters and object relationship model, generate valve control scheme, and send control commands to the corresponding irrigation equipment to execute zoned irrigation or automatic rotation irrigation tasks. The irrigation execution control module includes a zoned irrigation control submodule, a valve parameter configuration submodule, a hardware command issuance submodule, and an automatic rotation irrigation submodule; the irrigation execution control module is used to convert analysis results or rotation irrigation strategies into specific executable equipment control actions.

[0053] The zonal irrigation control submodule is used to determine the corresponding set of execution devices based on the target irrigation zone; it is also used to initiate smart irrigation operations for the selected zone. When a user clicks the "Smart Irrigation" entry for a zone in the zone list, the system will enter the analysis and execution page for that zone and load the zone's basic information, a list of associated valves, suggested irrigation parameters, and historical records. This submodule enables plot-level analysis results to be accurately applied to zonal-level execution objects.

[0054] The zoned irrigation control submodule supports initiating independent irrigation operations for a single irrigation zone, as well as allowing users to select multiple target zones simultaneously to initiate batch irrigation tasks. Once a target zone is selected, the submodule automatically matches and filters the corresponding valves, pumps, and fertigation units that need to be controlled for the current operation based on preset zone-equipment associations. This eliminates the need for users to manually select each associated device, effectively reducing operational complexity and workload. If the associated equipment for the target zone is offline, faulty, or in other abnormal states, the submodule will display a clear warning before the irrigation task is officially initiated, informing the user of the abnormal equipment's number and location. Users can also choose to replace the equipment with a backup or adjust the scope of the current irrigation task, preventing irrigation operations from failing due to equipment malfunctions and reducing water and fertilizer waste.

[0055] The valve parameter configuration submodule is used to configure the opening parameters, irrigation time parameters, and irrigation sequence parameters of corresponding valves based on the set of execution equipment. This submodule is also used to specifically configure the irrigation parameters of each valve within a zone. This submodule can display information such as the pump to which the valve belongs, valve name, number, status, irrigation radius, current opening degree, and irrigation time, arranged by irrigation sequence. Users can adjust the opening degree of a single valve to meet the differentiated water supply needs of different areas; they can also uniformly adjust the irrigation duration of a group of valves to achieve synchronous updates within the group; and they can also batch modify the parameters of the entire group to improve configuration efficiency.

[0056] The valve parameter configuration submodule automatically loads the suggested irrigation and fertilization parameters output by the intelligent analysis and decision-making module, displaying them as the default parameters on the current configuration page. Users no longer need to manually set all parameters from scratch; they only need to fine-tune them according to actual production conditions, significantly reducing configuration time. After completing the custom adjustments, users can bind and store the current configuration scheme with the corresponding target partition. When initiating irrigation operations in the same partition during the same crop growth period, the existing configuration scheme can be directly retrieved and reused, further reducing repetitive operations. In addition, this submodule performs a pre-validation of the legality of all configured parameters. For parameters exceeding the equipment's rated operating range, such as valve opening exceeding the equipment's allowable range or single-cycle irrigation duration exceeding the pump's rated maximum continuous operating value, a prompt will immediately pop up to guide the user to make corrections, preventing equipment damage or irrigation operation abnormalities due to unreasonable parameter configuration. At the same time, this submodule supports independent parameter configuration for different irrigation groups, with parameters of each group not interfering with each other. It can flexibly adapt to the differentiated management needs under different irrigation strategies and accommodate irrigation operation planning for complex fields.

[0057] The hardware command issuance submodule generates valve control schemes based on irrigation parameters, converts these schemes into equipment control commands, and sends them to the pumps, fertigation units, butterfly valves, and electric valves in the execution equipment set. It also converts valve configuration results into actual control commands and sends them to hardware terminals such as pumps, fertigation units, butterfly valves, and electric valves via IoT communication. Control commands may include opening commands, closing commands, valve opening setting commands, irrigation duration setting commands, and rotation irrigation node switching commands. After sending commands, the system can wait for terminal feedback and write the feedback results to the execution log to determine whether the commands have been successfully implemented.

[0058] The hardware command issuance submodule incorporates communication protocol adaptation templates for multiple mainstream irrigation equipment. It automatically matches the corresponding command format based on the model of the connected device, eliminating the need for users to develop customized conversion programs for different brands of hardware, significantly reducing the integration costs of upgrading existing irrigation systems. To address potential network fluctuations and unstable device signals during command issuance, this submodule includes a command retransmission mechanism: if no execution response is received from the target device within 5 seconds of issuing the command, the command will be automatically retransmitted, up to a maximum of 3 consecutive retransmissions. If a valid response is still not obtained after retransmission, the command issuance is considered a failure, and this anomaly is synchronized to the operation monitoring and early warning module to trigger an alarm. This prevents situations where equipment fails to execute control commands as required without the knowledge of management personnel, ensuring the smooth progress of irrigation operations according to plan. Simultaneously, this submodule fully archives the issuance time, target device number, command content, and response result of each control command. All records are traceable and queryable, providing complete data for subsequent equipment fault diagnosis and operational effect review.

[0059] The automatic rotation irrigation submodule generates automatic rotation irrigation tasks based on irrigation cycles, rotation irrigation nodes, and zone binding relationships. Before executing automatic rotation irrigation tasks, this submodule also verifies the online status of equipment, valve binding status, rotation irrigation node time relationships, and task overlap relationships, and determines the execution status of the automatic rotation irrigation task based on the verification results. The automatic rotation irrigation submodule manages rotation irrigation strategies in periodic irrigation or fertilization scenarios. This submodule allows users to input irrigation names, strategy types, and irrigation cycles, and configure multiple irrigation nodes in a timeline format. Each irrigation node can be bound to one or more irrigation zones, and automatically displays the butterfly valve and electric valve information associated with that zone. Users can set a uniform irrigation duration for each node and can also associate suggestions from intelligent analysis. The system automatically verifies whether node times conflict, whether the end time of the previous node is later than the start time of the next node, and whether the current cycle affects the start of the next cycle. If a conflict exists, the system will display a pop-up warning and prevent saving, thus ensuring the executability of the rotation irrigation plan.

[0060] The automatic rotation irrigation submodule in this embodiment also supports flexible adjustment of the created rotation irrigation plan. Users can pause or terminate the ongoing rotation irrigation task at any node, and can also manually adjust the execution order and time parameters of subsequent rotation irrigation nodes. It can flexibly adapt to the adjustment needs of the original plan in unexpected scenarios such as sudden rainfall and temporary agricultural operations. During the execution of the rotation irrigation task, the submodule will synchronize information such as the equipment operating status, actual irrigation duration, and actual water and fertilizer application of each rotation irrigation node in real time. After all tasks are completed, a complete rotation irrigation execution report will be automatically generated and stored synchronously in the equipment management and data interaction module for easy review of subsequent production management. If an abnormal alarm is triggered by a device associated with a node during the rotation irrigation execution, the submodule will automatically skip the abnormal node and prioritize the irrigation operation of subsequent normal nodes. At the same time, the abnormal information will be pushed to the bound user terminal for reminder processing to avoid delays in the entire rotation irrigation cycle due to the abnormality of a single node, ensuring that the overall agricultural progress is not significantly affected. In addition, this submodule supports the rapid reuse and editing of stored rotation irrigation strategies. Users can directly copy existing rotation irrigation strategies and generate new rotation irrigation plans by adjusting only a few parameters such as the corresponding planting cycle and target zone. There is no need to configure the complete strategy from scratch, which further reduces the operational cost of periodic rotation irrigation management.

[0061] In terms of actual control logic, the irrigation execution control module not only supports "one-click irrigation start" but also a "analysis first, then parameter-based execution" linkage mode. That is, the system can first analyze the target plot, obtain the total irrigation time, total flow rate, and suggested parameters, then map these parameters to a valve-level configuration list, which is then confirmed or fine-tuned by the user before execution. This mode retains the advantages of the system's intelligent analysis while also taking into account the experience-based correction needs of agricultural technicians.

[0062] The operation monitoring and early warning module is connected to the irrigation execution control module, the equipment management and data interaction module, and the multi-terminal display and recording traceability module, respectively. It is used to collect pressure data, flow data, equipment operating status data, and control feedback data during the irrigation execution process, generate abnormal early warning results according to preset early warning rules, and feed back the abnormal early warning results to the irrigation execution control module. The operation monitoring and early warning module includes a real-time data monitoring submodule, a threshold judgment submodule, an early warning rule configuration submodule, and an early warning history management submodule. The operation monitoring and early warning module is used to continuously track the equipment operation status, analyze anomalies, and trigger early warnings during irrigation.

[0063] The real-time data monitoring submodule collects real-time monitoring and control feedback data during the irrigation process and feeds the control feedback data back to the irrigation execution control module. Real-time monitoring data includes pressure data, flow data, and equipment operating status data. This submodule also displays the real-time operating data of valves, pumps, or other monitored objects. The system supports selecting a target valve and displaying the number of associated valves and the number successfully opened, as well as displaying pressure and flow data in a hyperbolic timeline. Pressure data can be displayed as a green curve, and flow data as a blue curve. Users can switch legends, zoom the timeline, and view data values ​​at specific time points. This submodule helps maintenance personnel quickly determine whether the irrigation process is stable.

[0064] The real-time data monitoring submodule in this embodiment adopts a high-frequency acquisition architecture with edge-cloud collaboration. Acquisition nodes deployed at the edge perform full sampling of pressure, flow, and equipment operating parameters at each monitoring point at a fixed interval of 100 milliseconds. The pre-processed and compressed dataset is uploaded to the system server via the MQTT lightweight IoT communication protocol, effectively reducing network bandwidth consumption while ensuring real-time data acquisition. For abnormal scenarios that significantly impact irrigation safety, such as sudden pressure spikes and drops or abnormal flow fluctuations, this submodule automatically triggers a high-frequency acquisition mechanism, automatically increasing the sampling frequency of abnormal points and caching full original sampling data within 5 minutes before and after the anomaly. When subsequent troubleshooting is needed, the original data for that period can be retrieved completely, avoiding the loss of anomaly details caused by conventional downsampling storage. Simultaneously, this submodule supports multi-terminal data synchronization, with real-time data refresh latency on web and mobile monitoring interfaces not exceeding 2 seconds. Administrators can view the latest operational data on any terminal, meeting the needs of dynamic management during irrigation operations.

[0065] The threshold judgment submodule compares real-time monitoring data with preset thresholds to identify abnormal states; it also performs anomaly judgment on real-time monitoring values. The system can directly mark thresholds such as high-pressure warning values, low-pressure warning values, and high-flow warning values ​​on the curve graph and highlight data points or data ranges exceeding the thresholds. In addition to static threshold judgment, this submodule can also perform conditional judgments based on equipment status and irrigation status, such as enabling a specific threshold monitoring only when the equipment is operating, thereby avoiding false alarms in standby mode.

[0066] The threshold judgment submodule supports multi-level hierarchical anomaly judgment rules, allowing for the setting of corresponding judgment thresholds and response strategies for different anomaly levels. Generally, it can be divided into two levels: alert-level anomalies and danger-level anomalies. When monitored data exceeds the preset alert threshold but does not reach the safe shutdown threshold, it is judged as an alert-level anomaly. Only an anomaly notification is pushed to management personnel; the currently executing irrigation operation is not actively interrupted, balancing operational stability and continuity. If monitored data exceeds the safe threshold, it is directly judged as a danger-level anomaly. A shutdown command is immediately issued to the irrigation execution control module, suspending the operation of the corresponding abnormal equipment and preventing safety accidents such as pipe bursts and pump overload burnout from the source. Simultaneously, this submodule supports dynamic threshold self-updating, periodically analyzing the distribution of normal operating data for the corresponding equipment over 8 months. It automatically corrects threshold offsets caused by equipment aging, component wear, and changes in pipeline pressure loss, significantly improving the accuracy of anomaly judgment and reducing the probability of false alarms and missed alarms compared to fixed threshold schemes.

[0067] The early warning rule configuration submodule is connected to the threshold judgment submodule and is used to configure the monitored object type, monitored data type, comparison conditions, and threshold content. The early warning rule configuration submodule allows users to flexibly configure early warning rules. Configuration items include at least the monitored object type, rule name, early warning time period, early warning message content, rule activation status, monitored data type, comparison conditions, and threshold content. Monitored object types can be water-fertilizer machines, water outlet valves, pipeline valves, etc.; monitored data types can be pressure values, flow values, solar panel voltage, etc.; comparison conditions can include greater than, greater than or equal to, less than, less than or equal to, equal to, and not equal to. Through the above configuration, the system can build differentiated early warning mechanisms for different objects and different business scenarios.

[0068] The aforementioned early warning rule configuration submodule allows users to set differentiated push targets and channels for different early warning rules: high-risk warnings related to equipment failure can be pushed to technicians responsible for equipment maintenance, while agricultural warnings such as abnormal irrigation water volume and fertilizer application deviations can be pushed to agricultural technicians responsible for field management. Push methods support multiple approaches, including system pop-up reminders, linked mobile phone SMS reminders, and WeChat / APP push reminders. Users can choose according to the urgency of the warning to ensure that important abnormal information reaches relevant personnel in a timely manner, avoiding delays in handling abnormalities. This submodule also supports batch activation and deactivation of rules. Users can adjust the effective status of all early warning rules with one click according to different agricultural stages such as irrigation season, fallow season, and crop rotation, without having to modify the rule status individually, effectively improving the management efficiency of early warning rules. Furthermore, this submodule allows users to configure multiple early warning rules of different levels for the same monitored object. Each rule operates independently with its own judgment logic, preventing rule overlap and fully meeting multi-level monitoring and early warning needs. All configured early warning rules will be uniformly stored in the device management and data interaction module, allowing administrators to view, edit, and delete them at any time. This facilitates dynamic adjustment of early warning strategies based on actual production management needs, adapting to early warning management requirements under different planting scenarios and equipment configurations.

[0069] The early warning history management submodule is used to store early warning time, device object, trigger value, threshold, and processing status.

[0070] The early warning history management submodule is used to record and manage all early warning events. Each early warning record can include at least the early warning time, device object, rule name, trigger value, threshold, current operating status, and processing status. By viewing the early warning history, administrators can analyze whether a device frequently experiences anomalies, whether a certain type of zone has long-term pressure fluctuations, or whether a certain irrigation rotation strategy has unreasonable design issues.

[0071] The early warning history management submodule supports multi-dimensional search and filtering functions. Users can filter and search for target early warning records by early warning level, occurrence time range, equipment zone, and early warning processing status, eliminating the need to browse all data page by page and significantly improving the efficiency of historical early warning information retrieval. For individual early warning records, managers can manually update the handling status, marking them as "pending handling," "handled," or "false alarm," and can also enter text notes explaining the cause of the anomaly, handling measures, and final handling results, completely preserving the entire process information of early warning handling. This submodule can also automatically generate early warning statistical analysis reports by time period, equipment type, and early warning type, intuitively displaying the frequency and distribution patterns of anomalies in different scenarios. This helps managers identify recurring problems in advance. For example, when low-pressure early warnings frequently occur in a certain irrigation zone, it can prompt managers to check for potential leaks in the pipeline network, promoting a shift in management from passive fault handling to proactive preventive maintenance and reducing the impact of sudden faults on irrigation operations. All historical warning data is bound to the corresponding equipment's full lifecycle operation file and stored. It supports one-click export of a complete list of warning records, which can provide complete and reliable data support for equipment fault tracing, maintenance plan optimization, and equipment scrapping assessment.

[0072] The equipment management and data interaction module is connected to the basic object access module, the sensing and acquisition module, the intelligent analysis and decision-making module, the irrigation execution control module, the operation monitoring and early warning module, and the multi-terminal display and record traceability module, respectively, to associate and store object data, sensing data, analysis results, irrigation control data, execution records, and early warning records; The equipment management and data interaction module includes an equipment file management submodule, a hardware binding submodule, a map positioning submodule, and a data storage and interaction submodule. The equipment management and data interaction module is used to complete the full lifecycle management of various equipment objects in the system and undertakes the responsibilities of data storage, querying, exchange, and archiving within the system.

[0073] The Equipment File Management submodule is used to create equipment files corresponding to monitoring equipment, pump rooms, water pumps, fertigation machines, butterfly valves, and electric valves. This submodule provides unified file management for monitoring equipment, pump rooms, water pumps, fertigation machines, butterfly valves, and electric valves. Users can perform operations such as adding, editing, deleting, viewing details, and filtering by conditions through this submodule. For example, when adding monitoring equipment, users can enter the equipment name, equipment type, terminal SN number, cover image, latitude and longitude, installation location, and activation status; when adding water pumps, users can enter the pump room, pump name, terminal SN number, minimum number of open outlets, and maximum number of open outlets; when adding valves, users can enter the land parcel, butterfly valve, valve type, valve rules, and location.

[0074] The Equipment File Management submodule records the entire lifecycle information of equipment from installation to decommissioning, including installation and commissioning records, maintenance records, fault handling records, and calibration records. Administrators can quickly obtain complete information about target equipment without having to consult paper files offline, significantly improving equipment management efficiency. This submodule supports quick retrieval of equipment by type, zone, and activation status. When an equipment anomaly warning is triggered during system operation, users can directly jump to the warning details page to view the corresponding equipment's complete file, helping maintenance personnel quickly grasp basic equipment information and historical fault details, shortening troubleshooting preparation time. Furthermore, the Equipment File Management submodule supports marking and sealing decommissioned equipment without deleting its historical operation records and associated work data. Historical information of decommissioned equipment can still be queried and traced as needed, ensuring the integrity of data throughout the entire lifecycle. For similar equipment installed in batches, this submodule supports batch import and creation of file templates. Users only need to upload a standardized and organized equipment information table to generate corresponding equipment files in batches, eliminating the need for manual entry and reducing a large amount of repetitive basic information entry work.

[0075] The hardware binding submodule is used to establish a data mapping relationship between IoT terminals and device files based on device identification information; it is also used to bind the actual IoT terminal to the system device file. Through terminal serial number binding, the system can route data reported by the terminal to the correct device object, thereby achieving digital mapping of the real hardware device. This submodule can also be used in complex scenarios such as device rebinding, terminal replacement, and multi-site association.

[0076] When an existing IoT terminal needs to be replaced due to malfunction or damage, the hardware binding submodule allows users to simply unbind the original terminal from its corresponding device file within the system and then rebind the new terminal's identification information to the original device file. This completes the configuration without modifying other basic information in the device file or losing any historical operational data, significantly simplifying the system adjustment process after terminal replacement and avoiding the repetitive workload of recreating a complete device file. For scenarios where multiple different types of monitoring terminals need to be deployed in the same irrigation zone, this submodule supports binding multiple terminals simultaneously to the corresponding zone's device file. This enables the aggregated storage and unified display of multi-dimensional monitoring data for that zone, allowing administrators to view all monitoring information for that zone on a single page. For integrated smart irrigation equipment with multiple acquisition components, such as a smart butterfly valve equipped with both pressure and flow acquisition components, the terminals corresponding to each acquisition component can be bound to a unified file for the device. This ensures that all acquired data corresponds to the correct device object, avoiding data misalignment and routing confusion. Furthermore, when field zoning is adjusted or equipment is relocated and redeployed, users can quickly unbind the original binding relationship and rebind it to the new target zoning file, flexibly adapting to the equipment management needs after adjustments to field planting plans. Accurate hardware binding relationships are the foundation for the entire system's data flow and precise command issuance. This submodule's flexible binding mechanism can adapt to various complex scenarios of field hardware deployment, effectively reducing the management costs of subsequent hardware updates and layout adjustments.

[0077] The map positioning submodule is used to record the spatial location of equipment; it is also used to perform spatial positioning and location management of equipment on the map. When adding new equipment, users can mark the location by entering the place name, searching by latitude and longitude, or directly clicking on the positioning point on the map; the system supports map zooming, positioning, layer switching, and satellite base map display. For objects such as valve zones that are highly spatially related, map positioning is not only used for display but also directly affects the rationality of irrigation control strategies.

[0078] The system can automatically analyze spatial topology based on device location information, clearly defining the irrigation zone and control level to which the device belongs. This provides a spatial basis for the accurate issuance of irrigation control commands and avoids misoperation of devices due to ambiguous zone affiliation. This submodule allows users to manually select and draw irrigation zone boundaries directly on the electronic map, automatically calculate the zone area, and batch associate all devices within a zone with the corresponding zone with one click. This results in a layered display of farmland equipment spatial layers: the global layer shows the irrigation network layout and equipment location distribution overview of the entire planting base, while zooming in to the zone layer allows viewing the specific location and real-time operating status of individual devices within that zone. Managers can quickly jump to the device details page by clicking on the device icon on the map to view operating data and issue control commands. Compared to a pure list-based device management mode, this is more intuitive and efficient.

[0079] To address the hierarchical management needs of large-scale contiguous planting bases, this submodule supports the division of independent visual layers according to management responsibilities. Field administrators in different areas can only view and operate the zones and equipment within their scope of responsibility, balancing the overall control of the base with the targeted management of grassroots units. Simultaneously, the map positioning submodule can directly link with the operation monitoring and early warning module. When the system triggers an equipment anomaly warning, it automatically highlights the specific location of the abnormal equipment on the map and supports navigation path navigation, helping maintenance personnel quickly locate the anomaly and take action, effectively shortening the response time for troubleshooting.

[0080] The data storage interaction submodule is used to associate and store object data, sensor data, analysis results, irrigation control data, execution records, and early warning records. This submodule provides unified storage and retrieval of master data, monitoring data, analysis results, control records, irrigation records, rotation irrigation records, and early warning records within the system. This submodule can store structured data and image data using a combination of database and file system methods, and provides query, export, and statistical interfaces to other modules. For example, pump start-up records and fertigation machine start-up records can be exported as files for archiving and analysis; valve start-up records and early warning records can serve as data sources for the irrigation traceability page.

[0081] The significance of the equipment management and data interaction module in this embodiment lies in the fact that, on the one hand, it ensures the unified management of all equipment objects and business objects, and on the other hand, it ensures that all analysis, control, early warning, and recording data can be collected around the same object system, thus supporting the entire smart irrigation closed loop from the bottom layer.

[0082] The multi-terminal display and recording traceability module is connected to the intelligent analysis and decision-making module, the operation monitoring and early warning module, and the equipment management and data interaction module respectively, and is used to display the entire irrigation process record; The multi-terminal display and record traceability module includes an overview dashboard sub-module, a farmland map sub-module, an irrigation record sub-module, and a rotation irrigation record sub-module. The multi-terminal display and record traceability module is used to provide differentiated but unified data display and business operation entry points to users with different roles, and to form a full-process traceability system.

[0083] The overview dashboard module displays the quantity, online status, and abnormal status of irrigation equipment; it also displays the operational overview of all key equipment on the homepage. The system can statistically analyze the total number of valves, their online and offline status, and the number of abnormal valves, as well as the total number of water pumps. Through this overview dashboard, managers can quickly determine the overall health status of the farm's irrigation equipment without navigating to specific pages.

[0084] The farmland map submodule displays the location relationships of plot boundaries, irrigation zones, pump houses, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment. It also displays the layout of various equipment, zones, and pipelines within the farm in a spatial map format. Users can freely select different layers such as weather stations, soil moisture stations, cameras, insect monitoring lights, valves, pump houses, butterfly valves, and fertigation machines, and can also view equipment details in pop-up windows. For irrigation zones, the system clearly marks their boundaries on the map using boundary colors; for pump houses, valves, and pipelines, the system can display their locations and routes on the base map. This submodule is a crucial component for the entire system's spatial management and visualized decision-making.

[0085] The irrigation record submodule displays irrigation parameters, execution time, operating status, and abnormal information corresponding to irrigation tasks. It also records and displays the irrigation history of a specific plot or zone. Each irrigation record can be associated with at least the plot, zone, irrigation name, valve parameter configuration, irrigation time, operating status, actual pressure-flow curve, and abnormal information for that irrigation session. After accessing the record details, users can view not only tabulated parameter information but also the spatial distribution of valves on a map and real-time data playback.

[0086] The irrigation rotation record submodule displays irrigation rotation strategies, irrigation rotation nodes, real-time data, and anomalies. It manages the historical results after the execution of automatic irrigation rotation strategies. This submodule allows filtering by pump, strategy type, and keywords, and viewing the associated zones, irrigation rotation nodes, status, real-time data, timeline, and anomalies for a specific irrigation rotation. Through the irrigation rotation record, administrators can conduct post-mortem analysis to review the effectiveness of a strategy, whether a node frequently encounters problems, and whether a zone needs adjustments to its duration or valve opening.

[0087] The multi-terminal display and record traceability module transforms the system from a mere back-end control platform into a unified business platform that can be used for business management, technical analysis, production control, and operational review.

[0088] The intelligent irrigation system of this invention can operate in different working modes according to different business scenarios, mainly including an intelligent analysis and decision-making mode and an automatic rotation irrigation execution and monitoring mode. The two modes can be used independently or in sequence, that is, analysis first, then execution, and then monitoring and tracing.

[0089] (1) Intelligent analysis and decision-making model The intelligent analysis and decision-making model is primarily geared towards business scenarios that prioritize analysis and implement on-demand irrigation. Its core objective is to complete intelligent diagnosis and parameter calculations for the target plot or zone before irrigation execution. The workflow of this model may include the following steps: 1. After logging into the system, users enter the smart irrigation analysis interface and first select the target farm, target plot, and target zone.

[0090] 2. The system reads the area, crops, growth period, historical diagnostic records and associated equipment information of the plot from the basic object access module, and loads its partition boundaries simultaneously.

[0091] 3. The sensing and acquisition module reads data associated with the plot, such as soil moisture, soil temperature, weather conditions, accumulated temperature, remote sensing nutrient heat map, and remote sensing water shortage heat map.

[0092] 4. The user inputs the target yield per acre and triggers the analysis task. The system then verifies the validity of the input data.

[0093] 5. The intelligent analysis and decision-making module first executes the nutrient analysis process to calculate the nitrogen, phosphorus, and potassium requirements under the current soil nutrient status, fertility level, and target yield conditions.

[0094] 6. The intelligent analysis and decision-making module then executes the water shortage analysis process, which integrates soil moisture content, evapotranspiration, soil temperature, and water shortage distribution to calculate the recommended irrigation duration, total flow rate, and water risk level.

[0095] 7. The system displays the analysis results in the form of overview cards, charts, heatmaps, and text suggestions, which users can switch between for viewing.

[0096] 8. If the user modifies the target output, the system will automatically recalculate and update the above results.

[0097] 9. If the user clicks "I want to irrigate", the system will bring the current analysis results into the irrigation execution control module and enter the execution preparation state.

[0098] The technical feature of this model is that the system does not simply display monitoring values, but rather transforms multiple data into executable, adjustable, and traceable business decision results after joint modeling.

[0099] (2) Automatic rotation irrigation execution and monitoring mode The automated rotation irrigation execution and monitoring mode is primarily designed for business scenarios characterized by "strategy-driven, automated execution, and process monitoring." Its core objective is to automatically complete zoned irrigation or periodic rotation irrigation tasks while ensuring safety and monitorability. The workflow of this mode may include the following steps: 1. Users can select an existing strategy or create a new rotation irrigation strategy on the irrigation execution page or rotation irrigation strategy page.

[0100] 2. The system reads the irrigation name, cycle, node timeline, partition binding relationship, and valve parameter configuration of the strategy.

[0101] 3. If the strategy is derived from the results of intelligent analysis, the system will also load the suggested values ​​from this analysis and allow the user to confirm or fine-tune them.

[0102] 4. Before irrigation starts, the system checks whether the relevant equipment is online, whether the relevant valves are properly bound, whether there are any time conflicts between nodes, and whether the current task overlaps with other tasks.

[0103] 5. After the verification is passed, the irrigation execution control module sends control commands to the corresponding water pump, fertigation unit, butterfly valve and electric valve according to the valve configuration and irrigation node sequence.

[0104] 6. After the equipment starts running, the operation monitoring and early warning module collects data such as pressure, flow rate, opening status, and number of successfully opened associated valves in real time, and continuously refreshes and displays it on the front-end interface.

[0105] 7. When the real-time monitoring value triggers the early warning rule, the system automatically generates an early warning event and highlights the relevant curve segment or equipment status.

[0106] 8. If the user manually terminates irrigation, the equipment reports an abnormality, or the warning reaches a severe level, the system can execute shutdown, valve shutdown, or task abort logic and record the reason for the abort.

[0107] 9. After irrigation is completed, the system automatically saves a complete record of the execution, including configuration parameters, execution time, real-time curves, early warning events, and final status.

[0108] The technical feature of this model is that the system can transform the irrigation process, which originally relied on manual operation, into an automated process that can be planned, started, monitored, and traced, making it particularly suitable for the periodic rotation irrigation management of large-scale farms.

[0109] The key innovation of this invention lies not in a single function, but in establishing a complete system solution that integrates analysis, control, monitoring, and traceability around smart irrigation, mainly reflected in the following aspects: 1. A joint analysis mechanism for irrigation and fertilization based on target yield has been established. In existing technologies, irrigation and fertilization are usually treated separately, while this invention uses target yield as a unified decision-making entry point, incorporating soil nutrient status and water shortage status into the same analysis link, outputting both fertilization parameters and irrigation parameters, enabling the system to provide more complete and practical decision-making suggestions for agricultural production.

[0110] 2. A spatial management mechanism integrating land parcels, zones, and equipment has been established. This invention does not manage individual devices in isolation, but rather maps farms, land parcels, zones, pump houses, water pumps, butterfly valves, electric valves, and monitoring equipment onto a unified map and object model, thereby achieving refined management of "finding equipment based on land parcels, finding valves based on zones, and viewing controlled objects based on maps."

[0111] 3. A refined control mechanism for valve groups and irrigation rotation nodes has been established. This invention supports independent adjustment of valve opening, synchronization of irrigation time within a group, batch adjustment of the entire group, and configuration of the irrigation rotation time axis, and can automatically verify conflicts between irrigation rotation nodes. This enables the system to adapt to complex irrigation scenarios with multiple valves, multiple zones, and multiple cycles, rather than simply being limited to the start and stop of single devices.

[0112] 4. A closed-loop monitoring and early warning mechanism for real-time operation has been established. This invention does not wait until irrigation is completed to view the results, but displays pressure, flow rate, equipment status, and abnormal thresholds in real time during operation, and allows for flexible early warning configuration based on equipment and rules, making the irrigation process safer and more controllable.

[0113] 5. A full-link traceability mechanism from analysis results to execution records has been established. This invention uniformly saves target yield input, remote sensing analysis results, irrigation suggestions, valve parameters, control commands, real-time curves, early warning events, and irrigation records, enabling the restoration of the causes and consequences of each irrigation, facilitating subsequent review, optimization, and responsibility determination.

[0114] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A full-chain management and control system for smart irrigation of farmland, characterized in that, The system includes a basic object access module, a sensing and acquisition module, an intelligent analysis and decision-making module, an irrigation execution and control module, an operation monitoring and early warning module, an equipment management and data interaction module, and a multi-terminal display and recording traceability module. The basic object access module is connected to the intelligent analysis and decision-making module, the irrigation execution control module, and the equipment management and data interaction module respectively. It is used to establish an object relationship model between farms, plots, irrigation zones, pump houses, water pumps, water and fertilizer machines, butterfly valves, electric valves, and monitoring equipment, and generate spatial boundary information, equipment association information, and historical diagnostic information corresponding to the target plot. The sensing and acquisition module is connected to the intelligent analysis and decision-making module, the operation monitoring and early warning module, and the equipment management and data interaction module respectively. It is used to collect environmental data, soil condition data, remote sensing analysis data, and irrigation equipment operation status data corresponding to the target plot, and to clean, time-align, and standardize the collected data to form sensing data in a unified format. The intelligent analysis and decision-making module is connected to the irrigation execution control module and is used to perform joint analysis based on target yield parameters, spatial boundary information, equipment association information, historical diagnostic information, and remote sensing analysis data, environmental data, and soil condition data in the sensing data to generate irrigation parameters for the corresponding target plot and send the irrigation parameters to the irrigation execution control module. The irrigation execution control module is connected to the equipment management and data interaction module and the operation monitoring and early warning module. It is used to determine the corresponding water pump, water and fertilizer machine, butterfly valve and electric valve according to the irrigation parameters and object relationship model, generate valve control scheme, and send control commands to the corresponding irrigation equipment to execute zoned irrigation or automatic rotation irrigation tasks. The operation monitoring and early warning module is connected to the equipment management and data interaction module and the multi-terminal display and recording traceability module respectively. It is used to collect pressure data, flow data, equipment operation status data and control feedback data during the irrigation process, generate abnormal early warning results according to preset early warning rules, and feed back the abnormal early warning results to the irrigation execution control module. The equipment management and data interaction module is connected to the multi-terminal display and record traceability module for associating and storing object data, sensing data, analysis results, irrigation control data, execution records, and early warning records. The multi-terminal display and recording traceability module is connected to the intelligent analysis and decision-making module to display the entire irrigation process record.

2. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The basic object access module includes a farm plot selection submodule, an irrigation zone modeling submodule, a historical diagnosis access submodule, and an object relationship mapping submodule; The farm plot selection submodule is used to obtain target farms, target plots and target irrigation zones, and load the corresponding equipment association information, spatial boundary information, area information, planted crop information, growth period information and equipment association information; The irrigation zoning modeling submodule is used to divide the target plot according to the plot boundary information and zoning rules, generate multiple irrigation zoning objects, and configure the name, number and spatial boundary of each irrigation zoning object. The historical diagnostic access submodule is used to obtain historical weather data, historical remote sensing analysis results, historical irrigation records and historical fertilization suggestions corresponding to the target plot; The object relationship mapping submodule is used to establish an object relationship model between plots of land, irrigation zones, pumping stations, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment.

3. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The sensing and acquisition module includes an environmental device access submodule, a remote sensing data access submodule, a device status acquisition submodule, and a map point encoding submodule. The environmental equipment access submodule is used to collect environmental data and soil condition data output by meteorological stations, moisture stations and other environmental monitoring equipment; The remote sensing data access submodule is used to acquire remote sensing analysis data corresponding to the target plot. The remote sensing analysis data includes soil nutrient distribution results and soil water shortage status results. The equipment status acquisition submodule is used to collect irrigation equipment operation status data, which includes the online status, working status, pressure data, flow data, current opening degree data, and control feedback data of water pumps, fertigation machines, butterfly valves, and electric valves. The map point coding submodule is used to bind each monitoring device and irrigation device to its corresponding spatial location.

4. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The intelligent analysis and decision-making module includes a target yield configuration submodule, a remote sensing nutrient analysis submodule, a remote sensing water shortage analysis submodule, a parameter calculation submodule, and a decision suggestion generation submodule. The target yield configuration submodule is used to obtain the target yield parameters corresponding to the target crop; The remote sensing nutrient analysis submodule is used to determine soil nutrient requirements based on the soil condition data and target yield parameters of the plot. The remote sensing water shortage analysis submodule is used to determine water shortage status information based on environmental data, remote sensing analysis data, historical diagnostic information, remote sensing water shortage status results, soil moisture data, soil temperature data, historical evapotranspiration, and weather conditions. The parameter calculation submodule is used to generate irrigation parameters based on spatial boundary information, equipment association information, nutrient demand information and water shortage status information. Irrigation parameters include irrigation time, irrigation flow rate, zonal irrigation duration and irrigation sequence. The decision suggestion generation submodule is used to visualize the output of irrigation parameters.

5. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The irrigation execution control module includes a zoned irrigation control submodule, a valve parameter configuration submodule, a hardware command issuance submodule, and an automatic rotation irrigation submodule; The zoned irrigation control submodule is used to determine the corresponding set of execution devices based on the target irrigation zone; The valve parameter configuration submodule is used to configure the opening parameters, irrigation time parameters, and irrigation sequence parameters of the corresponding valves according to the set of execution devices; The hardware instruction sending submodule is used to generate a valve control scheme based on irrigation parameters, convert the valve control scheme into equipment control instructions, and send them to the water pump, fertigation unit, butterfly valve, and electric valve in the set of executing equipment. The automatic irrigation rotation submodule is used to generate automatic irrigation rotation tasks based on irrigation cycle, irrigation rotation nodes and zone binding relationship; the automatic irrigation rotation submodule is also used to verify the online status of equipment, valve binding status, irrigation rotation node time relationship and task overlap relationship before executing the automatic irrigation rotation task, and determine the execution status of automatic irrigation rotation task based on the verification results.

6. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The operation monitoring and early warning module includes a real-time data monitoring submodule, a threshold judgment submodule, an early warning rule configuration submodule, and an early warning history management submodule; The real-time data monitoring submodule is used to collect real-time monitoring data and control feedback data during the irrigation process, and to feed the control feedback data back to the irrigation execution control module. The real-time monitoring data includes pressure data, flow data, and equipment operating status data; The threshold judgment submodule is used to compare real-time monitoring data with preset thresholds and determine abnormal states; The early warning rule configuration submodule is connected to the threshold judgment submodule and is used to configure the monitoring object type, monitoring data type, comparison conditions and threshold content; The warning history management submodule is used to store warning time, device object, trigger value, threshold and processing status.

7. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The device management and data interaction module includes a device file management submodule, a hardware binding submodule, a map positioning submodule, and a data storage and interaction submodule; The equipment file management submodule is used to create equipment files corresponding to monitoring equipment, pump rooms, water pumps, fertigation machines, butterfly valves, and electric valves. The hardware binding submodule is used to establish a data mapping relationship between IoT terminals and device files based on device identification information; The map positioning submodule is used to record the spatial location of the device; The data storage interaction submodule is used to associate and store object data, sensing data, analysis results, irrigation control data, execution records, and early warning records.

8. The end-to-end management and control system for smart irrigation of farmland as described in claim 1, characterized in that, The multi-terminal display and record traceability module includes an overview dashboard sub-module, a farmland map sub-module, an irrigation record sub-module, and a rotation irrigation record sub-module; The overview dashboard submodule is used to display the number of irrigation equipment, online status, and abnormal status. The farmland map submodule is used to display the location relationships of plot boundaries, irrigation zones, pumping stations, water pumps, fertigation machines, butterfly valves, electric valves, and monitoring equipment. The irrigation record submodule is used to display the irrigation parameters, execution time, running status and abnormal information corresponding to the irrigation task; The rotation irrigation record submodule is used to display the rotation irrigation strategy, rotation irrigation nodes, real-time data, and abnormal situations.

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