Irrigation dynamic control system based on weather forecast

Abstract

The application specifically relates to the technical field of intelligent irrigation, and discloses an irrigation dynamic regulation and control system based on meteorological prediction, which comprises a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback regulation module and a man-machine interaction module; the data acquisition module is integrated with a water quantity acquisition terminal, a crop state acquisition terminal and a meteorological monitoring terminal, and can acquire multi-source irrigation decision data of a target irrigation farmland in real time; the data analysis module is used for judging the water shortage risk level of the target irrigation farmland; the strategy generation module is used for generating an irrigation strategy; the strategy execution module is used for executing the irrigation strategy through an electrically-driven intelligent water-saving valve; the feedback regulation module is used for dynamically adjusting the irrigation strategy based on sensor feedback data; and the man-machine interaction module provides a data visual interface, dynamically optimizes the irrigation strategy in combination with real-time meteorological changes and irrigation feedback, and divides the water shortage risk level, so that the utilization efficiency of water resources is effectively improved, and the applicability and reliability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent irrigation technology, and more specifically, to an irrigation dynamic control system based on weather forecasting. Background Technology

[0002] With global population growth and economic development, water scarcity is becoming increasingly serious, particularly in agriculture where water demand is substantial. There is an urgent need to improve water resource utilization, reduce waste, and increase irrigation efficiency. As agricultural modernization progresses, electrical devices for controlling irrigation have become a key direction for achieving intelligent agricultural irrigation. With the rapid development of smart agriculture, irrigation systems based on electrical control and data analysis are becoming increasingly widespread. Through the collaboration of sensors, controllers, and actuators, automated control of the irrigation process is achieved. Furthermore, the development of meteorological monitoring equipment and forecasting technology provides more accurate and timely meteorological information for agricultural irrigation, offering significant reference value for agricultural irrigation decision-making.

[0003] The existing dynamic irrigation control system includes a data acquisition module, a data analysis module, a decision control module, a decision execution module, a fault diagnosis module, and a human-computer interaction module. The data acquisition module is used to collect soil and environmental data in real time. The data analysis module is used to convert the collected data into irrigation instructions. The decision control module is used to automatically generate irrigation plans. The decision execution module is used to automatically execute the irrigation plans. The fault diagnosis module is used to monitor the system's operating status. The human-computer interaction module is used to provide a graphical interface, enabling precise and efficient irrigation management.

[0004] However, it still has some shortcomings in actual use. First, the data collection dimension is limited. The existing irrigation dynamic control system can only collect soil data and some environmental data in real time. It cannot obtain long-term meteorological forecast information, which may lead to over-irrigation or waste. It cannot adjust the irrigation frequency in advance to cope with the peak water demand of crops.

[0005] Second, it lacks dynamic adjustment capabilities. The existing irrigation dynamic control system relies on fixed rules and lacks the ability to adapt to dynamic changes in weather. It cannot dynamically adjust irrigation strategies according to dynamic changes in weather, resulting in the waste of water resources.

[0006] Third, the lack of an emergency response mechanism means that the existing dynamic irrigation control system cannot identify the impact of meteorological risks on irrigation, lacks a targeted emergency response mechanism, and cannot dynamically optimize the allocation priority of water resources based on the probability of precipitation, resulting in low applicability of the dynamic irrigation control system. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide an irrigation dynamic control system based on meteorological forecasting. This system aggregates multi-source irrigation decision data collected in real time from a data acquisition module, a crop status acquisition terminal, and a meteorological monitoring terminal. It combines real-time meteorological changes and irrigation feedback to dynamically optimize irrigation strategies. An execution control terminal controls an intelligent water-saving valve to execute the irrigation strategy. The feedback adjustment module calculates irrigation deviations, constructs a water shortage risk level index, and classifies water shortage risk levels. This effectively solves the problems of single data acquisition dimensions, lack of dynamic adjustment capabilities, and lack of emergency response mechanisms mentioned in the background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an irrigation dynamic control system based on meteorological forecasting, comprising a water volume acquisition terminal, a crop status acquisition terminal, a meteorological monitoring terminal, a cloud server database, irrigation equipment, and a mobile terminal, and further comprising a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback adjustment module, and a human-computer interaction module.

[0009] The irrigation equipment specifically refers to electrically driven intelligent water-saving valves;

[0010] The data acquisition module is used to collect multi-source irrigation decision data of the target irrigated farmland, and to standardize the multi-source irrigation decision data to obtain a multi-source irrigation decision dataset, which is then transmitted to the data analysis module.

[0011] The data analysis module is used to construct a crop water demand model, calculate farmland irrigation water demand and water shortage risk index based on the multi-source irrigation decision dataset, determine the water shortage risk level of the target irrigated farmland, and transmit the analysis results to the strategy generation module.

[0012] The strategy generation module generates irrigation strategies based on the analysis results of the data analysis module and preset rules, presets irrigation strategies corresponding to different weather forecast results, and generates equipment control commands, which are then transmitted to the strategy execution module.

[0013] The strategy execution module is used to receive the device control instructions transmitted by the strategy generation module, control the irrigation equipment to execute the irrigation strategy according to the device control instructions, and collect the device feedback data in real time and upload it to the cloud server database.

[0014] The feedback adjustment module is used to monitor the irrigation execution results in real time, calculate the deviation value between the irrigation execution results and the preset target of the strategy, dynamically adjust the parameters of the crop water requirement model according to the deviation value, and dynamically adjust the irrigation strategy.

[0015] The human-computer interaction module is used to provide a data visualization interface, display farmland distribution and irrigation status in real time, push early warnings, set and manage multi-role permissions, and support manual adjustment of irrigation plans and switching of control modes.

[0016] The technical effects and advantages of this invention are as follows:

[0017] 1. This invention integrates multi-source irrigation decision data collected in real time by a data acquisition module from a data acquisition terminal, a crop status acquisition terminal, and a meteorological monitoring terminal, breaking the limitations of single-source data acquisition. Furthermore, by combining weather forecasts and extreme weather warnings with historical data and crop water requirement models, it improves the accuracy of predictions and provides a scientific basis for precision irrigation.

[0018] 2. This invention dynamically optimizes irrigation strategies by combining real-time weather changes and irrigation feedback. The feedback adjustment module automatically corrects crop water requirement model parameters by calculating irrigation deviations and dynamically adjusts irrigation time, water volume, and method in conjunction with weather forecasts, effectively reducing water waste and improving water resource utilization efficiency.

[0019] 3. This invention uses a data analysis module to analyze and process data to obtain farmland irrigation water demand and water shortage risk index, and constructs a water shortage risk level index to classify water shortage risk levels. The strategy generation module formulates corresponding irrigation strategies based on the water shortage risk level, dynamically adjusts the priority of water resource allocation, and supports manual intervention, thereby improving the applicability and reliability of the system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0021] Figure 2 This is a schematic diagram of the method steps of the present invention.

[0022] Figure 3 This is a schematic diagram illustrating the steps of the method for determining the water shortage risk level according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] As attached Figure 1 The irrigation dynamic control system based on meteorological forecasting shown includes a water volume acquisition terminal, a crop status acquisition terminal, a meteorological monitoring terminal, a cloud server database, irrigation equipment, and a mobile terminal. It also includes a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback adjustment module, and a human-computer interaction module.

[0025] In a more specific application of this invention, the water acquisition terminal is used to monitor the flow rate, water level, and water quality of irrigation water sources in real time, and to count the cumulative water consumption of each plot, providing data support for the subsequent allocation of water resources, ensuring that the irrigation water source is sufficient and the water quality meets the standards, and providing water quantity constraints for the generation of subsequent dynamic strategies. The water acquisition terminal is connected to the cloud server database through NB-IoT, and the water acquisition terminal can be an ultrasonic flow meter, an electromagnetic flow meter, or a water level sensor.

[0026] The crop status acquisition terminal is used to monitor soil moisture and crop physiological indicators, and identify early symptoms of crop diseases and pests. It provides "real-time evidence of crop water requirements" for the generation of dynamic strategies, avoiding the bias of relying solely on meteorological data. The crop status acquisition terminal is connected to the cloud server database through NB-IoT. The crop status acquisition terminal can be a multispectral camera, a stem diameter change sensor, a leaf surface humidity sensor, and a soil three-parameter sensor.

[0027] The meteorological monitoring terminal is used to collect real-time meteorological data and meteorological forecast data, calculate potential evapotranspiration and crop water stress index, and assess atmospheric demand pressure on crops for water. Local meteorological stations access cloud server databases via the MQTT protocol and obtain meteorological forecast data through API interfaces. The meteorological monitoring terminal includes a micro meteorological station and a meteorological radar data interface.

[0028] The cloud server database is used to store real-time data collected by all acquisition terminals in the irrigation dynamic control system, as well as historical data, model parameters, and user configuration information, ensuring data integrity, consistency, and security. It supports long-term data archiving and retrospective analysis. The cloud server database is connected to the front-end acquisition terminals, back-end application modules, and irrigation equipment to achieve seamless data flow and command interaction.

[0029] Irrigation equipment is used to execute irrigation commands, including timed and quantitative irrigation, pulse irrigation, and water-fertilizer coupled irrigation. It supports flow regulation and is connected to a cloud server through an IoT controller. It supports the MQTT bidirectional communication protocol. Specifically, irrigation equipment refers to electrically driven intelligent water-saving valves that receive digital control signals from the strategy execution module through solenoid valve groups to achieve opening degree regulation from 0% to 100%.

[0030] Mobile terminals are used to view farmland data and irrigation history records in real time, support manual adjustment of irrigation strategies, receive early warning information pushed by the system, generate visual reports, and assist users in decision-making. They can be smartphones or tablets and can be connected to cloud servers via the network.

[0031] The specific embodiments of the present invention include the following:

[0032] Data acquisition module: Collects multi-source irrigation decision data of the target irrigated farmland, performs standardization processing on the multi-source irrigation decision data to obtain a multi-source irrigation decision dataset, and transmits it to the data analysis module.

[0033] Furthermore, the multi-source irrigation decision data includes irrigation water source data, crop status data, real-time meteorological data, and meteorological forecast data. Irrigation water source data includes water level, flow rate, and water conveyance efficiency. Crop status data includes soil moisture, root depth, stem micro-changes, leaf surface humidity, and canopy temperature. Real-time meteorological data includes air temperature, relative humidity, wind direction, wind speed, solar radiation, and rainfall. Meteorological forecast data includes 7-day weather forecast information and extreme weather warning information. The multi-source irrigation decision data undergoes standardization processing, including outlier detection, sensor calibration, time series alignment, and unified data format.

[0034] This embodiment requires specific explanation regarding soil moisture conditions, which include soil water content, soil water potential, and soil stratification data. Irrigation water source data collection requires detecting the water level of the irrigation water source using a radar water level gauge and detecting the flow rate of the irrigation water source using an electromagnetic flow meter, with irrigation water source data collected every 15 minutes. Crop status data collection requires collecting soil moisture data using a soil moisture sensor, measuring micro-changes in the stem using a strain gauge stem growth sensor, and measuring the canopy temperature using an infrared canopy thermometer. Real-time meteorological data collection requires collecting real-time meteorological data every 5 minutes using a miniature weather station. Meteorological forecast data collection requires accessing the China Meteorological Administration API and is updated 4 times daily.

[0035] Data Analysis Module: Constructs a crop water requirement model, calculates farmland irrigation water requirement and water shortage risk index based on multi-source irrigation decision dataset, determines the water shortage risk level of target irrigated farmland, and transmits the analysis results to the strategy generation module.

[0036] Furthermore, the calculation of farmland irrigation water demand requires importing the multi-source irrigation decision dataset collected in real time by the data acquisition module into the crop water demand model to calculate the reference crop evapotranspiration ET0 for the target irrigated farmland. The reference crop evapotranspiration is then calculated using the formula ET0. c =K c The actual evapotranspiration ET is calculated using ×ET0. c To obtain the rainfall P, initial soil moisture V0, real-time soil moisture V, root layer depth d, and deep infiltration D from the multi-source irrigation decision dataset, the actual evapotranspiration, rainfall, initial soil moisture content, real-time soil moisture content, root layer depth, and deep infiltration are calculated using the following formula:

[0037] ,

[0038] The irrigation water requirement I for the target irrigated farmland is obtained, where P e For effective rainfall, P e The calculation method is as follows:

[0039] .

[0040] This embodiment requires a specific explanation: the calculation of crop transpiration needs to utilize the Penman-Montis formula, which incorporates air temperature T, wind speed at 2 meters per second (v), and saturated water vapor pressure (e). s Actual water vapor pressure e a Net radiation R n Substituting the soil heat flux G, the slope Δ of the saturated water vapor pressure curve, and the hygrometer constant γ into the formula:

[0041] ,

[0042] The reference crop transpiration rate ET0 was calculated, and the above data can be obtained by processing and analyzing a multi-source irrigation decision dataset.

[0043] In the above steps, (V0-V)×d represents soil water consumption, which refers to the reduction in the amount of water available for crop use in the soil profile during a certain period; effective rainfall refers to the actual amount of rainfall that can be used by crops, and is calculated using the FAO method based on actual evapotranspiration and rainfall; deep infiltration refers to the amount of water that migrates downward through the bottom of the crop root zone per unit time, and is obtained by calculating the ratio of the total hydraulic head difference to the vertical distance from the bottom of the root zone to the groundwater level and the negative product of the unsaturated hydraulic conductivity.

[0044] Furthermore, the calculation of the water shortage risk index requires obtaining the flow rate Q and water conveyance efficiency η of the irrigation water source from the multi-source irrigation decision dataset, and using the formula S=Q×t×η to calculate the available water volume S of the irrigation water source, where t is the planned irrigation time, and calculating the difference between the irrigation water demand and the available water volume of the irrigation water source to obtain the supply and demand balance difference.

[0045] Extract key meteorological factors from meteorological forecast data in multi-source irrigation decision data, including future cumulative rainfall P. f Predicted potential evapotranspiration ET 0f The drought meteorological warning level is calculated, and the difference between 1 and the ratio of future cumulative rainfall to irrigation water demand is obtained to obtain the precipitation deficit index RDI. The ratio of the predicted potential evapotranspiration to the historical average evapotranspiration is calculated to obtain the potential evapotranspiration stress index ETSI. The drought meteorological warning is divided into no warning, level 1 warning, level 2 warning and level 3 warning, and risk values ​​of 0, 30, 60 and 100 are assigned to them respectively to obtain the extreme weather risk factor EWR.

[0046] The supply-demand imbalance, precipitation deficit index, and potential evapotranspiration stress index are standardized to obtain standardized versions of these indices. The extreme weather risk factor, along with the standardized versions of these indices, are then substituted into the formula:

[0047] ,

[0048] The water shortage risk index SRI is obtained, where a1, a2, a3, and a4 represent the weighting coefficients of the supply-demand imbalance, precipitation deficit index, potential evapotranspiration stress index, and extreme weather risk factor, respectively, and a1+a2+a3+a4=1. S represents the available irrigation water supply, I represents the irrigation water demand of the target irrigated farmland, and RDI is calculated. norm The ETSI represents the standardized precipitation deficit index. norm This represents the standardized potential evapotranspiration stress index.

[0049] In this embodiment, it is important to note that the weighting coefficients a1, a2, a3, and a4 are set according to the degree of influence of each risk factor on the water shortage risk index. The supply-demand balance difference reflects the surplus or deficit of soil moisture. When the supply-demand balance difference is greater than 0, it indicates that the water demand is greater than the water supply, and there is a risk of water shortage. The more severe the water shortage, the larger the supply-demand balance difference. When the supply-demand balance difference is less than or equal to 0, it indicates that the water supply is sufficient. The precipitation deficit index reflects the ability of precipitation to replenish the crop's water demand during the forecast period. The larger the precipitation deficit index, the more severe the precipitation deficit and the higher the risk. The potential evapotranspiration stress index reflects the pressure of atmospheric evaporation demand on soil moisture. A potential evapotranspiration stress index greater than 1 indicates that the evaporation demand is higher than the historical average, accelerating soil moisture loss and increasing the risk. The standardized supply-demand balance difference, precipitation deficit index, and potential evapotranspiration stress index are all on a percentage scale.

[0050] Furthermore, determining the level of water shortage risk requires constructing a water shortage risk level index and classifying water shortage risk levels, including the first water shortage risk level, the second water shortage risk level, the third water shortage risk level, and the fourth water shortage risk level.

[0051] In this embodiment, it should be specifically noted that the water shortage risk level indicators include SRI1, SRI2, and SRI3, where SRI1 < SRI2 < SRI3. When 0 < SRI < SRI1, it indicates that the target irrigated farmland is at the first water shortage risk level, which means the target irrigated farmland is at a low risk, with sufficient water and no special intervention required; when SRI1 < SRI < SRI2, it indicates that the target irrigated farmland is at the second water shortage risk level, which means the target irrigated farmland is at a medium risk, and it is necessary to pay attention to the soil moisture and prepare to irrigate the farmland; when SRI2 < SRI < SRI3, it indicates that the target irrigated farmland is at the third water shortage risk level, which means the target irrigated farmland is at a high risk, and it is necessary to irrigate in a timely manner and activate the emergency plan; when SRI > SRI3, it indicates that the target irrigated farmland is at the fourth water shortage risk level, which means the target irrigated farmland is at an extremely high risk, and the target irrigated farmland is extremely short of water, and it is necessary to prioritize ensuring the water supply for the farmland.

[0052] Strategy generation module: Generate irrigation strategies based on the analysis results of the data analysis module and preset rules, preset irrigation strategies corresponding to different weather prediction results, and generate device control instructions, which are transmitted to the strategy execution module.

[0053] Furthermore, to generate the irrigation strategy, it is necessary to obtain the farmland irrigation water demand and water shortage risk level of the target irrigated farmland obtained by the data analysis module, preset crop growth stage rules, meteorological response rules, and equipment operation rules, formulate irrigation strategies according to the water shortage risk level, convert the strategy parameters into control instructions that the equipment can recognize, and generate a time sequence instruction queue.

[0054] In this embodiment, it should be specifically noted that the crop growth stage rules refer to the water demand thresholds and irrigation priorities corresponding to different growth stages of the crop; the meteorological response rules are to preset corresponding irrigation strategies based on weather forecasts. For example, when there is heavy rainfall, irrigation is stopped, and extreme weather avoidance rules are set, such as suspending irrigation during typhoon warnings and turning on the drainage system; the equipment operation rules refer to setting the maximum and minimum flow rate limits of the irrigation equipment, and setting the upper limit of the single irrigation duration to avoid excessive leaf surface humidity retention caused by night irrigation.

[0055] Developing irrigation strategies based on water shortage risk levels means initiating different priority strategies according to the water shortage risk index. When the water shortage risk level is at the first level, a conservative irrigation strategy is implemented, supplementing irrigation only when soil moisture is below the crop's critical value, prioritizing the use of natural precipitation. When the water shortage risk level is at the second level, a preventative irrigation strategy is implemented, irrigating in stages according to the irrigation water requirements of the target farmland, and adjusting in real time based on weather forecasts. When the water shortage risk level is at the third level, an emergency irrigation strategy is implemented, completing all required irrigation within 48 hours, prioritizing the supply of water to the root zone. When the water shortage risk level is at the fourth level, the highest priority irrigation strategy is implemented, immediately activating irrigation equipment and sending early warning notifications to mobile devices.

[0056] Strategy Execution Module: Receives device control commands from the strategy generation module, controls the irrigation equipment to execute irrigation strategies according to the device control commands, and collects device feedback data in real time and uploads it to the cloud server database.

[0057] Furthermore, the execution of the irrigation strategy requires receiving real-time equipment control commands from the strategy generation module, parsing and verifying the equipment control commands, controlling the intelligent water-saving valves to perform corresponding operations according to the irrigation strategy, and managing the execution sequence. The real-time collected equipment feedback data includes equipment operating status data, execution effect data, and environmental data.

[0058] This embodiment requires a specific explanation of how parsing and verifying device control commands involves format verification, permission verification, and conflict detection. Format verification refers to parsing the command fields and verifying whether they conform to the protocol specifications. Permission verification refers to checking the device's operating permissions. Conflict detection refers to checking the current device status to avoid duplicate commands that could cause device malfunctions.

[0059] The management execution sequence includes time triggering and time-based triggering. Time triggering refers to automatically executing the corresponding operation according to the time point set by the irrigation strategy. Time-based triggering refers to dynamically adjusting the on / off timing of irrigation equipment based on real-time data.

[0060] The equipment operation status data specifically includes valve opening, pipeline pressure, and equipment operating time. The execution effect data includes actual irrigation volume and irrigation uniformity. Environmental data refers to real-time meteorological data during the irrigation process. Based on the real-time collected equipment feedback data, it is determined whether the equipment has malfunctioned, and early warning notices are issued in a timely manner to take relevant solutions.

[0061] Feedback adjustment module: Monitors irrigation execution results in real time, calculates the deviation between the irrigation execution results and the preset target of the strategy, dynamically adjusts the parameters of the crop water requirement model based on the deviation, and dynamically adjusts the irrigation strategy.

[0062] Furthermore, the irrigation execution results include soil moisture content after irrigation, crop stem length, leaf surface moisture, and actual irrigation water volume. The difference between the actual irrigation water volume and the irrigation water requirement is calculated to obtain the water volume deviation value. The difference between the soil moisture content after irrigation and the target soil moisture content is calculated to obtain the soil moisture deviation value. The difference between the crop stem length and the target length is calculated to obtain the crop growth deviation value. Based on the water volume deviation value, soil moisture deviation value, and crop growth deviation value, a weighted analysis is performed to obtain the comprehensive deviation value. The parameters of the crop water requirement model and the irrigation strategy are dynamically adjusted according to the comprehensive deviation value.

[0063] In this embodiment, it is necessary to specifically explain that the parameters of the crop water requirement model include meteorological parameters, crop-related parameters, and soil-related parameters. If the soil moisture content of the target farmland is higher than the target value after irrigation and the crop is growing well, the crop coefficient should be appropriately reduced to decrease the subsequent predicted crop water requirement, and the soil evaporation coefficient should be lowered to optimize the soil moisture balance calculation. If there is insufficient water or the soil moisture is not up to standard during irrigation, it is necessary to add the prediction of future irrigation water requirement and adjust the calculation logic of irrigation duration and frequency.

[0064] Dynamically adjusting irrigation strategies requires changing irrigation schedules based on deviation analysis results. Based on water volume and soil moisture deviations, subsequent irrigation volumes should be increased or decreased proportionally. For the differences in irrigation effects on different target irrigated farmlands, regional irrigation strategies should be adjusted. Based on the actual implementation effect of the irrigation strategy, drip irrigation, sprinkler irrigation, and flood irrigation methods should be selected to improve water resource utilization.

[0065] Human-computer interaction module: Provides a data visualization interface to display farmland distribution and irrigation status in real time, pushes early warnings, sets and manages multi-role permissions, and supports manual adjustment of irrigation plans and switching of control modes.

[0066] Furthermore, the data visualization interface is used to display farmland distribution, farmland irrigation status, and multi-source irrigation decision data in real time, and to display soil moisture heat maps and air element distribution maps in real time. It supports historical data query and analysis, and sends early warning information to mobile terminals.

[0067] In this embodiment, it should be specifically explained that the roles in the irrigation dynamic control system include administrators, agricultural technicians, and farmers. Administrators can access all data and perform operations such as starting and stopping equipment, modifying strategies, and adjusting model parameters. They also have the authority to create or delete roles. Agricultural technicians can access data in their assigned area and perform operations such as adjusting irrigation plans and confirming risk levels. They also have the authority to customize risk warning thresholds. Farmers can access data for their own fields and can manually start and stop local equipment and view irrigation records.

[0068] like Figure 2As shown, this invention provides a dynamic irrigation control method based on meteorological forecasting, specifically including the following steps:

[0069] S1: Collect multi-source irrigation decision data of the target irrigated farmland in real time, and standardize the multi-source irrigation decision data to obtain a multi-source irrigation decision dataset;

[0070] S2: Construct a crop water demand model, calculate farmland irrigation water demand and water shortage risk index based on multi-source irrigation decision dataset, and determine the water shortage risk level of the target irrigated farmland;

[0071] S3: Generate irrigation strategies based on the water shortage risk level of the target irrigated farmland and preset rules, preset irrigation strategies corresponding to different weather forecast results, and generate equipment control instructions;

[0072] S4: Controls irrigation equipment to execute irrigation strategies according to equipment control instructions, and collects equipment feedback data in real time and uploads it to the cloud server database;

[0073] S5: Monitor irrigation execution results in real time, calculate the deviation between the irrigation execution results and the preset target of the strategy, dynamically adjust the parameters of the crop water requirement model based on the deviation, and dynamically adjust the irrigation strategy.

[0074] S6: Provides a data visualization interface to display farmland distribution and irrigation status in real time, pushes early warnings, sets and manages multi-role permissions, and supports manual adjustment of irrigation plans and switching of control modes.

[0075] like Figure 3 As shown, this invention provides a method for determining the level of water shortage risk, specifically including the following steps:

[0076] A1: Import the real-time collected multi-source irrigation decision dataset into the crop water requirement model, calculate the reference crop evapotranspiration of the target irrigated farmland, and use the reference crop evapotranspiration to calculate the actual evapotranspiration.

[0077] A2: The irrigation water demand of the target irrigated farmland is calculated using actual evapotranspiration, rainfall, initial soil moisture content, real-time soil moisture content, root layer depth, and deep percolation, and then the supply and demand balance difference is calculated.

[0078] A3: The water shortage risk index is calculated based on the supply and demand imbalance, precipitation deficit index, potential evapotranspiration stress index, and extreme weather risk factors.

[0079] A4: Construct a water shortage risk level index, compare the water shortage risk index with the water shortage risk level index, and classify the water shortage risk level.

[0080] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0081] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An irrigation dynamic regulation system based on weather prediction, comprising a water quantity collection terminal, a crop state collection terminal, a weather monitoring terminal, a cloud server database, an irrigation device, and a mobile terminal, characterized in that, It also includes a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback adjustment module and a human-computer interaction module: The irrigation equipment is specifically an intelligent water-saving valve driven by electricity. The data acquisition module is used for collecting multi-source irrigation decision data of the target irrigated farmland, and performing standardized processing on the multi-source irrigation decision data to obtain a multi-source irrigation decision dataset, which is transmitted to the data analysis module. The data analysis module is used for constructing a crop water requirement model, calculating farmland irrigation water requirement and water shortage risk index according to the multi-source irrigation decision dataset, judging the water shortage risk level of the target irrigated farmland, and transmitting the analysis result to the strategy generation module. The calculation of the water shortage risk index needs to obtain the flow Q and water delivery efficiency η of the irrigation water source from the multi-source irrigation decision dataset, and calculate the available water supply S of the irrigation water source by using the formula S=Q×t×η, wherein t is the planned irrigation time, and the difference between the irrigation water requirement and the available water supply of the irrigation water source is calculated. Key meteorological factors of meteorological prediction data in multi-source irrigation decision data are extracted, including future cumulative rainfall P f , potential evapotranspiration prediction value ET 0f , and drought meteorological warning level, a difference between the ratio of future cumulative rainfall and irrigation water requirement is calculated to obtain a rainfall deficit index RDI, a ratio of the potential evapotranspiration prediction value and the average value of historical evapotranspiration in the same period is calculated to obtain a potential evapotranspiration stress index ETSI, the drought meteorological warning is divided into no warning, first-level warning, second-level warning and third-level warning, and risk values 0, 30, 60 and 100 are respectively given to obtain an extreme weather risk factor EWR; The water shortage risk index is obtained by weighting the supply-demand balance difference, the precipitation deficit index, the potential evapotranspiration stress index and the extreme weather risk factor. The strategy generation module generates an irrigation strategy based on the analysis result of the data analysis module and a preset rule, generates a device control instruction corresponding to different meteorological prediction results, and transmits the device control instruction to the strategy execution module. The strategy execution module is used for receiving the device control instruction transmitted by the strategy generation module, controlling the irrigation equipment to execute the irrigation strategy according to the device control instruction, and uploading the device feedback data to the cloud server database in real time. The feedback adjustment module is used for monitoring the irrigation execution result in real time, calculating the deviation value of the irrigation execution result and the strategy preset target, dynamically adjusting the parameters of the crop water requirement model according to the deviation value, and dynamically adjusting the irrigation strategy. The human-computer interaction module is used for providing a data visualization interface, real-time display of farmland distribution and irrigation state, early warning push, setting and management of multi-role permissions, and support for manual adjustment of irrigation plan and switching of control mode.

2. The dynamic irrigation regulation system based on weather forecast according to claim 1, characterized in that: The multi-source irrigation decision data includes irrigation water source data, crop state data, real-time meteorological data and meteorological prediction data, the irrigation water source data includes water level, flow and water delivery efficiency, the crop state data includes soil moisture, root layer depth, stem micro change, leaf humidity and canopy temperature, the real-time meteorological data includes air temperature, relative humidity, wind direction, wind speed, solar radiation and rainfall, and the meteorological prediction data includes future 7-day weather forecast information and extreme weather warning information.

3. The dynamic irrigation regulation system based on weather forecast according to claim 1, characterized in that: The calculation of the farmland irrigation water requirement needs to import the multi-source irrigation decision data set collected by the data acquisition module in real time into a crop water requirement model to calculate the reference crop transpiration amount ET0 of the target irrigated farmland. The reference crop transpiration amount is calculated by using a formula ET c = K c × ET0 to obtain the actual evapotranspiration amount ET c . The rainfall P, initial soil water content V0, real-time soil water content V, root layer depth d and deep seepage amount D in the multi-source irrigation decision data set are obtained. The actual evapotranspiration amount, the rainfall, the initial soil water content, the real-time soil water content, the root layer depth and the deep seepage amount are calculated by using a formula: ， I, where P e is the effective rainfall, P e is calculated as: 。 4. The dynamic irrigation regulation system based on weather forecast of claim 1, wherein: The calculation of the water shortage risk index includes: The supply-demand balance difference, the precipitation deficit index and the potential evapotranspiration stress index are standardized to obtain the standardized supply-demand balance difference, the standardized precipitation deficit index and the standardized potential evapotranspiration stress index, and the extreme weather risk factor and the standardized supply-demand balance difference, the standardized precipitation deficit index and the standardized potential evapotranspiration stress index are substituted into the formula: ， a1, a2, a3, and a4 represent weight coefficients of the supply-demand balance difference, the precipitation deficit index, the potential evapotranspiration stress index, and the extreme weather risk factor respectively, a1+a2+a3+a4=1, S represents the irrigation water supply, I represents the farmland irrigation water requirement of the target irrigated farmland, and RDI norm represents the normalized precipitation deficit index, ETSI norm represents the normalized potential evapotranspiration stress index.

5. The dynamic irrigation regulation system based on weather forecast according to claim 1, characterized in that: The determination of the water shortage risk level requires constructing a water shortage risk level index and dividing the water shortage risk level into a first water shortage risk level, a second water shortage risk level, a third water shortage risk level and a fourth water shortage risk level.

6. The dynamic irrigation regulation system based on weather forecast of claim 1, wherein: The generation of the irrigation strategy requires obtaining the farmland irrigation water requirement and the water shortage risk level of the target farmland obtained by the data analysis module, presetting crop growth stage rules, meteorological corresponding rules and equipment operation rules, formulating the irrigation strategy according to the water shortage risk level, converting the strategy parameters into control instructions recognizable by the equipment, and generating a time sequence instruction queue.

7. The dynamic irrigation regulation system based on weather forecast of claim 1, wherein: The execution of the irrigation strategy requires receiving the equipment control instructions transmitted by the strategy generation module in real time, performing instruction analysis and verification on the equipment control instructions, controlling the intelligent water-saving valve to perform corresponding operations according to the irrigation strategy, and managing the execution time sequence. The equipment feedback data collected in real time includes equipment operation state data, execution effect data and environmental data.

8. The dynamic irrigation regulation system based on weather forecast of claim 1, wherein: The irrigation execution result includes the soil moisture content after irrigation, the stem length of the crop, the leaf humidity and the actual irrigation water quantity. The water quantity deviation value is obtained by calculating the difference between the actual irrigation water quantity and the irrigation water requirement. The soil moisture content deviation value is obtained by calculating the difference between the soil moisture content after irrigation and the target soil moisture content. The crop growth deviation value is obtained by calculating the difference between the stem length of the crop and the target length. The comprehensive deviation value is obtained by performing weighted analysis based on the water quantity deviation value, the soil moisture content deviation value and the crop growth deviation value. The parameters of the crop water requirement model and the irrigation strategy are dynamically adjusted according to the comprehensive deviation value.

9. The dynamic irrigation regulation system based on weather forecast of claim 1, wherein: The data visualization interface is used to display the farmland distribution, the farmland irrigation state and the multi-source irrigation decision data in real time, display the soil moisture content thermal diagram and the atmospheric element distribution diagram in real time, support historical data query and analysis, and send early warning information to a mobile terminal.

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